diff --git a/.circleci/config.yml b/.circleci/config.yml
index 2a15679801..4a44242697 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1050,6 +1050,51 @@ jobs:
ls
python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8
no_output_timeout: 120m
+ - run:
+ name: Rename the coverage files
+ command: |
+ mv coverage.xml litellm_mapped_tests_coverage.xml
+ mv .coverage litellm_mapped_tests_coverage
+
+ # Store test results
+ - store_test_results:
+ path: test-results
+ - persist_to_workspace:
+ root: .
+ paths:
+ - litellm_mapped_tests_coverage.xml
+ - litellm_mapped_tests_coverage
+ litellm_mapped_enterprise_tests:
+ docker:
+ - image: cimg/python:3.11
+ auth:
+ username: ${DOCKERHUB_USERNAME}
+ password: ${DOCKERHUB_PASSWORD}
+ working_directory: ~/project
+
+ steps:
+ - checkout
+ - setup_google_dns
+ - run:
+ name: Install Dependencies
+ command: |
+ python -m pip install --upgrade pip
+ python -m pip install -r requirements.txt
+ pip install "pytest-mock==3.12.0"
+ pip install "pytest==7.3.1"
+ pip install "pytest-retry==1.6.3"
+ pip install "pytest-cov==5.0.0"
+ pip install "pytest-asyncio==0.21.1"
+ pip install "respx==0.22.0"
+ pip install "hypercorn==0.17.3"
+ pip install "pydantic==2.10.2"
+ pip install "mcp==1.10.1"
+ pip install "requests-mock>=1.12.1"
+ pip install "responses==0.25.7"
+ pip install "pytest-xdist==3.6.1"
+ pip install "semantic_router==0.1.10"
+ pip install "fastapi-offline==1.7.3"
+ - setup_litellm_enterprise_pip
- run:
name: Run enterprise tests
command: |
@@ -1458,6 +1503,7 @@ jobs:
# - run: python ./tests/documentation_tests/test_general_setting_keys.py
- run: python ./tests/code_coverage_tests/check_licenses.py
- run: python ./tests/code_coverage_tests/router_code_coverage.py
+ - run: python ./tests/code_coverage_tests/test_chat_completion_imports.py
- run: python ./tests/code_coverage_tests/info_log_check.py
- run: python ./tests/code_coverage_tests/test_ban_set_verbose.py
- run: python ./tests/code_coverage_tests/code_qa_check_tests.py
@@ -1778,8 +1824,8 @@ jobs:
docker run -d \
-p 4000:4000 \
-e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \
- -e AZURE_API_KEY=$AZURE_BATCHES_API_KEY \
- -e AZURE_API_BASE=$AZURE_BATCHES_API_BASE \
+ -e AZURE_API_KEY=$AZURE_API_KEY \
+ -e AZURE_API_BASE=$AZURE_API_BASE \
-e AZURE_API_VERSION="2024-05-01-preview" \
-e REDIS_HOST=$REDIS_HOST \
-e REDIS_PASSWORD=$REDIS_PASSWORD \
@@ -2825,8 +2871,8 @@ jobs:
source "$NVM_DIR/bash_completion"
# Install and use Node version
- nvm install v18.17.0
- nvm use v18.17.0
+ nvm install v20
+ nvm use v20
cd ui/litellm-dashboard
@@ -2879,7 +2925,26 @@ jobs:
name: Install Playwright Browsers
command: |
npx playwright install
+ - run:
+ name: Run UI unit tests (Vitest)
+ command: |
+ # Use Node 20 (several deps require >=20)
+ export NVM_DIR="/opt/circleci/.nvm"
+ source "$NVM_DIR/nvm.sh"
+ nvm install 20
+ nvm use 20
+ cd ui/litellm-dashboard
+ npm ci || npm install
+
+ # CI run, with both LCOV (Codecov) and HTML (artifact you can click)
+ CI=true npm run test -- --run --coverage \
+ --coverage.provider=v8 \
+ --coverage.reporter=lcov \
+ --coverage.reporter=html \
+ --coverage.reportsDirectory=coverage/html
+
+
- run:
name: Build Docker image
command: docker build -t my-app:latest -f ./docker/Dockerfile.database .
@@ -3155,6 +3220,12 @@ workflows:
only:
- main
- /litellm_.*/
+ - litellm_mapped_enterprise_tests:
+ filters:
+ branches:
+ only:
+ - main
+ - /litellm_.*/
- litellm_mapped_tests:
filters:
branches:
@@ -3199,6 +3270,7 @@ workflows:
- guardrails_testing
- llm_responses_api_testing
- litellm_mapped_tests
+ - litellm_mapped_enterprise_tests
- batches_testing
- litellm_utils_testing
- pass_through_unit_testing
@@ -3259,6 +3331,7 @@ workflows:
- google_generate_content_endpoint_testing
- llm_responses_api_testing
- litellm_mapped_tests
+ - litellm_mapped_enterprise_tests
- batches_testing
- litellm_utils_testing
- pass_through_unit_testing
diff --git a/.gitignore b/.gitignore
index ed8c88c899..c2ac5137cb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -95,4 +95,5 @@ test.py
litellm_config.yaml
.cursor
.vscode/launch.json
-litellm/proxy/to_delete_loadtest_work/*
\ No newline at end of file
+litellm/proxy/to_delete_loadtest_work/*
+update_model_cost_map.py
diff --git a/Dockerfile b/Dockerfile
index addc109e10..f85582f992 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -41,9 +41,6 @@ RUN pip uninstall jwt -y
RUN pip uninstall PyJWT -y
RUN pip install PyJWT==2.9.0 --no-cache-dir
-# Build Admin UI
-RUN chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh
-
# Runtime stage
FROM $LITELLM_RUNTIME_IMAGE AS runtime
diff --git a/README.md b/README.md
index 46acf6cef0..f74889fbb2 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,7 @@ LiteLLM manages:
- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy)
-[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
+[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs)
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs)
🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle)
diff --git a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
index f24bc8f129..b0a24ef28e 100644
--- a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
+++ b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
@@ -4,10 +4,10 @@ This document provides comprehensive instructions for AI agents to generate rele
## Required Inputs
-1. **Release Version** (e.g., `v1.76.3-stable`)
+1. **Release Version** (e.g., `v1.77.3-stable`)
2. **PR Diff/Changelog** - List of PRs with titles and contributors
3. **Previous Version Commit Hash** - To compare model pricing changes
-4. **Reference Release Notes** - Previous release notes to follow style/format
+4. **Reference Release Notes** - Use recent stable releases (v1.76.3-stable, v1.77.2-stable) as templates for consistent formatting
## Step-by-Step Process
@@ -26,12 +26,12 @@ git diff HEAD -- model_prices_and_context_window.json
### 2. Release Notes Structure
-Follow this exact structure based on `docs/my-website/release_notes/v1.76.1-stable/index.md`:
+Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable):
```markdown
---
-title: "v1.76.X-stable - [Key Theme]"
-slug: "v1-76-X"
+title: "v1.77.X-stable - [Key Theme]"
+slug: "v1-77-X"
date: YYYY-MM-DDTHH:mm:ss
authors: [standard author block]
hide_table_of_contents: false
@@ -43,23 +43,42 @@ hide_table_of_contents: false
## Key Highlights
[3-5 bullet points of major features]
-## Major Changes
-[Critical changes users need to know]
-
-## Performance Improvements
-[Performance-related changes]
-
## New Models / Updated Models
-[Detailed model tables and provider updates]
+#### New Model Support
+[Model pricing table]
+
+#### Features
+[Provider-specific features organized by provider]
+
+### Bug Fixes
+[Provider-specific bug fixes organized by provider]
+
+#### New Provider Support
+[New provider integrations]
## LLM API Endpoints
-[API-related features and fixes]
+#### Features
+[API-specific features organized by API type]
+
+#### Bugs
+[General bug fixes]
## Management Endpoints / UI
-[Admin interface and management changes]
+#### Features
+[UI and management features]
+
+#### Bugs
+[Management-related bug fixes]
## Logging / Guardrail Integrations
-[Observability and security features]
+#### Features
+[Organized by integration provider with proper doc links]
+
+#### Guardrails
+[Guardrail-specific features and fixes]
+
+#### New Integration
+[Major new integrations]
## Performance / Loadbalancing / Reliability improvements
[Infrastructure improvements]
@@ -86,21 +105,27 @@ hide_table_of_contents: false
**New Models/Updated Models:**
- Extract from model_prices_and_context_window.json diff
- Create tables with: Provider, Model, Context Window, Input Cost, Output Cost, Features
-- Group by provider
-- Note pricing corrections
-- Highlight deprecated models
+- **Structure:**
+ - `#### New Model Support` - pricing table
+ - `#### Features` - organized by provider with documentation links
+ - `### Bug Fixes` - provider-specific bug fixes
+ - `#### New Provider Support` - major new provider integrations
+- Group by provider with proper doc links: `**[Provider Name](../../docs/providers/[provider])**`
+- Use bullet points under each provider for multiple features
+- Separate features from bug fixes clearly
-**Provider Features:**
-- Group by provider (Gemini, OpenAI, Anthropic, etc.)
-- Link to provider docs: `../../docs/providers/[provider_name]`
-- Separate features from bug fixes
-
-**API Endpoints:**
-- Images API
-- Video Generation (if applicable)
-- Responses API
-- Passthrough endpoints
-- General chat completions
+**LLM API Endpoints:**
+- **Structure:**
+ - `#### Features` - organized by API type (Responses API, Batch API, etc.)
+ - `#### Bugs` - general bug fixes under **General** category
+- **API Categories:**
+ - Responses API
+ - Batch API
+ - CountTokens API
+ - Images API
+ - Video Generation (if applicable)
+ - General (miscellaneous improvements)
+- Use proper documentation links for each API type
**UI/Management:**
- Authentication changes
@@ -108,11 +133,19 @@ hide_table_of_contents: false
- Team management
- Key management
-**Integrations:**
-- Logging providers (Datadog, Braintrust, etc.)
-- Guardrails
-- Cost tracking
-- Observability
+**Logging / Guardrail Integrations:**
+- **Structure:**
+ - `#### Features` - organized by integration provider with proper doc links
+ - `#### Guardrails` - guardrail-specific features and fixes
+ - `#### New Integration` - major new integrations
+- **Integration Categories:**
+ - **[DataDog](../../docs/proxy/logging#datadog)** - group all DataDog-related changes
+ - **[Langfuse](../../docs/proxy/logging#langfuse)** - Langfuse-specific features
+ - **[Prometheus](../../docs/proxy/logging#prometheus)** - monitoring improvements
+ - **[PostHog](../../docs/observability/posthog)** - observability integration
+ - Other logging providers with proper doc links
+- Use bullet points under each provider for multiple features
+- Separate logging features from guardrails clearly
### 4. Documentation Linking Strategy
@@ -211,10 +244,41 @@ This release has a known issue...
:::
```
-**Provider Features:**
+**Provider Features (New Models / Updated Models section):**
```markdown
+#### Features
+
- **[Provider Name](../../docs/providers/provider)**
- Feature description - [PR #XXXXX](link)
+ - Another feature description - [PR #YYYYY](link)
+```
+
+**API Features (LLM API Endpoints section):**
+```markdown
+#### Features
+
+- **[API Name](../../docs/api_path)**
+ - Feature description - [PR #XXXXX](link)
+ - Another feature - [PR #YYYYY](link)
+- **General**
+ - Miscellaneous improvements - [PR #ZZZZZ](link)
+```
+
+**Integration Features (Logging / Guardrail Integrations section):**
+```markdown
+#### Features
+
+- **[Integration Name](../../docs/proxy/logging#integration)**
+ - Feature description - [PR #XXXXX](link)
+ - Bug fix description - [PR #YYYYY](link)
+```
+
+**Bug Fixes Pattern:**
+```markdown
+### Bug Fixes
+
+- **[Provider/Component Name](../../docs/providers/provider)**
+ - Bug fix description - [PR #XXXXX](link)
```
### 10. Missing Documentation Check
diff --git a/docs/my-website/docs/completion/provider_specific_params.md b/docs/my-website/docs/completion/provider_specific_params.md
index a8307fc8a2..772ca13e29 100644
--- a/docs/my-website/docs/completion/provider_specific_params.md
+++ b/docs/my-website/docs/completion/provider_specific_params.md
@@ -433,4 +433,54 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
],
"adapater_id": "my-special-adapter-id" # 👈 PROVIDER-SPECIFIC PARAM
}'
+
+## Provider-Specific Metadata Parameters
+
+| Provider | Parameter | Use Case |
+|----------|-----------|----------|
+| **AWS Bedrock** | `requestMetadata` | Cost attribution, logging |
+| **Gemini/Vertex AI** | `labels` | Resource labeling |
+| **Anthropic** | `metadata` | User identification |
+
+
+
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+ messages=[{"role": "user", "content": "Hello!"}],
+ requestMetadata={"cost_center": "engineering"}
+)
+```
+
+
+
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="vertex_ai/gemini-pro",
+ messages=[{"role": "user", "content": "Hello!"}],
+ labels={"environment": "production"}
+)
+```
+
+
+
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="anthropic/claude-3-sonnet-20240229",
+ messages=[{"role": "user", "content": "Hello!"}],
+ metadata={"user_id": "user123"}
+)
+```
+
+
+
```
\ No newline at end of file
diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md
index 18c9905170..80b4c32d0a 100644
--- a/docs/my-website/docs/mcp.md
+++ b/docs/my-website/docs/mcp.md
@@ -114,7 +114,6 @@ mcp_servers:
description: "My custom MCP server"
auth_type: "api_key"
auth_value: "abc123"
- spec_version: "2025-03-26"
```
**Configuration Options:**
@@ -716,7 +715,6 @@ mcp_servers:
url: https://mcp.deepwiki.com/mcp
transport: "http"
auth_type: "none"
- spec_version: "2025-03-26"
access_groups: ["dev_group"]
```
diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md
index 9b807b8d0f..22ea051f7c 100644
--- a/docs/my-website/docs/observability/helicone_integration.md
+++ b/docs/my-website/docs/observability/helicone_integration.md
@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
# Helicone - OSS LLM Observability Platform
:::tip
@@ -9,9 +12,68 @@ https://github.com/BerriAI/litellm
[Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more.
-## Using Helicone with LiteLLM
+## Quick Start
-LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses.
+
+
+
+Use just 1 line of code to instantly log your responses **across all providers** with Helicone:
+
+```python
+import os
+from litellm import completion
+
+## Set env variables
+os.environ["HELICONE_API_KEY"] = "your-helicone-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-key"
+
+# Set callbacks
+litellm.success_callback = ["helicone"]
+
+# OpenAI call
+response = completion(
+ model="gpt-4o",
+ messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}],
+)
+
+print(response)
+```
+
+
+
+
+Add Helicone to your LiteLLM proxy configuration:
+
+```yaml title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+
+# Add Helicone callback
+litellm_settings:
+ success_callback: ["helicone"]
+
+# Set Helicone API key
+environment_variables:
+ HELICONE_API_KEY: "your-helicone-key"
+```
+
+Start the proxy:
+```bash
+litellm --config config.yaml
+```
+
+
+
+
+## Integration Methods
+
+There are two main approaches to integrate Helicone with LiteLLM:
+
+1. **Callbacks**: Log to Helicone while using any provider
+2. **Proxy Mode**: Use Helicone as a proxy for advanced features
### Supported LLM Providers
@@ -26,27 +88,16 @@ Helicone can log requests across [various LLM providers](https://docs.helicone.a
- Replicate
- And more
-### Integration Methods
+## Method 1: Using Callbacks
-There are two main approaches to integrate Helicone with LiteLLM:
+Log requests to Helicone while using any LLM provider directly.
-1. Using callbacks
-2. Using Helicone as a proxy
-
-Let's explore each method in detail.
-
-### Approach 1: Use Callbacks
-
-Use just 1 line of code to instantly log your responses **across all providers** with Helicone:
-
-```python
-litellm.success_callback = ["helicone"]
-```
-
-Complete Code
+
+
```python
import os
+import litellm
from litellm import completion
## Set env variables
@@ -66,28 +117,78 @@ response = completion(
print(response)
```
-### Approach 2: Use Helicone as a proxy
+
+
+
+```yaml title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+ - model_name: claude-3
+ litellm_params:
+ model: anthropic/claude-3-sonnet-20240229
+ api_key: os.environ/ANTHROPIC_API_KEY
+
+# Add Helicone logging
+litellm_settings:
+ success_callback: ["helicone"]
+
+# Environment variables
+environment_variables:
+ HELICONE_API_KEY: "your-helicone-key"
+ OPENAI_API_KEY: "your-openai-key"
+ ANTHROPIC_API_KEY: "your-anthropic-key"
+```
+
+Start the proxy:
+```bash
+litellm --config config.yaml
+```
+
+Make requests to your proxy:
+```python
+import openai
+
+client = openai.OpenAI(
+ api_key="anything", # proxy doesn't require real API key
+ base_url="http://localhost:4000"
+)
+
+response = client.chat.completions.create(
+ model="gpt-4", # This gets logged to Helicone
+ messages=[{"role": "user", "content": "Hello!"}]
+)
+```
+
+
+
+
+## Method 2: Using Helicone as a Proxy
Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more.
-To use Helicone as a proxy for your LLM requests:
+
+
-1. Set Helicone as your base URL via: litellm.api_base
-2. Pass in Helicone request headers via: litellm.metadata
-
-Complete Code:
+Set Helicone as your base URL and pass authentication headers:
```python
import os
import litellm
from litellm import completion
+# Configure LiteLLM to use Helicone proxy
litellm.api_base = "https://oai.hconeai.com/v1"
litellm.headers = {
- "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API
+ "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",
}
-response = litellm.completion(
+# Set your OpenAI API key
+os.environ["OPENAI_API_KEY"] = "your-openai-key"
+
+response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}]
)
@@ -136,36 +237,119 @@ litellm.metadata = {
}
```
-### Session Tracking and Tracing
+
+
+
+## Session Tracking and Tracing
Track multi-step and agentic LLM interactions using session IDs and paths:
-```python
-litellm.metadata = {
- "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API
- "Helicone-Session-Id": "session-abc-123", # The session ID you want to track
- "Helicone-Session-Path": "parent-trace/child-trace", # The path of the session
-}
-```
-
-- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together.
-- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace.
-
-By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows.
-
-### Retry and Fallback Mechanisms
-
-Set up retry mechanisms and fallback options:
+
+
```python
+import litellm
+
+litellm.api_base = "https://oai.hconeai.com/v1"
litellm.metadata = {
- "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API
- "Helicone-Retry-Enabled": "true", # Enable retry mechanism
- "helicone-retry-num": "3", # Set number of retries
- "helicone-retry-factor": "2", # Set exponential backoff factor
- "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models
+ "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",
+ "Helicone-Session-Id": "session-abc-123",
+ "Helicone-Session-Path": "parent-trace/child-trace",
}
+
+response = litellm.completion(
+ model="gpt-3.5-turbo",
+ messages=[{"role": "user", "content": "Start a conversation"}]
+)
```
+
+
+
+```python
+import openai
+
+client = openai.OpenAI(
+ api_key="anything",
+ base_url="http://localhost:4000"
+)
+
+# First request in session
+response1 = client.chat.completions.create(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_headers={
+ "Helicone-Session-Id": "session-abc-123",
+ "Helicone-Session-Path": "conversation/greeting"
+ }
+)
+
+# Follow-up request in same session
+response2 = client.chat.completions.create(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Tell me more"}],
+ extra_headers={
+ "Helicone-Session-Id": "session-abc-123",
+ "Helicone-Session-Path": "conversation/follow-up"
+ }
+)
+```
+
+
+
+
+- `Helicone-Session-Id`: Unique identifier for the session to group related requests
+- `Helicone-Session-Path`: Hierarchical path to represent parent/child traces (e.g., "parent/child")
+
+## Retry and Fallback Mechanisms
+
+
+
+
+```python
+import litellm
+
+litellm.api_base = "https://oai.hconeai.com/v1"
+litellm.metadata = {
+ "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",
+ "Helicone-Retry-Enabled": "true",
+ "helicone-retry-num": "3",
+ "helicone-retry-factor": "2", # Exponential backoff
+ "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]',
+}
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}]
+)
+```
+
+
+
+
+```yaml title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+ api_base: "https://oai.hconeai.com/v1"
+
+default_litellm_params:
+ headers:
+ Helicone-Auth: "Bearer ${HELICONE_API_KEY}"
+ Helicone-Retry-Enabled: "true"
+ helicone-retry-num: "3"
+ helicone-retry-factor: "2"
+ Helicone-Fallbacks: '["gpt-3.5-turbo", "gpt-4"]'
+
+environment_variables:
+ HELICONE_API_KEY: "your-helicone-key"
+ OPENAI_API_KEY: "your-openai-key"
+```
+
+
+
+
> **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start).
> By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM.
diff --git a/docs/my-website/docs/observability/posthog_integration.md b/docs/my-website/docs/observability/posthog_integration.md
new file mode 100644
index 0000000000..7e6a0e1076
--- /dev/null
+++ b/docs/my-website/docs/observability/posthog_integration.md
@@ -0,0 +1,216 @@
+# PostHog - Tracking LLM Usage Analytics
+
+## What is PostHog?
+
+PostHog is an open-source product analytics platform that helps you track and analyze how users interact with your product. For LLM applications, PostHog provides specialized AI features to track model usage, performance, and user interactions with your AI features.
+
+## Usage with LiteLLM Proxy (LLM Gateway)
+
+**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: gpt-3.5-turbo
+
+litellm_settings:
+ success_callback: ["posthog"]
+ failure_callback: ["posthog"]
+```
+
+**Step 2**: Set required environment variables
+
+```shell
+export POSTHOG_API_KEY="your-posthog-api-key"
+# Optional, defaults to https://app.posthog.com
+export POSTHOG_API_URL="https://app.posthog.com" # optional
+```
+
+**Step 3**: Start the proxy, make a test request
+
+Start proxy
+
+```shell
+litellm --config config.yaml --debug
+```
+
+Test Request
+
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "model": "gpt-3.5-turbo",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what llm are you"
+ }
+ ],
+ "metadata": {
+ "user_id": "user-123",
+ "custom_field": "custom_value"
+ }
+}'
+```
+
+## Usage with LiteLLM Python SDK
+
+### Quick Start
+
+Use just 2 lines of code, to instantly log your responses **across all providers** with PostHog:
+
+```python
+litellm.success_callback = ["posthog"]
+litellm.failure_callback = ["posthog"] # logs errors to posthog
+```
+```python
+import litellm
+import os
+
+# from PostHog
+os.environ["POSTHOG_API_KEY"] = ""
+# Optional, defaults to https://app.posthog.com
+os.environ["POSTHOG_API_URL"] = "" # optional
+
+# LLM API Keys
+os.environ['OPENAI_API_KEY']=""
+
+# set posthog as a callback, litellm will send the data to posthog
+litellm.success_callback = ["posthog"]
+
+# openai call
+response = litellm.completion(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "user", "content": "Hi - i'm openai"}
+ ],
+ metadata = {
+ "user_id": "user-123", # set posthog user ID
+ }
+)
+```
+
+### Advanced
+
+#### Set User ID and Custom Metadata
+
+Pass `user_id` in `metadata` to associate events with specific users in PostHog:
+
+**With LiteLLM Python SDK:**
+
+```python
+import litellm
+
+litellm.success_callback = ["posthog"]
+
+response = litellm.completion(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "user", "content": "Hello world"}
+ ],
+ metadata={
+ "user_id": "user-123", # Add user ID for PostHog tracking
+ "custom_field": "custom_value" # Add custom metadata
+ }
+)
+```
+
+**With LiteLLM Proxy using OpenAI Python SDK:**
+
+```python
+import openai
+
+client = openai.OpenAI(
+ api_key="sk-1234", # Your LiteLLM Proxy API key
+ base_url="http://0.0.0.0:4000" # Your LiteLLM Proxy URL
+)
+
+response = client.chat.completions.create(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "user", "content": "Hello world"}
+ ],
+ extra_body={
+ "metadata": {
+ "user_id": "user-123", # Add user ID for PostHog tracking
+ "project_name": "my-project", # Add custom metadata
+ "environment": "production"
+ }
+ }
+)
+```
+
+#### Disable Logging for Specific Calls
+
+Use the `no-log` flag to prevent logging for specific calls:
+
+```python
+import litellm
+
+litellm.success_callback = ["posthog"]
+
+response = litellm.completion(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "user", "content": "This won't be logged"}
+ ],
+ metadata={"no-log": True}
+)
+```
+
+## What's Logged to PostHog?
+
+When LiteLLM logs to PostHog, it captures detailed information about your LLM usage:
+
+### For Completion Calls
+- **Model Information**: Provider, model name, model parameters
+- **Usage Metrics**: Input tokens, output tokens, total cost
+- **Performance**: Latency, completion time
+- **Content**: Input messages, model responses (respects privacy settings)
+- **Metadata**: Custom fields, user ID, trace information
+
+### For Embedding Calls
+- **Model Information**: Provider, model name
+- **Usage Metrics**: Input tokens, total cost
+- **Performance**: Latency
+- **Content**: Input text (respects privacy settings)
+- **Metadata**: Custom fields, user ID, trace information
+
+### For Errors
+- **Error Details**: Error type, error message, stack trace
+- **Context**: Model, provider, input that caused the error
+- **Timing**: When the error occurred, request duration
+
+## Environment Variables
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `POSTHOG_API_KEY` | Yes | Your PostHog project API key |
+| `POSTHOG_API_URL` | No | PostHog API URL (defaults to https://app.posthog.com) |
+
+## Troubleshooting
+
+### 1. Missing API Key
+```
+Error: POSTHOG_API_KEY is not set
+```
+
+Set your PostHog API key:
+```python
+import os
+os.environ["POSTHOG_API_KEY"] = "your-api-key"
+```
+
+### 2. Custom PostHog Instance
+If you're using a self-hosted PostHog instance:
+```python
+import os
+os.environ["POSTHOG_API_URL"] = "https://your-posthog-instance.com"
+```
+
+### 3. Events Not Appearing
+- Check that your API key is correct
+- Verify network connectivity to PostHog
+- Events may take a few minutes to appear in PostHog dashboard
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md
index c191b74226..86e9ac5e3e 100644
--- a/docs/my-website/docs/providers/bedrock.md
+++ b/docs/my-website/docs/providers/bedrock.md
@@ -308,6 +308,65 @@ print(response)
+## Usage - Request Metadata
+
+Attach metadata to Bedrock requests for logging and cost attribution.
+
+
+
+
+```python
+import os
+from litellm import completion
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+response = completion(
+ model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+ messages=[{"role": "user", "content": "Hello, how are you?"}],
+ requestMetadata={
+ "cost_center": "engineering",
+ "user_id": "user123"
+ }
+)
+```
+
+
+
+**Set on yaml**
+
+```yaml
+model_list:
+ - model_name: bedrock-claude-v1
+ litellm_params:
+ model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0
+ requestMetadata:
+ cost_center: "engineering"
+```
+
+**Set on request**
+
+```python
+import openai
+client = openai.OpenAI(
+ api_key="anything",
+ base_url="http://0.0.0.0:4000"
+)
+
+response = client.chat.completions.create(
+ model="bedrock-claude-v1",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_body={
+ "requestMetadata": {"cost_center": "engineering"}
+ }
+)
+```
+
+
+
+
## Usage - Function Calling / Tool calling
LiteLLM supports tool calling via Bedrock's Converse and Invoke API's.
@@ -889,6 +948,19 @@ curl http://0.0.0.0:4000/v1/chat/completions \
Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html)
+### Selective Content Moderation with `guarded_text`
+
+LiteLLM supports selective content moderation using the `guarded_text` content type. This allows you to wrap only specific content that should be moderated by Bedrock Guardrails, rather than evaluating the entire conversation.
+
+**How it works:**
+- Content with `type: "guarded_text"` gets automatically wrapped in `guardrailConverseContent` blocks
+- Only the wrapped content is evaluated by Bedrock Guardrails
+- Regular content with `type: "text"` bypasses guardrail evaluation
+
+:::note
+If `guarded_text` is not used, the entire conversation history will be sent to the guardrail for evaluation, which can increase latency and costs.
+:::
+
@@ -915,6 +987,24 @@ response = completion(
"trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled"
},
)
+
+# Selective guardrail usage with guarded_text - only specific content is evaluated
+response_guard = completion(
+ model="anthropic.claude-v2",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What is the main topic of this legal document?"},
+ {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."}
+ ]
+ }
+ ],
+ guardrailConfig={
+ "guardrailIdentifier": "gr-abc123",
+ "guardrailVersion": "DRAFT"
+ }
+)
```
@@ -993,7 +1083,20 @@ response = client.chat.completions.create(model="bedrock-claude-v1", messages =
temperature=0.7
)
-print(response)
+# For adding selective guardrail usage with guarded_text
+response_guard = client.chat.completions.create(model="bedrock-claude-v1", messages = [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What is the main topic of this legal document?"},
+ {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."}
+ ]
+ }
+],
+temperature=0.7
+)
+
+print(response_guard)
```
@@ -1777,6 +1880,7 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re
| Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
| Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
+
## Bedrock Embedding
### API keys
@@ -1798,11 +1902,29 @@ response = embedding(
print(response)
```
+#### Titan V2 - encoding_format support
+```python
+from litellm import embedding
+# Float format (default)
+response = embedding(
+ model="bedrock/amazon.titan-embed-text-v2:0",
+ input=["good morning from litellm"],
+ encoding_format="float" # Returns float array
+)
+
+# Binary format
+response = embedding(
+ model="bedrock/amazon.titan-embed-text-v2:0",
+ input=["good morning from litellm"],
+ encoding_format="base64" # Returns base64 encoded binary
+)
+```
+
## Supported AWS Bedrock Embedding Models
| Model Name | Usage | Supported Additional OpenAI params |
|----------------------|---------------------------------------------|-----|
-| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) |
+| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | `dimensions`, `encoding_format` |
| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53)
| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) |
| Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18)
@@ -1891,6 +2013,39 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \
+### Using Inference Profiles with Image Generation
+
+For AWS Bedrock Application Inference Profiles with image generation, use the `model_id` parameter to specify the inference profile ARN:
+
+
+
+
+```python
+from litellm import image_generation
+
+response = image_generation(
+ model="bedrock/amazon.nova-canvas-v1:0",
+ model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0",
+ prompt="A cute baby sea otter"
+)
+print(f"response: {response}")
+```
+
+
+
+
+```yaml
+model_list:
+ - model_name: nova-canvas-inference-profile
+ litellm_params:
+ model: bedrock/amazon.nova-canvas-v1:0
+ model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0
+ aws_region_name: "eu-west-1"
+```
+
+
+
+
## Supported AWS Bedrock Image Generation Models
| Model Name | Function Call |
diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md
new file mode 100644
index 0000000000..95ee8d3d22
--- /dev/null
+++ b/docs/my-website/docs/providers/bedrock_embedding.md
@@ -0,0 +1,95 @@
+# Bedrock Embedding
+
+## Supported Embedding Models
+
+| Provider | LiteLLM Route | AWS Documentation |
+|----------|---------------|-------------------|
+| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) |
+| Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) |
+| TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) |
+
+### API keys
+This can be set as env variables or passed as **params to litellm.embedding()**
+```python
+import os
+os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key
+os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key
+os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2
+```
+
+## Usage
+### LiteLLM Python SDK
+```python
+from litellm import embedding
+response = embedding(
+ model="bedrock/amazon.titan-embed-text-v1",
+ input=["good morning from litellm"],
+)
+print(response)
+```
+
+### LiteLLM Proxy Server
+
+#### 1. Setup config.yaml
+```yaml
+model_list:
+ - model_name: titan-embed-v1
+ litellm_params:
+ model: bedrock/amazon.titan-embed-text-v1
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+ aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+ aws_region_name: us-east-1
+ - model_name: titan-embed-v2
+ litellm_params:
+ model: bedrock/amazon.titan-embed-text-v2:0
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+ aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+ aws_region_name: us-east-1
+```
+
+#### 2. Start Proxy
+```bash
+litellm --config /path/to/config.yaml
+```
+
+#### 3. Use with OpenAI Python SDK
+```python
+import openai
+client = openai.OpenAI(
+ api_key="anything",
+ base_url="http://0.0.0.0:4000"
+)
+
+response = client.embeddings.create(
+ input=["good morning from litellm"],
+ model="titan-embed-v1"
+)
+print(response)
+```
+
+#### 4. Use with LiteLLM Python SDK
+```python
+import litellm
+response = litellm.embedding(
+ model="titan-embed-v1", # model alias from config.yaml
+ input=["good morning from litellm"],
+ api_base="http://0.0.0.0:4000",
+ api_key="anything"
+)
+print(response)
+```
+
+## Supported AWS Bedrock Embedding Models
+
+| Model Name | Usage | Supported Additional OpenAI params |
+|----------------------|---------------------------------------------|-----|
+| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) |
+| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53)
+| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) |
+| TwelveLabs Marengo Embed 2.7 | `embedding(model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0", input=input)` | Supports multimodal input (text, video, audio, image) |
+| Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18)
+| Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18)
+
+### Advanced - [Drop Unsupported Params](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage)
+
+### Advanced - [Pass model/provider-specific Params](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage)
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/compactifai.md b/docs/my-website/docs/providers/compactifai.md
index 0e6e8f4ed3..1aa8146307 100644
--- a/docs/my-website/docs/providers/compactifai.md
+++ b/docs/my-website/docs/providers/compactifai.md
@@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem';
# CompactifAI
https://docs.compactif.ai/
-CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (<5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency.
+CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (under 5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency.
| Property | Details |
|-------|-------|
@@ -192,7 +192,7 @@ Common model formats:
## Benefits
- **Cost Efficient**: Up to 70% lower inference costs compared to standard models
-- **High Performance**: 4x throughput gains with minimal quality loss (<5%)
+- **High Performance**: 4x throughput gains with minimal quality loss (under 5%)
- **Low Latency**: Optimized for fast response times
- **Drop-in Replacement**: Full OpenAI API compatibility
- **Scalable**: Superior concurrency and resource efficiency
diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index fda0cee862..cb90b7434e 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -2509,150 +2509,6 @@ print("response from proxy", response)
-## **Batch APIs**
-
-Just add the following Vertex env vars to your environment.
-
-```bash
-# GCS Bucket settings, used to store batch prediction files in
-export GCS_BUCKET_NAME = "litellm-testing-bucket" # the bucket you want to store batch prediction files in
-export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file
-
-# Vertex /batch endpoint settings, used for LLM API requests
-export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file
-export VERTEXAI_LOCATION="us-central1" # can be any vertex location
-export VERTEXAI_PROJECT="my-test-project"
-```
-
-### Usage
-
-
-#### 1. Create a file of batch requests for vertex
-
-LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**
-
-Each `body` in the file should be an **OpenAI API request**
-
-Create a file called `vertex_batch_completions.jsonl` in the current working directory, the `model` should be the Vertex AI model name
-```
-{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
-{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
-```
-
-
-#### 2. Upload a File of batch requests
-
-For `vertex_ai` litellm will upload the file to the provided `GCS_BUCKET_NAME`
-
-```python
-import os
-oai_client = OpenAI(
- api_key="sk-1234", # litellm proxy API key
- base_url="http://localhost:4000" # litellm proxy base url
-)
-file_name = "vertex_batch_completions.jsonl" #
-_current_dir = os.path.dirname(os.path.abspath(__file__))
-file_path = os.path.join(_current_dir, file_name)
-file_obj = oai_client.files.create(
- file=open(file_path, "rb"),
- purpose="batch",
- extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use vertex_ai for this file upload
-)
-```
-
-**Expected Response**
-
-```json
-{
- "id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
- "bytes": 416,
- "created_at": 1733392026,
- "filename": "litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
- "object": "file",
- "purpose": "batch",
- "status": "uploaded",
- "status_details": null
-}
-```
-
-
-
-#### 3. Create a batch
-
-```python
-batch_input_file_id = file_obj.id # use `file_obj` from step 2
-create_batch_response = oai_client.batches.create(
- completion_window="24h",
- endpoint="/v1/chat/completions",
- input_file_id=batch_input_file_id, # example input_file_id = "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/c2b1b785-252b-448c-b180-033c4c63b3ce"
- extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request
-)
-```
-
-**Expected Response**
-
-```json
-{
- "id": "3814889423749775360",
- "completion_window": "24hrs",
- "created_at": 1733392026,
- "endpoint": "",
- "input_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
- "object": "batch",
- "status": "validating",
- "cancelled_at": null,
- "cancelling_at": null,
- "completed_at": null,
- "error_file_id": null,
- "errors": null,
- "expired_at": null,
- "expires_at": null,
- "failed_at": null,
- "finalizing_at": null,
- "in_progress_at": null,
- "metadata": null,
- "output_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001",
- "request_counts": null
-}
-```
-
-#### 4. Retrieve a batch
-
-```python
-retrieved_batch = oai_client.batches.retrieve(
- batch_id=create_batch_response.id,
- extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request
-)
-```
-
-**Expected Response**
-
-```json
-{
- "id": "3814889423749775360",
- "completion_window": "24hrs",
- "created_at": 1736500100,
- "endpoint": "",
- "input_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/7b2e47f5-3dd4-436d-920f-f9155bbdc952",
- "object": "batch",
- "status": "completed",
- "cancelled_at": null,
- "cancelling_at": null,
- "completed_at": null,
- "error_file_id": null,
- "errors": null,
- "expired_at": null,
- "expires_at": null,
- "failed_at": null,
- "finalizing_at": null,
- "in_progress_at": null,
- "metadata": null,
- "output_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001",
- "request_counts": null
-}
-```
-
-
## **Fine Tuning APIs**
@@ -2758,6 +2614,44 @@ curl http://localhost:4000/v1/fine_tuning/jobs \
+## Labels
+
+
+Google enables you to add custom metadata to its `generateContent` and `streamGenerateContent` calls.
+This mechanism is useful in Vertex AI because it allows costs and usage tracking over multiple
+different applications or users.
+
+
+### Usage
+
+You can use that feature through LiteLLM by sending `labels` or `metadata` field in your requests.
+
+If the client sets the `labels` field in the request to the LiteLLM,
+the LiteLLM will pass the `labels` field to the Vertex AI backend.
+
+If the client sets the `metadata` field in the request to the LiteLLM and the `labels` field is not set,
+the LiteLLM will create the `labels` field filled with `metadata` key/value pairs for all string values and
+pass it to the Vertex AI backend.
+
+
+Here is an example JSON request demonstrating the labels usage:
+
+```json
+{
+ "model": "gemini-2.0-flash-lite",
+ "messages": [
+ { "role": "user", "content": "respond in 20 words. who are you?" }
+ ],
+ "labels": {
+ "client_app": "acme_comp_financial_app",
+ "department": "finance",
+ "project": "acme_ai"
+ }
+}
+```
+
+
+
## Extra
### Using `GOOGLE_APPLICATION_CREDENTIALS`
diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md
new file mode 100644
index 0000000000..4eaa0d69d4
--- /dev/null
+++ b/docs/my-website/docs/providers/vertex_batch.md
@@ -0,0 +1,264 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## **Batch APIs**
+
+Just add the following Vertex env vars to your environment.
+
+```bash
+# GCS Bucket settings, used to store batch prediction files in
+export GCS_BUCKET_NAME="my-batch-bucket" # the bucket you want to store batch prediction files in
+export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file
+
+# Vertex /batch endpoint settings, used for LLM API requests
+export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file
+export VERTEXAI_LOCATION="us-central1" # can be any vertex location
+export VERTEXAI_PROJECT="my-project"
+```
+
+### Usage
+
+Follow this complete workflow: create JSONL file → upload file → create batch → retrieve batch status → get file content
+
+#### 1. Create a JSONL file of batch requests
+
+LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**.
+
+Each `body` in the file should be an **OpenAI API request**.
+
+Create a file called `batch_requests.jsonl` with your requests:
+```jsonl
+{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
+{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
+```
+
+#### 2. Upload the file
+
+Upload your JSONL file. For `vertex_ai`, the file will be stored in your configured GCS bucket provided by `GCS_BUCKET_NAME`.
+
+
+
+
+```python showLineNumbers title="upload_file.py"
+from openai import OpenAI
+
+oai_client = OpenAI(
+ api_key="sk-1234", # litellm proxy API key
+ base_url="http://localhost:4000" # litellm proxy base url
+)
+
+file_obj = oai_client.files.create(
+ file=open("batch_requests.jsonl", "rb"),
+ purpose="batch",
+ extra_body={"custom_llm_provider": "vertex_ai"}
+)
+
+print(f"File uploaded with ID: {file_obj.id}")
+```
+
+
+
+
+```bash showLineNumbers title="Upload File"
+curl --request POST \
+ --url http://localhost:4000/v1/files \
+ --header 'Content-Type: multipart/form-data' \
+ --form purpose=batch \
+ --form file=@batch_requests.jsonl \
+ --form custom_llm_provider=vertex_ai
+```
+
+
+
+
+**Expected Response:**
+
+```json
+{
+ "id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
+ "bytes": 416,
+ "created_at": 1758303684,
+ "filename": "litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
+ "object": "file",
+ "purpose": "batch",
+ "status": "uploaded",
+ "expires_at": null,
+ "status_details": null
+}
+```
+
+#### 3. Create a batch
+
+Create a batch job using the uploaded file ID.
+
+
+
+
+```python showLineNumbers title="create_batch.py"
+batch_input_file_id = file_obj.id # from step 2
+create_batch_response = oai_client.batches.create(
+ completion_window="24h",
+ endpoint="/v1/chat/completions",
+ input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd"
+ extra_body={"custom_llm_provider": "vertex_ai"}
+)
+
+print(f"Batch created with ID: {create_batch_response.id}")
+```
+
+
+
+
+```bash showLineNumbers title="Create Batch Request"
+curl --request POST \
+ --url http://localhost:4000/v1/batches \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
+ "endpoint": "/v1/chat/completions",
+ "completion_window": "24h",
+ "custom_llm_provider": "vertex_ai"
+}'
+```
+
+
+
+
+**Expected Response:**
+
+```json
+{
+ "id": "7814463557919047680",
+ "completion_window": "24hrs",
+ "created_at": 1758328011,
+ "endpoint": "",
+ "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
+ "object": "batch",
+ "status": "validating",
+ "cancelled_at": null,
+ "cancelling_at": null,
+ "completed_at": null,
+ "error_file_id": null,
+ "errors": null,
+ "expired_at": null,
+ "expires_at": null,
+ "failed_at": null,
+ "finalizing_at": null,
+ "in_progress_at": null,
+ "metadata": null,
+ "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite",
+ "request_counts": null,
+ "usage": null
+}
+```
+
+#### 4. Retrieve batch status
+
+Check the status of your batch job. The batch will progress through states: `validating` → `in_progress` → `completed`.
+
+
+
+
+```python showLineNumbers title="retrieve_batch.py"
+retrieved_batch = oai_client.batches.retrieve(
+ batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680
+ extra_body={"custom_llm_provider": "vertex_ai"}
+)
+
+print(f"Batch status: {retrieved_batch.status}")
+if retrieved_batch.status == "completed":
+ print(f"Output file: {retrieved_batch.output_file_id}")
+```
+
+
+
+
+```bash showLineNumbers title="Retrieve Batch Status"
+curl --request GET \
+ --url 'http://localhost:4000/batches/7814463557919047680?provider=vertex_ai' \
+ --header 'Authorization: Bearer sk-1234'
+```
+
+
+
+
+**Expected Response (when completed):**
+
+```json
+{
+ "id": "7814463557919047680",
+ "completion_window": "24hrs",
+ "created_at": 1758328011,
+ "endpoint": "",
+ "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
+ "object": "batch",
+ "status": "completed",
+ "cancelled_at": null,
+ "cancelling_at": null,
+ "completed_at": null,
+ "error_file_id": null,
+ "errors": null,
+ "expired_at": null,
+ "expires_at": null,
+ "failed_at": null,
+ "finalizing_at": null,
+ "in_progress_at": null,
+ "metadata": null,
+ "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/prediction-model-2025-09-19T21:26:51.569037Z/predictions.jsonl",
+ "request_counts": null,
+ "usage": null
+}
+```
+
+#### 5. Get file content
+
+Once the batch is completed, retrieve the results using the `output_file_id` from the batch response.
+
+**Important:** The `output_file_id` must be URL encoded when used in the request path.
+
+
+
+
+```python showLineNumbers title="get_file_content.py"
+import urllib.parse
+import json
+
+output_file_id = retrieved_batch.output_file_id
+# URL encode the file ID
+encoded_file_id = urllib.parse.quote_plus(output_file_id)
+
+# Get file content
+file_content = oai_client.files.content(
+ file_id=encoded_file_id,
+ extra_body={"custom_llm_provider": "vertex_ai"}
+)
+
+# Process the results
+for line in file_content.text.strip().split('\n'):
+ result = json.loads(line)
+ print(f"Request: {result['request']}")
+ print(f"Response: {result['response']}")
+ print("---")
+```
+
+
+
+
+```bash showLineNumbers title="Get File Content"
+# Note: The file ID must be URL encoded
+curl --request GET \
+ --url 'http://localhost:4000/files/gs%253A%252F%252Fmy-batch-bucket%252Flitellm-vertex-files%252Fpublishers%252Fgoogle%252Fmodels%252Fgemini-2.5-flash-lite%252Fprediction-model-2025-09-19T21%253A26%253A51.569037Z%252Fpredictions.jsonl/content?provider=vertex_ai' \
+ --header 'Authorization: Bearer sk-1234'
+```
+
+
+
+
+**Expected Response:**
+
+The response contains JSONL format with one result per line:
+
+```jsonl
+{"status":"","processed_time":"2025-09-19T21:29:47.352+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are a helpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.48079710006713866,"content":{"parts":[{"text":"Hello there! It's nice to meet you"}],"role":"model"},"finishReason":"MAX_TOKENS"}],"createTime":"2025-09-19T21:29:47.484619Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaIvKHdvshMIP_aOtuAg","usageMetadata":{"candidatesTokenCount":10,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":10}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":19,"trafficType":"ON_DEMAND"}}}
+{"status":"","processed_time":"2025-09-19T21:29:47.358+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are an unhelpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.6168075137668185,"content":{"parts":[{"text":"I am unable to assist with this request."}],"role":"model"},"finishReason":"STOP"}],"createTime":"2025-09-19T21:29:47.470889Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaOneHISShMIP28nA8QQ","usageMetadata":{"candidatesTokenCount":9,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":9}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":18,"trafficType":"ON_DEMAND"}}}
+```
diff --git a/docs/my-website/docs/proxy/budget_reset_and_tz.md b/docs/my-website/docs/proxy/budget_reset_and_tz.md
index 541ff6a2f0..340e33afe1 100644
--- a/docs/my-website/docs/proxy/budget_reset_and_tz.md
+++ b/docs/my-website/docs/proxy/budget_reset_and_tz.md
@@ -29,5 +29,6 @@ Common timezone values:
- `US/Pacific` - Pacific Time
- `Europe/London` - UK Time
- `Asia/Kolkata` - Indian Standard Time (IST)
+- `Asia/Bangkok` - Indochina Time (ICT)
- `Asia/Tokyo` - Japan Standard Time
- `Australia/Sydney` - Australian Eastern Time
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index ee857f700a..974e95a07b 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -93,6 +93,8 @@ callback_settings:
general_settings:
completion_model: string
+ store_prompts_in_spend_logs: boolean
+ forward_client_headers_to_llm_api: boolean
disable_spend_logs: boolean # turn off writing each transaction to the db
disable_master_key_return: boolean # turn off returning master key on UI (checked on '/user/info' endpoint)
disable_retry_on_max_parallel_request_limit_error: boolean # turn off retries when max parallel request limit is reached
@@ -121,6 +123,35 @@ general_settings:
alerting: ["slack", "email"]
alerting_threshold: 0
use_client_credentials_pass_through_routes: boolean # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints
+
+router_settings:
+ routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" - RECOMMENDED for best performance
+ redis_host: # string
+ redis_password: # string
+ redis_port: # string
+ enable_pre_call_checks: true # bool - Before call is made check if a call is within model context window
+ allowed_fails: 3 # cooldown model if it fails > 1 call in a minute.
+ cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
+ disable_cooldowns: True # bool - Disable cooldowns for all models
+ enable_tag_filtering: True # bool - Use tag based routing for requests
+ retry_policy: { # Dict[str, int]: retry policy for different types of exceptions
+ "AuthenticationErrorRetries": 3,
+ "TimeoutErrorRetries": 3,
+ "RateLimitErrorRetries": 3,
+ "ContentPolicyViolationErrorRetries": 4,
+ "InternalServerErrorRetries": 4
+ }
+ allowed_fails_policy: {
+ "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment
+ "AuthenticationErrorAllowedFails": 10, # int
+ "TimeoutErrorAllowedFails": 12, # int
+ "RateLimitErrorAllowedFails": 10000, # int
+ "ContentPolicyViolationErrorAllowedFails": 15, # int
+ "InternalServerErrorAllowedFails": 20, # int
+ }
+ content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations
+ fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors
+
```
### litellm_settings - Reference
@@ -659,6 +690,8 @@ router_settings:
| PILLAR_API_KEY | API key for Pillar API Guardrails
| PILLAR_ON_FLAGGED_ACTION | Action to take when content is flagged ('block' or 'monitor')
| POD_NAME | Pod name for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) as `POD_NAME`
+| POSTHOG_API_KEY | API key for PostHog analytics integration
+| POSTHOG_API_URL | Base URL for PostHog API (defaults to https://us.i.posthog.com)
| PREDIBASE_API_BASE | Base URL for Predibase API
| PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service
| PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service
@@ -739,3 +772,4 @@ router_settings:
| WEBHOOK_URL | URL for receiving webhooks from external services
| SPEND_LOG_RUN_LOOPS | Constant for setting how many runs of 1000 batch deletes should spend_log_cleanup task run |
| SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000 |
+| COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY | Maximum size for CoroutineChecker in-memory cache. Default is 1000 |
\ No newline at end of file
diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md
index cdb6f7018f..6a11d069fb 100644
--- a/docs/my-website/docs/proxy/deploy.md
+++ b/docs/my-website/docs/proxy/deploy.md
@@ -13,6 +13,7 @@ To start using Litellm, run the following commands in a shell:
```bash
# Get the code
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml
+curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml
# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
diff --git a/docs/my-website/docs/proxy/dynamic_rate_limit.md b/docs/my-website/docs/proxy/dynamic_rate_limit.md
new file mode 100644
index 0000000000..74a2d4c6c1
--- /dev/null
+++ b/docs/my-website/docs/proxy/dynamic_rate_limit.md
@@ -0,0 +1,241 @@
+
+# Dynamic TPM/RPM Allocation
+
+Prevent projects from gobbling too much tpm/rpm.
+
+Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125)
+
+## Quick Start Usage
+
+1. Setup config.yaml
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: my-fake-model
+ litellm_params:
+ model: gpt-3.5-turbo
+ api_key: my-fake-key
+ mock_response: hello-world
+ tpm: 60
+
+litellm_settings:
+ callbacks: ["dynamic_rate_limiter_v3"]
+
+general_settings:
+ master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
+ database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```python showLineNumbers title="test.py"
+"""
+- Run 2 concurrent teams calling same model
+- model has 60 TPM
+- Mock response returns 30 total tokens / request
+- Each team will only be able to make 1 request per minute
+"""
+
+import requests
+from openai import OpenAI, RateLimitError
+
+def create_key(api_key: str, base_url: str):
+ response = requests.post(
+ url="{}/key/generate".format(base_url),
+ json={},
+ headers={
+ "Authorization": "Bearer {}".format(api_key)
+ }
+ )
+
+ _response = response.json()
+
+ return _response["key"]
+
+key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+# call proxy with key 1 - works
+openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000")
+
+response = openai_client_1.chat.completions.with_raw_response.create(
+ model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
+)
+
+print("Headers for call 1 - {}".format(response.headers))
+_response = response.parse()
+print("Total tokens for call - {}".format(_response.usage.total_tokens))
+
+
+# call proxy with key 2 - works
+openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000")
+
+response = openai_client_2.chat.completions.with_raw_response.create(
+ model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
+)
+
+print("Headers for call 2 - {}".format(response.headers))
+_response = response.parse()
+print("Total tokens for call - {}".format(_response.usage.total_tokens))
+# call proxy with key 2 - fails
+try:
+ openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}])
+ raise Exception("This should have failed!")
+except RateLimitError as e:
+ print("This was rate limited b/c - {}".format(str(e)))
+
+```
+
+**Expected Response**
+
+```
+This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}
+```
+
+
+## [BETA] Set Priority / Reserve Quota
+
+Reserve TPM/RPM capacity for different environments or use cases. This ensures critical production workloads always have guaranteed capacity, while development or lower-priority tasks use remaining quota.
+
+**Use Cases:**
+- Production vs Development environments
+- Real-time applications vs batch processing
+- Critical services vs experimental features
+
+:::tip
+
+Reserving TPM/RPM on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it.
+:::
+
+### How Priority Reservation Works
+
+Priority reservation allocates a percentage of your model's total TPM/RPM to specific priority levels. Keys with higher priority get guaranteed access to their reserved quota first.
+
+**Example Scenario:**
+- Model has 10 RPM total capacity
+- Priority reservation: `{"prod": 0.9, "dev": 0.1}`
+- Result: Production keys get 9 RPM guaranteed, Development keys get 1 RPM guaranteed
+
+### Configuration
+
+#### 1. Setup config.yaml
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: "gpt-3.5-turbo"
+ api_key: os.environ/OPENAI_API_KEY
+ rpm: 10 # Total model capacity
+
+litellm_settings:
+ callbacks: ["dynamic_rate_limiter_v3"]
+ priority_reservation:
+ "prod": 0.9 # 90% reserved for production (9 RPM)
+ "dev": 0.1 # 10% reserved for development (1 RPM)
+
+general_settings:
+ master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
+ database_url: postgres://.. # OR set `DATABASE_URL=".."` in your.env
+```
+
+**Configuration Details:**
+
+`priority_reservation`: Dict[str, float]
+- **Key (str)**: Priority level name (can be any string like "prod", "dev", "critical", etc.)
+- **Value (float)**: Percentage of total TPM/RPM to reserve (0.0 to 1.0)
+- **Note**: Values should sum to 1.0 or less
+
+**Start Proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+#### 2. Create Keys with Priority Levels
+
+**Production Key:**
+```bash
+curl -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "metadata": {"priority": "prod"}
+}'
+```
+
+**Development Key:**
+```bash
+curl -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "metadata": {"priority": "dev"}
+}'
+```
+
+**Expected Response for both:**
+```json
+{
+ "key": "sk-...",
+ "metadata": {"priority": "prod"}, // or "dev"
+ ...
+}
+```
+
+#### 3. Test Priority Allocation
+
+**Test Production Key (should get 9 RPM):**
+```bash
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+ -H 'Content-Type: application/json' \
+ -H 'Authorization: Bearer sk-prod-key' \
+ -d '{
+ "model": "gpt-3.5-turbo",
+ "messages": [{"role": "user", "content": "Hello from prod"}]
+ }'
+```
+
+**Test Development Key (should get 1 RPM):**
+```bash
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+ -H 'Content-Type: application/json' \
+ -H 'Authorization: Bearer sk-dev-key' \
+ -d '{
+ "model": "gpt-3.5-turbo",
+ "messages": [{"role": "user", "content": "Hello from dev"}]
+ }'
+```
+
+### Expected Behavior
+
+With the configuration above:
+
+1. **Production keys** can make up to 9 requests per minute
+2. **Development keys** can make up to 1 request per minute
+3. Production requests are never blocked by development usage
+
+**Rate Limit Error Example:**
+```json
+{
+ "error": {
+ "message": "Key=sk-dev-... over available RPM=0. Model RPM=10, Reserved RPM for priority 'dev'=1, Active keys=1",
+ "type": "rate_limit_exceeded",
+ "code": 429
+ }
+}
+```
+
+### Demo Video
+
+This video walks through setting up dynamic rate limiting with priority reservation and locust tests to validate the behavior.
+
+
+
diff --git a/docs/my-website/docs/proxy/logging_spec.md b/docs/my-website/docs/proxy/logging_spec.md
index a39a62318e..5166b86ae1 100644
--- a/docs/my-website/docs/proxy/logging_spec.md
+++ b/docs/my-website/docs/proxy/logging_spec.md
@@ -61,6 +61,11 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds:
| `requester_metadata` | `Optional[dict]` | Additional requester metadata |
| `vector_store_request_metadata` | `Optional[List[StandardLoggingVectorStoreRequest]]` | Vector store request metadata |
| `requester_custom_headers` | Dict[str, str] | Any custom (`x-`) headers sent by the client to the proxy. |
+| `prompt_management_metadata` | `Optional[StandardLoggingPromptManagementMetadata]` | Prompt management and versioning metadata |
+| `mcp_tool_call_metadata` | `Optional[StandardLoggingMCPToolCall]` | MCP (Model Context Protocol) tool call information and cost tracking |
+| `applied_guardrails` | `Optional[List[str]]` | List of applied guardrail names |
+| `usage_object` | `Optional[dict]` | Raw usage object from the LLM provider |
+| `cold_storage_object_key` | `Optional[str]` | S3/GCS object key for cold storage retrieval |
| `guardrail_information` | `Optional[StandardLoggingGuardrailInformation]` | Guardrail information |
@@ -145,4 +150,82 @@ A literal type with two possible values:
| `duration` | `Optional[float]` | Duration of the guardrail in seconds |
| `masked_entity_count` | `Optional[Dict[str, int]]` | Count of masked entities |
+## StandardLoggingPromptManagementMetadata
+Used for tracking prompt versioning and management information.
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `prompt_id` | `str` | **Required**. Unique identifier for the prompt template or version |
+| `prompt_variables` | `Optional[dict]` | Variables/parameters used in the prompt template (e.g., `{"user_name": "John", "context": "support"}`) |
+| `prompt_integration` | `str` | **Required**. Integration or system managing the prompt (e.g., `"langfuse"`, `"promptlayer"`, `"custom"`) |
+
+## StandardLoggingMCPToolCall
+
+Used to track Model Context Protocol (MCP) tool calls within LiteLLM requests. This provides detailed logging for external tool integrations.
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `name` | `str` | **Required**. The name of the tool being called (e.g., `"get_weather"`, `"search_database"`) |
+| `arguments` | `dict` | **Required**. Arguments passed to the tool as key-value pairs |
+| `result` | `Optional[dict]` | The response/result returned by the tool execution (populated by custom logging hooks) |
+| `mcp_server_name` | `Optional[str]` | Name of the MCP server that handled the tool call (e.g., `"weather-service"`, `"database-connector"`) |
+| `mcp_server_logo_url` | `Optional[str]` | URL for the MCP server's logo (used for UI display in LiteLLM dashboard) |
+| `namespaced_tool_name` | `Optional[str]` | Fully qualified tool name including server prefix (e.g., `"deepwiki-mcp/get_page_content"`, `"github-mcp/create_issue"`) |
+| `mcp_server_cost_info` | `Optional[MCPServerCostInfo]` | Cost tracking information for the tool call |
+
+### MCPServerCostInfo
+
+Cost tracking structure for MCP server tool calls:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `default_cost_per_query` | `Optional[float]` | Default cost in USD for any tool call to this MCP server |
+| `tool_name_to_cost_per_query` | `Optional[Dict[str, float]]` | Per-tool cost mapping for granular pricing (e.g., `{"search": 0.01, "create": 0.05}`) |
+
+### Usage
+
+```python
+# Basic MCP tool call metadata
+mcp_tool_call = {
+ "name": "search_documents",
+ "arguments": {
+ "query": "machine learning tutorials",
+ "limit": 10,
+ "filter": "type:pdf"
+ },
+ "mcp_server_name": "document-search-service",
+ "namespaced_tool_name": "docs-mcp/search_documents",
+ "mcp_server_cost_info": {
+ "default_cost_per_query": 0.02,
+ "tool_name_to_cost_per_query": {
+ "search_documents": 0.02,
+ "get_document": 0.01
+ }
+ }
+}
+
+# optional result field (via custom logging hooks)
+mcp_tool_call_with_result = {
+ "name": "search_documents",
+ "arguments": {
+ "query": "machine learning tutorials",
+ "limit": 10,
+ "filter": "type:pdf"
+ },
+ "result": {
+ "documents": [...],
+ "total_found": 42,
+ "search_time_ms": 150
+ },
+ "mcp_server_name": "document-search-service",
+ "namespaced_tool_name": "docs-mcp/search_documents",
+ "mcp_server_cost_info": {
+ "default_cost_per_query": 0.02,
+ "tool_name_to_cost_per_query": {
+ "search_documents": 0.02,
+ "get_document": 0.01
+ }
+ }
+}
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md
index 3847406641..03d1879713 100644
--- a/docs/my-website/docs/proxy/team_budgets.md
+++ b/docs/my-website/docs/proxy/team_budgets.md
@@ -178,188 +178,3 @@ Expect to see this metric on prometheus to track the Remaining Budget for the te
```shell
litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06
```
-
-
-### Dynamic TPM/RPM Allocation
-
-Prevent projects from gobbling too much tpm/rpm.
-
-Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125)
-
-1. Setup config.yaml
-
-```yaml
-model_list:
- - model_name: my-fake-model
- litellm_params:
- model: gpt-3.5-turbo
- api_key: my-fake-key
- mock_response: hello-world
- tpm: 60
-
-litellm_settings:
- callbacks: ["dynamic_rate_limiter"]
-
-general_settings:
- master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
- database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
-```
-
-2. Start proxy
-
-```bash
-litellm --config /path/to/config.yaml
-```
-
-3. Test it!
-
-```python
-"""
-- Run 2 concurrent teams calling same model
-- model has 60 TPM
-- Mock response returns 30 total tokens / request
-- Each team will only be able to make 1 request per minute
-"""
-
-import requests
-from openai import OpenAI, RateLimitError
-
-def create_key(api_key: str, base_url: str):
- response = requests.post(
- url="{}/key/generate".format(base_url),
- json={},
- headers={
- "Authorization": "Bearer {}".format(api_key)
- }
- )
-
- _response = response.json()
-
- return _response["key"]
-
-key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
-key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
-
-# call proxy with key 1 - works
-openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000")
-
-response = openai_client_1.chat.completions.with_raw_response.create(
- model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
-)
-
-print("Headers for call 1 - {}".format(response.headers))
-_response = response.parse()
-print("Total tokens for call - {}".format(_response.usage.total_tokens))
-
-
-# call proxy with key 2 - works
-openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000")
-
-response = openai_client_2.chat.completions.with_raw_response.create(
- model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
-)
-
-print("Headers for call 2 - {}".format(response.headers))
-_response = response.parse()
-print("Total tokens for call - {}".format(_response.usage.total_tokens))
-# call proxy with key 2 - fails
-try:
- openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}])
- raise Exception("This should have failed!")
-except RateLimitError as e:
- print("This was rate limited b/c - {}".format(str(e)))
-
-```
-
-**Expected Response**
-
-```
-This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key= over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}
-```
-
-
-#### ✨ [BETA] Set Priority / Reserve Quota
-
-Reserve tpm/rpm capacity for projects in prod.
-
-:::tip
-
-Reserving tpm/rpm on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it.
-:::
-
-
-1. Setup config.yaml
-
-```yaml
-model_list:
- - model_name: gpt-3.5-turbo
- litellm_params:
- model: "gpt-3.5-turbo"
- api_key: os.environ/OPENAI_API_KEY
- rpm: 100
-
-litellm_settings:
- callbacks: ["dynamic_rate_limiter"]
- priority_reservation: {"dev": 0, "prod": 1}
-
-general_settings:
- master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
- database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
-```
-
-
-priority_reservation:
-- Dict[str, float]
- - str: can be any string
- - float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority.
-
-**Start Proxy**
-
-```
-litellm --config /path/to/config.yaml
-```
-
-2. Create a key with that priority
-
-```bash
-curl -X POST 'http://0.0.0.0:4000/key/generate' \
--H 'Authorization: Bearer ' \
--H 'Content-Type: application/json' \
--D '{
- "metadata": {"priority": "dev"} # 👈 KEY CHANGE
-}'
-```
-
-**Expected Response**
-
-```
-{
- ...
- "key": "sk-.."
-}
-```
-
-
-3. Test it!
-
-```bash
-curl -X POST 'http://0.0.0.0:4000/chat/completions' \
- -H 'Content-Type: application/json' \
- -H 'Authorization: sk-...' \ # 👈 key from step 2.
- -d '{
- "model": "gpt-3.5-turbo",
- "messages": [
- {
- "role": "user",
- "content": "what llm are you"
- }
- ],
-}'
-```
-
-**Expected Response**
-
-```
-Key=... over available RPM=0. Model RPM=100, Active keys=None
-```
-
diff --git a/docs/my-website/docs/sdk/headers.md b/docs/my-website/docs/sdk/headers.md
new file mode 100644
index 0000000000..a1fdaadc6f
--- /dev/null
+++ b/docs/my-website/docs/sdk/headers.md
@@ -0,0 +1,328 @@
+# SDK Header Support
+
+LiteLLM SDK provides comprehensive support for passing additional headers with API requests. This is essential for enterprise environments using API gateways, service meshes, and multi-tenant architectures.
+
+## Overview
+
+Headers can be passed to LiteLLM in three ways, with the following priority order:
+1. **Request-specific headers** (highest priority)
+2. **extra_headers parameter**
+3. **Global litellm.headers** (lowest priority)
+
+When the same header key is specified in multiple places, the higher priority value will be used.
+
+## Usage Methods
+
+### 1. Global Headers (litellm.headers)
+
+Set headers that will be included in all API requests:
+
+```python
+import litellm
+
+# Set global headers for all requests
+litellm.headers = {
+ "X-API-Gateway-Key": "your-gateway-key",
+ "X-Company-ID": "acme-corp",
+ "X-Environment": "production"
+}
+
+# Now all completion calls will include these headers
+response = litellm.completion(
+ model="claude-3-5-sonnet-latest",
+ messages=[{"role": "user", "content": "Hello"}]
+)
+```
+
+### 2. Per-Request Headers (extra_headers)
+
+Pass headers for specific requests using the `extra_headers` parameter:
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_headers={
+ "X-Request-ID": "req-12345",
+ "X-Tenant-ID": "tenant-abc",
+ "X-Custom-Auth": "bearer-token-xyz"
+ }
+)
+```
+
+### 3. Request Headers (headers parameter)
+
+Use the `headers` parameter for the highest priority header control:
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="claude-3-5-sonnet-latest",
+ messages=[{"role": "user", "content": "Hello"}],
+ headers={
+ "X-Priority-Header": "high-priority-value",
+ "Authorization": "Bearer custom-token"
+ }
+)
+```
+
+### 4. Combining All Methods
+
+You can combine all three methods. Headers will be merged with the priority order:
+
+```python
+import litellm
+
+# Global headers (lowest priority)
+litellm.headers = {
+ "X-Company-ID": "acme-corp",
+ "X-Shared-Header": "global-value"
+}
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_headers={
+ "X-Request-ID": "req-12345",
+ "X-Shared-Header": "extra-value" # Overrides global
+ },
+ headers={
+ "X-Priority-Header": "important",
+ "X-Shared-Header": "request-value" # Overrides both global and extra
+ }
+)
+
+# Final headers sent to API:
+# {
+# "X-Company-ID": "acme-corp", # From global
+# "X-Request-ID": "req-12345", # From extra_headers
+# "X-Priority-Header": "important", # From headers
+# "X-Shared-Header": "request-value" # From headers (highest priority)
+# }
+```
+
+## Enterprise Use Cases
+
+### API Gateway Integration (Apigee, Kong, AWS API Gateway)
+
+```python
+import litellm
+
+# Set up headers for API gateway routing and authentication
+litellm.headers = {
+ "X-API-Gateway-Key": "your-gateway-key",
+ "X-Route-Version": "v2"
+}
+
+# Per-tenant requests
+response = litellm.completion(
+ model="claude-3-5-sonnet-latest",
+ messages=[{"role": "user", "content": "Analyze this data"}],
+ extra_headers={
+ "X-Tenant-ID": "tenant-123",
+ "X-Department": "engineering"
+ }
+)
+```
+
+### Service Mesh (Istio, Linkerd)
+
+```python
+import litellm
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_headers={
+ "X-Trace-ID": "trace-abc-123",
+ "X-Service-Name": "ai-service",
+ "X-Version": "1.2.3"
+ }
+)
+```
+
+### Multi-Tenant SaaS Applications
+
+```python
+import litellm
+
+def make_ai_request(user_id, tenant_id, content):
+ return litellm.completion(
+ model="claude-3-5-sonnet-latest",
+ messages=[{"role": "user", "content": content}],
+ extra_headers={
+ "X-User-ID": user_id,
+ "X-Tenant-ID": tenant_id,
+ "X-Request-Time": str(int(time.time()))
+ }
+ )
+
+# Usage
+response = make_ai_request("user-456", "tenant-org-1", "Help me write code")
+```
+
+### Request Tracing and Debugging
+
+```python
+import litellm
+import uuid
+
+def traced_completion(model, messages, **kwargs):
+ trace_id = str(uuid.uuid4())
+
+ return litellm.completion(
+ model=model,
+ messages=messages,
+ extra_headers={
+ "X-Trace-ID": trace_id,
+ "X-Debug-Mode": "true",
+ "X-Source-Service": "my-app"
+ },
+ **kwargs
+ )
+
+# Usage
+response = traced_completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Debug this issue"}]
+)
+```
+
+### Custom Authentication
+
+```python
+import litellm
+
+def get_custom_auth_token():
+ # Your custom authentication logic
+ return "custom-auth-token"
+
+response = litellm.completion(
+ model="claude-3-5-sonnet-latest",
+ messages=[{"role": "user", "content": "Hello"}],
+ headers={
+ "X-Custom-Auth": get_custom_auth_token(),
+ "X-Auth-Type": "custom"
+ }
+)
+```
+
+## Provider Support
+
+Headers are supported across all LiteLLM providers including:
+
+- **OpenAI** (GPT models)
+- **Anthropic** (Claude models)
+- **Cohere**
+- **Hugging Face**
+- **Custom providers**
+- **Azure OpenAI**
+- **AWS Bedrock**
+- **Google Vertex AI**
+
+Each provider will receive your custom headers along with their required authentication and API-specific headers.
+
+## Best Practices
+
+### 1. Use Meaningful Header Names
+```python
+# Good
+extra_headers = {
+ "X-Request-ID": "req-12345",
+ "X-Tenant-ID": "org-456"
+}
+
+# Avoid
+extra_headers = {
+ "custom1": "value1",
+ "h2": "value2"
+}
+```
+
+### 2. Include Tracing Information
+```python
+extra_headers = {
+ "X-Trace-ID": trace_id,
+ "X-Span-ID": span_id,
+ "X-Service-Name": "ai-service"
+}
+```
+
+### 3. Handle Sensitive Information Carefully
+```python
+# Don't log sensitive headers
+import os
+
+if os.getenv("ENVIRONMENT") != "production":
+ extra_headers["X-Debug-User"] = user_id
+```
+
+### 4. Use Environment-Specific Headers
+```python
+import os
+
+environment = os.getenv("ENVIRONMENT", "development")
+
+litellm.headers = {
+ "X-Environment": environment,
+ "X-Service-Version": os.getenv("SERVICE_VERSION", "unknown")
+}
+```
+
+## Troubleshooting
+
+### Headers Not Being Passed
+
+If your headers aren't reaching the API:
+
+1. **Check Header Names**: Ensure header names don't conflict with provider-specific headers
+2. **Verify Priority**: Remember that `headers` > `extra_headers` > `litellm.headers`
+3. **Test with Logging**: Enable verbose logging to see what headers are being sent
+
+```python
+import litellm
+
+# Enable debug logging
+litellm.set_verbose = True
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "test"}],
+ extra_headers={"X-Debug": "test"}
+)
+```
+
+### Gateway or Proxy Issues
+
+If using API gateways or proxies:
+
+1. **Check Gateway Requirements**: Verify required headers for your gateway
+2. **Test Direct vs Gateway**: Compare direct API calls vs gateway calls
+3. **Validate Header Format**: Some gateways have header format requirements
+
+## Security Considerations
+
+1. **Don't Log Sensitive Headers**: Avoid logging authentication tokens or personal data
+2. **Use HTTPS**: Always use secure connections when passing sensitive headers
+3. **Validate Header Values**: Sanitize user-provided header values
+4. **Rotate Keys**: Regularly rotate any API keys passed in headers
+
+```python
+import litellm
+import re
+
+def safe_header_value(value):
+ # Remove potentially dangerous characters
+ return re.sub(r'[^\w\-.]', '', str(value))
+
+response = litellm.completion(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ extra_headers={
+ "X-User-ID": safe_header_value(user_id)
+ }
+)
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md
index b6a9c6a6b9..9d2b3757ee 100644
--- a/docs/my-website/docs/troubleshoot.md
+++ b/docs/my-website/docs/troubleshoot.md
@@ -2,7 +2,7 @@
[Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
[Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-[Community Slack 💭](https://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3)
+[Community Slack 💭](https://litellmossslack.slack.com/)
Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md
index 660c8cbcf0..d93568d49d 100644
--- a/docs/my-website/release_notes/v1.76.0-stable/index.md
+++ b/docs/my-website/release_notes/v1.76.0-stable/index.md
@@ -1,5 +1,5 @@
---
-title: "[PRE-RELEASE]v1.76.0-stable - RPS Improvements"
+title: "v1.76.0-stable - RPS Improvements"
slug: "v1-76-0"
date: 2025-08-23T10:00:00
authors:
diff --git a/docs/my-website/release_notes/v1.77.2-stable/index.md b/docs/my-website/release_notes/v1.77.2-stable/index.md
index 6d54db84df..fdd80693d0 100644
--- a/docs/my-website/release_notes/v1.77.2-stable/index.md
+++ b/docs/my-website/release_notes/v1.77.2-stable/index.md
@@ -1,5 +1,5 @@
---
-title: "[Pre-Release] v1.77.2-stable - Bedrock Batches API"
+title: "v1.77.2-stable - Bedrock Batches API"
slug: "v1-77-2"
date: 2025-09-13T10:00:00
authors:
@@ -21,22 +21,22 @@ import TabItem from '@theme/TabItem';
## Deploy this version
-:::info
-
-This release is not yet live.
-
-:::
-
``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:main-v1.77.2-stable
```
+
``` showLineNumbers title="pip install litellm"
+pip install litellm==1.77.2.post1
```
diff --git a/docs/my-website/release_notes/v1.77.3-stable/index.md b/docs/my-website/release_notes/v1.77.3-stable/index.md
new file mode 100644
index 0000000000..adea34f978
--- /dev/null
+++ b/docs/my-website/release_notes/v1.77.3-stable/index.md
@@ -0,0 +1,258 @@
+---
+title: "[Preview] v1.77.3-stable - Priority Based Rate Limiting"
+slug: "v1-77-3"
+date: 2025-09-21T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+ - name: Ishaan Jaff
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+
+
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:main-v1.77.3.rc.1
+```
+
+
+
+
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.77.3
+```
+
+
+
+
+---
+
+## Key Highlights
+
+- **+550 RPS Performance Improvements** - Optimizations in request handling and object initialization.
+- **Priority Quota Reservation** - Proxy admins can now reserve TPM/RPM capacity for specific keys.
+
+## Priority Quota Reservation
+
+This release adds support for priority quota reservation. This allows **Proxy Admins** to reserve TPM/RPM capacity for keys based on metadata priority levels, ensuring critical production workloads get guaranteed access regardless of development traffic volume.
+
+Get started [here](../../docs/proxy/dynamic_rate_limit#priority-quota-reservation)
+
+
+
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| SambaNova | `sambanova/deepseek-v3.1` | 128K | $0.90 | $0.90 | Chat completions |
+| SambaNova | `sambanova/gpt-oss-120b` | 128K | $0.72 | $0.72 | Chat completions |
+| OVHCloud | Various models | Varies | Contact provider | Contact provider | Chat completions |
+| CompactifAI | Various models | Varies | Contact provider | Contact provider | Chat completions |
+| TwelveLabs | `twelvelabs/marengo-embed-2.7` | 32K | $0.12 | $0.00 | Embeddings |
+
+#### Features
+
+- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)**
+ - New provider support with comprehensive model catalog - [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
+- **[CompactifAI](../../docs/providers/compactifai)**
+ - New provider integration - [PR #14532](https://github.com/BerriAI/litellm/pull/14532)
+- **[SambaNova](../../docs/providers/sambanova)**
+ - Added DeepSeek v3.1 and GPT-OSS-120B models - [PR #14500](https://github.com/BerriAI/litellm/pull/14500)
+- **[Bedrock](../../docs/providers/bedrock)**
+ - Cross-region inference profile cost calculation - [PR #14566](https://github.com/BerriAI/litellm/pull/14566)
+ - AWS external ID parameter support for authentication - [PR #14582](https://github.com/BerriAI/litellm/pull/14582)
+ - CountTokens API implementation - [PR #14557](https://github.com/BerriAI/litellm/pull/14557)
+ - Titan V2 encoding_format parameter support - [PR #14687](https://github.com/BerriAI/litellm/pull/14687)
+ - Nova Canvas image generation inference profiles - [PR #14578](https://github.com/BerriAI/litellm/pull/14578)
+ - Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14618](https://github.com/BerriAI/litellm/pull/14618)
+ - Bedrock Twelve Labs embedding provider support - [PR #14697](https://github.com/BerriAI/litellm/pull/14697)
+- **[Vertex AI](../../docs/providers/vertex)**
+ - Gemini labels field provider-aware filtering - [PR #14563](https://github.com/BerriAI/litellm/pull/14563)
+ - Gemini Batch API support - [PR #14733](https://github.com/BerriAI/litellm/pull/14733)
+- **[Volcengine](../../docs/providers/volcengine)**
+ - Fixed thinking parameters when disabled - [PR #14569](https://github.com/BerriAI/litellm/pull/14569)
+- **[Cohere](../../docs/providers/cohere)**
+ - Handle Generate API deprecation, default to chat endpoints - [PR #14676](https://github.com/BerriAI/litellm/pull/14676)
+- **[TwelveLabs](../../docs/providers/twelvelabs)**
+ - Added Marengo Embed 2.7 embedding support - [PR #14674](https://github.com/BerriAI/litellm/pull/14674)
+
+### Bug Fixes
+
+- **[Bedrock](../../docs/providers/bedrock)**
+ - Empty arguments handling in tool call invocation - [PR #14583](https://github.com/BerriAI/litellm/pull/14583)
+- **[Vertex AI](../../docs/providers/vertex)**
+ - Avoid deepcopy crash with non-pickleables in Gemini/Vertex - [PR #14418](https://github.com/BerriAI/litellm/pull/14418)
+- **[XAI](../../docs/providers/xai)**
+ - Fix unsupported stop parameter for grok-code models - [PR #14565](https://github.com/BerriAI/litellm/pull/14565)
+- **[Gemini](../../docs/providers/gemini)**
+ - Updated error message for Gemini API - [PR #14589](https://github.com/BerriAI/litellm/pull/14589)
+ - Fixed 2.5 Flash Image Preview model routing - [PR #14715](https://github.com/BerriAI/litellm/pull/14715)
+ - API key passing for token counting endpoints - [PR #14744](https://github.com/BerriAI/litellm/pull/14744)
+
+#### New Provider Support
+
+- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)**
+ - Complete provider integration with model catalog and authentication - [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
+- **[CompactifAI](../../docs/providers/compactifai)**
+ - New provider support with documentation - [PR #14532](https://github.com/BerriAI/litellm/pull/14532)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[/responses](../../docs/response_api)**
+ - Added cancel endpoint support for non-admin users - [PR #14594](https://github.com/BerriAI/litellm/pull/14594)
+ - Improved response session handling and cold storage configuration with s3 - [PR #14534](https://github.com/BerriAI/litellm/pull/14534)
+ - Added OpenAI & Azure /responses/cancel endpoint support - [PR #14561](https://github.com/BerriAI/litellm/pull/14561)
+- **General**
+ - Enhanced rate limit error messages with details - [PR #14736](https://github.com/BerriAI/litellm/pull/14736)
+ - Middle-truncation for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
+
+#### Bugs
+
+- **[/chat/completions](../../docs/completion/input)**
+ - Fixed completion chat ID handling - [PR #14548](https://github.com/BerriAI/litellm/pull/14548)
+ - Prevent AttributeError for _get_tags_from_request_kwargs - [PR #14735](https://github.com/BerriAI/litellm/pull/14735)
+- **[/responses](../../docs/response_api)**
+ - Fixed cost calculation - [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
+- **General**
+ - Rate limiter AttributeError fix - [PR #14609](https://github.com/BerriAI/litellm/pull/14609)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Responses API Cost Calculation** fix - [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
+- **Anthropic Cache Token Pricing** - Separate 1-hour vs 5-minute cache creation costs - [PR #14620](https://github.com/BerriAI/litellm/pull/14620), [PR #14652](https://github.com/BerriAI/litellm/pull/14652)
+- **Indochina Time Timezone** support for budget resets - [PR #14666](https://github.com/BerriAI/litellm/pull/14666)
+- **Soft Budget Alert Cache Issues** - Resolved soft budget alert cache issues - [PR #14491](https://github.com/BerriAI/litellm/pull/14491)
+- **Dynamic Rate Limiter v3** - Priority routing improvements - [PR #14734](https://github.com/BerriAI/litellm/pull/14734)
+- **Enhanced Rate Limit Errors** - More detailed error messages - [PR #14736](https://github.com/BerriAI/litellm/pull/14736)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Team Member Service Account Keys** - Allow team members to view keys they create - [PR #14619](https://github.com/BerriAI/litellm/pull/14619)
+- **Default Budget for JWT Teams** - Auto-assign budgets to generated teams - [PR #14514](https://github.com/BerriAI/litellm/pull/14514)
+- **SSO Access Control Groups** - Enhanced token info endpoint integration - [PR #14738](https://github.com/BerriAI/litellm/pull/14738)
+- **Health Test Connect Protection** - Restrict access based on model creation permissions - [PR #14650](https://github.com/BerriAI/litellm/pull/14650)
+- **Amazon Bedrock Guardrail Info View** - Enhanced logging visualization - [PR #14696](https://github.com/BerriAI/litellm/pull/14696)
+
+#### Bug Fixes
+
+- **SCIM v2** - Fix group PUSH and PUT operations for non-existent members - [PR #14581](https://github.com/BerriAI/litellm/pull/14581)
+- **Guardrail View/Edit/Delete** behavior fixes - [PR #14622](https://github.com/BerriAI/litellm/pull/14622)
+- **In-Memory Guardrail** update failures - [PR #14653](https://github.com/BerriAI/litellm/pull/14653)
+
+---
+
+## Logging / Guardrail Integrations
+
+#### Features
+
+- **[DataDog](../../docs/proxy/logging#datadog)**
+ - Enhanced spend tracking metrics - [PR #14555](https://github.com/BerriAI/litellm/pull/14555)
+ - Stream support with is_streamed_request parameter - [PR #14673](https://github.com/BerriAI/litellm/pull/14673)
+ - Fixed tool calls metadata passing - [PR #14531](https://github.com/BerriAI/litellm/pull/14531)
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+ - Added logging support for Responses API - [PR #14597](https://github.com/BerriAI/litellm/pull/14597)
+- **[Langsmith](../../docs/proxy/logging#langsmith)**
+ - Langsmith Sampling Rate - Key/Team-level tracing configuration - [PR #14740](https://github.com/BerriAI/litellm/pull/14740)
+- **[Prometheus](../../docs/proxy/logging#prometheus)**
+ - Multi-worker support improvements - [PR #14530](https://github.com/BerriAI/litellm/pull/14530)
+ - User email labels in monitoring - [PR #14520](https://github.com/BerriAI/litellm/pull/14520)
+- **[Opik](../../docs/proxy/logging#opik)**
+ - Fixed timezone issue - [PR #14708](https://github.com/BerriAI/litellm/pull/14708)
+
+### Bug Fixes
+
+- **[S3](../../docs/proxy/logging#s3-buckets)**
+ - Fixed 404 error when using s3_endpoint_url - [PR #14559](https://github.com/BerriAI/litellm/pull/14559)
+
+#### Guardrails
+
+- **Tool Permission Guardrail** - Fine-grained tool access control - [PR #14519](https://github.com/BerriAI/litellm/pull/14519)
+- **Bedrock Guardrails** - Selective guarding support with runtime endpoint configuration - [PR #14575](https://github.com/BerriAI/litellm/pull/14575), [PR #14650](https://github.com/BerriAI/litellm/pull/14650)
+- **Default Last Message** in guardrails - [PR #14640](https://github.com/BerriAI/litellm/pull/14640)
+- **AWS exceptions handling despite 200 response** - [PR #14658](https://github.com/BerriAI/litellm/pull/14658)
+#### New Integration
+
+- **[PostHog](../../docs/observability/posthog)** - Complete observability integration for LiteLLM usage tracking and analytics - [PR #14610](https://github.com/BerriAI/litellm/pull/14610)
+
+---
+
+
+## MCP Gateway
+
+- **MCP Server Alias Parsing** - Multi-part URL path support - [PR #14558](https://github.com/BerriAI/litellm/pull/14558)
+- **MCP Filter Recomputation** - After server deletion - [PR #14542](https://github.com/BerriAI/litellm/pull/14542)
+- **MCP Gateway Tools List** improvements - [PR #14695](https://github.com/BerriAI/litellm/pull/14695)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **+500 RPS Performance Boost** when sending the `user` field - [PR #14616](https://github.com/BerriAI/litellm/pull/14616)
+- **+50 RPS** by removing iscoroutine from hot path - [PR #14649](https://github.com/BerriAI/litellm/pull/14649)
+- **7% reduction** in __init__ overhead - [PR #14689](https://github.com/BerriAI/litellm/pull/14689)
+- **Generic Object Pool** implementation for better resource management - [PR #14702](https://github.com/BerriAI/litellm/pull/14702)
+
+---
+
+## General Proxy Improvements
+
+- **Middle-Truncation** for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
+
+#### Security
+
+- **Security Update** - Bump aiohttp==3.12.14, fix CVE-2025-53643 - [PR #14638](https://github.com/BerriAI/litellm/pull/14638)
+
+---
+
+## New Contributors
+
+* @luisfucros made their first contribution in [PR #14500](https://github.com/BerriAI/litellm/pull/14500)
+* @hanakannzashi made their first contribution in [PR #14548](https://github.com/BerriAI/litellm/pull/14548)
+* @eliasto made their first contribution in [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
+* @Rasmusafj made their first contribution in [PR #14491](https://github.com/BerriAI/litellm/pull/14491)
+* @LingXuanYin made their first contribution in [PR #14569](https://github.com/BerriAI/litellm/pull/14569)
+* @ronaldpereira made their first contribution in [PR #14613](https://github.com/BerriAI/litellm/pull/14613)
+* @hula-la made their first contribution in [PR #14534](https://github.com/BerriAI/litellm/pull/14534)
+* @carlos-marchal-ph made their first contribution in [PR #14610](https://github.com/BerriAI/litellm/pull/14610)
+* @akraines made their first contribution in [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
+* @mrFranklin made their first contribution in [PR #14708](https://github.com/BerriAI/litellm/pull/14708)
+* @tcx4c70 made their first contribution in [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
+* @michaeltansg made their first contribution in [PR #14666](https://github.com/BerriAI/litellm/pull/14666)
+* @tosi29 made their first contribution in [PR #14725](https://github.com/BerriAI/litellm/pull/14725)
+* @gmdfalk made their first contribution in [PR #14735](https://github.com/BerriAI/litellm/pull/14735)
+* @FelipeRodriguesGare made their first contribution in [PR #14733](https://github.com/BerriAI/litellm/pull/14733)
+* @mritunjaysharma394 made their first contribution in [PR #14678](https://github.com/BerriAI/litellm/pull/14678)
+
+---
+
+## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.2.rc.1...v1.77.3.rc.1)**
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index f3bab0219f..fe6dbc2729 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -201,7 +201,7 @@ const sidebars = {
{
type: "category",
label: "Budgets + Rate Limits",
- items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/customers"],
+ items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/dynamic_rate_limit", "proxy/customers"],
},
{
type: "link",
@@ -392,6 +392,7 @@ const sidebars = {
"providers/vertex",
"providers/vertex_partner",
"providers/vertex_image",
+ "providers/vertex_batch",
]
},
{
@@ -411,6 +412,7 @@ const sidebars = {
label: "Bedrock",
items: [
"providers/bedrock",
+ "providers/bedrock_embedding",
"providers/bedrock_agents",
"providers/bedrock_batches",
"providers/bedrock_vector_store",
@@ -522,6 +524,7 @@ const sidebars = {
"completion/batching",
"completion/mock_requests",
"completion/reliable_completions",
+ "proxy/veo_video_generation",
]
},
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
index 1028a443a4..d4964b9667 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
@@ -109,6 +109,9 @@ class PagerDutyAlerting(SlackAlerting):
error_llm_provider=error_info.get("llm_provider"),
user_api_key_hash=_meta.get("user_api_key_hash"),
user_api_key_alias=_meta.get("user_api_key_alias"),
+ user_api_key_spend=_meta.get("user_api_key_spend"),
+ user_api_key_max_budget=_meta.get("user_api_key_max_budget"),
+ user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"),
user_api_key_org_id=_meta.get("user_api_key_org_id"),
user_api_key_team_id=_meta.get("user_api_key_team_id"),
user_api_key_user_id=_meta.get("user_api_key_user_id"),
@@ -191,6 +194,9 @@ class PagerDutyAlerting(SlackAlerting):
error_llm_provider="HangingRequest",
user_api_key_hash=user_api_key_dict.api_key,
user_api_key_alias=user_api_key_dict.key_alias,
+ user_api_key_spend=user_api_key_dict.spend,
+ user_api_key_max_budget=user_api_key_dict.max_budget,
+ user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None,
user_api_key_org_id=user_api_key_dict.org_id,
user_api_key_team_id=user_api_key_dict.team_id,
user_api_key_user_id=user_api_key_dict.user_id,
diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py
index efee1a7783..4a1e8d7543 100644
--- a/enterprise/litellm_enterprise/integrations/prometheus.py
+++ b/enterprise/litellm_enterprise/integrations/prometheus.py
@@ -102,7 +102,9 @@ class PrometheusLogger(CustomLogger):
# "team",
# "team_alias",
# ],
- labelnames=self.get_labels_for_metric("litellm_llm_api_time_to_first_token_metric"),
+ labelnames=self.get_labels_for_metric(
+ "litellm_llm_api_time_to_first_token_metric"
+ ),
buckets=LATENCY_BUCKETS,
)
@@ -240,14 +242,14 @@ class PrometheusLogger(CustomLogger):
self.litellm_deployment_state = self._gauge_factory(
"litellm_deployment_state",
"LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage",
- labelnames=self.get_labels_for_metric("litellm_deployment_state")
+ labelnames=self.get_labels_for_metric("litellm_deployment_state"),
)
self.litellm_deployment_cooled_down = self._counter_factory(
"litellm_deployment_cooled_down",
"LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down",
# labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
- labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down")
+ labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down"),
)
self.litellm_deployment_success_responses = self._counter_factory(
@@ -1039,20 +1041,12 @@ class PrometheusLogger(CustomLogger):
_labels = prometheus_label_factory(
supported_enum_labels=self.get_labels_for_metric(
- metric_name="litellm_proxy_total_requests_metric"
+ metric_name="litellm_spend_metric"
),
enum_values=enum_values,
)
- self.litellm_spend_metric.labels(
- end_user_id,
- user_api_key,
- user_api_key_alias,
- model,
- user_api_team,
- user_api_team_alias,
- user_id,
- ).inc(response_cost)
+ self.litellm_spend_metric.labels(**_labels).inc(response_cost)
def _set_virtual_key_rate_limit_metrics(
self,
@@ -2280,7 +2274,9 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]:
return result
-def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: str) -> bool:
+def _tag_matches_wildcard_configured_pattern(
+ tags: List[str], configured_tag: str
+) -> bool:
"""
Check if any of the request tags matches a wildcard configured pattern
@@ -2305,6 +2301,7 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st
import re
from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
+
pattern_router = PatternMatchRouter()
regex_pattern = pattern_router._pattern_to_regex(configured_tag)
return any(re.match(pattern=regex_pattern, string=tag) for tag in tags)
@@ -2313,11 +2310,11 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st
def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
"""
Get custom labels from tags based on admin configuration.
-
+
Supports both exact matches and wildcard patterns:
- Exact match: "prod" matches "prod" exactly
- - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0"
-
+ - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0"
+
Reuses PatternMatchRouter for wildcard pattern matching.
Returns dict of label_name: "true" if the tag matches the configured tag, "false" otherwise
@@ -2345,17 +2342,19 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
for configured_tag in configured_tags:
label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}")
-
+
# Check for exact match first (backwards compatibility)
if configured_tag in tags:
result[label_name] = "true"
continue
-
+
# Use PatternMatchRouter for wildcard pattern matching
- if "*" in configured_tag and _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag):
+ if "*" in configured_tag and _tag_matches_wildcard_configured_pattern(
+ tags=tags, configured_tag=configured_tag
+ ):
result[label_name] = "true"
continue
-
+
# No match found
result[label_name] = "false"
diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml
index 217bb753f4..1d1fa64549 100644
--- a/enterprise/pyproject.toml
+++ b/enterprise/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-enterprise"
-version = "0.1.19"
+version = "0.1.20"
description = "Package for LiteLLM Enterprise features"
authors = ["BerriAI"]
readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
-version = "0.1.19"
+version = "0.1.20"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-enterprise==",
diff --git a/examples/sdk_headers_example.py b/examples/sdk_headers_example.py
new file mode 100644
index 0000000000..4ce31df4a7
--- /dev/null
+++ b/examples/sdk_headers_example.py
@@ -0,0 +1,195 @@
+#!/usr/bin/env python3
+"""
+Example demonstrating LiteLLM SDK header support for enterprise environments.
+
+This example shows how to use additional headers with API gateways, service meshes,
+and multi-tenant architectures.
+"""
+
+import litellm
+import os
+from typing import Dict, Any
+
+def example_global_headers():
+ """Example: Set global headers for all requests"""
+ print("=== Global Headers Example ===")
+
+ # Set global headers that will be included in all API requests
+ litellm.headers = {
+ "X-API-Gateway-Key": "your-gateway-key-here",
+ "X-Company-ID": "acme-corp",
+ "X-Environment": "production"
+ }
+
+ print("Global headers set:", litellm.headers)
+
+ # These headers will now be included in all completion calls
+ # (Note: This example doesn't actually make API calls)
+ print("Global headers will be included in all subsequent completion() calls")
+
+
+def example_per_request_headers():
+ """Example: Using extra_headers for specific requests"""
+ print("\n=== Per-Request Headers Example ===")
+
+ headers_to_send = {
+ "X-Request-ID": "req-12345",
+ "X-Tenant-ID": "tenant-abc",
+ "X-Custom-Auth": "bearer-token-xyz"
+ }
+
+ print("Per-request headers:", headers_to_send)
+
+ # Example of how you would use extra_headers in a real call
+ # response = litellm.completion(
+ # model="claude-3-5-sonnet-latest",
+ # messages=[{"role": "user", "content": "Hello"}],
+ # extra_headers=headers_to_send
+ # )
+
+
+def example_header_priority():
+ """Example: Demonstrating header priority and merging"""
+ print("\n=== Header Priority Example ===")
+
+ # Set global headers
+ litellm.headers = {
+ "X-Company-ID": "acme-corp",
+ "X-Shared-Header": "global-value"
+ }
+
+ # Headers that would be sent in a request
+ extra_headers = {
+ "X-Request-ID": "req-12345",
+ "X-Shared-Header": "extra-value" # Overrides global
+ }
+
+ request_headers = {
+ "X-Priority-Header": "important",
+ "X-Shared-Header": "request-value" # Overrides both global and extra
+ }
+
+ print("Global headers:", litellm.headers)
+ print("Extra headers:", extra_headers)
+ print("Request headers:", request_headers)
+ print("\nFinal headers would be:")
+ print(" X-Company-ID: acme-corp (from global)")
+ print(" X-Request-ID: req-12345 (from extra)")
+ print(" X-Priority-Header: important (from request)")
+ print(" X-Shared-Header: request-value (request wins - highest priority)")
+
+
+def example_enterprise_api_gateway():
+ """Example: Enterprise API Gateway scenario"""
+ print("\n=== Enterprise API Gateway Example ===")
+
+ # Simulate enterprise environment with Apigee or similar
+ gateway_config = {
+ "X-API-Gateway-Key": os.getenv("API_GATEWAY_KEY", "demo-key"),
+ "X-Route-Version": "v2",
+ "X-Rate-Limit-Group": "premium"
+ }
+
+ # Set gateway headers globally
+ litellm.headers = gateway_config
+ print("Gateway headers configured:", gateway_config)
+
+ # Function to make tenant-specific requests
+ def make_tenant_request(tenant_id: str, user_id: str, content: str) -> Dict[str, Any]:
+ """Make an AI request with tenant-specific headers"""
+
+ tenant_headers = {
+ "X-Tenant-ID": tenant_id,
+ "X-User-ID": user_id,
+ "X-Request-Time": "2024-01-01T00:00:00Z",
+ "X-Service-Name": "ai-assistant"
+ }
+
+ print(f"Making request for tenant {tenant_id}, user {user_id}")
+ print("Tenant-specific headers:", tenant_headers)
+
+ # In a real scenario, this would make the actual API call:
+ # return litellm.completion(
+ # model="claude-3-5-sonnet-latest",
+ # messages=[{"role": "user", "content": content}],
+ # extra_headers=tenant_headers
+ # )
+
+ # For demo purposes, return mock data
+ return {"mock": "response", "headers_used": {**gateway_config, **tenant_headers}}
+
+ # Example usage
+ result = make_tenant_request("tenant-123", "user-456", "Analyze this data")
+ print("Response:", result)
+
+
+def example_service_mesh():
+ """Example: Service mesh integration (Istio, Linkerd)"""
+ print("\n=== Service Mesh Example ===")
+
+ service_mesh_headers = {
+ "X-Trace-ID": "trace-abc-123",
+ "X-Span-ID": "span-def-456",
+ "X-Service-Name": "ai-service",
+ "X-Version": "1.2.3",
+ "X-Cluster": "prod-us-west-2"
+ }
+
+ print("Service mesh headers:", service_mesh_headers)
+
+ # Example of using these headers for distributed tracing
+ # response = litellm.completion(
+ # model="gpt-4",
+ # messages=[{"role": "user", "content": "Hello"}],
+ # extra_headers=service_mesh_headers
+ # )
+
+
+def example_debugging_and_monitoring():
+ """Example: Request debugging and monitoring"""
+ print("\n=== Debugging and Monitoring Example ===")
+
+ import uuid
+ import time
+
+ # Generate unique identifiers for request tracking
+ trace_id = str(uuid.uuid4())
+ request_id = f"req-{int(time.time())}"
+
+ debug_headers = {
+ "X-Trace-ID": trace_id,
+ "X-Request-ID": request_id,
+ "X-Debug-Mode": "true",
+ "X-Source-Service": "customer-support-bot",
+ "X-Request-Priority": "high"
+ }
+
+ print("Debug headers:", debug_headers)
+ print(f"Trace ID: {trace_id}")
+ print(f"Request ID: {request_id}")
+
+ # These headers help with:
+ # 1. Distributed tracing across services
+ # 2. Request correlation in logs
+ # 3. Debug mode enablement
+ # 4. Priority-based routing
+
+
+if __name__ == "__main__":
+ print("LiteLLM SDK Header Support Examples")
+ print("=" * 50)
+
+ example_global_headers()
+ example_per_request_headers()
+ example_header_priority()
+ example_enterprise_api_gateway()
+ example_service_mesh()
+ example_debugging_and_monitoring()
+
+ print("\n" + "=" * 50)
+ print("All examples completed!")
+ print("\nTo use in your application:")
+ print("1. Set litellm.headers for global headers")
+ print("2. Use extra_headers parameter for request-specific headers")
+ print("3. Use headers parameter for highest priority headers")
+ print("4. Headers are merged with priority: headers > extra_headers > litellm.headers")
\ No newline at end of file
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl
new file mode 100644
index 0000000000..c035bb4421
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz
new file mode 100644
index 0000000000..85069c622b
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz differ
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql
new file mode 100644
index 0000000000..5686876b37
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql
@@ -0,0 +1,8 @@
+/*
+ Warnings:
+
+ - You are about to drop the column `spec_version` on the `LiteLLM_MCPServerTable` table. All the data in the column will be lost.
+
+*/
+-- AlterTable
+ALTER TABLE "public"."LiteLLM_MCPServerTable" DROP COLUMN "spec_version";
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index b8f2201d6b..2b1e20820f 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -171,7 +171,6 @@ model LiteLLM_MCPServerTable {
description String?
url String?
transport String @default("sse")
- spec_version String @default("2025-03-26")
auth_type String?
created_at DateTime? @default(now()) @map("created_at")
created_by String?
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 0cb9c35fa6..1c368d5807 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-proxy-extras"
-version = "0.2.18"
+version = "0.2.19"
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
authors = ["BerriAI"]
readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
-version = "0.2.18"
+version = "0.2.19"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 8404cf5125..aa8357b6c7 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -68,6 +68,7 @@ from litellm.constants import (
bedrock_embedding_models,
known_tokenizer_config,
BEDROCK_INVOKE_PROVIDERS_LITERAL,
+ BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
BEDROCK_CONVERSE_MODELS,
DEFAULT_MAX_TOKENS,
DEFAULT_SOFT_BUDGET,
@@ -117,6 +118,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"logfire",
"literalai",
"dynamic_rate_limiter",
+ "dynamic_rate_limiter_v3",
"langsmith",
"prometheus",
"otel",
@@ -148,6 +150,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"vector_store_pre_call_hook",
"dotprompt",
"cloudzero",
+ "posthog",
]
configured_cold_storage_logger: Optional[
_custom_logger_compatible_callbacks_literal
@@ -1051,7 +1054,6 @@ from .llms.databricks.chat.transformation import DatabricksConfig
from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig
from .llms.predibase.chat.transformation import PredibaseConfig
from .llms.replicate.chat.transformation import ReplicateConfig
-from .llms.cohere.completion.transformation import CohereTextConfig as CohereConfig
from .llms.snowflake.chat.transformation import SnowflakeConfig
from .llms.cohere.rerank.transformation import CohereRerankConfig
from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config
diff --git a/litellm/batches/main.py b/litellm/batches/main.py
index e5c7a2e4b6..37b9aff4ef 100644
--- a/litellm/batches/main.py
+++ b/litellm/batches/main.py
@@ -340,7 +340,7 @@ def create_batch(
@client
async def aretrieve_batch(
batch_id: str,
- custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai",
metadata: Optional[Dict[str, str]] = None,
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
@@ -378,11 +378,129 @@ async def aretrieve_batch(
except Exception as e:
raise e
+def _handle_retrieve_batch_providers_without_provider_config(
+ batch_id: str,
+ optional_params: GenericLiteLLMParams,
+ timeout: Union[float, httpx.Timeout],
+ litellm_params: dict,
+ _retrieve_batch_request: RetrieveBatchRequest,
+ _is_async: bool,
+ custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai",
+):
+ api_base: Optional[str] = None
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_BASE_URL")
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+
+ response = openai_batches_instance.retrieve_batch(
+ _is_async=_is_async,
+ retrieve_batch_data=_retrieve_batch_request,
+ api_base=api_base,
+ api_key=api_key,
+ organization=organization,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ )
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or get_secret_str("AZURE_API_BASE")
+ )
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret_str("AZURE_API_VERSION")
+ )
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret_str("AZURE_OPENAI_API_KEY")
+ or get_secret_str("AZURE_API_KEY")
+ )
+
+ extra_body = optional_params.get("extra_body", {})
+ if extra_body is not None:
+ extra_body.pop("azure_ad_token", None)
+ else:
+ get_secret_str("AZURE_AD_TOKEN") # type: ignore
+
+ response = azure_batches_instance.retrieve_batch(
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ api_version=api_version,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ retrieve_batch_data=_retrieve_batch_request,
+ litellm_params=litellm_params,
+ )
+ elif custom_llm_provider == "vertex_ai":
+ api_base = optional_params.api_base or ""
+ vertex_ai_project = (
+ optional_params.vertex_project
+ or litellm.vertex_project
+ or get_secret_str("VERTEXAI_PROJECT")
+ )
+ vertex_ai_location = (
+ optional_params.vertex_location
+ or litellm.vertex_location
+ or get_secret_str("VERTEXAI_LOCATION")
+ )
+ vertex_credentials = optional_params.vertex_credentials or get_secret_str(
+ "VERTEXAI_CREDENTIALS"
+ )
+
+ response = vertex_ai_batches_instance.retrieve_batch(
+ _is_async=_is_async,
+ batch_id=batch_id,
+ api_base=api_base,
+ vertex_project=vertex_ai_project,
+ vertex_location=vertex_ai_location,
+ vertex_credentials=vertex_credentials,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
@client
def retrieve_batch(
batch_id: str,
- custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai",
metadata: Optional[Dict[str, str]] = None,
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
@@ -430,115 +548,59 @@ def retrieve_batch(
)
_is_async = kwargs.pop("aretrieve_batch", False) is True
- api_base: Optional[str] = None
- if custom_llm_provider == "openai":
- # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
- api_base = (
- optional_params.api_base
- or litellm.api_base
- or os.getenv("OPENAI_BASE_URL")
- or os.getenv("OPENAI_API_BASE")
- or "https://api.openai.com/v1"
- )
- organization = (
- optional_params.organization
- or litellm.organization
- or os.getenv("OPENAI_ORGANIZATION", None)
- or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
- )
- # set API KEY
- api_key = (
- optional_params.api_key
- or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
- or litellm.openai_key
- or os.getenv("OPENAI_API_KEY")
- )
-
- response = openai_batches_instance.retrieve_batch(
- _is_async=_is_async,
- retrieve_batch_data=_retrieve_batch_request,
- api_base=api_base,
- api_key=api_key,
- organization=organization,
- timeout=timeout,
- max_retries=optional_params.max_retries,
- )
- elif custom_llm_provider == "azure":
- api_base = (
- optional_params.api_base
- or litellm.api_base
- or get_secret_str("AZURE_API_BASE")
- )
- api_version = (
- optional_params.api_version
- or litellm.api_version
- or get_secret_str("AZURE_API_VERSION")
- )
-
- api_key = (
- optional_params.api_key
- or litellm.api_key
- or litellm.azure_key
- or get_secret_str("AZURE_OPENAI_API_KEY")
- or get_secret_str("AZURE_API_KEY")
- )
-
- extra_body = optional_params.get("extra_body", {})
- if extra_body is not None:
- extra_body.pop("azure_ad_token", None)
- else:
- get_secret_str("AZURE_AD_TOKEN") # type: ignore
-
- response = azure_batches_instance.retrieve_batch(
- _is_async=_is_async,
- api_base=api_base,
- api_key=api_key,
- api_version=api_version,
- timeout=timeout,
- max_retries=optional_params.max_retries,
- retrieve_batch_data=_retrieve_batch_request,
- litellm_params=litellm_params,
- )
- elif custom_llm_provider == "vertex_ai":
- api_base = optional_params.api_base or ""
- vertex_ai_project = (
- optional_params.vertex_project
- or litellm.vertex_project
- or get_secret_str("VERTEXAI_PROJECT")
- )
- vertex_ai_location = (
- optional_params.vertex_location
- or litellm.vertex_location
- or get_secret_str("VERTEXAI_LOCATION")
- )
- vertex_credentials = optional_params.vertex_credentials or get_secret_str(
- "VERTEXAI_CREDENTIALS"
- )
-
- response = vertex_ai_batches_instance.retrieve_batch(
- _is_async=_is_async,
- batch_id=batch_id,
- api_base=api_base,
- vertex_project=vertex_ai_project,
- vertex_location=vertex_ai_location,
- vertex_credentials=vertex_credentials,
- timeout=timeout,
- max_retries=optional_params.max_retries,
+ client = kwargs.get("client", None)
+
+ # Try to use provider config first (for providers like bedrock)
+ model: Optional[str] = kwargs.get("model", None)
+ if model is not None:
+ provider_config = ProviderConfigManager.get_provider_batches_config(
+ model=model,
+ provider=LlmProviders(custom_llm_provider),
)
else:
- raise litellm.exceptions.BadRequestError(
- message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
- custom_llm_provider
- ),
- model="n/a",
- llm_provider=custom_llm_provider,
- response=httpx.Response(
- status_code=400,
- content="Unsupported provider",
- request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ provider_config = None
+
+ if provider_config is not None:
+ response = base_llm_http_handler.retrieve_batch(
+ batch_id=batch_id,
+ provider_config=provider_config,
+ litellm_params=litellm_params,
+ headers=extra_headers or {},
+ api_base=optional_params.api_base,
+ api_key=optional_params.api_key,
+ logging_obj=litellm_logging_obj or LiteLLMLoggingObj(
+ model=model or "bedrock/unknown",
+ messages=[],
+ stream=False,
+ call_type="batch_retrieve",
+ start_time=None,
+ litellm_call_id="batch_retrieve_" + batch_id,
+ function_id="batch_retrieve",
),
+ _is_async=_is_async,
+ client=client
+ if client is not None
+ and isinstance(client, (HTTPHandler, AsyncHTTPHandler))
+ else None,
+ timeout=timeout,
+ model=model,
)
- return response
+ return response
+
+
+ #########################################################
+ # Handle providers without provider config
+ #########################################################
+ return _handle_retrieve_batch_providers_without_provider_config(
+ batch_id=batch_id,
+ custom_llm_provider=custom_llm_provider,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ _retrieve_batch_request=_retrieve_batch_request,
+ _is_async=_is_async,
+ timeout=timeout,
+ )
+
except Exception as e:
raise e
diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py
index 47bc0222ed..af7468ba14 100644
--- a/litellm/caching/redis_cache.py
+++ b/litellm/caching/redis_cache.py
@@ -19,6 +19,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast
import litellm
from litellm._logging import print_verbose, verbose_logger
from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs
+from litellm.litellm_core_utils.coroutine_checker import coroutine_checker
from litellm.types.caching import RedisPipelineIncrementOperation
from litellm.types.services import ServiceTypes
@@ -138,7 +139,7 @@ class RedisCache(BaseCache):
self.redis_flush_size = redis_flush_size
self.redis_version = "Unknown"
try:
- if not inspect.iscoroutinefunction(self.redis_client):
+ if not coroutine_checker.is_async_callable(self.redis_client):
self.redis_version = self.redis_client.info()["redis_version"] # type: ignore
except Exception:
pass
diff --git a/litellm/constants.py b/litellm/constants.py
index bb8da97a8f..005eb2bb6d 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -179,7 +179,7 @@ NON_LLM_CONNECTION_TIMEOUT = int(
os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15)
) # timeout for adjacent services (e.g. jwt auth)
MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000))
-MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 1000))
+MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 2048))
BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75))
REPLICATE_POLLING_DELAY_SECONDS = float(
os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5)
@@ -805,6 +805,12 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
"deepseek_r1",
]
+BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[
+ "cohere",
+ "amazon",
+ "twelvelabs",
+]
+
BEDROCK_CONVERSE_MODELS = [
"openai.gpt-oss-20b-1:0",
"openai.gpt-oss-120b-1:0",
@@ -858,6 +864,7 @@ bedrock_embedding_models: set = set(
"amazon.titan-embed-text-v1",
"cohere.embed-english-v3",
"cohere.embed-multilingual-v3",
+ "twelvelabs.marengo-embed-2-7-v1:0",
]
)
@@ -976,6 +983,7 @@ HEALTH_CHECK_TIMEOUT_SECONDS = int(
os.getenv("HEALTH_CHECK_TIMEOUT_SECONDS", 60)
) # 60 seconds
LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME = "litellm-internal-health-check"
+LITTELM_CLI_SERVICE_ACCOUNT_NAME = "litellm-cli"
UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard"
LITELLM_PROXY_ADMIN_NAME = "default_user_id"
@@ -1099,3 +1107,8 @@ SENTRY_PII_DENYLIST = [
"SMTP_SENDER_EMAIL",
"TEST_EMAIL_ADDRESS",
]
+
+# CoroutineChecker cache configuration
+COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int(
+ os.getenv("COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY", 1000)
+)
diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py
index c97da6624a..ecb58e1822 100644
--- a/litellm/experimental_mcp_client/client.py
+++ b/litellm/experimental_mcp_client/client.py
@@ -19,8 +19,6 @@ from litellm._logging import verbose_logger
from litellm.types.mcp import (
MCPAuth,
MCPAuthType,
- MCPSpecVersion,
- MCPSpecVersionType,
MCPStdioConfig,
MCPTransport,
MCPTransportType,
@@ -48,7 +46,6 @@ class MCPClient:
auth_value: Optional[str] = None,
timeout: float = 60.0,
stdio_config: Optional[MCPStdioConfig] = None,
- protocol_version: MCPSpecVersionType = MCPSpecVersion.jun_2025,
):
self.server_url: str = server_url
self.transport_type: MCPTransport = transport_type
@@ -62,7 +59,6 @@ class MCPClient:
self._session_ctx = None
self._task: Optional[asyncio.Task] = None
self.stdio_config: Optional[MCPStdioConfig] = stdio_config
- self.protocol_version: MCPSpecVersionType = protocol_version
# handle the basic auth value if provided
if auth_value:
@@ -84,22 +80,24 @@ class MCPClient:
"""Initialize the transport and session."""
if self._session:
return # Already connected
-
+
try:
if self.transport_type == MCPTransport.stdio:
# For stdio transport, use stdio_client with command-line parameters
if not self.stdio_config:
raise ValueError("stdio_config is required for stdio transport")
-
+
server_params = StdioServerParameters(
command=self.stdio_config.get("command", ""),
args=self.stdio_config.get("args", []),
- env=self.stdio_config.get("env", {})
+ env=self.stdio_config.get("env", {}),
)
-
+
self._transport_ctx = stdio_client(server_params)
self._transport = await self._transport_ctx.__aenter__()
- self._session_ctx = ClientSession(self._transport[0], self._transport[1])
+ self._session_ctx = ClientSession(
+ self._transport[0], self._transport[1]
+ )
self._session = await self._session_ctx.__aenter__()
await self._session.initialize()
elif self.transport_type == MCPTransport.sse:
@@ -110,7 +108,9 @@ class MCPClient:
headers=headers,
)
self._transport = await self._transport_ctx.__aenter__()
- self._session_ctx = ClientSession(self._transport[0], self._transport[1])
+ self._session_ctx = ClientSession(
+ self._transport[0], self._transport[1]
+ )
self._session = await self._session_ctx.__aenter__()
await self._session.initialize()
else: # http
@@ -121,7 +121,9 @@ class MCPClient:
headers=headers,
)
self._transport = await self._transport_ctx.__aenter__()
- self._session_ctx = ClientSession(self._transport[0], self._transport[1])
+ self._session_ctx = ClientSession(
+ self._transport[0], self._transport[1]
+ )
self._session = await self._session_ctx.__aenter__()
await self._session.initialize()
except ValueError as e:
@@ -184,8 +186,10 @@ class MCPClient:
def _get_auth_headers(self) -> dict:
"""Generate authentication headers based on auth type."""
- headers = {}
-
+ headers = {
+ "MCP-Protocol-Version": "2025-06-18"
+ }
+
if self._mcp_auth_value:
if self.auth_type == MCPAuth.bearer_token:
headers["Authorization"] = f"Bearer {self._mcp_auth_value}"
@@ -196,18 +200,8 @@ class MCPClient:
elif self.auth_type == MCPAuth.authorization:
headers["Authorization"] = self._mcp_auth_value
- # Handle protocol version - it might be a string or enum
- if hasattr(self.protocol_version, 'value'):
- # It's an enum
- protocol_version_str = self.protocol_version.value
- else:
- # It's a string
- protocol_version_str = str(self.protocol_version)
-
- headers["MCP-Protocol-Version"] = protocol_version_str
return headers
-
async def list_tools(self) -> List[MCPTool]:
"""List available tools from the server."""
if not self._session:
@@ -216,7 +210,7 @@ class MCPClient:
except Exception as e:
verbose_logger.warning(f"MCP client connection failed: {str(e)}")
return []
-
+
if self._session is None:
verbose_logger.warning("MCP client session is not initialized")
return []
@@ -245,17 +239,20 @@ class MCPClient:
except Exception as e:
verbose_logger.warning(f"MCP client connection failed: {str(e)}")
return MCPCallToolResult(
- content=[TextContent(type="text", text=f"{str(e)}")],
- isError=True
+ content=[TextContent(type="text", text=f"{str(e)}")], isError=True
)
if self._session is None:
verbose_logger.warning("MCP client session is not initialized")
return MCPCallToolResult(
- content=[TextContent(type="text", text="MCP client session is not initialized")],
+ content=[
+ TextContent(
+ type="text", text="MCP client session is not initialized"
+ )
+ ],
isError=True,
)
-
+
try:
tool_result = await self._session.call_tool(
name=call_tool_request_params.name,
@@ -270,8 +267,8 @@ class MCPClient:
await self.disconnect()
# Return a default error result instead of raising
return MCPCallToolResult(
- content=[TextContent(type="text", text=f"{str(e)}")], # Empty content for error case
+ content=[
+ TextContent(type="text", text=f"{str(e)}")
+ ], # Empty content for error case
isError=True,
)
-
-
diff --git a/litellm/files/main.py b/litellm/files/main.py
index 299e52895b..18be2c702b 100644
--- a/litellm/files/main.py
+++ b/litellm/files/main.py
@@ -731,7 +731,7 @@ def file_list(
async def afile_content(
file_id: str,
- custom_llm_provider: Literal["openai", "azure"] = "openai",
+ custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
@@ -887,6 +887,32 @@ def file_content(
client=client,
litellm_params=litellm_params_dict,
)
+ elif custom_llm_provider == "vertex_ai":
+ api_base = optional_params.api_base or ""
+ vertex_ai_project = (
+ optional_params.vertex_project
+ or litellm.vertex_project
+ or get_secret_str("VERTEXAI_PROJECT")
+ )
+ vertex_ai_location = (
+ optional_params.vertex_location
+ or litellm.vertex_location
+ or get_secret_str("VERTEXAI_LOCATION")
+ )
+ vertex_credentials = optional_params.vertex_credentials or get_secret_str(
+ "VERTEXAI_CREDENTIALS"
+ )
+
+ response = vertex_ai_files_instance.file_content(
+ _is_async=_is_async,
+ file_content_request=_file_content_request,
+ api_base=api_base,
+ vertex_credentials=vertex_credentials,
+ vertex_project=vertex_ai_project,
+ vertex_location=vertex_ai_location,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ )
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format(
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index 40d2137a7f..6b77557cd3 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -357,6 +357,7 @@ class CustomGuardrail(CustomLogger):
end_time: Optional[float] = None,
duration: Optional[float] = None,
masked_entity_count: Optional[Dict[str, int]] = None,
+ guardrail_provider: Optional[str] = None,
) -> None:
"""
Builds `StandardLoggingGuardrailInformation` and adds it to the request metadata so it can be used for logging to DataDog, Langfuse, etc.
@@ -367,6 +368,7 @@ class CustomGuardrail(CustomLogger):
slg = StandardLoggingGuardrailInformation(
guardrail_name=self.guardrail_name,
+ guardrail_provider=guardrail_provider,
guardrail_mode=(
GuardrailMode(**self.event_hook.model_dump()) # type: ignore
if isinstance(self.event_hook, Mode)
@@ -487,7 +489,8 @@ class CustomGuardrail(CustomLogger):
"""
Update the guardrails litellm params in memory
"""
- pass
+ for key, value in vars(litellm_params).items():
+ setattr(self, key, value)
def log_guardrail_information(func):
diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py
index adab0ca659..2702192f63 100644
--- a/litellm/integrations/datadog/datadog_llm_obs.py
+++ b/litellm/integrations/datadog/datadog_llm_obs.py
@@ -148,10 +148,22 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
),
),
}
- verbose_logger.debug("payload %s", json.dumps(payload, indent=4))
+
+ # serialize datetime objects - for budget reset time in spend metrics
+ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
+ try:
+ verbose_logger.debug("payload %s", safe_dumps(payload))
+ except Exception as debug_error:
+ verbose_logger.debug(
+ "payload serialization failed: %s", str(debug_error)
+ )
+
+ json_payload = safe_dumps(payload)
+
response = await self.async_client.post(
url=self.intake_url,
- json=payload,
+ content=json_payload,
headers={
"DD-API-KEY": self.DD_API_KEY,
"Content-Type": "application/json",
@@ -486,6 +498,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
"guardrail_information": standard_logging_payload.get(
"guardrail_information", None
),
+ "is_streamed_request": self._get_stream_value_from_payload(standard_logging_payload),
}
#########################################################
@@ -494,6 +507,12 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
latency_metrics = self._get_latency_metrics(standard_logging_payload)
_metadata.update({"latency_metrics": dict(latency_metrics)})
+ #########################################################
+ # Add spend metrics to metadata
+ #########################################################
+ spend_metrics = self._get_spend_metrics(standard_logging_payload)
+ _metadata.update({"spend_metrics": dict(spend_metrics)})
+
## extract tool calls and add to metadata
tool_call_metadata = self._extract_tool_call_metadata(standard_logging_payload)
_metadata.update(tool_call_metadata)
@@ -543,6 +562,96 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
return latency_metrics
+ def _get_stream_value_from_payload(self, standard_logging_payload: StandardLoggingPayload) -> bool:
+ """
+ Extract the stream value from standard logging payload.
+
+ The stream field in StandardLoggingPayload is only set to True for completed streaming responses.
+ For non-streaming requests, it's None. The original stream parameter is in model_parameters.
+
+ Returns:
+ bool: True if this was a streaming request, False otherwise
+ """
+ # Check top-level stream field first (only True for completed streaming)
+ stream_value = standard_logging_payload.get("stream")
+ if stream_value is True:
+ return True
+
+ # Fallback to model_parameters.stream for original request parameters
+ model_params = standard_logging_payload.get("model_parameters", {})
+ if isinstance(model_params, dict):
+ stream_value = model_params.get("stream")
+ if stream_value is True:
+ return True
+
+ # Default to False for non-streaming requests
+ return False
+
+ def _get_spend_metrics(
+ self, standard_logging_payload: StandardLoggingPayload
+ ) -> DDLLMObsSpendMetrics:
+ """
+ Get the spend metrics from the standard logging payload
+ """
+ spend_metrics: DDLLMObsSpendMetrics = DDLLMObsSpendMetrics()
+
+ # send response cost
+ spend_metrics["response_cost"] = standard_logging_payload.get(
+ "response_cost", 0.0
+ )
+
+ # Get budget information from metadata
+ metadata = standard_logging_payload.get("metadata", {})
+
+ # API key max budget
+ user_api_key_max_budget = metadata.get("user_api_key_max_budget")
+ if user_api_key_max_budget is not None:
+ spend_metrics["user_api_key_max_budget"] = float(user_api_key_max_budget)
+
+ # API key spend
+ user_api_key_spend = metadata.get("user_api_key_spend")
+ if user_api_key_spend is not None:
+ try:
+ spend_metrics["user_api_key_spend"] = float(user_api_key_spend)
+ except (ValueError, TypeError):
+ verbose_logger.debug(
+ f"Invalid user_api_key_spend value: {user_api_key_spend}"
+ )
+
+ # API key budget reset datetime
+ user_api_key_budget_reset_at = metadata.get("user_api_key_budget_reset_at")
+ if user_api_key_budget_reset_at is not None:
+ try:
+ from datetime import datetime, timezone
+
+ budget_reset_at = None
+ if isinstance(user_api_key_budget_reset_at, str):
+ # Handle ISO format strings that might have 'Z' suffix
+ iso_string = user_api_key_budget_reset_at.replace("Z", "+00:00")
+ budget_reset_at = datetime.fromisoformat(iso_string)
+ elif isinstance(user_api_key_budget_reset_at, datetime):
+ budget_reset_at = user_api_key_budget_reset_at
+
+ if budget_reset_at is not None:
+ # Preserve timezone info if already present
+ if budget_reset_at.tzinfo is None:
+ budget_reset_at = budget_reset_at.replace(tzinfo=timezone.utc)
+
+ # Convert to ISO string format for JSON serialization
+ # This prevents circular reference issues and ensures proper timezone representation
+ iso_string = budget_reset_at.isoformat()
+ spend_metrics["user_api_key_budget_reset_at"] = iso_string
+
+ # Debug logging to verify the conversion
+ verbose_logger.debug(
+ f"Converted budget_reset_at to ISO format: {iso_string}"
+ )
+ except Exception as e:
+ verbose_logger.debug(f"Error processing budget reset datetime: {e}")
+ verbose_logger.debug(f"Original value: {user_api_key_budget_reset_at}")
+
+ return spend_metrics
+
def _process_input_messages_preserving_tool_calls(
self, messages: List[Any]
) -> List[Dict[str, Any]]:
diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py
index 9c3f07fa1a..2457773138 100644
--- a/litellm/integrations/langfuse/langfuse.py
+++ b/litellm/integrations/langfuse/langfuse.py
@@ -15,7 +15,7 @@ from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info
from litellm.llms.custom_httpx.http_handler import _get_httpx_client
from litellm.secret_managers.main import str_to_bool
from litellm.types.integrations.langfuse import *
-from litellm.types.llms.openai import HttpxBinaryResponseContent
+from litellm.types.llms.openai import HttpxBinaryResponseContent, ResponsesAPIResponse
from litellm.types.utils import (
EmbeddingResponse,
ImageResponse,
@@ -196,6 +196,7 @@ class LangFuseLogger:
TranscriptionResponse,
RerankResponse,
HttpxBinaryResponseContent,
+ ResponsesAPIResponse,
],
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None,
@@ -305,6 +306,7 @@ class LangFuseLogger:
TranscriptionResponse,
RerankResponse,
HttpxBinaryResponseContent,
+ ResponsesAPIResponse,
],
prompt: dict,
level: str,
@@ -369,6 +371,11 @@ class LangFuseLogger:
):
input = prompt
output = response_obj.results
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.ResponsesAPIResponse
+ ):
+ input = prompt
+ output = self._get_responses_api_content_for_langfuse(response_obj)
elif (
kwargs.get("call_type") is not None
and kwargs.get("call_type") == "_arealtime"
@@ -768,6 +775,19 @@ class LangFuseLogger:
else:
return None
+ @staticmethod
+ def _get_responses_api_content_for_langfuse(
+ response_obj: ResponsesAPIResponse,
+ ):
+ """
+ Get the responses API content for Langfuse logging
+ """
+ if hasattr(response_obj, 'output') and response_obj.output:
+ # ResponsesAPIResponse.output is a list of strings
+ return response_obj.output
+ else:
+ return None
+
@staticmethod
def _get_langfuse_tags(
standard_logging_object: Optional[StandardLoggingPayload],
diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py
index 7035aa3a81..7783b704b4 100644
--- a/litellm/integrations/langsmith.py
+++ b/litellm/integrations/langsmith.py
@@ -39,6 +39,7 @@ class LangsmithLogger(CustomBatchLogger):
langsmith_api_key: Optional[str] = None,
langsmith_project: Optional[str] = None,
langsmith_base_url: Optional[str] = None,
+ langsmith_sampling_rate: Optional[float] = None,
**kwargs,
):
self.flush_lock = asyncio.Lock()
@@ -49,7 +50,8 @@ class LangsmithLogger(CustomBatchLogger):
langsmith_base_url=langsmith_base_url,
)
self.sampling_rate: float = (
- float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore
+ langsmith_sampling_rate
+ or float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore
if os.getenv("LANGSMITH_SAMPLING_RATE") is not None
and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore
else 1.0
@@ -76,26 +78,14 @@ class LangsmithLogger(CustomBatchLogger):
langsmith_base_url: Optional[str] = None,
) -> LangsmithCredentialsObject:
_credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY")
- if _credentials_api_key is None:
- raise Exception(
- "Invalid Langsmith API Key given. _credentials_api_key=None."
- )
_credentials_project = (
langsmith_project or os.getenv("LANGSMITH_PROJECT") or "litellm-completion"
)
- if _credentials_project is None:
- raise Exception(
- "Invalid Langsmith API Key given. _credentials_project=None."
- )
_credentials_base_url = (
langsmith_base_url
or os.getenv("LANGSMITH_BASE_URL")
or "https://api.smith.langchain.com"
)
- if _credentials_base_url is None:
- raise Exception(
- "Invalid Langsmith API Key given. _credentials_base_url=None."
- )
return LangsmithCredentialsObject(
LANGSMITH_API_KEY=_credentials_api_key,
@@ -200,12 +190,7 @@ class LangsmithLogger(CustomBatchLogger):
def log_success_event(self, kwargs, response_obj, start_time, end_time):
try:
- sampling_rate = (
- float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore
- if os.getenv("LANGSMITH_SAMPLING_RATE") is not None
- and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore
- else 1.0
- )
+ sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
@@ -219,6 +204,7 @@ class LangsmithLogger(CustomBatchLogger):
kwargs,
response_obj,
)
+
credentials = self._get_credentials_to_use_for_request(kwargs=kwargs)
data = self._prepare_log_data(
kwargs=kwargs,
@@ -245,7 +231,7 @@ class LangsmithLogger(CustomBatchLogger):
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
try:
- sampling_rate = self.sampling_rate
+ sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
@@ -286,7 +272,7 @@ class LangsmithLogger(CustomBatchLogger):
)
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
- sampling_rate = self.sampling_rate
+ sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
@@ -417,6 +403,17 @@ class LangsmithLogger(CustomBatchLogger):
for queue_object in self.log_queue:
credentials = queue_object["credentials"]
+ # if credential missing, skip - log warning
+ if (
+ credentials["LANGSMITH_API_KEY"] is None
+ or credentials["LANGSMITH_PROJECT"] is None
+ ):
+ verbose_logger.warning(
+ "Langsmith Logging - credentials missing - api_key: %s, project: %s",
+ credentials["LANGSMITH_API_KEY"],
+ credentials["LANGSMITH_PROJECT"],
+ )
+ continue
key = CredentialsKey(
api_key=credentials["LANGSMITH_API_KEY"],
project=credentials["LANGSMITH_PROJECT"],
@@ -432,6 +429,19 @@ class LangsmithLogger(CustomBatchLogger):
return log_queue_by_credentials
+ def _get_sampling_rate_to_use_for_request(self, kwargs: Dict[str, Any]) -> float:
+ standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = (
+ kwargs.get("standard_callback_dynamic_params", None)
+ )
+ sampling_rate: float = self.sampling_rate
+ if standard_callback_dynamic_params is not None:
+ _sampling_rate = standard_callback_dynamic_params.get(
+ "langsmith_sampling_rate"
+ )
+ if _sampling_rate is not None:
+ sampling_rate = float(_sampling_rate)
+ return sampling_rate
+
def _get_credentials_to_use_for_request(
self, kwargs: Dict[str, Any]
) -> LangsmithCredentialsObject:
@@ -442,9 +452,9 @@ class LangsmithLogger(CustomBatchLogger):
Otherwise, use the default credentials.
"""
- standard_callback_dynamic_params: Optional[
- StandardCallbackDynamicParams
- ] = kwargs.get("standard_callback_dynamic_params", None)
+ standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = (
+ kwargs.get("standard_callback_dynamic_params", None)
+ )
if standard_callback_dynamic_params is not None:
credentials = self.get_credentials_from_env(
langsmith_api_key=standard_callback_dynamic_params.get(
diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py
index 8cbfb9e653..9f90d2384d 100644
--- a/litellm/integrations/opik/opik.py
+++ b/litellm/integrations/opik/opik.py
@@ -3,6 +3,7 @@ Opik Logger that logs LLM events to an Opik server
"""
import asyncio
+from datetime import timezone
import json
import traceback
from typing import Dict, List
@@ -291,8 +292,8 @@ class OpikLogger(CustomBatchLogger):
"project_name": project_name,
"id": trace_id,
"name": trace_name,
- "start_time": start_time.isoformat() + "Z",
- "end_time": end_time.isoformat() + "Z",
+ "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
+ "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
"input": input_data,
"output": output_data,
"metadata": metadata,
@@ -312,8 +313,8 @@ class OpikLogger(CustomBatchLogger):
"parent_span_id": parent_span_id,
"name": span_name,
"type": "llm",
- "start_time": start_time.isoformat() + "Z",
- "end_time": end_time.isoformat() + "Z",
+ "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
+ "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
"input": input_data,
"output": output_data,
"metadata": metadata,
diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py
new file mode 100644
index 0000000000..d321135f28
--- /dev/null
+++ b/litellm/integrations/posthog.py
@@ -0,0 +1,333 @@
+"""
+PostHog Integration - sends LLM analytics events to PostHog
+
+Follows PostHog's LLM Analytics format: https://posthog.com/docs/llm-analytics/manual-capture
+
+async_log_success_event: stores batch of events in memory and flushes to PostHog
+async_log_failure_event: logs failed LLM calls with error information
+
+For batching specific details see CustomBatchLogger class
+"""
+
+import asyncio
+import os
+import uuid
+from typing import Any, Dict, Optional
+
+
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.llms.custom_httpx.http_handler import (
+ _get_httpx_client,
+ get_async_httpx_client,
+ httpxSpecialProvider,
+)
+from litellm.types.integrations.posthog import (
+ POSTHOG_MAX_BATCH_SIZE,
+ PostHogEventPayload,
+)
+from litellm.types.utils import StandardLoggingPayload
+
+
+class PostHogLogger(CustomBatchLogger):
+ def __init__(self, **kwargs):
+ """
+ Initializes the PostHog logger, checks if the correct env variables are set
+
+ Required environment variables:
+ `POSTHOG_API_KEY` - your PostHog API key
+ `POSTHOG_API_URL` - your PostHog API URL (defaults to https://app.posthog.com)
+ """
+ try:
+ verbose_logger.debug("PostHog: in init posthog logger")
+ if os.getenv("POSTHOG_API_KEY", None) is None:
+ raise Exception("POSTHOG_API_KEY is not set, set 'POSTHOG_API_KEY=<>'")
+
+ self.async_client = get_async_httpx_client(
+ llm_provider=httpxSpecialProvider.LoggingCallback
+ )
+ self.sync_client = _get_httpx_client()
+
+ self.POSTHOG_API_KEY = os.getenv("POSTHOG_API_KEY")
+ posthog_api_url = os.getenv("POSTHOG_API_URL", "https://us.i.posthog.com")
+ self.posthog_host = posthog_api_url.rstrip('/')
+ self.capture_url = f"{self.posthog_host}/batch/"
+
+ self._async_initialized = False
+ self.flush_lock = None
+ self.log_queue = []
+
+ super().__init__(
+ **kwargs, flush_lock=None, batch_size=POSTHOG_MAX_BATCH_SIZE
+ )
+
+ except Exception as e:
+ verbose_logger.exception(
+ f"PostHog: Got exception on init PostHog client {str(e)}"
+ )
+ raise e
+
+ def log_success_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ verbose_logger.debug(
+ "PostHog: Sync logging - Enters logging function for model %s", kwargs
+ )
+
+ event_payload = self.create_posthog_event_payload(kwargs)
+
+ headers = {
+ "Content-Type": "application/json",
+ }
+
+ payload = self._create_posthog_payload([event_payload])
+
+ response = self.sync_client.post(
+ url=self.capture_url,
+ json=payload,
+ headers=headers,
+ )
+ response.raise_for_status()
+
+ if response.status_code != 200:
+ raise Exception(
+ f"Response from PostHog API status_code: {response.status_code}, text: {response.text}"
+ )
+
+ verbose_logger.debug("PostHog: Sync event successfully sent")
+
+ except Exception as e:
+ verbose_logger.exception(f"PostHog Sync Layer Error - {str(e)}")
+
+ async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ verbose_logger.debug(
+ "PostHog: Async logging - Enters logging function for model %s", kwargs
+ )
+ self._ensure_async_setup() # Lazy initialization
+ await self._log_async_event(kwargs, response_obj, start_time, end_time)
+ except Exception as e:
+ verbose_logger.exception(f"PostHog Layer Error - {str(e)}")
+ pass
+
+ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ verbose_logger.debug(
+ "PostHog: Async logging - Enters logging function for model %s", kwargs
+ )
+ self._ensure_async_setup() # Lazy initialization
+ await self._log_async_event(kwargs, response_obj, start_time, end_time)
+ except Exception as e:
+ verbose_logger.exception(f"PostHog Layer Error - {str(e)}")
+ pass
+
+ async def _log_async_event(self, kwargs, response_obj=None, start_time=0.0, end_time=0.0):
+ # Note: response_obj, start_time, end_time not used - all data comes from kwargs
+ event_payload = self.create_posthog_event_payload(kwargs)
+
+ self.log_queue.append(event_payload)
+ verbose_logger.debug(
+ f"PostHog, event added to queue. Will flush in {self.flush_interval} seconds..."
+ )
+
+ if len(self.log_queue) >= self.batch_size:
+ await self.flush_queue()
+
+ def create_posthog_event_payload(self, kwargs: Dict[str, Any]) -> PostHogEventPayload:
+ """
+ Helper function to create a PostHog event payload for logging
+
+ Args:
+ kwargs (Dict[str, Any]): request kwargs containing standard_logging_object
+
+ Returns:
+ PostHogEventPayload: defined in types.py
+ """
+ standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
+ "standard_logging_object", None
+ )
+ if standard_logging_object is None:
+ raise ValueError("standard_logging_object not found in kwargs")
+
+ call_type = standard_logging_object.get("call_type", "")
+ event_name = "$ai_embedding" if call_type == "embedding" else "$ai_generation"
+
+ properties = self._create_posthog_properties(
+ standard_logging_object=standard_logging_object,
+ kwargs=kwargs,
+ event_name=event_name,
+ )
+
+ distinct_id = self._get_distinct_id(standard_logging_object, kwargs)
+
+ return PostHogEventPayload(
+ event=event_name,
+ properties=properties,
+ distinct_id=distinct_id,
+ )
+
+ def _create_posthog_properties(
+ self,
+ standard_logging_object: StandardLoggingPayload,
+ kwargs: Dict[str, Any],
+ event_name: str,
+ ) -> Dict[str, Any]:
+ """Create PostHog properties following LLM Analytics spec"""
+ properties = {}
+
+ # Core model information
+ properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "")
+ properties["$ai_provider"] = self._safe_get(standard_logging_object, "custom_llm_provider", "")
+
+ # Input/Output data
+ messages = self._safe_get(standard_logging_object, "messages")
+ if messages is not None:
+ properties["$ai_input"] = messages
+
+ if event_name == "$ai_generation":
+ response = self._safe_get(standard_logging_object, "response")
+ if response is not None:
+ properties["$ai_output_choices"] = response
+
+ # Token information
+ properties["$ai_input_tokens"] = self._safe_get(standard_logging_object, "prompt_tokens", 0)
+ if event_name == "$ai_generation":
+ properties["$ai_output_tokens"] = self._safe_get(standard_logging_object, "completion_tokens", 0)
+
+ # Cost and performance
+ response_cost = self._safe_get(standard_logging_object, "response_cost")
+ if response_cost is not None:
+ properties["$ai_total_cost_usd"] = response_cost
+
+ properties["$ai_latency"] = self._safe_get(standard_logging_object, "response_time", 0.0)
+
+ # Error handling
+ if self._safe_get(standard_logging_object, "status") == "failure":
+ properties["$ai_is_error"] = True
+ error_str = self._safe_get(standard_logging_object, "error_str")
+ if error_str is not None:
+ properties["$ai_error"] = error_str
+
+ # Add trace properties
+ self._add_trace_properties(properties, kwargs)
+
+ # Add custom metadata fields
+ self._add_custom_metadata_properties(properties, kwargs)
+
+ return properties
+
+ def _add_trace_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]):
+ standard_logging_object = self._safe_get(kwargs, "standard_logging_object", {})
+
+ trace_id = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid())
+ properties["$ai_trace_id"] = trace_id
+
+ span_id = self._safe_get(standard_logging_object, "id", self._safe_uuid())
+ properties["$ai_span_id"] = span_id
+
+ metadata = self._extract_metadata(kwargs)
+ parent_id = metadata.get("parent_run_id") or metadata.get("parent_id")
+ if parent_id:
+ properties["$ai_parent_id"] = parent_id
+
+ def _add_custom_metadata_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]):
+ """Add custom metadata fields to PostHog properties"""
+ metadata = self._extract_metadata(kwargs)
+ if not isinstance(metadata, dict):
+ return
+
+ litellm_internal_fields = {
+ "endpoint", "caching_groups", "user_api_key_hash", "user_api_key_alias",
+ "user_api_key_team_id", "user_api_key_user_id", "user_api_key_org_id",
+ "user_api_key_team_alias", "user_api_key_end_user_id", "user_api_key_user_email",
+ "user_api_key", "user_api_end_user_max_budget", "litellm_api_version",
+ "global_max_parallel_requests", "user_api_key_team_max_budget", "user_api_key_team_spend",
+ "user_api_key_spend", "user_api_key_max_budget", "user_api_key_model_max_budget",
+ "user_api_key_metadata", "headers", "litellm_parent_otel_span", "requester_ip_address",
+ "model_group", "model_group_size", "deployment", "model_info", "api_base",
+ "caching_groups", "hidden_params", "parent_run_id", "parent_id", "user_id"
+ }
+
+ for key, value in metadata.items():
+ if key not in litellm_internal_fields:
+ properties[key] = value
+
+ def _get_distinct_id(
+ self, standard_logging_object: StandardLoggingPayload, kwargs: Dict[str, Any]
+ ) -> str:
+ metadata = self._extract_metadata(kwargs)
+ user_id = self._safe_get(metadata, "user_id")
+ if user_id:
+ return str(user_id)
+ end_user = self._safe_get(standard_logging_object, "end_user")
+ if end_user:
+ return str(end_user)
+ trace_id = self._safe_get(standard_logging_object, "trace_id")
+ if trace_id:
+ return str(trace_id)
+
+ return self._safe_uuid()
+
+ async def async_send_batch(self):
+ """
+ Sends the in memory logs queue to PostHog API
+
+ Raises:
+ Raises a NON Blocking verbose_logger.exception if an error occurs
+ """
+ try:
+ if not self.log_queue:
+ return
+
+ verbose_logger.debug(
+ f"PostHog: Sending batch of {len(self.log_queue)} events"
+ )
+
+ headers = {
+ "Content-Type": "application/json",
+ }
+
+ payload = self._create_posthog_payload(list(self.log_queue))
+
+ response = await self.async_client.post(
+ url=self.capture_url,
+ json=payload,
+ headers=headers,
+ )
+ response.raise_for_status()
+
+ if response.status_code != 200:
+ raise Exception(
+ f"Response from PostHog API status_code: {response.status_code}, text: {response.text}"
+ )
+
+ verbose_logger.debug(
+ f"PostHog: Batch of {len(self.log_queue)} events successfully sent"
+ )
+ except Exception as e:
+ verbose_logger.exception(f"PostHog Error sending batch API - {str(e)}")
+
+ def _ensure_async_setup(self):
+ if not self._async_initialized:
+ try:
+ self.flush_lock = asyncio.Lock()
+ asyncio.create_task(self.periodic_flush())
+ self._async_initialized = True
+ verbose_logger.debug("PostHog: Async components initialized")
+ except Exception as e:
+ verbose_logger.error(f"PostHog: Failed to initialize async components: {str(e)}")
+ raise
+
+ def _extract_metadata(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ return litellm_params.get("metadata", {}) or {}
+
+ def _safe_uuid(self) -> str:
+ return str(uuid.uuid4())
+
+ def _create_posthog_payload(self, events: list) -> Dict[str, Any]:
+ return {"api_key": self.POSTHOG_API_KEY, "batch": events}
+
+ def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any:
+ if obj is None or not hasattr(obj, 'get'):
+ return default
+ return obj.get(key, default)
diff --git a/litellm/litellm_core_utils/cached_imports.py b/litellm/litellm_core_utils/cached_imports.py
new file mode 100644
index 0000000000..c3ab292e9c
--- /dev/null
+++ b/litellm/litellm_core_utils/cached_imports.py
@@ -0,0 +1,56 @@
+"""
+Cached imports module for LiteLLM.
+
+This module provides cached import functionality to avoid repeated imports
+inside functions that are critical to performance.
+"""
+
+from typing import TYPE_CHECKING, Callable, Optional, Type
+
+# Type annotations for cached imports
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging
+ from litellm.litellm_core_utils.coroutine_checker import CoroutineChecker
+
+# Global cache variables
+_LiteLLMLogging: Optional[Type["Logging"]] = None
+_coroutine_checker: Optional["CoroutineChecker"] = None
+_set_callbacks: Optional[Callable] = None
+
+
+def get_litellm_logging_class() -> Type["Logging"]:
+ """Get the cached LiteLLM Logging class, initializing if needed."""
+ global _LiteLLMLogging
+ if _LiteLLMLogging is not None:
+ return _LiteLLMLogging
+ from litellm.litellm_core_utils.litellm_logging import Logging
+ _LiteLLMLogging = Logging
+ return _LiteLLMLogging
+
+
+def get_coroutine_checker() -> "CoroutineChecker":
+ """Get the cached coroutine checker instance, initializing if needed."""
+ global _coroutine_checker
+ if _coroutine_checker is not None:
+ return _coroutine_checker
+ from litellm.litellm_core_utils.coroutine_checker import coroutine_checker
+ _coroutine_checker = coroutine_checker
+ return _coroutine_checker
+
+
+def get_set_callbacks() -> Callable:
+ """Get the cached set_callbacks function, initializing if needed."""
+ global _set_callbacks
+ if _set_callbacks is not None:
+ return _set_callbacks
+ from litellm.litellm_core_utils.litellm_logging import set_callbacks
+ _set_callbacks = set_callbacks
+ return _set_callbacks
+
+
+def clear_cached_imports() -> None:
+ """Clear all cached imports. Useful for testing or memory management."""
+ global _LiteLLMLogging, _coroutine_checker, _set_callbacks
+ _LiteLLMLogging = None
+ _coroutine_checker = None
+ _set_callbacks = None
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
index 4aeb9d4d64..7423e55b62 100644
--- a/litellm/litellm_core_utils/core_helpers.py
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -228,9 +228,11 @@ def safe_deep_copy(data):
"""
Safe Deep Copy
- The LiteLLM Request has some object that can-not be pickled / deep copied
-
- Use this function to safely deep copy the LiteLLM Request
+ The LiteLLM request may contain objects that cannot be pickled/deep-copied
+ (e.g., tracing spans, locks, clients).
+
+ This helper deep-copies each top-level key independently; on failure keeps
+ original ref
"""
import copy
@@ -255,9 +257,22 @@ def safe_deep_copy(data):
"litellm_parent_otel_span"
)
data["litellm_metadata"]["litellm_parent_otel_span"] = "placeholder"
- new_data = copy.deepcopy(data)
- # Step 2: re-add the litellm_parent_otel_span after doing a deep copy
+ # Step 2: Per-key deepcopy with fallback
+ if isinstance(data, dict):
+ new_data = {}
+ for k, v in data.items():
+ try:
+ new_data[k] = copy.deepcopy(v)
+ except Exception:
+ new_data[k] = v
+ else:
+ try:
+ new_data = copy.deepcopy(data)
+ except Exception:
+ new_data = data
+
+ # Step 3: re-add the litellm_parent_otel_span after doing a deep copy
if isinstance(data, dict) and litellm_parent_otel_span is not None:
if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]:
data["metadata"]["litellm_parent_otel_span"] = litellm_parent_otel_span
@@ -268,4 +283,4 @@ def safe_deep_copy(data):
data["litellm_metadata"][
"litellm_parent_otel_span"
] = litellm_parent_otel_span
- return new_data
+ return new_data
\ No newline at end of file
diff --git a/litellm/litellm_core_utils/coroutine_checker.py b/litellm/litellm_core_utils/coroutine_checker.py
new file mode 100644
index 0000000000..368aee62ed
--- /dev/null
+++ b/litellm/litellm_core_utils/coroutine_checker.py
@@ -0,0 +1,63 @@
+# CoroutineChecker utility for checking if functions/callables are coroutines or coroutine functions
+
+import inspect
+from typing import Any
+from weakref import WeakKeyDictionary
+from litellm.constants import (
+ COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY,
+)
+
+
+class CoroutineChecker:
+ """Utility class for checking coroutine status of functions and callables.
+
+ Simple bounded cache using WeakKeyDictionary to avoid memory leaks.
+ """
+
+ def __init__(self):
+ self._cache = WeakKeyDictionary()
+ self._max_size = COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY
+
+ def is_async_callable(self, callback: Any) -> bool:
+ """Fast, cached check for whether a callback is an async function.
+ Falls back gracefully if the object cannot be weak-referenced or cached.
+ 2.59x speedup.
+ """
+ # Fast path: check cache first (most common case)
+ try:
+ cached = self._cache.get(callback)
+ if cached is not None:
+ return cached
+ except Exception:
+ pass
+
+ # Determine target - optimized path for common cases
+ target = callback
+ if not inspect.isfunction(target) and not inspect.ismethod(target):
+ try:
+ call_attr = getattr(target, "__call__", None)
+ if call_attr is not None:
+ target = call_attr
+ except Exception:
+ pass
+
+ # Compute result
+ try:
+ result = inspect.iscoroutinefunction(target)
+ except Exception:
+ result = False
+
+ # Cache the result with size enforcement
+ try:
+ # Simple size enforcement: clear cache if it gets too large
+ if len(self._cache) >= self._max_size:
+ self._cache.clear()
+
+ self._cache[callback] = result
+ except Exception:
+ pass
+
+ return result
+
+# Global instance for backward compatibility and convenience
+coroutine_checker = CoroutineChecker()
diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py
index af51fe9ab7..bb8fa580e3 100644
--- a/litellm/litellm_core_utils/custom_logger_registry.py
+++ b/litellm/litellm_core_utils/custom_logger_registry.py
@@ -33,6 +33,7 @@ from litellm.integrations.mlflow import MlflowLogger
from litellm.integrations.openmeter import OpenMeterLogger
from litellm.integrations.opentelemetry import OpenTelemetry
from litellm.integrations.opik.opik import OpikLogger
+from litellm.integrations.posthog import PostHogLogger
try:
from litellm_enterprise.integrations.prometheus import PrometheusLogger
@@ -46,6 +47,7 @@ from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook i
VectorStorePreCallHook,
)
from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler
+from litellm.proxy.hooks.dynamic_rate_limiter_v3 import _PROXY_DynamicRateLimitHandlerV3
class CustomLoggerRegistry:
@@ -85,9 +87,11 @@ class CustomLoggerRegistry:
"s3_v2": S3Logger,
"aws_sqs": SQSLogger,
"dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler,
+ "dynamic_rate_limiter_v3": _PROXY_DynamicRateLimitHandlerV3,
"vector_store_pre_call_hook": VectorStorePreCallHook,
"dotprompt": DotpromptManager,
"cloudzero": CloudZeroLogger,
+ "posthog": PostHogLogger,
}
try:
diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py
index 08e5323c30..9a317cfcf0 100644
--- a/litellm/litellm_core_utils/duration_parser.py
+++ b/litellm/litellm_core_utils/duration_parser.py
@@ -158,6 +158,7 @@ def _setup_timezone(
"US/Eastern": timezone(timedelta(hours=-4)), # EDT
"US/Pacific": timezone(timedelta(hours=-7)), # PDT
"Asia/Kolkata": timezone(timedelta(hours=5, minutes=30)), # IST
+ "Asia/Bangkok": timezone(timedelta(hours=7)), # ICT (Indochina Time)
"Europe/London": timezone(timedelta(hours=1)), # BST
"UTC": timezone.utc,
}
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
index 25ae0269ab..409f5ebe0b 100644
--- a/litellm/litellm_core_utils/exception_mapping_utils.py
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -6,6 +6,7 @@ import httpx
import litellm
from litellm._logging import verbose_logger
+from litellm.types.utils import LlmProviders
from ..exceptions import (
APIConnectionError,
@@ -556,7 +557,7 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
llm_provider="anthropic",
)
- elif "overloaded_error" in error_str:
+ elif "overloaded_error" in error_str or "Overloaded" in error_str:
exception_mapping_worked = True
raise InternalServerError(
message="AnthropicError - {}".format(error_str),
@@ -762,7 +763,7 @@ def exception_type( # type: ignore # noqa: PLR0915
error_str += "XXXXXXX" + '"'
raise AuthenticationError(
- message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}",
llm_provider=custom_llm_provider,
model=model,
response=getattr(original_exception, "response", None),
@@ -771,14 +772,14 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "model's maximum context limit" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
- message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}",
model=model,
llm_provider=custom_llm_provider,
)
elif "token_quota_reached" in error_str:
exception_mapping_worked = True
raise RateLimitError(
- message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}",
llm_provider=custom_llm_provider,
model=model,
response=getattr(original_exception, "response", None),
@@ -789,14 +790,14 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
elif "model_no_support_for_function" in error_str:
exception_mapping_worked = True
raise BadRequestError(
- message=f"{custom_llm_provider}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
@@ -804,7 +805,7 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 500:
exception_mapping_worked = True
raise litellm.InternalServerError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
@@ -814,28 +815,28 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise AuthenticationError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
elif original_exception.status_code == 400:
exception_mapping_worked = True
raise BadRequestError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
elif original_exception.status_code == 404:
exception_mapping_worked = True
raise NotFoundError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
elif original_exception.status_code == 408:
exception_mapping_worked = True
raise Timeout(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -846,7 +847,7 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise BadRequestError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -854,7 +855,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -862,7 +863,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 503:
exception_mapping_worked = True
raise ServiceUnavailableError(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -870,7 +871,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 504: # gateway timeout error
exception_mapping_worked = True
raise Timeout(
- message=f"{custom_llm_provider}Exception - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -1168,9 +1169,9 @@ def exception_type( # type: ignore # noqa: PLR0915
exception_status_code=original_exception.status_code,
)
elif (
- custom_llm_provider == "vertex_ai"
- or custom_llm_provider == "vertex_ai_beta"
- or custom_llm_provider == "gemini"
+ custom_llm_provider == LlmProviders.VERTEX_AI
+ or custom_llm_provider == LlmProviders.VERTEX_AI_BETA
+ or custom_llm_provider == LlmProviders.GEMINI
):
if (
"Vertex AI API has not been used in project" in error_str
@@ -1178,9 +1179,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise BadRequestError(
- message=f"litellm.BadRequestError: VertexAIException - {error_str}",
+ message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
request=httpx.Request(
@@ -1193,7 +1194,7 @@ def exception_type( # type: ignore # noqa: PLR0915
if "400 Request payload size exceeds" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
- message=f"VertexException - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
llm_provider=custom_llm_provider,
)
@@ -1203,9 +1204,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
- message=f"litellm.InternalServerError: VertexAIException - {error_str}",
+ message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=500,
content=str(original_exception),
@@ -1216,7 +1217,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "API key not valid." in error_str:
exception_mapping_worked = True
raise AuthenticationError(
- message=f"{custom_llm_provider}Exception - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@@ -1224,9 +1225,9 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "403" in error_str:
exception_mapping_worked = True
raise BadRequestError(
- message=f"VertexAIException BadRequestError - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=403,
request=httpx.Request(
@@ -1243,9 +1244,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise ContentPolicyViolationError(
- message=f"VertexAIException ContentPolicyViolationError - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=400,
@@ -1264,9 +1265,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise RateLimitError(
- message=f"litellm.RateLimitError: VertexAIException - {error_str}",
+ message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=429,
@@ -1282,18 +1283,18 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
- message=f"litellm.InternalServerError: VertexAIException - {error_str}",
+ message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
if hasattr(original_exception, "status_code"):
if original_exception.status_code == 400:
exception_mapping_worked = True
raise BadRequestError(
- message=f"VertexAIException BadRequestError - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=400,
@@ -1306,21 +1307,35 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 401:
exception_mapping_worked = True
raise AuthenticationError(
- message=f"VertexAIException - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
+ if original_exception.status_code == 403:
+ exception_mapping_worked = True
+ raise PermissionDeniedError(
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
+ llm_provider=custom_llm_provider,
+ model=model,
+ response=httpx.Response(
+ status_code=403,
+ request=httpx.Request(
+ method="POST",
+ url="https://cloud.google.com/vertex-ai/",
+ ),
+ ),
+ )
if original_exception.status_code == 404:
exception_mapping_worked = True
raise NotFoundError(
- message=f"VertexAIException - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
if original_exception.status_code == 408:
exception_mapping_worked = True
raise Timeout(
- message=f"VertexAIException - {original_exception.message}",
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
@@ -1328,9 +1343,9 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
- message=f"litellm.RateLimitError: VertexAIException - {error_str}",
+ message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=429,
@@ -1343,9 +1358,9 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 500:
exception_mapping_worked = True
raise litellm.InternalServerError(
- message=f"VertexAIException InternalServerError - {error_str}",
+ message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}",
model=model,
- llm_provider="vertex_ai",
+ llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=500,
@@ -1353,71 +1368,20 @@ def exception_type( # type: ignore # noqa: PLR0915
request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
- if original_exception.status_code == 503:
+ if original_exception.status_code == 502:
exception_mapping_worked = True
- raise ServiceUnavailableError(
- message=f"VertexAIException - {original_exception.message}",
+ raise APIConnectionError(
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
- elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
- if "503 Getting metadata" in error_str:
- # auth errors look like this
- # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate.
- exception_mapping_worked = True
- raise BadRequestError(
- message="GeminiException - Invalid api key",
- model=model,
- llm_provider="palm",
- response=getattr(original_exception, "response", None),
- )
- if (
- "504 Deadline expired before operation could complete." in error_str
- or "504 Deadline Exceeded" in error_str
- ):
- exception_mapping_worked = True
- raise Timeout(
- message=f"GeminiException - {original_exception.message}",
- model=model,
- llm_provider="palm",
- exception_status_code=original_exception.status_code,
- )
- if "400 Request payload size exceeds" in error_str:
- exception_mapping_worked = True
- raise ContextWindowExceededError(
- message=f"GeminiException - {error_str}",
- model=model,
- llm_provider="palm",
- response=getattr(original_exception, "response", None),
- )
- if (
- "500 An internal error has occurred." in error_str
- or "list index out of range" in error_str
- ):
- exception_mapping_worked = True
- raise APIError(
- status_code=getattr(original_exception, "status_code", 500),
- message=f"GeminiException - {original_exception.message}",
- llm_provider="palm",
- model=model,
- request=httpx.Response(
- status_code=429,
- request=httpx.Request(
- method="POST",
- url=" https://cloud.google.com/vertex-ai/",
- ),
- ),
- )
- if hasattr(original_exception, "status_code"):
- if original_exception.status_code == 400:
+ if original_exception.status_code == 503:
exception_mapping_worked = True
- raise BadRequestError(
- message=f"GeminiException - {error_str}",
+ raise ServiceUnavailableError(
+ message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
+ llm_provider=custom_llm_provider,
model=model,
- llm_provider="palm",
- response=getattr(original_exception, "response", None),
)
- # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes
elif custom_llm_provider == "cloudflare":
if "Authentication error" in error_str:
exception_mapping_worked = True
@@ -1449,6 +1413,14 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
response=getattr(original_exception, "response", None),
)
+ elif "invalid type: parameter" in error_str:
+ exception_mapping_worked = True
+ raise BadRequestError(
+ message=f"CohereException - {original_exception.message}",
+ llm_provider="cohere",
+ model=model,
+ response=getattr(original_exception, "response", None),
+ )
elif "too many tokens" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py
index d60e79069c..06e650f938 100644
--- a/litellm/litellm_core_utils/get_supported_openai_params.py
+++ b/litellm/litellm_core_utils/get_supported_openai_params.py
@@ -94,9 +94,7 @@ def get_supported_openai_params( # noqa: PLR0915
return litellm.VLLMConfig().get_supported_openai_params(model=model)
elif custom_llm_provider == "deepseek":
return litellm.DeepSeekChatConfig().get_supported_openai_params(model=model)
- elif custom_llm_provider == "cohere":
- return litellm.CohereConfig().get_supported_openai_params(model=model)
- elif custom_llm_provider == "cohere_chat":
+ elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere":
return litellm.CohereChatConfig().get_supported_openai_params(model=model)
elif custom_llm_provider == "maritalk":
return litellm.MaritalkConfig().get_supported_openai_params(model=model)
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 2f21d28089..059fd9f1fc 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -138,6 +138,7 @@ from ..integrations.logfire_logger import LogfireLevel, LogfireLogger
from ..integrations.lunary import LunaryLogger
from ..integrations.openmeter import OpenMeterLogger
from ..integrations.opik.opik import OpikLogger
+from ..integrations.posthog import PostHogLogger
from ..integrations.prompt_layer import PromptLayerLogger
from ..integrations.s3 import S3Logger
from ..integrations.s3_v2 import S3Logger as S3V2Logger
@@ -193,7 +194,6 @@ _in_memory_loggers: List[Any] = []
sentry_sdk_instance = None
capture_exception = None
add_breadcrumb = None
-posthog = None
slack_app = None
alerts_channel = None
heliconeLogger = None
@@ -300,9 +300,9 @@ class Logging(LiteLLMLoggingBaseClass):
self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4())
self.function_id = function_id
self.streaming_chunks: List[Any] = [] # for generating complete stream response
- self.sync_streaming_chunks: List[Any] = (
- []
- ) # for generating complete stream response
+ self.sync_streaming_chunks: List[
+ Any
+ ] = [] # for generating complete stream response
self.log_raw_request_response = log_raw_request_response
# Initialize dynamic callbacks
@@ -672,24 +672,23 @@ class Logging(LiteLLMLoggingBaseClass):
if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook(
non_default_params
):
- self.model_call_details["prompt_integration"] = (
- anthropic_cache_control_logger.__class__.__name__
- )
+ self.model_call_details[
+ "prompt_integration"
+ ] = anthropic_cache_control_logger.__class__.__name__
return anthropic_cache_control_logger
#########################################################
# Vector Store / Knowledge Base hooks
#########################################################
if litellm.vector_store_registry is not None:
-
vector_store_custom_logger = _init_custom_logger_compatible_class(
logging_integration="vector_store_pre_call_hook",
internal_usage_cache=None,
llm_router=None,
)
- self.model_call_details["prompt_integration"] = (
- vector_store_custom_logger.__class__.__name__
- )
+ self.model_call_details[
+ "prompt_integration"
+ ] = vector_store_custom_logger.__class__.__name__
return vector_store_custom_logger
return None
@@ -741,9 +740,9 @@ class Logging(LiteLLMLoggingBaseClass):
model
): # if model name was changes pre-call, overwrite the initial model call name with the new one
self.model_call_details["model"] = model
- self.model_call_details["litellm_params"]["api_base"] = (
- self._get_masked_api_base(additional_args.get("api_base", ""))
- )
+ self.model_call_details["litellm_params"][
+ "api_base"
+ ] = self._get_masked_api_base(additional_args.get("api_base", ""))
def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915
# Log the exact input to the LLM API
@@ -772,10 +771,10 @@ class Logging(LiteLLMLoggingBaseClass):
try:
# [Non-blocking Extra Debug Information in metadata]
if turn_off_message_logging is True:
- _metadata["raw_request"] = (
- "redacted by litellm. \
+ _metadata[
+ "raw_request"
+ ] = "redacted by litellm. \
'litellm.turn_off_message_logging=True'"
- )
else:
curl_command = self._get_request_curl_command(
api_base=additional_args.get("api_base", ""),
@@ -786,32 +785,32 @@ class Logging(LiteLLMLoggingBaseClass):
_metadata["raw_request"] = str(curl_command)
# split up, so it's easier to parse in the UI
- self.model_call_details["raw_request_typed_dict"] = (
- RawRequestTypedDict(
- raw_request_api_base=str(
- additional_args.get("api_base") or ""
- ),
- raw_request_body=self._get_raw_request_body(
- additional_args.get("complete_input_dict", {})
- ),
- raw_request_headers=self._get_masked_headers(
- additional_args.get("headers", {}) or {},
- ignore_sensitive_headers=True,
- ),
- error=None,
- )
+ self.model_call_details[
+ "raw_request_typed_dict"
+ ] = RawRequestTypedDict(
+ raw_request_api_base=str(
+ additional_args.get("api_base") or ""
+ ),
+ raw_request_body=self._get_raw_request_body(
+ additional_args.get("complete_input_dict", {})
+ ),
+ raw_request_headers=self._get_masked_headers(
+ additional_args.get("headers", {}) or {},
+ ignore_sensitive_headers=True,
+ ),
+ error=None,
)
except Exception as e:
- self.model_call_details["raw_request_typed_dict"] = (
- RawRequestTypedDict(
- error=str(e),
- )
+ self.model_call_details[
+ "raw_request_typed_dict"
+ ] = RawRequestTypedDict(
+ error=str(e),
)
- _metadata["raw_request"] = (
- "Unable to Log \
+ _metadata[
+ "raw_request"
+ ] = "Unable to Log \
raw request: {}".format(
- str(e)
- )
+ str(e)
)
if getattr(self, "logger_fn", None) and callable(self.logger_fn):
try:
@@ -1112,13 +1111,13 @@ class Logging(LiteLLMLoggingBaseClass):
for callback in callbacks:
try:
if isinstance(callback, CustomLogger):
- response: Optional[MCPPostCallResponseObject] = (
- await callback.async_post_mcp_tool_call_hook(
- kwargs=kwargs,
- response_obj=post_mcp_tool_call_response_obj,
- start_time=start_time,
- end_time=end_time,
- )
+ response: Optional[
+ MCPPostCallResponseObject
+ ] = await callback.async_post_mcp_tool_call_hook(
+ kwargs=kwargs,
+ response_obj=post_mcp_tool_call_response_obj,
+ start_time=start_time,
+ end_time=end_time,
)
######################################################################
# if any of the callbacks modify the response, use the modified response
@@ -1238,9 +1237,9 @@ class Logging(LiteLLMLoggingBaseClass):
verbose_logger.debug(
f"response_cost_failure_debug_information: {debug_info}"
)
- self.model_call_details["response_cost_failure_debug_information"] = (
- debug_info
- )
+ self.model_call_details[
+ "response_cost_failure_debug_information"
+ ] = debug_info
return None
try:
@@ -1265,9 +1264,9 @@ class Logging(LiteLLMLoggingBaseClass):
verbose_logger.debug(
f"response_cost_failure_debug_information: {debug_info}"
)
- self.model_call_details["response_cost_failure_debug_information"] = (
- debug_info
- )
+ self.model_call_details[
+ "response_cost_failure_debug_information"
+ ] = debug_info
return None
@@ -1411,9 +1410,9 @@ class Logging(LiteLLMLoggingBaseClass):
end_time = datetime.datetime.now()
if self.completion_start_time is None:
self.completion_start_time = end_time
- self.model_call_details["completion_start_time"] = (
- self.completion_start_time
- )
+ self.model_call_details[
+ "completion_start_time"
+ ] = self.completion_start_time
self.model_call_details["log_event_type"] = "successful_api_call"
self.model_call_details["end_time"] = end_time
self.model_call_details["cache_hit"] = cache_hit
@@ -1466,39 +1465,39 @@ class Logging(LiteLLMLoggingBaseClass):
"response_cost"
]
else:
- self.model_call_details["response_cost"] = (
- self._response_cost_calculator(result=logging_result)
- )
+ self.model_call_details[
+ "response_cost"
+ ] = self._response_cost_calculator(result=logging_result)
## STANDARDIZED LOGGING PAYLOAD
- self.model_call_details["standard_logging_object"] = (
- get_standard_logging_object_payload(
- kwargs=self.model_call_details,
- init_response_obj=logging_result,
- start_time=start_time,
- end_time=end_time,
- logging_obj=self,
- status="success",
- standard_built_in_tools_params=self.standard_built_in_tools_params,
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj=logging_result,
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="success",
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
)
elif isinstance(result, dict) or isinstance(result, list):
## STANDARDIZED LOGGING PAYLOAD
- self.model_call_details["standard_logging_object"] = (
- get_standard_logging_object_payload(
- kwargs=self.model_call_details,
- init_response_obj=result,
- start_time=start_time,
- end_time=end_time,
- logging_obj=self,
- status="success",
- standard_built_in_tools_params=self.standard_built_in_tools_params,
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="success",
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
)
elif standard_logging_object is not None:
- self.model_call_details["standard_logging_object"] = (
- standard_logging_object
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = standard_logging_object
else: # streaming chunks + image gen.
self.model_call_details["response_cost"] = None
@@ -1597,7 +1596,6 @@ class Logging(LiteLLMLoggingBaseClass):
)
if complete_streaming_response is not None:
-
self.success_handler(result=complete_streaming_response)
return
@@ -1650,23 +1648,23 @@ class Logging(LiteLLMLoggingBaseClass):
verbose_logger.debug(
"Logging Details LiteLLM-Success Call streaming complete"
)
- self.model_call_details["complete_streaming_response"] = (
- complete_streaming_response
- )
- self.model_call_details["response_cost"] = (
- self._response_cost_calculator(result=complete_streaming_response)
- )
+ self.model_call_details[
+ "complete_streaming_response"
+ ] = complete_streaming_response
+ self.model_call_details[
+ "response_cost"
+ ] = self._response_cost_calculator(result=complete_streaming_response)
## STANDARDIZED LOGGING PAYLOAD
- self.model_call_details["standard_logging_object"] = (
- get_standard_logging_object_payload(
- kwargs=self.model_call_details,
- init_response_obj=complete_streaming_response,
- start_time=start_time,
- end_time=end_time,
- logging_obj=self,
- status="success",
- standard_built_in_tools_params=self.standard_built_in_tools_params,
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj=complete_streaming_response,
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="success",
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
)
callbacks = self.get_combined_callback_list(
dynamic_success_callbacks=self.dynamic_success_callbacks,
@@ -1994,10 +1992,10 @@ class Logging(LiteLLMLoggingBaseClass):
)
else:
if self.stream and complete_streaming_response:
- self.model_call_details["complete_response"] = (
- self.model_call_details.get(
- "complete_streaming_response", {}
- )
+ self.model_call_details[
+ "complete_response"
+ ] = self.model_call_details.get(
+ "complete_streaming_response", {}
)
result = self.model_call_details["complete_response"]
openMeterLogger.log_success_event(
@@ -2036,10 +2034,10 @@ class Logging(LiteLLMLoggingBaseClass):
)
else:
if self.stream and complete_streaming_response:
- self.model_call_details["complete_response"] = (
- self.model_call_details.get(
- "complete_streaming_response", {}
- )
+ self.model_call_details[
+ "complete_response"
+ ] = self.model_call_details.get(
+ "complete_streaming_response", {}
)
result = self.model_call_details["complete_response"]
@@ -2141,10 +2139,12 @@ class Logging(LiteLLMLoggingBaseClass):
result.usage = batch_usage
elif not is_base64_unified_file_id: # only run for non-unified file ids
- response_cost, batch_usage, batch_models = (
- await _handle_completed_batch(
- batch=result, custom_llm_provider=self.custom_llm_provider
- )
+ (
+ response_cost,
+ batch_usage,
+ batch_models,
+ ) = await _handle_completed_batch(
+ batch=result, custom_llm_provider=self.custom_llm_provider
)
result._hidden_params["response_cost"] = response_cost
@@ -2175,9 +2175,9 @@ class Logging(LiteLLMLoggingBaseClass):
if complete_streaming_response is not None:
print_verbose("Async success callbacks: Got a complete streaming response")
- self.model_call_details["async_complete_streaming_response"] = (
- complete_streaming_response
- )
+ self.model_call_details[
+ "async_complete_streaming_response"
+ ] = complete_streaming_response
try:
if self.model_call_details.get("cache_hit", False) is True:
@@ -2188,10 +2188,10 @@ class Logging(LiteLLMLoggingBaseClass):
model_call_details=self.model_call_details
)
# base_model defaults to None if not set on model_info
- self.model_call_details["response_cost"] = (
- self._response_cost_calculator(
- result=complete_streaming_response
- )
+ self.model_call_details[
+ "response_cost"
+ ] = self._response_cost_calculator(
+ result=complete_streaming_response
)
verbose_logger.debug(
@@ -2204,16 +2204,16 @@ class Logging(LiteLLMLoggingBaseClass):
self.model_call_details["response_cost"] = None
## STANDARDIZED LOGGING PAYLOAD
- self.model_call_details["standard_logging_object"] = (
- get_standard_logging_object_payload(
- kwargs=self.model_call_details,
- init_response_obj=complete_streaming_response,
- start_time=start_time,
- end_time=end_time,
- logging_obj=self,
- status="success",
- standard_built_in_tools_params=self.standard_built_in_tools_params,
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj=complete_streaming_response,
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="success",
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
)
callbacks = self.get_combined_callback_list(
dynamic_success_callbacks=self.dynamic_async_success_callbacks,
@@ -2426,18 +2426,18 @@ class Logging(LiteLLMLoggingBaseClass):
## STANDARDIZED LOGGING PAYLOAD
- self.model_call_details["standard_logging_object"] = (
- get_standard_logging_object_payload(
- kwargs=self.model_call_details,
- init_response_obj={},
- start_time=start_time,
- end_time=end_time,
- logging_obj=self,
- status="failure",
- error_str=str(exception),
- original_exception=exception,
- standard_built_in_tools_params=self.standard_built_in_tools_params,
- )
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj={},
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="failure",
+ error_str=str(exception),
+ original_exception=exception,
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
)
return start_time, end_time
@@ -3068,7 +3068,7 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915
"""
Globally sets the callback client
"""
- global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger
+ global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger
try:
for callback in callback_list:
@@ -3107,19 +3107,6 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915
)
capture_exception = sentry_sdk_instance.capture_exception
add_breadcrumb = sentry_sdk_instance.add_breadcrumb
- elif callback == "posthog":
- try:
- from posthog import Posthog
- except ImportError:
- print_verbose("Package 'posthog' is missing. Installing it...")
- subprocess.check_call(
- [sys.executable, "-m", "pip", "install", "posthog"]
- )
- from posthog import Posthog
- posthog = Posthog(
- project_api_key=os.environ.get("POSTHOG_API_KEY"),
- host=os.environ.get("POSTHOG_API_URL"),
- )
elif callback == "slack":
try:
from slack_bolt import App
@@ -3214,6 +3201,14 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
_openmeter_logger = OpenMeterLogger()
_in_memory_loggers.append(_openmeter_logger)
return _openmeter_logger # type: ignore
+ elif logging_integration == "posthog":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, PostHogLogger):
+ return callback # type: ignore
+
+ _posthog_logger = PostHogLogger()
+ _in_memory_loggers.append(_posthog_logger)
+ return _posthog_logger # type: ignore
elif logging_integration == "braintrust":
from litellm.integrations.braintrust_logging import BraintrustLogger
@@ -3326,9 +3321,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
endpoint=arize_config.endpoint,
)
- os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
- f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
- )
+ os.environ[
+ "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
+ ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
for callback in _in_memory_loggers:
if (
isinstance(callback, ArizeLogger)
@@ -3352,9 +3347,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
# auth can be disabled on local deployments of arize phoenix
if arize_phoenix_config.otlp_auth_headers is not None:
- os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
- arize_phoenix_config.otlp_auth_headers
- )
+ os.environ[
+ "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
+ ] = arize_phoenix_config.otlp_auth_headers
for callback in _in_memory_loggers:
if (
@@ -3449,6 +3444,30 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
dynamic_rate_limiter_obj.update_variables(llm_router=llm_router)
_in_memory_loggers.append(dynamic_rate_limiter_obj)
return dynamic_rate_limiter_obj # type: ignore
+ elif logging_integration == "dynamic_rate_limiter_v3":
+ from litellm.proxy.hooks.dynamic_rate_limiter_v3 import (
+ _PROXY_DynamicRateLimitHandlerV3,
+ )
+
+ for callback in _in_memory_loggers:
+ if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3):
+ return callback # type: ignore
+
+ if internal_usage_cache is None:
+ raise Exception(
+ "Internal Error: Cache cannot be empty - internal_usage_cache={}".format(
+ internal_usage_cache
+ )
+ )
+
+ dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3(
+ internal_usage_cache=internal_usage_cache
+ )
+
+ if llm_router is not None and isinstance(llm_router, litellm.Router):
+ dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router)
+ _in_memory_loggers.append(dynamic_rate_limiter_obj_v3)
+ return dynamic_rate_limiter_obj_v3 # type: ignore
elif logging_integration == "langtrace":
if "LANGTRACE_API_KEY" not in os.environ:
raise ValueError("LANGTRACE_API_KEY not found in environment variables")
@@ -3462,9 +3481,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
exporter="otlp_http",
endpoint="https://langtrace.ai/api/trace",
)
- os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
- f"api_key={os.getenv('LANGTRACE_API_KEY')}"
- )
+ os.environ[
+ "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
+ ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}"
for callback in _in_memory_loggers:
if (
isinstance(callback, OpenTelemetry)
@@ -3712,6 +3731,14 @@ def get_custom_logger_compatible_class( # noqa: PLR0915
for callback in _in_memory_loggers:
if isinstance(callback, _PROXY_DynamicRateLimitHandler):
return callback # type: ignore
+ elif logging_integration == "dynamic_rate_limiter_v3":
+ from litellm.proxy.hooks.dynamic_rate_limiter_v3 import (
+ _PROXY_DynamicRateLimitHandlerV3,
+ )
+
+ for callback in _in_memory_loggers:
+ if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3):
+ return callback # type: ignore
elif logging_integration == "langtrace":
from litellm.integrations.opentelemetry import OpenTelemetry
@@ -3910,22 +3937,25 @@ class StandardLoggingPayloadSetup:
clean_metadata = StandardLoggingMetadata(
user_api_key_hash=None,
user_api_key_alias=None,
+ user_api_key_spend=None,
+ user_api_key_max_budget=None,
+ user_api_key_budget_reset_at=None,
user_api_key_team_id=None,
user_api_key_org_id=None,
user_api_key_user_id=None,
user_api_key_team_alias=None,
user_api_key_user_email=None,
+ user_api_key_end_user_id=None,
+ user_api_key_request_route=None,
spend_logs_metadata=None,
requester_ip_address=None,
requester_metadata=None,
- user_api_key_end_user_id=None,
prompt_management_metadata=prompt_management_metadata,
applied_guardrails=applied_guardrails,
mcp_tool_call_metadata=mcp_tool_call_metadata,
vector_store_request_metadata=vector_store_request_metadata,
usage_object=usage_object,
requester_custom_headers=None,
- user_api_key_request_route=None,
cold_storage_object_key=None,
)
if isinstance(metadata, dict):
@@ -4114,10 +4144,10 @@ class StandardLoggingPayloadSetup:
for key in StandardLoggingHiddenParams.__annotations__.keys():
if key in hidden_params:
if key == "additional_headers":
- clean_hidden_params["additional_headers"] = (
- StandardLoggingPayloadSetup.get_additional_headers(
- hidden_params[key]
- )
+ clean_hidden_params[
+ "additional_headers"
+ ] = StandardLoggingPayloadSetup.get_additional_headers(
+ hidden_params[key]
)
else:
clean_hidden_params[key] = hidden_params[key] # type: ignore
@@ -4150,15 +4180,28 @@ class StandardLoggingPayloadSetup:
from litellm.integrations.s3 import get_s3_object_key
# Only generate object key if cold storage is configured
- if litellm.configured_cold_storage_logger is None:
+ configured_cold_storage_logger = litellm.configured_cold_storage_logger
+ if configured_cold_storage_logger is None:
return None
try:
# Generate file name in same format as litellm.utils.get_logging_id
s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}"
+ # Get the actual s3_path from the configured cold storage logger instance
+ s3_path = "" # default value
+
+ # Try to get the actual logger instance from the logger name
+ try:
+ custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name(configured_cold_storage_logger)
+ if custom_logger and hasattr(custom_logger, 's3_path') and custom_logger.s3_path:
+ s3_path = custom_logger.s3_path
+ except Exception:
+ # If any error occurs in getting the logger instance, use default empty s3_path
+ pass
+
s3_object_key = get_s3_object_key(
- s3_path="", # Use empty path as default
+ s3_path=s3_path, # Use actual s3_path from logger configuration
team_alias_prefix="", # Don't split by team alias for cold storage
start_time=start_time,
s3_file_name=s3_file_name,
@@ -4553,6 +4596,9 @@ def get_standard_logging_metadata(
clean_metadata = StandardLoggingMetadata(
user_api_key_hash=None,
user_api_key_alias=None,
+ user_api_key_spend=None,
+ user_api_key_max_budget=None,
+ user_api_key_budget_reset_at=None,
user_api_key_team_id=None,
user_api_key_org_id=None,
user_api_key_user_id=None,
@@ -4572,14 +4618,10 @@ def get_standard_logging_metadata(
cold_storage_object_key=None,
)
if isinstance(metadata, dict):
- # Filter the metadata dictionary to include only the specified keys
- clean_metadata = StandardLoggingMetadata(
- **{ # type: ignore
- key: metadata[key]
- for key in StandardLoggingMetadata.__annotations__.keys()
- if key in metadata
- }
- )
+ # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields
+ for key in StandardLoggingMetadata.__annotations__.keys():
+ if key in metadata:
+ clean_metadata[key] = metadata[key] # type: ignore
if metadata.get("user_api_key") is not None:
if is_valid_sha256_hash(str(metadata.get("user_api_key"))):
@@ -4602,9 +4644,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]):
):
for k, v in metadata["user_api_key_metadata"].items():
if k == "logging": # prevent logging user logging keys
- cleaned_user_api_key_metadata[k] = (
- "scrubbed_by_litellm_for_sensitive_keys"
- )
+ cleaned_user_api_key_metadata[
+ k
+ ] = "scrubbed_by_litellm_for_sensitive_keys"
else:
cleaned_user_api_key_metadata[k] = v
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index c851ec06a6..60a3119841 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -1,11 +1,12 @@
# What is this?
## Helper utilities for cost_per_token()
-from typing import Any, Literal, Optional, Tuple, cast
+from typing import Any, Literal, Optional, Tuple, TypedDict, cast
import litellm
from litellm._logging import verbose_logger
from litellm.types.utils import (
+ CacheCreationTokenDetails,
CallTypes,
ImageResponse,
ModelInfo,
@@ -113,20 +114,34 @@ def _generic_cost_per_character(
return prompt_cost, completion_cost
-def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, float, float, float]:
+def _get_token_base_cost(
+ model_info: ModelInfo, usage: Usage
+) -> Tuple[float, float, float, float, float]:
"""
Return prompt cost, completion cost, and cache costs for a given model and usage.
If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set,
then we use the corresponding threshold cost for all token types.
-
+
Returns:
Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost)
"""
- prompt_base_cost = cast(float, _get_cost_per_unit(model_info, "input_cost_per_token"))
- completion_base_cost = cast(float, _get_cost_per_unit(model_info, "output_cost_per_token"))
- cache_creation_cost = cast(float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost"))
- cache_read_cost = cast(float, _get_cost_per_unit(model_info, "cache_read_input_token_cost"))
+ prompt_base_cost = cast(
+ float, _get_cost_per_unit(model_info, "input_cost_per_token")
+ )
+ completion_base_cost = cast(
+ float, _get_cost_per_unit(model_info, "output_cost_per_token")
+ )
+ cache_creation_cost = cast(
+ float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost")
+ )
+ cache_creation_cost_above_1hr = cast(
+ float,
+ _get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"),
+ )
+ cache_read_cost = cast(
+ float, _get_cost_per_unit(model_info, "cache_read_input_token_cost")
+ )
## CHECK IF ABOVE THRESHOLD
threshold: Optional[float] = None
@@ -140,34 +155,57 @@ def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, fl
)
if usage.prompt_tokens > threshold:
- prompt_base_cost = cast(float, _get_cost_per_unit(model_info, key, prompt_base_cost))
- completion_base_cost = cast(float, _get_cost_per_unit(
- model_info,
- f"output_cost_per_token_above_{threshold_str}_tokens",
- completion_base_cost,
- ))
-
+ prompt_base_cost = cast(
+ float, _get_cost_per_unit(model_info, key, prompt_base_cost)
+ )
+ completion_base_cost = cast(
+ float,
+ _get_cost_per_unit(
+ model_info,
+ f"output_cost_per_token_above_{threshold_str}_tokens",
+ completion_base_cost,
+ ),
+ )
+
# Apply tiered pricing to cache costs
- cache_creation_tiered_key = f"cache_creation_input_token_cost_above_{threshold_str}_tokens"
- cache_read_tiered_key = f"cache_read_input_token_cost_above_{threshold_str}_tokens"
-
+ cache_creation_tiered_key = (
+ f"cache_creation_input_token_cost_above_{threshold_str}_tokens"
+ )
+ cache_read_tiered_key = (
+ f"cache_read_input_token_cost_above_{threshold_str}_tokens"
+ )
+
if cache_creation_tiered_key in model_info:
- cache_creation_cost = cast(float, _get_cost_per_unit(
- model_info, cache_creation_tiered_key, cache_creation_cost
- ))
-
+ cache_creation_cost = cast(
+ float,
+ _get_cost_per_unit(
+ model_info,
+ cache_creation_tiered_key,
+ cache_creation_cost,
+ ),
+ )
+
if cache_read_tiered_key in model_info:
- cache_read_cost = cast(float, _get_cost_per_unit(
- model_info, cache_read_tiered_key, cache_read_cost
- ))
-
+ cache_read_cost = cast(
+ float,
+ _get_cost_per_unit(
+ model_info, cache_read_tiered_key, cache_read_cost
+ ),
+ )
+
break
except (IndexError, ValueError):
continue
except Exception:
continue
- return prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost
+ return (
+ prompt_base_cost,
+ completion_base_cost,
+ cache_creation_cost,
+ cache_creation_cost_above_1hr,
+ cache_read_cost,
+ )
def calculate_cost_component(
@@ -195,7 +233,9 @@ def calculate_cost_component(
return 0.0
-def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0) -> Optional[float]:
+def _get_cost_per_unit(
+ model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0
+) -> Optional[float]:
# Sometimes the cost per unit is a string (e.g.: If a value like "3e-7" was read from the config.yaml)
cost_per_unit = model_info.get(cost_key)
if isinstance(cost_per_unit, float):
@@ -210,7 +250,196 @@ def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Opti
f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0"
)
return default_value
-
+
+
+def calculate_cache_writing_cost(
+ cache_creation_tokens: int,
+ cache_creation_token_details: Optional[CacheCreationTokenDetails],
+ cache_creation_cost_above_1hr: float,
+ cache_creation_cost: float,
+) -> float:
+ """
+ Adjust cost of cache creation tokens based on the cache creation token details.
+ """
+ total_cost: float = 0.0
+ if cache_creation_token_details is not None:
+ # get the number of 5m and 1h cache creation tokens
+ cache_creation_tokens_5m = (
+ cache_creation_token_details.ephemeral_5m_input_tokens
+ )
+ cache_creation_tokens_1h = (
+ cache_creation_token_details.ephemeral_1h_input_tokens
+ )
+ # add the number of 5m and 1h cache creation tokens to the cache creation tokens
+ total_cost += (
+ cache_creation_tokens_5m * cache_creation_cost
+ if cache_creation_tokens_5m is not None
+ else 0.0
+ )
+ total_cost += (
+ cache_creation_tokens_1h * cache_creation_cost_above_1hr
+ if cache_creation_tokens_1h is not None
+ else 0.0
+ )
+ else:
+ total_cost += cache_creation_tokens * cache_creation_cost
+ return total_cost
+
+
+class PromptTokensDetailsResult(TypedDict):
+ cache_hit_tokens: int
+ cache_creation_tokens: int
+ cache_creation_token_details: Optional[CacheCreationTokenDetails]
+ text_tokens: int
+ audio_tokens: int
+ character_count: int
+ image_count: int
+ video_length_seconds: int
+
+
+def _parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult:
+ cache_hit_tokens = (
+ cast(Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0))
+ or 0
+ )
+ cache_creation_tokens = (
+ cast(
+ Optional[int],
+ getattr(usage.prompt_tokens_details, "cache_creation_tokens", 0),
+ )
+ or 0
+ )
+ cache_creation_token_details = (
+ cast(
+ Optional[CacheCreationTokenDetails],
+ getattr(usage.prompt_tokens_details, "cache_creation_token_details", None),
+ )
+ or None
+ )
+ text_tokens = (
+ cast(Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None))
+ or 0 # default to prompt tokens, if this field is not set
+ )
+ audio_tokens = (
+ cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0))
+ or 0
+ )
+ character_count = (
+ cast(
+ Optional[int],
+ getattr(usage.prompt_tokens_details, "character_count", 0),
+ )
+ or 0
+ )
+ image_count = (
+ cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) or 0
+ )
+ video_length_seconds = (
+ cast(
+ Optional[int],
+ getattr(usage.prompt_tokens_details, "video_length_seconds", 0),
+ )
+ or 0
+ )
+
+ return PromptTokensDetailsResult(
+ cache_hit_tokens=cache_hit_tokens,
+ cache_creation_tokens=cache_creation_tokens,
+ cache_creation_token_details=cache_creation_token_details,
+ text_tokens=text_tokens,
+ audio_tokens=audio_tokens,
+ character_count=character_count,
+ image_count=image_count,
+ video_length_seconds=video_length_seconds,
+ )
+
+
+class CompletionTokensDetailsResult(TypedDict):
+ audio_tokens: int
+ text_tokens: int
+ reasoning_tokens: int
+
+
+def _parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult:
+ audio_tokens = (
+ cast(
+ Optional[int],
+ getattr(usage.completion_tokens_details, "audio_tokens", 0),
+ )
+ or 0
+ )
+ text_tokens = (
+ cast(
+ Optional[int],
+ getattr(usage.completion_tokens_details, "text_tokens", None),
+ )
+ or 0 # default to completion tokens, if this field is not set
+ )
+ reasoning_tokens = (
+ cast(
+ Optional[int],
+ getattr(usage.completion_tokens_details, "reasoning_tokens", 0),
+ )
+ or 0
+ )
+
+ return CompletionTokensDetailsResult(
+ audio_tokens=audio_tokens,
+ text_tokens=text_tokens,
+ reasoning_tokens=reasoning_tokens,
+ )
+
+
+def _calculate_input_cost(
+ prompt_tokens_details: PromptTokensDetailsResult,
+ model_info: ModelInfo,
+ prompt_base_cost: float,
+ cache_read_cost: float,
+ cache_creation_cost: float,
+ cache_creation_cost_above_1hr: float,
+) -> float:
+ """
+ Calculates the input cost for a given model, prompt tokens, and completion tokens.
+ """
+ prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost
+
+ ### CACHE READ COST - Now uses tiered pricing
+ prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost
+
+ ### AUDIO COST
+ prompt_cost += calculate_cost_component(
+ model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"]
+ )
+
+ ### CACHE WRITING COST - Now uses tiered pricing
+ prompt_cost += calculate_cache_writing_cost(
+ cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"],
+ cache_creation_token_details=prompt_tokens_details[
+ "cache_creation_token_details"
+ ],
+ cache_creation_cost_above_1hr=cache_creation_cost_above_1hr,
+ cache_creation_cost=cache_creation_cost,
+ )
+
+ ### CHARACTER COST
+
+ prompt_cost += calculate_cost_component(
+ model_info, "input_cost_per_character", prompt_tokens_details["character_count"]
+ )
+
+ ### IMAGE COUNT COST
+ prompt_cost += calculate_cost_component(
+ model_info, "input_cost_per_image", prompt_tokens_details["image_count"]
+ )
+
+ ### VIDEO LENGTH COST
+ prompt_cost += calculate_cost_component(
+ model_info,
+ "input_cost_per_video_per_second",
+ prompt_tokens_details["video_length_seconds"],
+ )
+
+ return prompt_cost
def generic_cost_per_token(
@@ -236,83 +465,45 @@ def generic_cost_per_token(
### Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing)
prompt_cost = 0.0
### PROCESSING COST
- text_tokens = usage.prompt_tokens
- cache_hit_tokens = 0
- audio_tokens = 0
- character_count = 0
- image_count = 0
- video_length_seconds = 0
+ prompt_tokens_details = PromptTokensDetailsResult(
+ cache_hit_tokens=0,
+ cache_creation_tokens=0,
+ cache_creation_token_details=None,
+ text_tokens=usage.prompt_tokens,
+ audio_tokens=0,
+ character_count=0,
+ image_count=0,
+ video_length_seconds=0,
+ )
if usage.prompt_tokens_details:
- cache_hit_tokens = (
- cast(
- Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0)
- )
- or 0
- )
- text_tokens = (
- cast(
- Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None)
- )
- or 0 # default to prompt tokens, if this field is not set
- )
- audio_tokens = (
- cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0))
- or 0
- )
- character_count = (
- cast(
- Optional[int],
- getattr(usage.prompt_tokens_details, "character_count", 0),
- )
- or 0
- )
- image_count = (
- cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0))
- or 0
- )
- video_length_seconds = (
- cast(
- Optional[int],
- getattr(usage.prompt_tokens_details, "video_length_seconds", 0),
- )
- or 0
- )
+ prompt_tokens_details = _parse_prompt_tokens_details(usage)
## EDGE CASE - text tokens not set inside PromptTokensDetails
- if text_tokens == 0:
- text_tokens = usage.prompt_tokens - cache_hit_tokens - audio_tokens
- prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost = _get_token_base_cost(
- model_info=model_info, usage=usage
- )
+ if prompt_tokens_details["text_tokens"] == 0:
+ text_tokens = (
+ usage.prompt_tokens
+ - prompt_tokens_details["cache_hit_tokens"]
+ - prompt_tokens_details["audio_tokens"]
+ - prompt_tokens_details["cache_creation_tokens"]
+ )
+ prompt_tokens_details["text_tokens"] = text_tokens
- prompt_cost = float(text_tokens) * prompt_base_cost
+ (
+ prompt_base_cost,
+ completion_base_cost,
+ cache_creation_cost,
+ cache_creation_cost_above_1hr,
+ cache_read_cost,
+ ) = _get_token_base_cost(model_info=model_info, usage=usage)
- ### CACHE READ COST - Now uses tiered pricing
- prompt_cost += float(cache_hit_tokens) * cache_read_cost
-
- ### AUDIO COST
- prompt_cost += calculate_cost_component(
- model_info, "input_cost_per_audio_token", audio_tokens
- )
-
- ### CACHE WRITING COST - Now uses tiered pricing
- prompt_cost += float(usage._cache_creation_input_tokens or 0) * cache_creation_cost
-
- ### CHARACTER COST
-
- prompt_cost += calculate_cost_component(
- model_info, "input_cost_per_character", character_count
- )
-
- ### IMAGE COUNT COST
- prompt_cost += calculate_cost_component(
- model_info, "input_cost_per_image", image_count
- )
-
- ### VIDEO LENGTH COST
- prompt_cost += calculate_cost_component(
- model_info, "input_cost_per_video_per_second", video_length_seconds
+ prompt_cost = _calculate_input_cost(
+ prompt_tokens_details=prompt_tokens_details,
+ model_info=model_info,
+ prompt_base_cost=prompt_base_cost,
+ cache_read_cost=cache_read_cost,
+ cache_creation_cost=cache_creation_cost,
+ cache_creation_cost_above_1hr=cache_creation_cost_above_1hr,
)
## CALCULATE OUTPUT COST
@@ -321,27 +512,10 @@ def generic_cost_per_token(
reasoning_tokens = 0
is_text_tokens_total = False
if usage.completion_tokens_details is not None:
- audio_tokens = (
- cast(
- Optional[int],
- getattr(usage.completion_tokens_details, "audio_tokens", 0),
- )
- or 0
- )
- text_tokens = (
- cast(
- Optional[int],
- getattr(usage.completion_tokens_details, "text_tokens", None),
- )
- or 0 # default to completion tokens, if this field is not set
- )
- reasoning_tokens = (
- cast(
- Optional[int],
- getattr(usage.completion_tokens_details, "reasoning_tokens", 0),
- )
- or 0
- )
+ completion_tokens_details = _parse_completion_tokens_details(usage)
+ audio_tokens = completion_tokens_details["audio_tokens"]
+ text_tokens = completion_tokens_details["text_tokens"]
+ reasoning_tokens = completion_tokens_details["reasoning_tokens"]
if text_tokens == 0:
text_tokens = usage.completion_tokens
@@ -350,8 +524,12 @@ def generic_cost_per_token(
## TEXT COST
completion_cost = float(text_tokens) * completion_base_cost
- _output_cost_per_audio_token = _get_cost_per_unit(model_info, "output_cost_per_audio_token", None)
- _output_cost_per_reasoning_token = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None)
+ _output_cost_per_audio_token = _get_cost_per_unit(
+ model_info, "output_cost_per_audio_token", None
+ )
+ _output_cost_per_reasoning_token = _get_cost_per_unit(
+ model_info, "output_cost_per_reasoning_token", None
+ )
## AUDIO COST
if not is_text_tokens_total and audio_tokens is not None and audio_tokens > 0:
@@ -397,7 +575,7 @@ class CostCalculatorUtils:
]:
return True
return False
-
+
@staticmethod
def route_image_generation_cost_calculator(
model: str,
diff --git a/litellm/litellm_core_utils/object_pooling.py b/litellm/litellm_core_utils/object_pooling.py
new file mode 100644
index 0000000000..846e6536f8
--- /dev/null
+++ b/litellm/litellm_core_utils/object_pooling.py
@@ -0,0 +1,137 @@
+"""
+Generic object pooling utilities for LiteLLM.
+
+This module provides a flexible object pooling system that can be used
+to pool any type of object, reducing memory allocation overhead and
+improving performance for frequently created/destroyed objects.
+
+Memory Management Strategy:
+- Balanced eviction-based memory control to optimize reuse ratio
+- Moderate eviction frequency (300s) to maintain high object reuse
+- Conservative eviction weight (0.3) to avoid destroying useful objects
+- Lower pre-warm count (5) to reduce initial memory footprint
+- Always keeps at least one object available for high availability
+- Unlimited pools when maxsize is not specified (eviction controls actual usage)
+"""
+
+from typing import Any, Callable, Optional, Type, TypeVar
+
+from pond import Pond, PooledObject, PooledObjectFactory
+
+T = TypeVar('T')
+
+class GenericPooledObjectFactory(PooledObjectFactory):
+ """Generic factory class for creating pooled objects of any type."""
+
+ def __init__(
+ self,
+ object_class: Type[T],
+ pooled_maxsize: Optional[int] = None, # None = unlimited pool with eviction-based memory control
+ least_one: bool = True, # Always keep at least one for high concurrency
+ initializer: Optional[Callable[[T], None]] = None
+ ):
+ # Only pass maxsize to Pond if user specified it - otherwise let Pond handle unlimited pools
+ if pooled_maxsize is not None:
+ super().__init__(pooled_maxsize=pooled_maxsize, least_one=least_one)
+ else:
+ super().__init__(least_one=least_one)
+ self.object_class = object_class
+ self.initializer = initializer
+ self._user_maxsize = pooled_maxsize # Store original user preference
+
+ def createInstance(self) -> PooledObject:
+ """Create a new instance wrapped in a PooledObject."""
+ # Create a properly initialized instance
+ obj = self.object_class()
+ return PooledObject(obj)
+
+ def destroy(self, pooled_object: PooledObject):
+ """Destroy the pooled object."""
+ if hasattr(pooled_object.keeped_object, '__dict__'):
+ pooled_object.keeped_object.__dict__.clear()
+ del pooled_object
+
+ def reset(self, pooled_object: PooledObject, **kwargs: Any) -> PooledObject:
+ """Reset the pooled object to a clean state."""
+ obj = pooled_object.keeped_object
+ # Reset the object by calling its reset method if it exists
+ if hasattr(obj, 'reset') and callable(getattr(obj, 'reset')):
+ obj.reset()
+ else:
+ # Fallback: clear all attributes to reset the object
+ if hasattr(obj, '__dict__'):
+ obj.__dict__.clear()
+ return pooled_object
+
+ def validate(self, pooled_object: PooledObject) -> bool:
+ """Validate if the pooled object is still usable."""
+ return pooled_object.keeped_object is not None
+
+# Global pond instances
+_pools: dict[str, Pond] = {}
+
+def get_object_pool(
+ pool_name: str,
+ object_class: Type[T],
+ pooled_maxsize: Optional[int] = None, # None = unlimited pool with eviction-based memory control
+ least_one: bool = True, # Always keep at least one
+ borrowed_timeout: int = 10, # Longer timeout for high concurrency
+ time_between_eviction_runs: int = 300, # Less frequent eviction to maintain high reuse ratio
+ eviction_weight: float = 0.3, # Less aggressive eviction for better reuse
+ prewarm_count: int = 5 # Lower pre-warm count to reduce initial memory usage
+) -> Pond:
+ """Get or create a global object pool instance with balanced eviction-based memory control.
+
+ Memory is controlled through moderate eviction to balance reuse ratio and memory usage:
+ - Moderate eviction frequency (300s) to maintain high object reuse ratio
+ - Conservative eviction weight (0.3) to avoid destroying useful objects
+ - Lower pre-warm count (5) to reduce initial memory footprint
+
+ Args:
+ pool_name: Unique name for the pool
+ object_class: The class type to pool
+ pooled_maxsize: Maximum number of objects in the pool (None = truly unlimited)
+ least_one: Whether to keep at least one object in the pool (default: True)
+ borrowed_timeout: Timeout for borrowing objects (seconds, default: 10)
+ time_between_eviction_runs: Time between eviction runs (seconds, default: 300)
+ eviction_weight: Weight for eviction algorithm (default: 0.3, conservative)
+ prewarm_count: Number of objects to pre-warm the pool with (default: 5)
+
+ Returns:
+ Pond instance for the specified object type
+ """
+
+ if pool_name in _pools:
+ return _pools[pool_name]
+
+ # Create new pond
+ pond = Pond(
+ borrowed_timeout=borrowed_timeout,
+ time_between_eviction_runs=time_between_eviction_runs,
+ thread_daemon=True,
+ eviction_weight=eviction_weight
+ )
+
+ # Register the factory with user's maxsize preference
+ factory = GenericPooledObjectFactory(
+ object_class=object_class,
+ pooled_maxsize=pooled_maxsize,
+ least_one=least_one
+ )
+ pond.register(factory, name=f"{pool_name}Factory")
+
+ # Pre-warm the pool
+ _prewarm_pool(pond, pool_name, prewarm_count)
+
+ _pools[pool_name] = pond
+ return pond
+
+def _prewarm_pool(pond: Pond, pool_name: str, prewarm_count: int = 20) -> None:
+ """Pre-warm the pool with initial objects for high concurrency."""
+ for _ in range(prewarm_count):
+ try:
+ pooled_obj = pond.borrow(name=f"{pool_name}Factory")
+ pond.recycle(pooled_obj, name=f"{pool_name}Factory")
+ except Exception:
+ # If pre-warming fails, just continue
+ break
\ No newline at end of file
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 65f49cf08b..b9cc5e50c3 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -16,8 +16,8 @@ from litellm import verbose_logger
from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client
from litellm.types.files import get_file_extension_from_mime_type
from litellm.types.llms.anthropic import *
-from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
from litellm.types.llms.bedrock import CachePointBlock
+from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
from litellm.types.llms.custom_http import httpxSpecialProvider
from litellm.types.llms.ollama import OllamaVisionModelObject
from litellm.types.llms.openai import (
@@ -1067,10 +1067,10 @@ def convert_to_gemini_tool_call_invoke(
if tool_calls is not None:
for tool in tool_calls:
if "function" in tool:
- gemini_function_call: Optional[VertexFunctionCall] = (
- _gemini_tool_call_invoke_helper(
- function_call_params=tool["function"]
- )
+ gemini_function_call: Optional[
+ VertexFunctionCall
+ ] = _gemini_tool_call_invoke_helper(
+ function_call_params=tool["function"]
)
if gemini_function_call is not None:
_parts_list.append(
@@ -1589,9 +1589,9 @@ def anthropic_messages_pt( # noqa: PLR0915
)
if "cache_control" in _content_element:
- _anthropic_content_element["cache_control"] = (
- _content_element["cache_control"]
- )
+ _anthropic_content_element[
+ "cache_control"
+ ] = _content_element["cache_control"]
user_content.append(_anthropic_content_element)
elif m.get("type", "") == "text":
m = cast(ChatCompletionTextObject, m)
@@ -1629,9 +1629,9 @@ def anthropic_messages_pt( # noqa: PLR0915
)
if "cache_control" in _content_element:
- _anthropic_content_text_element["cache_control"] = (
- _content_element["cache_control"]
- )
+ _anthropic_content_text_element[
+ "cache_control"
+ ] = _content_element["cache_control"]
user_content.append(_anthropic_content_text_element)
@@ -2482,8 +2482,7 @@ class BedrockImageProcessor:
if is_document:
return BedrockImageProcessor._get_document_format(
- mime_type=mime_type,
- supported_doc_formats=supported_doc_formats
+ mime_type=mime_type, supported_doc_formats=supported_doc_formats
)
else:
@@ -2495,12 +2494,9 @@ class BedrockImageProcessor:
f"Unsupported image format: {image_format}. Supported formats: {supported_image_and_video_formats}"
)
return image_format
-
+
@staticmethod
- def _get_document_format(
- mime_type: str,
- supported_doc_formats: List[str]
- ) -> str:
+ def _get_document_format(mime_type: str, supported_doc_formats: List[str]) -> str:
"""
Get the document format from the mime type
@@ -2519,13 +2515,9 @@ class BedrockImageProcessor:
The document format
"""
valid_extensions: Optional[List[str]] = None
- potential_extensions = mimetypes.guess_all_extensions(
- mime_type, strict=False
- )
+ potential_extensions = mimetypes.guess_all_extensions(mime_type, strict=False)
valid_extensions = [
- ext[1:]
- for ext in potential_extensions
- if ext[1:] in supported_doc_formats
+ ext[1:] for ext in potential_extensions if ext[1:] in supported_doc_formats
]
# Fallback to types/files.py if mimetypes doesn't return valid extensions
@@ -2689,10 +2681,12 @@ def _convert_to_bedrock_tool_call_invoke(
)
bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool)
_parts_list.append(bedrock_content_block)
-
+
# Check for cache_control and add a separate cachePoint block
if tool.get("cache_control", None) is not None:
- cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default"))
+ cache_point_block = BedrockContentBlock(
+ cachePoint=CachePointBlock(type="default")
+ )
_parts_list.append(cache_point_block)
return _parts_list
except Exception as e:
@@ -2754,7 +2748,7 @@ def _convert_to_bedrock_tool_call_result(
for content in content_list:
if content["type"] == "text":
content_str += content["text"]
-
+
message.get("name", "")
id = str(message.get("tool_call_id", str(uuid.uuid4())))
@@ -2763,7 +2757,7 @@ def _convert_to_bedrock_tool_call_result(
content=[tool_result_content_block],
toolUseId=id,
)
-
+
content_block = BedrockContentBlock(toolResult=tool_result)
return content_block
@@ -3128,6 +3122,12 @@ class BedrockConverseMessagesProcessor:
if element["type"] == "text":
_part = BedrockContentBlock(text=element["text"])
_parts.append(_part)
+ elif element["type"] == "guarded_text":
+ # Wrap guarded_text in guardContent block
+ _part = BedrockContentBlock(
+ guardContent={"text": {"text": element["text"]}}
+ )
+ _parts.append(_part)
elif element["type"] == "image_url":
format: Optional[str] = None
if isinstance(element["image_url"], dict):
@@ -3199,26 +3199,29 @@ class BedrockConverseMessagesProcessor:
current_message = messages[msg_i]
tool_call_result = _convert_to_bedrock_tool_call_result(current_message)
tool_content.append(tool_call_result)
-
+
# Check if we need to add a separate cachePoint block
has_cache_control = False
-
+
# Check for message-level cache_control
if current_message.get("cache_control", None) is not None:
has_cache_control = True
# Check for content-level cache_control in list content
elif isinstance(current_message.get("content"), list):
for content_element in current_message["content"]:
- if (isinstance(content_element, dict) and
- content_element.get("cache_control", None) is not None):
+ if (
+ isinstance(content_element, dict)
+ and content_element.get("cache_control", None) is not None
+ ):
has_cache_control = True
break
-
+
# Add a separate cachePoint block if cache_control is present
if has_cache_control:
- cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default"))
+ cache_point_block = BedrockContentBlock(
+ cachePoint=CachePointBlock(type="default")
+ )
tool_content.append(cache_point_block)
-
msg_i += 1
if tool_content:
@@ -3299,7 +3302,7 @@ class BedrockConverseMessagesProcessor:
image_url=image_url
)
assistants_parts.append(assistants_part)
- # Add cache point block for assistant content elements
+ # Add cache point block for assistant content elements
_cache_point_block = (
litellm.AmazonConverseConfig()._get_cache_point_block(
message_block=cast(
@@ -3311,8 +3314,12 @@ class BedrockConverseMessagesProcessor:
if _cache_point_block is not None:
assistants_parts.append(_cache_point_block)
assistant_content.extend(assistants_parts)
- elif _assistant_content is not None and isinstance(_assistant_content, str):
- assistant_content.append(BedrockContentBlock(text=_assistant_content))
+ elif _assistant_content is not None and isinstance(
+ _assistant_content, str
+ ):
+ assistant_content.append(
+ BedrockContentBlock(text=_assistant_content)
+ )
# Add cache point block for assistant string content
_cache_point_block = (
litellm.AmazonConverseConfig()._get_cache_point_block(
@@ -3496,6 +3503,12 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
if element["type"] == "text":
_part = BedrockContentBlock(text=element["text"])
_parts.append(_part)
+ elif element["type"] == "guarded_text":
+ # Wrap guarded_text in guardContent block
+ _part = BedrockContentBlock(
+ guardContent={"text": {"text": element["text"]}}
+ )
+ _parts.append(_part)
elif element["type"] == "image_url":
format: Optional[str] = None
if isinstance(element["image_url"], dict):
@@ -3565,29 +3578,33 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
while msg_i < len(messages) and messages[msg_i]["role"] == "tool":
tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i])
current_message = messages[msg_i]
-
+
# Add the tool result first
tool_content.append(tool_call_result)
-
+
# Check if we need to add a separate cachePoint block
has_cache_control = False
-
+
# Check for message-level cache_control
if current_message.get("cache_control", None) is not None:
has_cache_control = True
# Check for content-level cache_control in list content
elif isinstance(current_message.get("content"), list):
for content_element in current_message["content"]:
- if (isinstance(content_element, dict) and
- content_element.get("cache_control", None) is not None):
+ if (
+ isinstance(content_element, dict)
+ and content_element.get("cache_control", None) is not None
+ ):
has_cache_control = True
break
-
+
# Add a separate cachePoint block if cache_control is present
if has_cache_control:
- cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default"))
+ cache_point_block = BedrockContentBlock(
+ cachePoint=CachePointBlock(type="default")
+ )
tool_content.append(cache_point_block)
-
+
msg_i += 1
if tool_content:
# if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles)
@@ -3852,10 +3869,9 @@ def function_call_prompt(messages: list, functions: list):
if isinstance(message["content"], str):
message["content"] += f""" {function_prompt}"""
else:
- message["content"].append({
- "type": "text",
- "text": f""" {function_prompt}"""
- })
+ message["content"].append(
+ {"type": "text", "text": f""" {function_prompt}"""}
+ )
function_added_to_prompt = True
if function_added_to_prompt is False:
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index ce874bfde9..e54aeaf995 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -45,7 +45,10 @@ from litellm.types.llms.openai import (
OpenAIMcpServerTool,
OpenAIWebSearchOptions,
)
-from litellm.types.utils import CompletionTokensDetailsWrapper
+from litellm.types.utils import (
+ CacheCreationTokenDetails,
+ CompletionTokensDetailsWrapper,
+)
from litellm.types.utils import Message as LitellmMessage
from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
from litellm.utils import (
@@ -200,8 +203,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
)
_allowed_properties = set(AnthropicInputSchema.__annotations__.keys())
- input_schema_filtered = {k: v for k, v in _input_schema.items() if k in _allowed_properties}
- input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema(**input_schema_filtered)
+ input_schema_filtered = {
+ k: v for k, v in _input_schema.items() if k in _allowed_properties
+ }
+ input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema(
+ **input_schema_filtered
+ )
_tool = AnthropicMessagesTool(
name=tool["function"]["name"],
@@ -816,12 +823,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
_usage = usage_object
cache_creation_input_tokens: int = 0
cache_read_input_tokens: int = 0
+ cache_creation_token_details: Optional[CacheCreationTokenDetails] = None
web_search_requests: Optional[int] = None
if (
"cache_creation_input_tokens" in _usage
and _usage["cache_creation_input_tokens"] is not None
):
cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
+ prompt_tokens += cache_creation_input_tokens
if (
"cache_read_input_tokens" in _usage
and _usage["cache_read_input_tokens"] is not None
@@ -837,8 +846,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
int, _usage["server_tool_use"]["web_search_requests"]
)
+ if "cache_creation" in _usage and _usage["cache_creation"] is not None:
+ cache_creation_token_details = CacheCreationTokenDetails(
+ ephemeral_5m_input_tokens=_usage["cache_creation"].get(
+ "ephemeral_5m_input_tokens"
+ ),
+ ephemeral_1h_input_tokens=_usage["cache_creation"].get(
+ "ephemeral_1h_input_tokens"
+ ),
+ )
+
prompt_tokens_details = PromptTokensDetailsWrapper(
cached_tokens=cache_read_input_tokens,
+ cache_creation_tokens=cache_read_input_tokens,
+ cache_creation_token_details=cache_creation_token_details,
)
completion_token_details = (
CompletionTokensDetailsWrapper(
diff --git a/litellm/llms/base_llm/batches/transformation.py b/litellm/llms/base_llm/batches/transformation.py
index 1d3e54fae6..9e67689fcd 100644
--- a/litellm/llms/base_llm/batches/transformation.py
+++ b/litellm/llms/base_llm/batches/transformation.py
@@ -158,6 +158,48 @@ class BaseBatchesConfig(ABC):
"""
pass
+ @abstractmethod
+ def transform_retrieve_batch_request(
+ self,
+ batch_id: str,
+ optional_params: dict,
+ litellm_params: dict,
+ ) -> Union[bytes, str, Dict[str, Any]]:
+ """
+ Transform the batch retrieval request to provider-specific format.
+
+ Args:
+ batch_id: Batch ID to retrieve
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+
+ Returns:
+ Transformed request data
+ """
+ pass
+
+ @abstractmethod
+ def transform_retrieve_batch_response(
+ self,
+ model: Optional[str],
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ litellm_params: dict,
+ ) -> LiteLLMBatch:
+ """
+ Transform provider-specific batch retrieval response to LiteLLM format.
+
+ Args:
+ model: Model name
+ raw_response: Raw HTTP response
+ logging_obj: Logging object
+ litellm_params: LiteLLM parameters
+
+ Returns:
+ LiteLLM batch object
+ """
+ pass
+
@abstractmethod
def get_error_class(
self, error_message: str, status_code: int, headers: Union[Dict, Headers]
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 0ddf8896fd..8211addaf9 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -20,7 +20,11 @@ from pydantic import BaseModel
from litellm._logging import verbose_logger
from litellm.caching.caching import DualCache
-from litellm.constants import BEDROCK_INVOKE_PROVIDERS_LITERAL, BEDROCK_MAX_POLICY_SIZE
+from litellm.constants import (
+ BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
+ BEDROCK_INVOKE_PROVIDERS_LITERAL,
+ BEDROCK_MAX_POLICY_SIZE,
+)
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.secret_managers.main import get_secret, get_secret_str
@@ -189,23 +193,32 @@ class BaseAWSLLM:
# Check if we're in IRSA and trying to assume the same role we already have
current_role_arn = os.getenv("AWS_ROLE_ARN")
web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE")
-
+
# In IRSA environments, we should skip role assumption if we're already running as the target role
# This is true when:
# 1. We have AWS_ROLE_ARN set (current role)
# 2. We have AWS_WEB_IDENTITY_TOKEN_FILE set (IRSA environment)
# 3. The current role matches the requested role
- if (current_role_arn and web_identity_token_file and
- current_role_arn == aws_role_name):
- verbose_logger.debug("Using IRSA same-role optimization: calling _auth_with_env_vars")
+ if (
+ current_role_arn
+ and web_identity_token_file
+ and current_role_arn == aws_role_name
+ ):
+ verbose_logger.debug(
+ "Using IRSA same-role optimization: calling _auth_with_env_vars"
+ )
# We're already running as this role via IRSA, no need to assume it again
# Use the default boto3 credentials (which will use the IRSA credentials)
credentials, _cache_ttl = self._auth_with_env_vars()
else:
- verbose_logger.debug("Using role assumption: calling _auth_with_aws_role")
+ verbose_logger.debug(
+ "Using role assumption: calling _auth_with_aws_role"
+ )
# If aws_session_name is not provided, generate a default one
if aws_session_name is None:
- aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}"
+ aws_session_name = (
+ f"litellm-session-{int(datetime.now().timestamp())}"
+ )
credentials, _cache_ttl = self._auth_with_aws_role(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
@@ -318,6 +331,40 @@ class BaseAWSLLM:
return provider
return None
+ @staticmethod
+ def get_bedrock_embedding_provider(
+ model: str,
+ ) -> Optional[BEDROCK_EMBEDDING_PROVIDERS_LITERAL]:
+ """
+ Helper function to get the bedrock embedding provider from the model
+
+ Handles scenarios like:
+ 1. model=cohere.embed-english-v3:0 -> Returns `cohere`
+ 2. model=amazon.titan-embed-text-v1 -> Returns `amazon`
+ 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs`
+ 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs`
+ """
+ # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0
+ if "." in model:
+ parts = model.split(".")
+ # Check if the second part (after potential region) is a known provider
+ if len(parts) >= 2:
+ potential_provider = parts[1] # e.g., "twelvelabs" from "us.twelvelabs.marengo-embed-2-7-v1:0"
+ if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL):
+ return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider)
+
+ # Check if the first part is a known provider (standard format)
+ potential_provider = parts[0] # e.g., "cohere" from "cohere.embed-english-v3:0"
+ if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL):
+ return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider)
+
+ # Fallback: check if any provider name appears in the model string
+ for provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL):
+ if provider in model:
+ return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, provider)
+
+ return None
+
def _get_aws_region_name(
self,
optional_params: dict,
@@ -479,55 +526,67 @@ class BaseAWSLLM:
iam_creds = session.get_credentials()
return iam_creds, self._get_default_ttl_for_boto3_credentials()
- def _handle_irsa_cross_account(self, irsa_role_arn: str, aws_role_name: str,
- aws_session_name: str, region: str, web_identity_token_file: str,
- aws_external_id: Optional[str] = None) -> dict:
+ def _handle_irsa_cross_account(
+ self,
+ irsa_role_arn: str,
+ aws_role_name: str,
+ aws_session_name: str,
+ region: str,
+ web_identity_token_file: str,
+ aws_external_id: Optional[str] = None,
+ ) -> dict:
"""Handle cross-account role assumption for IRSA."""
import boto3
-
+
verbose_logger.debug("Cross-account role assumption detected")
-
+
# Read the web identity token
- with open(web_identity_token_file, 'r') as f:
+ with open(web_identity_token_file, "r") as f:
web_identity_token = f.read().strip()
-
+
# Create an STS client without credentials
with tracer.trace("boto3.client(sts) for manual IRSA"):
- sts_client = boto3.client('sts', region_name=region)
-
+ sts_client = boto3.client("sts", region_name=region)
+
# Manually assume the IRSA role with the session name
- verbose_logger.debug(f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}")
+ verbose_logger.debug(
+ f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}"
+ )
irsa_response = sts_client.assume_role_with_web_identity(
RoleArn=irsa_role_arn,
RoleSessionName=aws_session_name,
- WebIdentityToken=web_identity_token
+ WebIdentityToken=web_identity_token,
)
-
+
# Extract the credentials from the IRSA assumption
irsa_creds = irsa_response["Credentials"]
-
+
# Create a new STS client with the IRSA credentials
with tracer.trace("boto3.client(sts) with manual IRSA credentials"):
sts_client_with_creds = boto3.client(
- 'sts',
+ "sts",
region_name=region,
aws_access_key_id=irsa_creds["AccessKeyId"],
aws_secret_access_key=irsa_creds["SecretAccessKey"],
- aws_session_token=irsa_creds["SessionToken"]
+ aws_session_token=irsa_creds["SessionToken"],
)
-
+
# Get current caller identity for debugging
try:
caller_identity = sts_client_with_creds.get_caller_identity()
- verbose_logger.debug(f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}")
+ verbose_logger.debug(
+ f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}"
+ )
except Exception as e:
verbose_logger.debug(f"Failed to get caller identity: {e}")
-
+
# Now assume the target role
- verbose_logger.debug(f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}")
+ verbose_logger.debug(
+ f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}"
+ )
assume_role_params = {
"RoleArn": aws_role_name,
- "RoleSessionName": aws_session_name
+ "RoleSessionName": aws_session_name,
}
# Add ExternalId parameter if provided
@@ -536,27 +595,36 @@ class BaseAWSLLM:
return sts_client_with_creds.assume_role(**assume_role_params)
- def _handle_irsa_same_account(self, aws_role_name: str, aws_session_name: str, region: str,
- aws_external_id: Optional[str] = None) -> dict:
+ def _handle_irsa_same_account(
+ self,
+ aws_role_name: str,
+ aws_session_name: str,
+ region: str,
+ aws_external_id: Optional[str] = None,
+ ) -> dict:
"""Handle same-account role assumption for IRSA."""
import boto3
-
+
verbose_logger.debug("Same account role assumption, using automatic IRSA")
with tracer.trace("boto3.client(sts) with automatic IRSA"):
sts_client = boto3.client("sts", region_name=region)
-
+
# Get current caller identity for debugging
try:
caller_identity = sts_client.get_caller_identity()
- verbose_logger.debug(f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}")
+ verbose_logger.debug(
+ f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}"
+ )
except Exception as e:
verbose_logger.debug(f"Failed to get caller identity: {e}")
-
+
# Assume the role
- verbose_logger.debug(f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}")
+ verbose_logger.debug(
+ f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}"
+ )
assume_role_params = {
"RoleArn": aws_role_name,
- "RoleSessionName": aws_session_name
+ "RoleSessionName": aws_session_name,
}
# Add ExternalId parameter if provided
@@ -565,20 +633,24 @@ class BaseAWSLLM:
return sts_client.assume_role(**assume_role_params)
- def _extract_credentials_and_ttl(self, sts_response: dict) -> Tuple[Credentials, Optional[int]]:
+ def _extract_credentials_and_ttl(
+ self, sts_response: dict
+ ) -> Tuple[Credentials, Optional[int]]:
"""Extract credentials and TTL from STS response."""
from botocore.credentials import Credentials
-
+
sts_credentials = sts_response["Credentials"]
credentials = Credentials(
access_key=sts_credentials["AccessKeyId"],
secret_key=sts_credentials["SecretAccessKey"],
token=sts_credentials["SessionToken"],
)
-
+
expiration_time = sts_credentials["Expiration"]
- ttl = int((expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds())
-
+ ttl = int(
+ (expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds()
+ )
+
return credentials, ttl
@tracer.wrap()
@@ -600,34 +672,51 @@ class BaseAWSLLM:
# Check if we're in an EKS/IRSA environment
web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE")
irsa_role_arn = os.getenv("AWS_ROLE_ARN")
-
+
# If we have IRSA environment variables and no explicit credentials,
# we need to use the web identity token flow
- if (web_identity_token_file and irsa_role_arn and
- aws_access_key_id is None and aws_secret_access_key is None):
+ if (
+ web_identity_token_file
+ and irsa_role_arn
+ and aws_access_key_id is None
+ and aws_secret_access_key is None
+ ):
# For cross-account role assumption with specific session names,
# we need to manually assume the IRSA role first with the correct session name
- verbose_logger.debug(f"IRSA detected: using web identity token from {web_identity_token_file}")
-
+ verbose_logger.debug(
+ f"IRSA detected: using web identity token from {web_identity_token_file}"
+ )
+
try:
# Get region from environment
- region = os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1"
-
+ region = (
+ os.getenv("AWS_REGION")
+ or os.getenv("AWS_DEFAULT_REGION")
+ or "us-east-1"
+ )
+
# Check if we need to do cross-account role assumption
if aws_role_name != irsa_role_arn:
sts_response = self._handle_irsa_cross_account(
- irsa_role_arn, aws_role_name, aws_session_name, region, web_identity_token_file, aws_external_id
+ irsa_role_arn,
+ aws_role_name,
+ aws_session_name,
+ region,
+ web_identity_token_file,
+ aws_external_id,
)
else:
sts_response = self._handle_irsa_same_account(
aws_role_name, aws_session_name, region, aws_external_id
)
-
+
return self._extract_credentials_and_ttl(sts_response)
-
+
except Exception as e:
verbose_logger.debug(f"Failed to assume role via IRSA: {e}")
- if "AccessDenied" in str(e) and "is not authorized to perform: sts:AssumeRole" in str(e):
+ if "AccessDenied" in str(
+ e
+ ) and "is not authorized to perform: sts:AssumeRole" in str(e):
# Provide a more helpful error message for trust policy issues
verbose_logger.error(
f"Access denied when trying to assume role {aws_role_name}. "
@@ -636,7 +725,7 @@ class BaseAWSLLM:
)
# Re-raise the exception instead of falling through
raise
-
+
# In EKS/IRSA environments, use ambient credentials (no explicit keys needed)
# This allows the web identity token to work automatically
if aws_access_key_id is None and aws_secret_access_key is None:
@@ -653,7 +742,7 @@ class BaseAWSLLM:
assume_role_params = {
"RoleArn": aws_role_name,
- "RoleSessionName": aws_session_name
+ "RoleSessionName": aws_session_name,
}
# Add ExternalId parameter if provided
@@ -782,14 +871,14 @@ class BaseAWSLLM:
)
# Determine proxy_endpoint_url
- if env_aws_bedrock_runtime_endpoint and isinstance(
- env_aws_bedrock_runtime_endpoint, str
- ):
- proxy_endpoint_url = env_aws_bedrock_runtime_endpoint
- elif aws_bedrock_runtime_endpoint is not None and isinstance(
+ if aws_bedrock_runtime_endpoint is not None and isinstance(
aws_bedrock_runtime_endpoint, str
):
proxy_endpoint_url = aws_bedrock_runtime_endpoint
+ elif env_aws_bedrock_runtime_endpoint and isinstance(
+ env_aws_bedrock_runtime_endpoint, str
+ ):
+ proxy_endpoint_url = env_aws_bedrock_runtime_endpoint
else:
proxy_endpoint_url = endpoint_url
diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py
index 3338450f79..2f3d00dddd 100644
--- a/litellm/llms/bedrock/batches/transformation.py
+++ b/litellm/llms/bedrock/batches/transformation.py
@@ -7,7 +7,6 @@ from httpx import Headers, Response
from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.types.llms.bedrock import (
- BedrockBatchJobStatus,
BedrockCreateBatchRequest,
BedrockCreateBatchResponse,
BedrockInputDataConfig,
@@ -200,19 +199,23 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
# Extract information from typed Bedrock response
job_arn = response_data.get("jobArn", "")
- status: BedrockBatchJobStatus = response_data.get("status", "Submitted")
+ status_str: str = str(response_data.get("status", "Submitted"))
# Map Bedrock status to OpenAI-compatible status
- status_mapping: Dict[BedrockBatchJobStatus, str] = {
+ status_mapping: Dict[str, str] = {
"Submitted": "validating",
+ "Validating": "validating",
+ "Scheduled": "in_progress",
"InProgress": "in_progress",
+ "PartiallyCompleted": "completed",
"Completed": "completed",
"Failed": "failed",
"Stopping": "cancelling",
- "Stopped": "cancelled"
+ "Stopped": "cancelled",
+ "Expired": "expired",
}
- openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status, "validating"))
+ openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating"))
# Get original request data from litellm_params if available
original_request = litellm_params.get("original_batch_request", {})
@@ -229,7 +232,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
output_file_id=None, # Will be populated when job completes
error_file_id=None,
created_at=int(time.time()),
- in_progress_at=int(time.time()) if status == "InProgress" else None,
+ in_progress_at=int(time.time()) if status_str == "InProgress" else None,
expires_at=None,
finalizing_at=None,
completed_at=None,
@@ -241,6 +244,203 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
metadata=original_request.get("metadata", {}),
)
+ def transform_retrieve_batch_request(
+ self,
+ batch_id: str,
+ optional_params: dict,
+ litellm_params: dict,
+ ) -> Dict[str, Any]:
+ """
+ Transform batch retrieval request for Bedrock.
+
+ Args:
+ batch_id: Bedrock job ARN
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+
+ Returns:
+ Transformed request data for Bedrock GetModelInvocationJob API
+ """
+ # For Bedrock, batch_id should be the full job ARN
+ # The GetModelInvocationJob API expects the full ARN as the identifier
+ if not batch_id.startswith("arn:aws:bedrock:"):
+ raise ValueError(f"Invalid batch_id format. Expected ARN, got: {batch_id}")
+
+ # Extract the job identifier from the ARN - use the full ARN path part
+ # ARN format: arn:aws:bedrock:region:account:model-invocation-job/job-name
+ arn_parts = batch_id.split(":")
+ if len(arn_parts) < 6:
+ raise ValueError(f"Invalid ARN format: {batch_id}")
+
+ region = arn_parts[3]
+ # arn_parts[5] contains "model-invocation-job/{jobId}"
+
+ # Build the endpoint URL for GetModelInvocationJob
+ # AWS API format: GET /model-invocation-job/{jobIdentifier}
+ # Use the FULL ARN as jobIdentifier and URL-encode it (includes ':' and '/')
+ import urllib.parse as _ul
+ encoded_arn = _ul.quote(batch_id, safe="")
+ endpoint_url = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}"
+
+ # Use common utility for AWS signing
+ signed_headers, _ = self.common_utils.sign_aws_request(
+ service_name="bedrock",
+ data={}, # GET request has no body
+ endpoint_url=endpoint_url,
+ optional_params=optional_params,
+ method="GET"
+ )
+
+ # Return pre-signed request format
+ return {
+ "method": "GET",
+ "url": endpoint_url,
+ "headers": signed_headers,
+ "data": None
+ }
+
+ def _parse_timestamps_and_status(self, response_data, status_str: str):
+ """Helper to parse timestamps based on status."""
+ import datetime
+ def parse_timestamp(ts_str: Optional[str]) -> Optional[int]:
+ if not ts_str:
+ return None
+ try:
+ dt = datetime.datetime.fromisoformat(ts_str.replace('Z', '+00:00'))
+ return int(dt.timestamp())
+ except Exception:
+ return None
+
+ created_at = parse_timestamp(str(response_data.get("submitTime")) if response_data.get("submitTime") is not None else None)
+ in_progress_states = {"InProgress", "Validating", "Scheduled"}
+ in_progress_at = (
+ parse_timestamp(str(response_data.get("lastModifiedTime")) if response_data.get("lastModifiedTime") is not None else None)
+ if status_str in in_progress_states
+ else None
+ )
+ completed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str in {"Completed", "PartiallyCompleted"} else None
+ failed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Failed" else None
+ cancelled_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Stopped" else None
+ expires_at = parse_timestamp(str(response_data.get("jobExpirationTime")) if response_data.get("jobExpirationTime") is not None else None)
+
+ return created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at
+
+ def _extract_file_configs(self, response_data):
+ """Helper to extract input and output file configurations."""
+ # Extract input file ID
+ input_file_id = ""
+ input_data_config = response_data.get("inputDataConfig", {})
+ if isinstance(input_data_config, dict):
+ s3_input_config = input_data_config.get("s3InputDataConfig", {})
+ if isinstance(s3_input_config, dict):
+ input_file_id = s3_input_config.get("s3Uri", "")
+
+ # Extract output file ID
+ output_file_id = None
+ output_data_config = response_data.get("outputDataConfig", {})
+ if isinstance(output_data_config, dict):
+ s3_output_config = output_data_config.get("s3OutputDataConfig", {})
+ if isinstance(s3_output_config, dict):
+ output_file_id = s3_output_config.get("s3Uri", "")
+
+ return input_file_id, output_file_id
+
+ def _extract_errors_and_metadata(self, response_data, raw_response):
+ """Helper to extract errors and enriched metadata."""
+ # Extract errors
+ message = response_data.get("message")
+ errors = None
+ if message:
+ from openai.types.batch import Errors
+ from openai.types.batch_error import BatchError
+ errors = Errors(
+ data=[BatchError(message=message, code=str(raw_response.status_code))],
+ object="list"
+ )
+
+ # Enrich metadata with useful Bedrock fields
+ enriched_metadata_raw: Dict[str, Any] = {
+ "jobName": response_data.get("jobName"),
+ "clientRequestToken": response_data.get("clientRequestToken"),
+ "modelId": response_data.get("modelId"),
+ "roleArn": response_data.get("roleArn"),
+ "timeoutDurationInHours": response_data.get("timeoutDurationInHours"),
+ "vpcConfig": response_data.get("vpcConfig"),
+ }
+ import json as _json
+ enriched_metadata: Dict[str, str] = {}
+ for _k, _v in enriched_metadata_raw.items():
+ if _v is None:
+ continue
+ if isinstance(_v, (dict, list)):
+ try:
+ enriched_metadata[_k] = _json.dumps(_v)
+ except Exception:
+ enriched_metadata[_k] = str(_v)
+ else:
+ enriched_metadata[_k] = str(_v)
+
+ return errors, enriched_metadata
+
+ def transform_retrieve_batch_response(
+ self,
+ model: Optional[str],
+ raw_response: Response,
+ logging_obj: Any,
+ litellm_params: dict,
+ ) -> LiteLLMBatch:
+ """
+ Transform Bedrock batch retrieval response to LiteLLM format.
+ """
+ from litellm.types.llms.bedrock import BedrockGetBatchResponse
+ try:
+ response_data: BedrockGetBatchResponse = raw_response.json()
+ except Exception as e:
+ raise ValueError(f"Failed to parse Bedrock batch response: {e}")
+
+ job_arn = response_data.get("jobArn", "")
+ status_str: str = str(response_data.get("status", "Submitted"))
+
+ # Map Bedrock status to OpenAI-compatible status
+ status_mapping: Dict[str, str] = {
+ "Submitted": "validating", "Validating": "validating", "Scheduled": "in_progress",
+ "InProgress": "in_progress", "PartiallyCompleted": "completed", "Completed": "completed",
+ "Failed": "failed", "Stopping": "cancelling", "Stopped": "cancelled", "Expired": "expired"
+ }
+ openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating"))
+
+ # Parse timestamps
+ created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at = self._parse_timestamps_and_status(response_data, status_str)
+
+ # Extract file configurations
+ input_file_id, output_file_id = self._extract_file_configs(response_data)
+
+ # Extract errors and metadata
+ errors, enriched_metadata = self._extract_errors_and_metadata(response_data, raw_response)
+
+ return LiteLLMBatch(
+ id=job_arn,
+ object="batch",
+ endpoint="/v1/chat/completions",
+ errors=errors,
+ input_file_id=input_file_id,
+ completion_window="24h",
+ status=openai_status,
+ output_file_id=output_file_id,
+ error_file_id=None,
+ created_at=created_at or int(time.time()),
+ in_progress_at=in_progress_at,
+ expires_at=expires_at,
+ finalizing_at=None,
+ completed_at=completed_at,
+ failed_at=failed_at,
+ expired_at=None,
+ cancelling_at=None,
+ cancelled_at=cancelled_at,
+ request_counts=None,
+ metadata=enriched_metadata,
+ )
+
def get_error_class(
self, error_message: str, status_code: int, headers: Union[Dict, Headers]
) -> BaseLLMException:
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index e3d65be8bb..e809444433 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -102,6 +102,61 @@ class AmazonConverseConfig(BaseConfig):
"performanceConfig": PerformanceConfigBlock,
}
+ @staticmethod
+ def _convert_consecutive_user_messages_to_guarded_text(
+ messages: List[AllMessageValues], optional_params: dict
+ ) -> List[AllMessageValues]:
+ """
+ Convert consecutive user messages at the end to guarded_text type if guardrailConfig is present
+ and no guarded_text is already present in those messages.
+ """
+ # Check if guardrailConfig is present
+ if "guardrailConfig" not in optional_params:
+ return messages
+
+ # Find all consecutive user messages at the end
+ consecutive_user_message_indices = []
+ for i in range(len(messages) - 1, -1, -1):
+ if messages[i].get("role") == "user":
+ consecutive_user_message_indices.append(i)
+ else:
+ break
+
+ if not consecutive_user_message_indices:
+ return messages
+
+ # Process each consecutive user message
+ messages_copy = copy.deepcopy(messages)
+ for user_message_index in consecutive_user_message_indices:
+ user_message = messages_copy[user_message_index]
+ content = user_message.get("content", [])
+
+ if isinstance(content, list):
+ has_guarded_text = any(
+ isinstance(item, dict) and item.get("type") == "guarded_text"
+ for item in content
+ )
+ if has_guarded_text:
+ continue # Skip this message if it already has guarded_text
+
+ # Convert text elements to guarded_text
+ new_content = []
+ for item in content:
+ if isinstance(item, dict) and item.get("type") == "text":
+ new_item = {"type": "guarded_text", "text": item["text"]} # type: ignore
+ new_content.append(new_item)
+ else:
+ new_content.append(item)
+
+ messages_copy[user_message_index]["content"] = new_content # type: ignore
+ elif isinstance(content, str):
+ # If content is a string, convert it to guarded_text
+ messages_copy[user_message_index]["content"] = [ # type: ignore
+ {"type": "guarded_text", "text": content} # type: ignore
+ ]
+
+ return messages_copy
+
@classmethod
def get_config(cls):
return {
@@ -120,6 +175,77 @@ class AmazonConverseConfig(BaseConfig):
and v is not None
}
+ def _validate_request_metadata(self, metadata: dict) -> None:
+ """
+ Validate requestMetadata according to AWS Bedrock Converse API constraints.
+
+ Constraints:
+ - Maximum of 16 items
+ - Keys: 1-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{1,256}
+ - Values: 0-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{0,256}
+ """
+ import re
+
+ if not isinstance(metadata, dict):
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata must be a dictionary",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if len(metadata) > 16:
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata can contain a maximum of 16 items",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ key_pattern = re.compile(r'^[a-zA-Z0-9\s:_@$#=/+,.-]{1,256}$')
+ value_pattern = re.compile(r'^[a-zA-Z0-9\s:_@$#=/+,.-]{0,256}$')
+
+ for key, value in metadata.items():
+ if not isinstance(key, str):
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata keys must be strings",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if not isinstance(value, str):
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata values must be strings",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if len(key) == 0 or len(key) > 256:
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata key length must be 1-256 characters",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if len(value) > 256:
+ raise litellm.exceptions.BadRequestError(
+ message="requestMetadata value length must be 0-256 characters",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if not key_pattern.match(key):
+ raise litellm.exceptions.BadRequestError(
+ message=f"requestMetadata key '{key}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
+ if not value_pattern.match(value):
+ raise litellm.exceptions.BadRequestError(
+ message=f"requestMetadata value '{value}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]",
+ model="bedrock",
+ llm_provider="bedrock",
+ )
+
def get_supported_openai_params(self, model: str) -> List[str]:
from litellm.utils import supports_function_calling
@@ -133,6 +259,7 @@ class AmazonConverseConfig(BaseConfig):
"top_p",
"extra_headers",
"response_format",
+ "requestMetadata",
]
if (
@@ -442,6 +569,10 @@ class AmazonConverseConfig(BaseConfig):
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
value
)
+ if param == "requestMetadata":
+ if value is not None and isinstance(value, dict):
+ self._validate_request_metadata(value) # type: ignore
+ optional_params["requestMetadata"] = value
# Only update thinking tokens for non-GPT-OSS models
if "gpt-oss" not in model:
@@ -501,7 +632,6 @@ class AmazonConverseConfig(BaseConfig):
)
and not is_thinking_enabled
):
-
optional_params["tool_choice"] = ToolChoiceValuesBlock(
tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME)
)
@@ -632,34 +762,8 @@ class AmazonConverseConfig(BaseConfig):
return {}
- def _transform_request_helper(
- self,
- model: str,
- system_content_blocks: List[SystemContentBlock],
- optional_params: dict,
- messages: Optional[List[AllMessageValues]] = None,
- headers: Optional[dict] = None,
- ) -> CommonRequestObject:
- ## VALIDATE REQUEST
- """
- Bedrock doesn't support tool calling without `tools=` param specified.
- """
- if (
- "tools" not in optional_params
- and messages is not None
- and has_tool_call_blocks(messages)
- ):
- if litellm.modify_params:
- optional_params["tools"] = add_dummy_tool(
- custom_llm_provider="bedrock_converse"
- )
- else:
- raise litellm.UnsupportedParamsError(
- message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
- model="",
- llm_provider="bedrock",
- )
-
+ def _prepare_request_params(self, optional_params: dict, model: str) -> tuple[dict, dict, dict]:
+ """Prepare and separate request parameters."""
inference_params = copy.deepcopy(optional_params)
supported_converse_params = list(
AmazonConverseConfig.__annotations__.keys()
@@ -673,6 +777,11 @@ class AmazonConverseConfig(BaseConfig):
)
inference_params.pop("json_mode", None) # used for handling json_schema
+ # Extract requestMetadata before processing other parameters
+ request_metadata = inference_params.pop("requestMetadata", None)
+ if request_metadata is not None:
+ self._validate_request_metadata(request_metadata)
+
# keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params'
additional_request_params = {
k: v for k, v in inference_params.items() if k not in total_supported_params
@@ -686,9 +795,10 @@ class AmazonConverseConfig(BaseConfig):
self._handle_top_k_value(model, inference_params)
)
- original_tools = inference_params.pop("tools", [])
+ return inference_params, additional_request_params, request_metadata
- # Initialize bedrock_tools
+ def _process_tools_and_beta(self, original_tools: list, model: str, headers: Optional[dict], additional_request_params: dict) -> tuple[List[ToolBlock], list]:
+ """Process tools and collect anthropic_beta values."""
bedrock_tools: List[ToolBlock] = []
# Collect anthropic_beta values from user headers
@@ -730,6 +840,44 @@ class AmazonConverseConfig(BaseConfig):
seen.add(beta)
additional_request_params["anthropic_beta"] = unique_betas
+ return bedrock_tools, anthropic_beta_list
+
+ def _transform_request_helper(
+ self,
+ model: str,
+ system_content_blocks: List[SystemContentBlock],
+ optional_params: dict,
+ messages: Optional[List[AllMessageValues]] = None,
+ headers: Optional[dict] = None,
+ ) -> CommonRequestObject:
+ ## VALIDATE REQUEST
+ """
+ Bedrock doesn't support tool calling without `tools=` param specified.
+ """
+ if (
+ "tools" not in optional_params
+ and messages is not None
+ and has_tool_call_blocks(messages)
+ ):
+ if litellm.modify_params:
+ optional_params["tools"] = add_dummy_tool(
+ custom_llm_provider="bedrock_converse"
+ )
+ else:
+ raise litellm.UnsupportedParamsError(
+ message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
+ model="",
+ llm_provider="bedrock",
+ )
+
+ # Prepare and separate parameters
+ inference_params, additional_request_params, request_metadata = self._prepare_request_params(optional_params, model)
+
+ original_tools = inference_params.pop("tools", [])
+
+ # Process tools and collect beta values
+ bedrock_tools, anthropic_beta_list = self._process_tools_and_beta(original_tools, model, headers, additional_request_params)
+
bedrock_tool_config: Optional[ToolConfigBlock] = None
if len(bedrock_tools) > 0:
tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
@@ -759,6 +907,10 @@ class AmazonConverseConfig(BaseConfig):
if bedrock_tool_config is not None:
data["toolConfig"] = bedrock_tool_config
+ # Request Metadata (top-level field)
+ if request_metadata is not None:
+ data["requestMetadata"] = request_metadata
+
return data
async def _async_transform_request(
@@ -770,6 +922,11 @@ class AmazonConverseConfig(BaseConfig):
headers: Optional[dict] = None,
) -> RequestObject:
messages, system_content_blocks = self._transform_system_message(messages)
+
+ # Convert last user message to guarded_text if guardrailConfig is present
+ messages = self._convert_consecutive_user_messages_to_guarded_text(
+ messages, optional_params
+ )
## TRANSFORMATION ##
_data: CommonRequestObject = self._transform_request_helper(
@@ -822,6 +979,11 @@ class AmazonConverseConfig(BaseConfig):
) -> RequestObject:
messages, system_content_blocks = self._transform_system_message(messages)
+ # Convert last user message to guarded_text if guardrailConfig is present
+ messages = self._convert_consecutive_user_messages_to_guarded_text(
+ messages, optional_params
+ )
+
_data: CommonRequestObject = self._transform_request_helper(
model=model,
system_content_blocks=system_content_blocks,
@@ -995,7 +1157,9 @@ class AmazonConverseConfig(BaseConfig):
return message, returned_finish_reason
- def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[
+ def _translate_message_content(
+ self, content_blocks: List[ContentBlock]
+ ) -> Tuple[
str,
List[ChatCompletionToolCallChunk],
Optional[List[BedrockConverseReasoningContentBlock]],
@@ -1010,9 +1174,9 @@ class AmazonConverseConfig(BaseConfig):
"""
content_str = ""
tools: List[ChatCompletionToolCallChunk] = []
- reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
- None
- )
+ reasoningContentBlocks: Optional[
+ List[BedrockConverseReasoningContentBlock]
+ ] = None
for idx, content in enumerate(content_blocks):
"""
- Content is either a tool response or text
@@ -1133,9 +1297,9 @@ class AmazonConverseConfig(BaseConfig):
chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
content_str = ""
tools: List[ChatCompletionToolCallChunk] = []
- reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
- None
- )
+ reasoningContentBlocks: Optional[
+ List[BedrockConverseReasoningContentBlock]
+ ] = None
if message is not None:
(
@@ -1148,12 +1312,12 @@ class AmazonConverseConfig(BaseConfig):
chat_completion_message["provider_specific_fields"] = {
"reasoningContentBlocks": reasoningContentBlocks,
}
- chat_completion_message["reasoning_content"] = (
- self._transform_reasoning_content(reasoningContentBlocks)
- )
- chat_completion_message["thinking_blocks"] = (
- self._transform_thinking_blocks(reasoningContentBlocks)
- )
+ chat_completion_message[
+ "reasoning_content"
+ ] = self._transform_reasoning_content(reasoningContentBlocks)
+ chat_completion_message[
+ "thinking_blocks"
+ ] = self._transform_thinking_blocks(reasoningContentBlocks)
chat_completion_message["content"] = content_str
if (
json_mode is True
@@ -1171,7 +1335,6 @@ class AmazonConverseConfig(BaseConfig):
# Bedrock returns the response wrapped in a "properties" object
# We need to extract the actual content from this wrapper
try:
-
response_data = json.loads(json_mode_content_str)
# If Bedrock wrapped the response in "properties", extract the content
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index 831a6da93b..c7f7acf331 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -738,19 +738,24 @@ class CommonBatchFilesUtils:
)
# Prepare the request data
- if isinstance(data, dict):
- import json
- request_data = json.dumps(data)
+ method_upper = method.upper()
+ if method_upper == "GET":
+ # GET requests should be signed with an empty payload
+ request_data = ""
+ headers = {}
else:
- request_data = data
-
- # Prepare headers
- headers = {"Content-Type": "application/json"}
+ if isinstance(data, dict):
+ import json
+ request_data = json.dumps(data)
+ else:
+ request_data = data
+ # Prepare headers for non-GET requests
+ headers = {"Content-Type": "application/json"}
# Create AWS request and sign it
sigv4 = SigV4Auth(credentials, service_name, aws_region_name)
request = AWSRequest(
- method=method.upper(), url=endpoint_url, data=request_data, headers=headers
+ method=method_upper, url=endpoint_url, data=request_data, headers=headers
)
sigv4.add_auth(request)
prepped = request.prepare()
diff --git a/litellm/llms/bedrock/count_tokens/handler.py b/litellm/llms/bedrock/count_tokens/handler.py
new file mode 100644
index 0000000000..d4355c0c36
--- /dev/null
+++ b/litellm/llms/bedrock/count_tokens/handler.py
@@ -0,0 +1,123 @@
+"""
+AWS Bedrock CountTokens API handler.
+
+Simplified handler leveraging existing LiteLLM Bedrock infrastructure.
+"""
+
+from typing import Any, Dict
+
+from fastapi import HTTPException
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.llms.bedrock.count_tokens.transformation import BedrockCountTokensConfig
+from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
+
+
+class BedrockCountTokensHandler(BedrockCountTokensConfig):
+ """
+ Simplified handler for AWS Bedrock CountTokens API requests.
+
+ Uses existing LiteLLM infrastructure for authentication and request handling.
+ """
+
+ async def handle_count_tokens_request(
+ self,
+ request_data: Dict[str, Any],
+ litellm_params: Dict[str, Any],
+ resolved_model: str,
+ ) -> Dict[str, Any]:
+ """
+ Handle a CountTokens request using existing LiteLLM patterns.
+
+ Args:
+ request_data: The incoming request payload
+ litellm_params: LiteLLM configuration parameters
+ resolved_model: The actual model ID resolved from router
+
+ Returns:
+ Dictionary containing token count response
+ """
+ try:
+ # Validate the request
+ self.validate_count_tokens_request(request_data)
+
+ verbose_logger.debug(
+ f"Processing CountTokens request for resolved model: {resolved_model}"
+ )
+
+ # Get AWS region using existing LiteLLM function
+ aws_region_name = self._get_aws_region_name(
+ optional_params=litellm_params,
+ model=resolved_model,
+ model_id=None,
+ )
+
+ verbose_logger.debug(f"Retrieved AWS region: {aws_region_name}")
+
+ # Transform request to Bedrock format (supports both Converse and InvokeModel)
+ bedrock_request = self.transform_anthropic_to_bedrock_count_tokens(
+ request_data=request_data
+ )
+
+ verbose_logger.debug(f"Transformed request: {bedrock_request}")
+
+ # Get endpoint URL using simplified function
+ endpoint_url = self.get_bedrock_count_tokens_endpoint(
+ resolved_model, aws_region_name
+ )
+
+ verbose_logger.debug(f"Making request to: {endpoint_url}")
+
+ # Use existing _sign_request method from BaseAWSLLM
+ headers = {"Content-Type": "application/json"}
+ signed_headers, signed_body = self._sign_request(
+ service_name="bedrock",
+ headers=headers,
+ optional_params=litellm_params,
+ request_data=bedrock_request,
+ api_base=endpoint_url,
+ model=resolved_model,
+ )
+
+ async_client = get_async_httpx_client(llm_provider=litellm.LlmProviders.BEDROCK)
+
+ response = await async_client.post(
+ endpoint_url,
+ headers=signed_headers,
+ data=signed_body,
+ timeout=30.0,
+ )
+
+ verbose_logger.debug(f"Response status: {response.status_code}")
+
+ if response.status_code != 200:
+ error_text = response.text
+ verbose_logger.error(f"AWS Bedrock error: {error_text}")
+ raise HTTPException(
+ status_code=400,
+ detail={"error": f"AWS Bedrock error: {error_text}"},
+ )
+
+ bedrock_response = response.json()
+
+ verbose_logger.debug(f"Bedrock response: {bedrock_response}")
+
+ # Transform response back to expected format
+ final_response = self.transform_bedrock_response_to_anthropic(
+ bedrock_response
+ )
+
+ verbose_logger.debug(f"Final response: {final_response}")
+
+ return final_response
+
+ except HTTPException:
+ # Re-raise HTTP exceptions as-is
+ raise
+ except Exception as e:
+ verbose_logger.error(f"Error in CountTokens handler: {str(e)}")
+ raise HTTPException(
+ status_code=500,
+ detail={"error": f"CountTokens processing error: {str(e)}"},
+ )
diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py
new file mode 100644
index 0000000000..d46ed3aa45
--- /dev/null
+++ b/litellm/llms/bedrock/count_tokens/transformation.py
@@ -0,0 +1,213 @@
+"""
+AWS Bedrock CountTokens API transformation logic.
+
+This module handles the transformation of requests from Anthropic Messages API format
+to AWS Bedrock's CountTokens API format and vice versa.
+"""
+
+from typing import Any, Dict, List
+
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+from litellm.llms.bedrock.common_utils import BedrockModelInfo
+
+
+class BedrockCountTokensConfig(BaseAWSLLM):
+ """
+ Configuration and transformation logic for AWS Bedrock CountTokens API.
+
+ AWS Bedrock CountTokens API Specification:
+ - Endpoint: POST /model/{modelId}/count-tokens
+ - Input formats: 'invokeModel' or 'converse'
+ - Response: {"inputTokens": }
+ """
+
+ def _detect_input_type(self, request_data: Dict[str, Any]) -> str:
+ """
+ Detect whether to use 'converse' or 'invokeModel' input format.
+
+ Args:
+ request_data: The original request data
+
+ Returns:
+ 'converse' or 'invokeModel'
+ """
+ # If the request has messages in the expected Anthropic format, use converse
+ if "messages" in request_data and isinstance(request_data["messages"], list):
+ return "converse"
+
+ # For raw text or other formats, use invokeModel
+ # This handles cases where the input is prompt-based or already in raw Bedrock format
+ return "invokeModel"
+
+ def transform_anthropic_to_bedrock_count_tokens(
+ self,
+ request_data: Dict[str, Any],
+ ) -> Dict[str, Any]:
+ """
+ Transform request to Bedrock CountTokens format.
+ Supports both Converse and InvokeModel input types.
+
+ Input (Anthropic format):
+ {
+ "model": "claude-3-5-sonnet",
+ "messages": [{"role": "user", "content": "Hello!"}]
+ }
+
+ Output (Bedrock CountTokens format for Converse):
+ {
+ "input": {
+ "converse": {
+ "messages": [...],
+ "system": [...] (if present)
+ }
+ }
+ }
+
+ Output (Bedrock CountTokens format for InvokeModel):
+ {
+ "input": {
+ "invokeModel": {
+ "body": "{...raw model input...}"
+ }
+ }
+ }
+ """
+ input_type = self._detect_input_type(request_data)
+
+ if input_type == "converse":
+ return self._transform_to_converse_format(request_data.get("messages", []))
+ else:
+ return self._transform_to_invoke_model_format(request_data)
+
+ def _transform_to_converse_format(
+ self, messages: List[Dict[str, Any]]
+ ) -> Dict[str, Any]:
+ """Transform to Converse input format."""
+ # Extract system messages if present
+ system_messages = []
+ user_messages = []
+
+ for message in messages:
+ if message.get("role") == "system":
+ system_messages.append({"text": message.get("content", "")})
+ else:
+ # Transform message content to Bedrock format
+ transformed_message: Dict[str, Any] = {"role": message.get("role"), "content": []}
+
+ # Handle content - ensure it's in the correct array format
+ content = message.get("content", "")
+ if isinstance(content, str):
+ # String content -> convert to text block
+ transformed_message["content"].append({"text": content})
+ elif isinstance(content, list):
+ # Already in blocks format - use as is
+ transformed_message["content"] = content
+
+ user_messages.append(transformed_message)
+
+ # Build the converse input format
+ converse_input = {"messages": user_messages}
+
+ # Add system messages if present
+ if system_messages:
+ converse_input["system"] = system_messages
+
+ # Build the complete request
+ return {"input": {"converse": converse_input}}
+
+ def _transform_to_invoke_model_format(
+ self, request_data: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """Transform to InvokeModel input format."""
+ import json
+
+ # For InvokeModel, we need to provide the raw body that would be sent to the model
+ # Remove the 'model' field from the body as it's not part of the model input
+ body_data = {k: v for k, v in request_data.items() if k != "model"}
+
+ return {"input": {"invokeModel": {"body": json.dumps(body_data)}}}
+
+ def get_bedrock_count_tokens_endpoint(
+ self, model: str, aws_region_name: str
+ ) -> str:
+ """
+ Construct the AWS Bedrock CountTokens API endpoint using existing LiteLLM functions.
+
+ Args:
+ model: The resolved model ID from router lookup
+ aws_region_name: AWS region (e.g., "eu-west-1")
+
+ Returns:
+ Complete endpoint URL for CountTokens API
+ """
+ # Use existing LiteLLM function to get the base model ID (removes region prefix)
+ model_id = BedrockModelInfo.get_base_model(model)
+
+ # Remove bedrock/ prefix if present
+ if model_id.startswith("bedrock/"):
+ model_id = model_id[8:] # Remove "bedrock/" prefix
+
+ base_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
+ endpoint = f"{base_url}/model/{model_id}/count-tokens"
+
+ return endpoint
+
+ def transform_bedrock_response_to_anthropic(
+ self, bedrock_response: Dict[str, Any]
+ ) -> Dict[str, Any]:
+ """
+ Transform Bedrock CountTokens response to Anthropic format.
+
+ Input (Bedrock response):
+ {
+ "inputTokens": 123
+ }
+
+ Output (Anthropic format):
+ {
+ "input_tokens": 123
+ }
+ """
+ input_tokens = bedrock_response.get("inputTokens", 0)
+
+ return {"input_tokens": input_tokens}
+
+ def validate_count_tokens_request(self, request_data: Dict[str, Any]) -> None:
+ """
+ Validate the incoming count tokens request.
+ Supports both Converse and InvokeModel input formats.
+
+ Args:
+ request_data: The request payload
+
+ Raises:
+ ValueError: If the request is invalid
+ """
+ if not request_data.get("model"):
+ raise ValueError("model parameter is required")
+
+ input_type = self._detect_input_type(request_data)
+
+ if input_type == "converse":
+ # Validate Converse format (messages-based)
+ messages = request_data.get("messages", [])
+ if not messages:
+ raise ValueError("messages parameter is required for Converse input")
+
+ if not isinstance(messages, list):
+ raise ValueError("messages must be a list")
+
+ for i, message in enumerate(messages):
+ if not isinstance(message, dict):
+ raise ValueError(f"Message {i} must be a dictionary")
+
+ if "role" not in message:
+ raise ValueError(f"Message {i} must have a 'role' field")
+
+ if "content" not in message:
+ raise ValueError(f"Message {i} must have a 'content' field")
+ else:
+ # For InvokeModel format, we need at least some content to count tokens
+ # The content structure varies by model, so we do minimal validation
+ if len(request_data) <= 1: # Only has 'model' field
+ raise ValueError("Request must contain content to count tokens")
diff --git a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
index 8056e9e9b2..ff748b58e8 100644
--- a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
+++ b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
@@ -10,7 +10,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-tit
"""
import types
-from typing import List, Optional
+from typing import List, Optional, Union
from litellm.types.llms.bedrock import (
AmazonTitanV2EmbeddingRequest,
@@ -30,9 +30,7 @@ class AmazonTitanV2Config:
normalize: Optional[bool] = None
dimensions: Optional[int] = None
- def __init__(
- self, normalize: Optional[bool] = None, dimensions: Optional[int] = None
- ) -> None:
+ def __init__(self, normalize: Optional[bool] = None, dimensions: Optional[int] = None) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
@@ -57,32 +55,56 @@ class AmazonTitanV2Config:
}
def get_supported_openai_params(self) -> List[str]:
- return ["dimensions"]
+ return ["dimensions", "encoding_format"]
- def map_openai_params(
- self, non_default_params: dict, optional_params: dict
- ) -> dict:
+ def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict:
for k, v in non_default_params.items():
if k == "dimensions":
optional_params["dimensions"] = v
+ elif k == "encoding_format":
+ # Map OpenAI encoding_format to AWS embeddingTypes
+ if v == "float":
+ optional_params["embeddingTypes"] = ["float"]
+ elif v == "base64":
+ # base64 maps to binary format in AWS
+ optional_params["embeddingTypes"] = ["binary"]
+ else:
+ # For any other encoding format, default to float
+ optional_params["embeddingTypes"] = ["float"]
return optional_params
- def _transform_request(
- self, input: str, inference_params: dict
- ) -> AmazonTitanV2EmbeddingRequest:
+ def _transform_request(self, input: str, inference_params: dict) -> AmazonTitanV2EmbeddingRequest:
return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore
- def _transform_response(
- self, response_list: List[dict], model: str
- ) -> EmbeddingResponse:
+ def _transform_response(self, response_list: List[dict], model: str) -> EmbeddingResponse:
total_prompt_tokens = 0
transformed_responses: List[Embedding] = []
for index, response in enumerate(response_list):
_parsed_response = AmazonTitanV2EmbeddingResponse(**response) # type: ignore
+
+ # According to AWS docs, embeddingsByType is always present
+ # If binary was requested (encoding_format="base64"), use binary data
+ # Otherwise, use float data from embeddingsByType or fallback to embedding field
+ embedding_data: Union[List[float], List[int]]
+
+ if ("embeddingsByType" in _parsed_response and
+ "binary" in _parsed_response["embeddingsByType"]):
+ # Use binary data if available (for encoding_format="base64")
+ embedding_data = _parsed_response["embeddingsByType"]["binary"]
+ elif ("embeddingsByType" in _parsed_response and
+ "float" in _parsed_response["embeddingsByType"]):
+ # Use float data from embeddingsByType
+ embedding_data = _parsed_response["embeddingsByType"]["float"]
+ elif "embedding" in _parsed_response:
+ # Fallback to legacy embedding field
+ embedding_data = _parsed_response["embedding"]
+ else:
+ raise ValueError(f"No embedding data found in response: {response}")
+
transformed_responses.append(
Embedding(
- embedding=_parsed_response["embedding"],
+ embedding=embedding_data,
index=index,
object="embedding",
)
diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py
index 0824905f51..d4dd716a1f 100644
--- a/litellm/llms/bedrock/embed/embedding.py
+++ b/litellm/llms/bedrock/embed/embedding.py
@@ -4,12 +4,13 @@ Handles embedding calls to Bedrock's `/invoke` endpoint
import copy
import json
-from typing import Any, Callable, List, Optional, Tuple, Union
import urllib.parse
+from typing import Any, Callable, List, Optional, Tuple, Union, get_args
import httpx
import litellm
+from litellm.constants import BEDROCK_EMBEDDING_PROVIDERS_LITERAL
from litellm.llms.cohere.embed.handler import embedding as cohere_embedding
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
@@ -18,7 +19,11 @@ from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
)
from litellm.secret_managers.main import get_secret
-from litellm.types.llms.bedrock import AmazonEmbeddingRequest, CohereEmbeddingRequest
+from litellm.types.llms.bedrock import (
+ AmazonEmbeddingRequest,
+ CohereEmbeddingRequest,
+ TwelveLabsMarengoEmbeddingRequest,
+)
from litellm.types.utils import EmbeddingResponse
from ..base_aws_llm import BaseAWSLLM
@@ -29,6 +34,7 @@ from .amazon_titan_multimodal_transformation import (
)
from .amazon_titan_v2_transformation import AmazonTitanV2Config
from .cohere_transformation import BedrockCohereEmbeddingConfig
+from .twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig
class BedrockEmbedding(BaseAWSLLM):
@@ -145,6 +151,44 @@ class BedrockEmbedding(BaseAWSLLM):
raise BedrockError(status_code=408, message="Timeout error occurred.")
return response.json()
+
+ def _transform_response(
+ self, response_list: List[dict], model: str, provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL
+ ) -> Optional[EmbeddingResponse]:
+ """
+ Transforms the response from the Bedrock embedding provider to the OpenAI format.
+ """
+ returned_response: Optional[EmbeddingResponse] = None
+ if model == "amazon.titan-embed-image-v1":
+ returned_response = (
+ AmazonTitanMultimodalEmbeddingG1Config()._transform_response(
+ response_list=response_list, model=model
+ )
+ )
+ elif model == "amazon.titan-embed-text-v1":
+ returned_response = AmazonTitanG1Config()._transform_response(
+ response_list=response_list, model=model
+ )
+ elif model == "amazon.titan-embed-text-v2:0":
+ returned_response = AmazonTitanV2Config()._transform_response(
+ response_list=response_list, model=model
+ )
+ elif provider == "twelvelabs":
+ returned_response = TwelveLabsMarengoEmbeddingConfig()._transform_response(
+ response_list=response_list, model=model
+ )
+
+
+ ##########################################################
+ # Validate returned response
+ ##########################################################
+ if returned_response is None:
+ raise Exception(
+ "Unable to map model response to known provider format. model={}".format(
+ model
+ )
+ )
+ return returned_response
def _single_func_embeddings(
self,
@@ -157,6 +201,7 @@ class BedrockEmbedding(BaseAWSLLM):
aws_region_name: str,
model: str,
logging_obj: Any,
+ provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
api_key: Optional[str] = None,
):
responses: List[dict] = []
@@ -164,16 +209,16 @@ class BedrockEmbedding(BaseAWSLLM):
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
-
+
prepped = self.get_request_headers(
- credentials=credentials,
- aws_region_name=aws_region_name,
- extra_headers=extra_headers,
- endpoint_url=endpoint_url,
- data=json.dumps(data),
- headers=headers,
- api_key=api_key
- )
+ credentials=credentials,
+ aws_region_name=aws_region_name,
+ extra_headers=extra_headers,
+ endpoint_url=endpoint_url,
+ data=json.dumps(data),
+ headers=headers,
+ api_key=api_key,
+ )
## LOGGING
logging_obj.pre_call(
@@ -203,32 +248,9 @@ class BedrockEmbedding(BaseAWSLLM):
responses.append(response)
- returned_response: Optional[EmbeddingResponse] = None
-
- ## TRANSFORM RESPONSE ##
- if model == "amazon.titan-embed-image-v1":
- returned_response = (
- AmazonTitanMultimodalEmbeddingG1Config()._transform_response(
- response_list=responses, model=model
- )
- )
- elif model == "amazon.titan-embed-text-v1":
- returned_response = AmazonTitanG1Config()._transform_response(
- response_list=responses, model=model
- )
- elif model == "amazon.titan-embed-text-v2:0":
- returned_response = AmazonTitanV2Config()._transform_response(
- response_list=responses, model=model
- )
-
- if returned_response is None:
- raise Exception(
- "Unable to map model response to known provider format. model={}".format(
- model
- )
- )
-
- return returned_response
+ return self._transform_response(
+ response_list=responses, model=model, provider=provider
+ )
async def _async_single_func_embeddings(
self,
@@ -241,6 +263,7 @@ class BedrockEmbedding(BaseAWSLLM):
aws_region_name: str,
model: str,
logging_obj: Any,
+ provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
api_key: Optional[str] = None,
):
responses: List[dict] = []
@@ -248,16 +271,16 @@ class BedrockEmbedding(BaseAWSLLM):
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
-
+
prepped = self.get_request_headers(
- credentials=credentials,
- aws_region_name=aws_region_name,
- extra_headers=extra_headers,
- endpoint_url=endpoint_url,
- data=json.dumps(data),
- headers=headers,
- api_key=api_key,
- )
+ credentials=credentials,
+ aws_region_name=aws_region_name,
+ extra_headers=extra_headers,
+ endpoint_url=endpoint_url,
+ data=json.dumps(data),
+ headers=headers,
+ api_key=api_key,
+ )
## LOGGING
logging_obj.pre_call(
@@ -286,33 +309,10 @@ class BedrockEmbedding(BaseAWSLLM):
)
responses.append(response)
-
- returned_response: Optional[EmbeddingResponse] = None
-
## TRANSFORM RESPONSE ##
- if model == "amazon.titan-embed-image-v1":
- returned_response = (
- AmazonTitanMultimodalEmbeddingG1Config()._transform_response(
- response_list=responses, model=model
- )
- )
- elif model == "amazon.titan-embed-text-v1":
- returned_response = AmazonTitanG1Config()._transform_response(
- response_list=responses, model=model
- )
- elif model == "amazon.titan-embed-text-v2:0":
- returned_response = AmazonTitanV2Config()._transform_response(
- response_list=responses, model=model
- )
-
- if returned_response is None:
- raise Exception(
- "Unable to map model response to known provider format. model={}".format(
- model
- )
- )
-
- return returned_response
+ return self._transform_response(
+ response_list=responses, model=model, provider=provider
+ )
def embeddings(
self,
@@ -336,7 +336,7 @@ class BedrockEmbedding(BaseAWSLLM):
### TRANSFORMATION ###
unencoded_model_id = (
optional_params.pop("model_id", None) or model
- ) # default to model if not passed
+ ) # default to model if not passed
modelId = urllib.parse.quote(unencoded_model_id, safe="")
aws_region_name = self._get_aws_region_name(
optional_params=optional_params,
@@ -344,7 +344,12 @@ class BedrockEmbedding(BaseAWSLLM):
model_id=unencoded_model_id,
)
- provider = model.split(".")[0]
+ provider = self.get_bedrock_embedding_provider(model)
+ if provider is None:
+ raise Exception(
+ f"Unable to determine bedrock embedding provider for model: {model}. "
+ f"Supported providers: {list(get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL))}"
+ )
inference_params = copy.deepcopy(optional_params)
inference_params = {
k: v
@@ -394,6 +399,15 @@ class BedrockEmbedding(BaseAWSLLM):
)
)
batch_data.append(transformed_request)
+ elif provider == "twelvelabs":
+ batch_data = []
+ for i in input:
+ twelvelabs_request: (
+ TwelveLabsMarengoEmbeddingRequest
+ ) = TwelveLabsMarengoEmbeddingConfig()._transform_request(
+ input=i, inference_params=inference_params
+ )
+ batch_data.append(twelvelabs_request)
### SET RUNTIME ENDPOINT ###
endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint(
@@ -422,8 +436,9 @@ class BedrockEmbedding(BaseAWSLLM):
model=model,
logging_obj=logging_obj,
api_key=api_key,
+ provider=provider,
)
- return self._single_func_embeddings(
+ returned_response = self._single_func_embeddings(
client=(
client
if client is not None and isinstance(client, HTTPHandler)
@@ -438,14 +453,18 @@ class BedrockEmbedding(BaseAWSLLM):
model=model,
logging_obj=logging_obj,
api_key=api_key,
+ provider=provider,
)
+ if returned_response is None:
+ raise Exception("Unable to map Bedrock request to provider")
+ return returned_response
elif data is None:
raise Exception("Unable to map Bedrock request to provider")
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
-
+
prepped = self.get_request_headers(
credentials=credentials,
aws_region_name=aws_region_name,
diff --git a/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py
new file mode 100644
index 0000000000..fdad8a6504
--- /dev/null
+++ b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py
@@ -0,0 +1,140 @@
+"""
+Transformation logic from OpenAI /v1/embeddings format to Bedrock TwelveLabs Marengo /invoke format.
+
+Why separate file? Make it easy to see how transformation works
+
+Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html
+"""
+
+from typing import List
+
+from litellm.types.llms.bedrock import (
+ TwelveLabsMarengoEmbeddingRequest,
+)
+from litellm.types.utils import Embedding, EmbeddingResponse, Usage
+from litellm.utils import get_base64_str, is_base64_encoded
+
+
+class TwelveLabsMarengoEmbeddingConfig:
+ """
+ Reference - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html
+
+ Supports text and image inputs for Phase 1.
+ Video and audio support will be added in Phase 2.
+ """
+
+ def __init__(self) -> None:
+ pass
+
+ def get_supported_openai_params(self) -> List[str]:
+ return ["encoding_format", "textTruncate", "embeddingOption"]
+
+ def map_openai_params(
+ self, non_default_params: dict, optional_params: dict
+ ) -> dict:
+ for k, v in non_default_params.items():
+ if k == "encoding_format":
+ # TwelveLabs doesn't have encoding_format, but we can map it to embeddingOption
+ if v == "float":
+ optional_params["embeddingOption"] = ["visual-text", "visual-image"]
+ elif k == "textTruncate":
+ optional_params["textTruncate"] = v
+ elif k == "embeddingOption":
+ optional_params["embeddingOption"] = v
+ return optional_params
+
+ def _transform_request(
+ self, input: str, inference_params: dict
+ ) -> TwelveLabsMarengoEmbeddingRequest:
+ """
+ Transform OpenAI-style input to TwelveLabs Marengo format.
+ Phase 1: Supports text and image inputs only.
+ """
+ # Check if input is base64 encoded image
+ is_encoded = is_base64_encoded(input)
+
+ if is_encoded:
+ # Image input
+ b64_str = get_base64_str(input)
+ transformed_request = TwelveLabsMarengoEmbeddingRequest(
+ inputType="image", mediaSource={"base64String": b64_str}
+ )
+ else:
+ # Text input
+ transformed_request = TwelveLabsMarengoEmbeddingRequest(
+ inputType="text", inputText=input
+ )
+
+ # Set default textTruncate if not specified
+ if "textTruncate" not in inference_params:
+ transformed_request["textTruncate"] = "end"
+
+ # Apply any additional inference parameters
+ for k, v in inference_params.items():
+ if k not in [
+ "inputType",
+ "inputText",
+ "mediaSource",
+ ]: # Don't override core fields
+ transformed_request[k] = v # type: ignore
+
+ return transformed_request
+
+ def _transform_response(
+ self, response_list: List[dict], model: str
+ ) -> EmbeddingResponse:
+ """
+ Transform TwelveLabs response to OpenAI format.
+ Handles the actual TwelveLabs response format: {"data": [{"embedding": [...]}]}
+ """
+ embeddings: List[Embedding] = []
+ total_tokens = 0
+
+ for response in response_list:
+ # TwelveLabs response format has a "data" field containing the embeddings
+ if "data" in response and isinstance(response["data"], list):
+ for item in response["data"]:
+ if "embedding" in item:
+ # Single embedding response
+ embedding = Embedding(
+ embedding=item["embedding"],
+ index=len(embeddings),
+ object="embedding",
+ )
+ embeddings.append(embedding)
+
+ # Estimate token count (rough approximation)
+ if "inputTextTokenCount" in item:
+ total_tokens += item["inputTextTokenCount"]
+ else:
+ # Rough estimate: 1 token per 4 characters for text, or use embedding size
+ total_tokens += len(item["embedding"]) // 4
+ elif "embedding" in response:
+ # Direct embedding response (fallback for other formats)
+ embedding = Embedding(
+ embedding=response["embedding"],
+ index=len(embeddings),
+ object="embedding",
+ )
+ embeddings.append(embedding)
+
+ # Estimate token count (rough approximation)
+ if "inputTextTokenCount" in response:
+ total_tokens += response["inputTextTokenCount"]
+ else:
+ # Rough estimate: 1 token per 4 characters for text
+ total_tokens += len(response.get("inputText", "")) // 4
+ elif "embeddings" in response:
+ # Multiple embeddings response (from video/audio)
+ for i, emb in enumerate(response["embeddings"]):
+ embedding = Embedding(
+ embedding=emb["embedding"],
+ index=len(embeddings),
+ object="embedding",
+ )
+ embeddings.append(embedding)
+ total_tokens += len(emb["embedding"]) // 4 # Rough estimate
+
+ usage = Usage(prompt_tokens=total_tokens, total_tokens=total_tokens)
+
+ return EmbeddingResponse(data=embeddings, model=model, usage=usage)
diff --git a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py
index 3ef7a40e9a..cd33e62af1 100644
--- a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py
+++ b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py
@@ -7,12 +7,12 @@ from litellm.types.llms.bedrock import (
AmazonNovaCanvasColorGuidedGenerationParams,
AmazonNovaCanvasColorGuidedRequest,
AmazonNovaCanvasImageGenerationConfig,
+ AmazonNovaCanvasInpaintingParams,
+ AmazonNovaCanvasInpaintingRequest,
AmazonNovaCanvasRequestBase,
AmazonNovaCanvasTextToImageParams,
AmazonNovaCanvasTextToImageRequest,
AmazonNovaCanvasTextToImageResponse,
- AmazonNovaCanvasInpaintingParams,
- AmazonNovaCanvasInpaintingRequest,
)
from litellm.types.utils import ImageResponse
@@ -67,6 +67,11 @@ class AmazonNovaCanvasConfig:
"""
task_type = optional_params.pop("taskType", "TEXT_IMAGE")
image_generation_config = optional_params.pop("imageGenerationConfig", {})
+
+ # Extract model_id parameter to prevent "extraneous key" error from Bedrock API
+ # Following the same pattern as chat completions and embeddings
+ unencoded_model_id = optional_params.pop("model_id", None) # noqa: F841
+
image_generation_config = {**image_generation_config, **optional_params}
if task_type == "TEXT_IMAGE":
text_to_image_params: Dict[str, Any] = image_generation_config.pop(
diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py
index 55d94675d1..0103f190d3 100644
--- a/litellm/llms/bedrock/image/image_handler.py
+++ b/litellm/llms/bedrock/image/image_handler.py
@@ -233,7 +233,17 @@ class BedrockImageGeneration(BaseAWSLLM):
Returns:
dict: The request body to use for the Bedrock Image Generation API
"""
- provider = model.split(".")[0]
+ # Use the existing ARN-aware provider detection method
+ bedrock_provider = self.get_bedrock_invoke_provider(model)
+
+ if bedrock_provider == "amazon" or bedrock_provider == "nova":
+ # Handle Amazon Nova Canvas models
+ provider = "amazon"
+ elif bedrock_provider == "stability":
+ provider = "stability"
+ else:
+ # Fallback to original logic for backward compatibility
+ provider = model.split(".")[0]
inference_params = copy.deepcopy(optional_params)
inference_params.pop(
"user", None
diff --git a/litellm/llms/cohere/completion/handler.py b/litellm/llms/cohere/completion/handler.py
deleted file mode 100644
index 6a77951146..0000000000
--- a/litellm/llms/cohere/completion/handler.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-Cohere /generate API - uses `llm_http_handler.py` to make httpx requests
-
-Request/Response transformation is handled in `transformation.py`
-"""
diff --git a/litellm/llms/cohere/completion/transformation.py b/litellm/llms/cohere/completion/transformation.py
deleted file mode 100644
index f96ef89d3c..0000000000
--- a/litellm/llms/cohere/completion/transformation.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import time
-from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
-
-import httpx
-
-import litellm
-from litellm.litellm_core_utils.prompt_templates.common_utils import (
- convert_content_list_to_str,
-)
-from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
-from litellm.types.llms.openai import AllMessageValues
-from litellm.types.utils import Choices, Message, ModelResponse, Usage
-
-from ..common_utils import CohereError
-from ..common_utils import ModelResponseIterator as CohereModelResponseIterator
-from ..common_utils import validate_environment as cohere_validate_environment
-
-if TYPE_CHECKING:
- from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
-
- LiteLLMLoggingObj = _LiteLLMLoggingObj
-else:
- LiteLLMLoggingObj = Any
-
-
-class CohereTextConfig(BaseConfig):
- """
- Reference: https://docs.cohere.com/reference/generate
-
- The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters:
-
- - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5.
-
- - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20.
-
- - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END.
-
- - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75.
-
- - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc.
-
- - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text.
-
- - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text.
-
- - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0.
-
- - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0.
-
- - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens.
-
- - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared.
-
- - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE.
-
- - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233}
- """
-
- num_generations: Optional[int] = None
- max_tokens: Optional[int] = None
- truncate: Optional[str] = None
- temperature: Optional[int] = None
- preset: Optional[str] = None
- end_sequences: Optional[list] = None
- stop_sequences: Optional[list] = None
- k: Optional[int] = None
- p: Optional[int] = None
- frequency_penalty: Optional[int] = None
- presence_penalty: Optional[int] = None
- return_likelihoods: Optional[str] = None
- logit_bias: Optional[dict] = None
-
- def __init__(
- self,
- num_generations: Optional[int] = None,
- max_tokens: Optional[int] = None,
- truncate: Optional[str] = None,
- temperature: Optional[int] = None,
- preset: Optional[str] = None,
- end_sequences: Optional[list] = None,
- stop_sequences: Optional[list] = None,
- k: Optional[int] = None,
- p: Optional[int] = None,
- frequency_penalty: Optional[int] = None,
- presence_penalty: Optional[int] = None,
- return_likelihoods: Optional[str] = None,
- logit_bias: Optional[dict] = None,
- ) -> None:
- locals_ = locals().copy()
- for key, value in locals_.items():
- if key != "self" and value is not None:
- setattr(self.__class__, key, value)
-
- @classmethod
- def get_config(cls):
- return super().get_config()
-
- def validate_environment(
- self,
- headers: dict,
- model: str,
- messages: List[AllMessageValues],
- optional_params: dict,
- litellm_params: dict,
- api_key: Optional[str] = None,
- api_base: Optional[str] = None,
- ) -> dict:
- return cohere_validate_environment(
- headers=headers,
- model=model,
- messages=messages,
- optional_params=optional_params,
- api_key=api_key,
- )
-
- def get_error_class(
- self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
- ) -> BaseLLMException:
- return CohereError(status_code=status_code, message=error_message)
-
- def get_supported_openai_params(self, model: str) -> List:
- return [
- "stream",
- "temperature",
- "max_tokens",
- "logit_bias",
- "top_p",
- "frequency_penalty",
- "presence_penalty",
- "stop",
- "n",
- "extra_headers",
- ]
-
- def map_openai_params(
- self,
- non_default_params: dict,
- optional_params: dict,
- model: str,
- drop_params: bool,
- ) -> dict:
- for param, value in non_default_params.items():
- if param == "stream":
- optional_params["stream"] = value
- elif param == "temperature":
- optional_params["temperature"] = value
- elif param == "max_tokens":
- optional_params["max_tokens"] = value
- elif param == "n":
- optional_params["num_generations"] = value
- elif param == "logit_bias":
- optional_params["logit_bias"] = value
- elif param == "top_p":
- optional_params["p"] = value
- elif param == "frequency_penalty":
- optional_params["frequency_penalty"] = value
- elif param == "presence_penalty":
- optional_params["presence_penalty"] = value
- elif param == "stop":
- optional_params["stop_sequences"] = value
- return optional_params
-
- def transform_request(
- self,
- model: str,
- messages: List[AllMessageValues],
- optional_params: dict,
- litellm_params: dict,
- headers: dict,
- ) -> dict:
- prompt = " ".join(
- convert_content_list_to_str(message=message) for message in messages
- )
-
- ## Load Config
- config = litellm.CohereConfig.get_config()
- for k, v in config.items():
- if (
- k not in optional_params
- ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
- optional_params[k] = v
-
- ## Handle Tool Calling
- if "tools" in optional_params:
- _is_function_call = True
- tool_calling_system_prompt = self._construct_cohere_tool_for_completion_api(
- tools=optional_params["tools"]
- )
- optional_params["tools"] = tool_calling_system_prompt
-
- data = {
- "model": model,
- "prompt": prompt,
- **optional_params,
- }
-
- return data
-
- def transform_response(
- self,
- model: str,
- raw_response: httpx.Response,
- model_response: ModelResponse,
- logging_obj: LiteLLMLoggingObj,
- request_data: dict,
- messages: List[AllMessageValues],
- optional_params: dict,
- litellm_params: dict,
- encoding: Any,
- api_key: Optional[str] = None,
- json_mode: Optional[bool] = None,
- ) -> ModelResponse:
- prompt = " ".join(
- convert_content_list_to_str(message=message) for message in messages
- )
- completion_response = raw_response.json()
- choices_list = []
- for idx, item in enumerate(completion_response["generations"]):
- if len(item["text"]) > 0:
- message_obj = Message(content=item["text"])
- else:
- message_obj = Message(content=None)
- choice_obj = Choices(
- finish_reason=item["finish_reason"],
- index=idx + 1,
- message=message_obj,
- )
- choices_list.append(choice_obj)
- model_response.choices = choices_list # type: ignore
-
- ## CALCULATING USAGE
- prompt_tokens = len(encoding.encode(prompt))
- completion_tokens = len(
- encoding.encode(model_response["choices"][0]["message"].get("content", ""))
- )
-
- model_response.created = int(time.time())
- model_response.model = model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- )
- setattr(model_response, "usage", usage)
- return model_response
-
- def _construct_cohere_tool_for_completion_api(
- self,
- tools: Optional[List] = None,
- ) -> dict:
- if tools is None:
- tools = []
- return {"tools": tools}
-
- def get_model_response_iterator(
- self,
- streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
- sync_stream: bool,
- json_mode: Optional[bool] = False,
- ):
- return CohereModelResponseIterator(
- streaming_response=streaming_response,
- sync_stream=sync_stream,
- json_mode=json_mode,
- )
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 8b925a375a..dc64fea1a3 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -2520,6 +2520,95 @@ class BaseLLMHTTPHandler:
litellm_params=litellm_params_with_request,
)
+ def retrieve_batch(
+ self,
+ batch_id: str,
+ litellm_params: dict,
+ provider_config: "BaseBatchesConfig",
+ headers: dict,
+ api_base: Optional[str],
+ api_key: Optional[str],
+ logging_obj: "LiteLLMLoggingObj",
+ _is_async: bool = False,
+ client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ model: Optional[str] = None,
+ ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]:
+ """
+ Retrieve a batch using provider-specific configuration.
+ """
+ # Transform the request using provider config
+ transformed_request = provider_config.transform_retrieve_batch_request(
+ batch_id=batch_id,
+ optional_params=litellm_params,
+ litellm_params=litellm_params,
+ )
+
+ if _is_async:
+ return self.async_retrieve_batch(
+ transformed_request=transformed_request,
+ litellm_params=litellm_params,
+ provider_config=provider_config,
+ headers=headers,
+ api_base=api_base,
+ logging_obj=logging_obj,
+ client=client,
+ timeout=timeout,
+ batch_id=batch_id,
+ model=model,
+ )
+
+ if client is None or not isinstance(client, HTTPHandler):
+ sync_httpx_client = _get_httpx_client()
+ else:
+ sync_httpx_client = client
+
+ try:
+ if (
+ isinstance(transformed_request, dict)
+ and "method" in transformed_request
+ ):
+ # Handle pre-signed requests (e.g., from Bedrock with AWS auth)
+ method = transformed_request["method"].lower()
+ request_kwargs = {
+ "url": transformed_request["url"],
+ "headers": transformed_request["headers"],
+ }
+
+ # Only add data for non-GET requests
+ if method != "get" and transformed_request.get("data") is not None:
+ request_kwargs["data"] = transformed_request["data"]
+
+ batch_response = getattr(sync_httpx_client, method)(**request_kwargs)
+ elif isinstance(transformed_request, dict) and api_base:
+ # For other providers that use JSON requests
+ batch_response = sync_httpx_client.get(
+ url=api_base,
+ headers={**headers, "Content-Type": "application/json"},
+ params=transformed_request,
+ )
+ else:
+ # Handle other request types if needed
+ if not api_base:
+ raise ValueError("api_base is required for non-pre-signed requests")
+ batch_response = sync_httpx_client.get(
+ url=api_base,
+ headers=headers,
+ )
+ except Exception as e:
+ verbose_logger.exception(f"Error retrieving batch: {e}")
+ raise self._handle_error(
+ e=e,
+ provider_config=provider_config,
+ )
+
+ return provider_config.transform_retrieve_batch_response(
+ model=model,
+ raw_response=batch_response,
+ logging_obj=logging_obj,
+ litellm_params=litellm_params,
+ )
+
async def async_create_batch(
self,
transformed_request: Union[bytes, str, dict],
@@ -2606,6 +2695,89 @@ class BaseLLMHTTPHandler:
litellm_params=litellm_params_with_request,
)
+ async def async_retrieve_batch(
+ self,
+ transformed_request: Union[bytes, str, dict],
+ litellm_params: dict,
+ provider_config: "BaseBatchesConfig",
+ headers: dict,
+ api_base: Optional[str],
+ logging_obj: "LiteLLMLoggingObj",
+ client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ batch_id: Optional[str] = None,
+ model: Optional[str] = None,
+ ):
+ """
+ Async version of retrieve_batch
+ """
+ if client is None or not isinstance(client, AsyncHTTPHandler):
+ async_httpx_client = get_async_httpx_client(
+ llm_provider=provider_config.custom_llm_provider
+ )
+ else:
+ async_httpx_client = client
+
+ #########################################################
+ # Debug Logging
+ #########################################################
+ logging_obj.pre_call(
+ input="",
+ api_key="",
+ additional_args={
+ "complete_input_dict": transformed_request,
+ "api_base": api_base,
+ "headers": headers,
+ "batch_id": batch_id,
+ },
+ )
+
+ try:
+ if (
+ isinstance(transformed_request, dict)
+ and "method" in transformed_request
+ ):
+ # Handle pre-signed requests (e.g., from Bedrock with AWS auth)
+ method = transformed_request["method"].lower()
+ request_kwargs = {
+ "url": transformed_request["url"],
+ "headers": transformed_request["headers"],
+ }
+
+ # Only add data for non-GET requests
+ if method != "get" and transformed_request.get("data") is not None:
+ request_kwargs["data"] = transformed_request["data"]
+
+ batch_response = await getattr(async_httpx_client, method)(**request_kwargs)
+ elif isinstance(transformed_request, dict) and api_base:
+ # For other providers that use JSON requests
+ batch_response = await async_httpx_client.get(
+ url=api_base,
+ headers={**headers, "Content-Type": "application/json"},
+ params=transformed_request,
+ )
+ else:
+ # Handle other request types if needed
+ if not api_base:
+ raise ValueError("api_base is required for non-pre-signed requests")
+ batch_response = await async_httpx_client.get(
+ url=api_base,
+ headers=headers,
+ )
+ except Exception as e:
+ verbose_logger.exception(f"Error retrieving batch: {e}")
+ raise self._handle_error(
+ e=e,
+ provider_config=provider_config,
+ )
+
+ return provider_config.transform_retrieve_batch_response(
+ model=model,
+ raw_response=batch_response,
+ logging_obj=logging_obj,
+ litellm_params=litellm_params,
+ )
+
def cancel_response_api_handler(
self,
response_id: str,
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index 28142f7273..f38c772e35 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -121,7 +121,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
default_headers = {
"Content-Type": "application/json",
}
- gemini_api_key = self._get_google_ai_studio_api_key(dict(litellm_params or {}))
+ # Use the passed api_key first, then fall back to litellm_params and environment
+ gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {}))
if gemini_api_key is not None:
default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
if headers is not None:
diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py
index e57364fd28..f136bd0a40 100644
--- a/litellm/llms/gemini/image_generation/transformation.py
+++ b/litellm/llms/gemini/image_generation/transformation.py
@@ -85,17 +85,25 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
) -> str:
"""
Get the complete url for the request
-
- Google AI API format: https://generativelanguage.googleapis.com/v1beta/models/{model}:predict
+
+ Gemini 2.5 Flash Image Preview: :generateContent
+ Other Imagen models: :predict
"""
complete_url: str = (
- api_base
- or get_secret_str("GEMINI_API_BASE")
+ api_base
+ or get_secret_str("GEMINI_API_BASE")
or self.DEFAULT_BASE_URL
)
complete_url = complete_url.rstrip("/")
- complete_url = f"{complete_url}/models/{model}:predict"
+
+ # Gemini 2.5 Flash Image Preview uses generateContent endpoint
+ if "2.5-flash-image-preview" in model:
+ complete_url = f"{complete_url}/models/{model}:generateContent"
+ else:
+ # All other Imagen models use predict endpoint
+ complete_url = f"{complete_url}/models/{model}:predict"
+
return complete_url
def validate_environment(
@@ -128,35 +136,52 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
headers: dict,
) -> dict:
"""
- Transform the image generation request to Google AI Imagen format
-
- Google AI API format:
+ Transform the image generation request to Gemini format
+
+ For Gemini 2.5 Flash Image Preview, use the standard Gemini format with response_modalities:
{
- "instances": [
+ "contents": [
{
- "prompt": "Robot holding a red skateboard"
+ "parts": [
+ {"text": "Generate an image of..."}
+ ]
}
],
- "parameters": {
- "sampleCount": 4,
- "aspectRatio": "1:1",
- "personGeneration": "allow_adult"
+ "generationConfig": {
+ "response_modalities": ["IMAGE", "TEXT"]
}
}
"""
- from litellm.types.llms.gemini import (
- GeminiImageGenerationInstance,
- GeminiImageGenerationParameters,
- )
- request_body: GeminiImageGenerationRequest = GeminiImageGenerationRequest(
- instances=[
- GeminiImageGenerationInstance(
- prompt=prompt
- )
- ],
- parameters=GeminiImageGenerationParameters(**optional_params)
- )
- return request_body.model_dump(exclude_none=True)
+ # For Gemini 2.5 Flash Image Preview, use standard Gemini format
+ if "2.5-flash-image-preview" in model:
+ request_body: dict = {
+ "contents": [
+ {
+ "parts": [
+ {"text": prompt}
+ ]
+ }
+ ],
+ "generationConfig": {
+ "response_modalities": ["IMAGE", "TEXT"]
+ }
+ }
+ return request_body
+ else:
+ # For other Imagen models, use the original Imagen format
+ from litellm.types.llms.gemini import (
+ GeminiImageGenerationInstance,
+ GeminiImageGenerationParameters,
+ )
+ request_body_obj: GeminiImageGenerationRequest = GeminiImageGenerationRequest(
+ instances=[
+ GeminiImageGenerationInstance(
+ prompt=prompt
+ )
+ ],
+ parameters=GeminiImageGenerationParameters(**optional_params)
+ )
+ return request_body_obj.model_dump(exclude_none=True)
def transform_image_generation_response(
self,
@@ -185,14 +210,30 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
if not model_response.data:
model_response.data = []
-
- # Google AI returns predictions with generated images
- predictions = response_data.get("predictions", [])
- for prediction in predictions:
- # Google AI returns base64 encoded images in the prediction
- model_response.data.append(ImageObject(
- b64_json=prediction.get("bytesBase64Encoded", None),
- url=None, # Google AI returns base64, not URLs
- ))
-
+
+ # Handle different response formats based on model
+ if "2.5-flash-image-preview" in model:
+ # Gemini 2.5 Flash Image Preview returns in candidates format
+ candidates = response_data.get("candidates", [])
+ for candidate in candidates:
+ content = candidate.get("content", {})
+ parts = content.get("parts", [])
+ for part in parts:
+ # Look for inlineData with image
+ if "inlineData" in part:
+ inline_data = part["inlineData"]
+ if "data" in inline_data:
+ model_response.data.append(ImageObject(
+ b64_json=inline_data["data"],
+ url=None,
+ ))
+ else:
+ # Original Imagen format - predictions with generated images
+ predictions = response_data.get("predictions", [])
+ for prediction in predictions:
+ # Google AI returns base64 encoded images in the prediction
+ model_response.data.append(ImageObject(
+ b64_json=prediction.get("bytesBase64Encoded", None),
+ url=None, # Google AI returns base64, not URLs
+ ))
return model_response
\ No newline at end of file
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index 3be373ca5e..6755cab22e 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -378,7 +378,7 @@ class OCIChatConfig(BaseConfig):
or not oci_compartment_id
):
raise Exception(
- "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
+ "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id "
"and at least one of oci_key or oci_key_file."
)
diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py
index a97f312d48..5b6d21b594 100644
--- a/litellm/llms/vertex_ai/batches/transformation.py
+++ b/litellm/llms/vertex_ai/batches/transformation.py
@@ -114,7 +114,14 @@ class VertexAIBatchTransformation:
"""
Gets the output file id from the Vertex AI Batch response
"""
- output_file_id: str = ""
+
+ output_file_id: str = (
+ response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "")
+ + "/predictions.jsonl"
+ )
+ if output_file_id != "/predictions.jsonl":
+ return output_file_id
+
output_config = response.get("outputConfig")
if output_config is None:
return output_file_id
diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py
index a666a2c37f..6636bccd6a 100644
--- a/litellm/llms/vertex_ai/files/handler.py
+++ b/litellm/llms/vertex_ai/files/handler.py
@@ -1,5 +1,6 @@
import asyncio
-from typing import Any, Coroutine, Optional, Union
+import urllib.parse
+from typing import Any, Coroutine, Optional, Tuple, Union
import httpx
@@ -9,7 +10,12 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import (
GCSLoggingConfig,
)
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
-from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject
+from litellm.types.llms.openai import (
+ CreateFileRequest,
+ FileContentRequest,
+ HttpxBinaryResponseContent,
+ OpenAIFileObject,
+)
from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
from .transformation import VertexAIJsonlFilesTransformation
@@ -105,3 +111,136 @@ class VertexAIFilesHandler(GCSBucketBase):
max_retries=max_retries,
)
)
+
+ def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]:
+ """
+ Extract bucket name and object path from URL-encoded file_id.
+
+ Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile
+ Which decodes to: gs://bucket-name/path/to/file
+
+ Returns:
+ tuple: (bucket_name, url_encoded_object_path)
+ - bucket_name: "bucket-name"
+ - url_encoded_object_path: "path%2Fto%2Ffile"
+ """
+ decoded_path = urllib.parse.unquote(file_id)
+
+ if decoded_path.startswith("gs://"):
+ full_path = decoded_path[5:] # Remove 'gs://' prefix
+ else:
+ full_path = decoded_path
+
+ if "/" in full_path:
+ bucket_name, object_path = full_path.split("/", 1)
+ else:
+ bucket_name = full_path
+ object_path = ""
+
+ encoded_object_path = urllib.parse.quote(object_path, safe="")
+
+ return bucket_name, encoded_object_path
+
+ async def afile_content(
+ self,
+ file_content_request: FileContentRequest,
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ ) -> HttpxBinaryResponseContent:
+ """
+ Download file content from GCS bucket for VertexAI files.
+
+ Args:
+ file_content_request: Contains file_id (URL-encoded GCS path)
+ vertex_credentials: VertexAI credentials
+ vertex_project: VertexAI project ID
+ vertex_location: VertexAI location
+ timeout: Request timeout
+ max_retries: Max retry attempts
+
+ Returns:
+ HttpxBinaryResponseContent: Binary content wrapped in compatible response format
+ """
+ file_id = file_content_request.get("file_id")
+ if not file_id:
+ raise ValueError("file_id is required in file_content_request")
+
+ bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id(
+ file_id
+ )
+
+ download_kwargs = {
+ "standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name}
+ }
+
+ file_content = await self.download_gcs_object(
+ object_name=encoded_object_path, **download_kwargs
+ )
+
+ if file_content is None:
+ decoded_path = urllib.parse.unquote(file_id)
+ raise ValueError(f"Failed to download file from GCS: {decoded_path}")
+
+ decoded_path = urllib.parse.unquote(file_id)
+ mock_response = httpx.Response(
+ status_code=200,
+ content=file_content,
+ headers={"content-type": "application/octet-stream"},
+ request=httpx.Request(method="GET", url=decoded_path),
+ )
+
+ return HttpxBinaryResponseContent(response=mock_response)
+
+ def file_content(
+ self,
+ _is_async: bool,
+ file_content_request: FileContentRequest,
+ api_base: Optional[str],
+ vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ ) -> Union[
+ HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+ ]:
+ """
+ Download file content from GCS bucket for VertexAI files.
+ Supports both sync and async operations.
+
+ Args:
+ _is_async: Whether to run asynchronously
+ file_content_request: Contains file_id (URL-encoded GCS path)
+ api_base: API base (unused for GCS operations)
+ vertex_credentials: VertexAI credentials
+ vertex_project: VertexAI project ID
+ vertex_location: VertexAI location
+ timeout: Request timeout
+ max_retries: Max retry attempts
+
+ Returns:
+ HttpxBinaryResponseContent or Coroutine: Binary content wrapped in compatible response format
+ """
+ if _is_async:
+ return self.afile_content(
+ file_content_request=file_content_request,
+ vertex_credentials=vertex_credentials,
+ vertex_project=vertex_project,
+ vertex_location=vertex_location,
+ timeout=timeout,
+ max_retries=max_retries,
+ )
+ else:
+ return asyncio.run(
+ self.afile_content(
+ file_content_request=file_content_request,
+ vertex_credentials=vertex_credentials,
+ vertex_project=vertex_project,
+ vertex_location=vertex_location,
+ timeout=timeout,
+ max_retries=max_retries,
+ )
+ )
diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py
index 1066a8f036..f2e5a5b5d2 100644
--- a/litellm/llms/vertex_ai/files/transformation.py
+++ b/litellm/llms/vertex_ai/files/transformation.py
@@ -261,10 +261,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
raise ValueError("file is required")
extracted_file_data = extract_file_data(file_data)
extracted_file_data_content = extracted_file_data.get("content")
-
+
if extracted_file_data_content is None:
raise ValueError("file content is required")
-
+
if FilesAPIUtils.is_batch_jsonl_file(
create_file_data=create_file_data,
extracted_file_data=extracted_file_data,
@@ -283,7 +283,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
openai_jsonl_content
)
)
- return json.dumps(vertex_jsonl_content)
+ return "\n".join(json.dumps(item) for item in vertex_jsonl_content)
elif isinstance(extracted_file_data_content, bytes):
return extracted_file_data_content
else:
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index 76998e7669..0f0bc776cc 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -239,6 +239,7 @@ class VertexBase:
stream=stream,
auth_header=None,
url=default_api_base,
+ model=model,
)
return api_base
@@ -292,6 +293,7 @@ class VertexBase:
stream: Optional[bool],
auth_header: Optional[str],
url: str,
+ model: Optional[str] = None,
) -> Tuple[Optional[str], str]:
"""
for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317
@@ -301,7 +303,12 @@ class VertexBase:
"""
if api_base:
if custom_llm_provider == "gemini":
- url = "{}:{}".format(api_base, endpoint)
+ # For Gemini (Google AI Studio), construct the full path like other providers
+ if model is None:
+ raise ValueError(
+ "Model parameter is required for Gemini custom API base URLs"
+ )
+ url = "{}/models/{}:{}".format(api_base, model, endpoint)
if gemini_api_key is None:
raise ValueError(
"Missing gemini_api_key, please set `GEMINI_API_KEY`"
@@ -373,6 +380,7 @@ class VertexBase:
endpoint=endpoint,
stream=stream,
url=url,
+ model=model,
)
def _handle_reauthentication(
@@ -384,19 +392,19 @@ class VertexBase:
) -> Tuple[str, str]:
"""
Handle reauthentication when credentials refresh fails.
-
+
This method clears the cached credentials and attempts to reload them once.
It should only be called when "Reauthentication is needed" error occurs.
-
+
Args:
credentials: The original credentials
project_id: The project ID
credential_cache_key: The cache key to clear
error: The original error that triggered reauthentication
-
+
Returns:
Tuple of (access_token, project_id)
-
+
Raises:
The original error if reauthentication fails
"""
@@ -404,11 +412,11 @@ class VertexBase:
f"Handling reauthentication for project_id: {project_id}. "
f"Clearing cache and retrying once."
)
-
+
# Clear the cached credentials
if credential_cache_key in self._credentials_project_mapping:
del self._credentials_project_mapping[credential_cache_key]
-
+
# Retry once with _retry_reauth=True to prevent infinite recursion
try:
return self.get_access_token(
@@ -438,12 +446,12 @@ class VertexBase:
3. Check if loaded credentials have expired
4. If expired, refresh credentials
5. Return access token and project id
-
+
Args:
credentials: The credentials to use for authentication
project_id: The Google Cloud project ID
_retry_reauth: Internal flag to prevent infinite recursion during reauthentication
-
+
Returns:
Tuple of (access_token, project_id)
"""
diff --git a/litellm/main.py b/litellm/main.py
index 70dbdde887..5493c7e34e 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -116,6 +116,7 @@ from litellm.utils import (
from ._logging import verbose_logger
from .caching.caching import disable_cache, enable_cache, update_cache
+from .litellm_core_utils.core_helpers import safe_deep_copy
from .litellm_core_utils.fallback_utils import (
async_completion_with_fallbacks,
completion_with_fallbacks,
@@ -1003,7 +1004,15 @@ def completion( # type: ignore # noqa: PLR0915
provider_specific_header = cast(
Optional[ProviderSpecificHeader], kwargs.get("provider_specific_header", None)
)
- headers = kwargs.get("headers", None) or extra_headers
+ # Properly merge headers with priority: request headers > extra_headers > global litellm.headers
+ headers = {}
+ if litellm.headers is not None and isinstance(litellm.headers, dict):
+ headers.update(litellm.headers)
+ if extra_headers is not None and isinstance(extra_headers, dict):
+ headers.update(extra_headers)
+ request_headers = kwargs.get("headers", None)
+ if request_headers is not None and isinstance(request_headers, dict):
+ headers.update(request_headers)
ensure_alternating_roles: Optional[bool] = kwargs.get(
"ensure_alternating_roles", None
@@ -1014,10 +1023,6 @@ def completion( # type: ignore # noqa: PLR0915
assistant_continue_message: Optional[ChatCompletionAssistantMessage] = kwargs.get(
"assistant_continue_message", None
)
- if headers is None:
- headers = {}
- if extra_headers is not None:
- headers.update(extra_headers)
num_retries = kwargs.get(
"num_retries", None
) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor.
@@ -1074,7 +1079,6 @@ def completion( # type: ignore # noqa: PLR0915
prompt_id=prompt_id, non_default_params=non_default_params
)
):
-
(
model,
messages,
@@ -1427,8 +1431,7 @@ def completion( # type: ignore # noqa: PLR0915
"azure_ad_token_provider", None
)
- headers = headers or litellm.headers
-
+ # Use the consolidated headers that were already merged at the top of the function
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
if max_retries is not None:
@@ -1693,8 +1696,7 @@ def completion( # type: ignore # noqa: PLR0915
or get_secret("OPENAI_API_KEY")
)
- headers = headers or litellm.headers
-
+ # Use the consolidated headers that were already merged at the top of the function
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
@@ -2032,7 +2034,6 @@ def completion( # type: ignore # noqa: PLR0915
try:
if use_base_llm_http_handler:
-
response = base_llm_http_handler.completion(
model=model,
messages=messages,
@@ -2395,47 +2396,7 @@ def completion( # type: ignore # noqa: PLR0915
)
return response
response = model_response
- elif custom_llm_provider == "cohere":
- cohere_key = (
- api_key
- or litellm.cohere_key
- or get_secret("COHERE_API_KEY")
- or get_secret("CO_API_KEY")
- or litellm.api_key
- )
-
- api_base = (
- api_base
- or litellm.api_base
- or get_secret("COHERE_API_BASE")
- or "https://api.cohere.ai/v1/generate"
- )
-
- headers = headers or litellm.headers or {}
- if headers is None:
- headers = {}
-
- if extra_headers is not None:
- headers.update(extra_headers)
-
- response = base_llm_http_handler.completion(
- model=model,
- stream=stream,
- messages=messages,
- acompletion=acompletion,
- api_base=api_base,
- model_response=model_response,
- optional_params=optional_params,
- litellm_params=litellm_params,
- custom_llm_provider="cohere",
- timeout=timeout,
- headers=headers,
- encoding=encoding,
- api_key=cohere_key,
- logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
- client=client,
- )
- elif custom_llm_provider == "cohere_chat":
+ elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere":
cohere_key = (
api_key
or litellm.cohere_key
@@ -2451,12 +2412,8 @@ def completion( # type: ignore # noqa: PLR0915
or "https://api.cohere.ai/v1/chat"
)
- headers = headers or litellm.headers or {}
- if headers is None:
- headers = {}
-
- if extra_headers is not None:
- headers.update(extra_headers)
+ # Use the consolidated headers that were already merged at the top of the function
+ # No need for additional merging here as it's already done
response = base_llm_http_handler.completion(
model=model,
@@ -2552,15 +2509,10 @@ def completion( # type: ignore # noqa: PLR0915
)
elif custom_llm_provider == "compactifai":
api_key = (
- api_key
- or get_secret_str("COMPACTIFAI_API_KEY")
- or litellm.api_key
+ api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key
)
- api_base = (
- api_base
- or "https://api.compactif.ai/v1"
- )
+ api_base = api_base or "https://api.compactif.ai/v1"
## COMPLETION CALL
response = base_llm_http_handler.completion(
@@ -2848,8 +2800,7 @@ def completion( # type: ignore # noqa: PLR0915
)
api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE")
-
- new_params = deepcopy(optional_params)
+ new_params = safe_deep_copy(optional_params or {})
response = vertex_chat_completion.completion( # type: ignore
model=model,
messages=messages,
@@ -2893,7 +2844,7 @@ def completion( # type: ignore # noqa: PLR0915
api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE")
- new_params = deepcopy(optional_params)
+ new_params = safe_deep_copy(optional_params or {})
if vertex_partner_models_chat_completion.is_vertex_partner_model(model):
model_response = vertex_partner_models_chat_completion.completion(
model=model,
@@ -3147,9 +3098,9 @@ def completion( # type: ignore # noqa: PLR0915
"aws_region_name" not in optional_params
or optional_params["aws_region_name"] is None
):
- optional_params["aws_region_name"] = (
- aws_bedrock_client.meta.region_name
- )
+ optional_params[
+ "aws_region_name"
+ ] = aws_bedrock_client.meta.region_name
bedrock_route = BedrockModelInfo.get_bedrock_route(model)
if bedrock_route == "converse":
@@ -3491,7 +3442,6 @@ def completion( # type: ignore # noqa: PLR0915
)
raise e
elif custom_llm_provider == "gradient_ai":
-
api_base = litellm.api_base or api_base
response = base_llm_http_handler.completion(
model=model,
@@ -3851,7 +3801,7 @@ def embedding(
*,
aembedding: Literal[True],
**kwargs,
-) -> Coroutine[Any, Any, EmbeddingResponse]:
+) -> Coroutine[Any, Any, EmbeddingResponse]:
...
@@ -3877,7 +3827,7 @@ def embedding(
*,
aembedding: Literal[False] = False,
**kwargs,
-) -> EmbeddingResponse:
+) -> EmbeddingResponse:
...
# fmt: on
@@ -4188,10 +4138,8 @@ def embedding( # noqa: PLR0915
or litellm.api_key
)
- if extra_headers is not None and isinstance(extra_headers, dict):
- headers = extra_headers
- else:
- headers = {}
+ # Use the consolidated headers that were already merged at the top of the function
+ # No need for additional merging here as it's already done
response = base_llm_http_handler.embedding(
model=model,
@@ -5151,9 +5099,9 @@ def adapter_completion(
new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore
- translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = (
- None
- )
+ translated_response: Optional[
+ Union[BaseModel, AdapterCompletionStreamWrapper]
+ ] = None
if isinstance(response, ModelResponse):
translated_response = translation_obj.translate_completion_output_params(
response=response
@@ -6141,9 +6089,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(content_chunks) > 0:
- response["choices"][0]["message"]["content"] = (
- processor.get_combined_content(content_chunks)
- )
+ response["choices"][0]["message"][
+ "content"
+ ] = processor.get_combined_content(content_chunks)
thinking_blocks = [
chunk
@@ -6154,9 +6102,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(thinking_blocks) > 0:
- response["choices"][0]["message"]["thinking_blocks"] = (
- processor.get_combined_thinking_content(thinking_blocks)
- )
+ response["choices"][0]["message"][
+ "thinking_blocks"
+ ] = processor.get_combined_thinking_content(thinking_blocks)
reasoning_chunks = [
chunk
@@ -6167,9 +6115,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(reasoning_chunks) > 0:
- response["choices"][0]["message"]["reasoning_content"] = (
- processor.get_combined_reasoning_content(reasoning_chunks)
- )
+ response["choices"][0]["message"][
+ "reasoning_content"
+ ] = processor.get_combined_reasoning_content(reasoning_chunks)
audio_chunks = [
chunk
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 96e19d022c..43ea3af320 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -1,2570 +1,881 @@
{
- "sample_spec": {
- "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.",
- "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
- "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
- "input_cost_per_token": 0.0,
- "output_cost_per_token": 0.0,
- "output_cost_per_reasoning_token": 0.0,
- "input_cost_per_audio_token": 0.0,
- "litellm_provider": "one of https://docs.litellm.ai/docs/providers",
- "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_vision": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_prompt_caching": true,
- "supports_response_schema": true,
- "supports_system_messages": true,
- "supports_reasoning": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.0,
- "search_context_size_medium": 0.0,
- "search_context_size_high": 0.0
- },
- "file_search_cost_per_1k_calls": 0.0,
- "file_search_cost_per_gb_per_day": 0.0,
- "vector_store_cost_per_gb_per_day": 0.0,
- "computer_use_input_cost_per_1k_tokens": 0.0,
- "computer_use_output_cost_per_1k_tokens": 0.0,
- "code_interpreter_cost_per_session": 0.0,
- "supported_regions": [
- "global",
- "us-west-2",
- "eu-west-1",
- "ap-southeast-1",
- "ap-northeast-1"
- ],
- "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD"
- },
- "omni-moderation-latest": {
- "max_tokens": 32768,
- "max_input_tokens": 32768,
- "max_output_tokens": 0,
- "input_cost_per_token": 0.0,
- "output_cost_per_token": 0.0,
- "litellm_provider": "openai",
- "mode": "moderation"
- },
- "omni-moderation-latest-intents": {
- "max_tokens": 32768,
- "max_input_tokens": 32768,
- "max_output_tokens": 0,
- "input_cost_per_token": 0.0,
- "output_cost_per_token": 0.0,
- "litellm_provider": "openai",
- "mode": "moderation"
- },
- "omni-moderation-2024-09-26": {
- "max_tokens": 32768,
- "max_input_tokens": 32768,
- "max_output_tokens": 0,
- "input_cost_per_token": 0.0,
- "output_cost_per_token": 0.0,
- "litellm_provider": "openai",
- "mode": "moderation"
- },
- "gpt-4": {
- "max_tokens": 4096,
- "max_input_tokens": 8192,
- "max_output_tokens": 4096,
- "input_cost_per_token": 3e-05,
- "output_cost_per_token": 6e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4.1": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 2e-06,
- "output_cost_per_token": 8e-06,
- "input_cost_per_token_batches": 1e-06,
- "output_cost_per_token_batches": 4e-06,
- "cache_read_input_token_cost": 5e-07,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4.1-2025-04-14": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 2e-06,
- "output_cost_per_token": 8e-06,
- "input_cost_per_token_batches": 1e-06,
- "output_cost_per_token_batches": 4e-06,
- "cache_read_input_token_cost": 5e-07,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4.1-mini": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 4e-07,
- "output_cost_per_token": 1.6e-06,
- "input_cost_per_token_batches": 2e-07,
- "output_cost_per_token_batches": 8e-07,
- "cache_read_input_token_cost": 1e-07,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4.1-mini-2025-04-14": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 4e-07,
- "output_cost_per_token": 1.6e-06,
- "input_cost_per_token_batches": 2e-07,
- "output_cost_per_token_batches": 8e-07,
- "cache_read_input_token_cost": 1e-07,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4.1-nano": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 1e-07,
- "output_cost_per_token": 4e-07,
- "input_cost_per_token_batches": 5e-08,
- "output_cost_per_token_batches": 2e-07,
- "cache_read_input_token_cost": 2.5e-08,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4.1-nano-2025-04-14": {
- "max_tokens": 32768,
- "max_input_tokens": 1047576,
- "max_output_tokens": 32768,
- "input_cost_per_token": 1e-07,
- "output_cost_per_token": 4e-07,
- "input_cost_per_token_batches": 5e-08,
- "output_cost_per_token_batches": 2e-07,
- "cache_read_input_token_cost": 2.5e-08,
- "litellm_provider": "openai",
- "mode": "chat",
- "supported_endpoints": [
- "/v1/chat/completions",
- "/v1/batch",
- "/v1/responses"
- ],
- "supported_modalities": [
- "text",
- "image"
- ],
- "supported_output_modalities": [
- "text"
- ],
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_native_streaming": true
- },
- "gpt-4o": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "output_cost_per_token": 1e-05,
- "input_cost_per_token_batches": 1.25e-06,
- "output_cost_per_token_batches": 5e-06,
- "cache_read_input_token_cost": 1.25e-06,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "watsonx/ibm/granite-3-8b-instruct": {
- "max_tokens": 8192,
- "max_input_tokens": 8192,
- "max_output_tokens": 1024,
- "input_cost_per_token": 0.0002,
- "output_cost_per_token": 0.0002,
- "litellm_provider": "watsonx",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_parallel_function_calling": false,
- "supports_vision": false,
- "supports_audio_input": false,
- "supports_audio_output": false,
- "supports_prompt_caching": true,
- "supports_response_schema": true,
- "supports_system_messages": true
- },
- "watsonx/mistralai/mistral-large": {
- "max_tokens": 131072,
- "max_input_tokens": 131072,
- "max_output_tokens": 16384,
- "input_cost_per_token": 3e-06,
- "output_cost_per_token": 1e-05,
- "litellm_provider": "watsonx",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_parallel_function_calling": false,
- "supports_vision": false,
- "supports_audio_input": false,
- "supports_audio_output": false,
- "supports_prompt_caching": true,
- "supports_response_schema": true,
- "supports_system_messages": true
- },
- "gpt-4o-search-preview-2025-03-11": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "output_cost_per_token": 1e-05,
- "input_cost_per_token_batches": 1.25e-06,
- "output_cost_per_token_batches": 5e-06,
- "cache_read_input_token_cost": 1.25e-06,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-search-preview": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "output_cost_per_token": 1e-05,
- "input_cost_per_token_batches": 1.25e-06,
- "output_cost_per_token_batches": 5e-06,
- "cache_read_input_token_cost": 1.25e-06,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_web_search": true,
- "search_context_cost_per_query": {
- "search_context_size_low": 0.03,
- "search_context_size_medium": 0.035,
- "search_context_size_high": 0.05
- }
- },
- "gpt-4.5-preview": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 7.5e-05,
- "output_cost_per_token": 0.00015,
- "input_cost_per_token_batches": 3.75e-05,
- "output_cost_per_token_batches": 7.5e-05,
- "cache_read_input_token_cost": 3.75e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4.5-preview-2025-02-27": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 7.5e-05,
- "output_cost_per_token": 0.00015,
- "input_cost_per_token_batches": 3.75e-05,
- "output_cost_per_token_batches": 7.5e-05,
- "cache_read_input_token_cost": 3.75e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "deprecation_date": "2025-07-14"
- },
- "gpt-4o-audio-preview": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "input_cost_per_audio_token": 0.0001,
- "output_cost_per_token": 1e-05,
- "output_cost_per_audio_token": 0.0002,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-audio-preview-2024-12-17": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "input_cost_per_audio_token": 4e-05,
- "output_cost_per_token": 1e-05,
- "output_cost_per_audio_token": 8e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-audio-preview-2024-10-01": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "input_cost_per_audio_token": 0.0001,
- "output_cost_per_token": 1e-05,
- "output_cost_per_audio_token": 0.0002,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-audio-preview-2025-06-03": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 2.5e-06,
- "input_cost_per_audio_token": 4e-05,
- "output_cost_per_token": 1e-05,
- "output_cost_per_audio_token": 8e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-mini-audio-preview": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 1.5e-07,
- "input_cost_per_audio_token": 1e-05,
- "output_cost_per_token": 6e-07,
- "output_cost_per_audio_token": 2e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-mini-audio-preview-2024-12-17": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 1.5e-07,
- "input_cost_per_audio_token": 1e-05,
- "output_cost_per_token": 6e-07,
- "output_cost_per_audio_token": 2e-05,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_audio_input": true,
- "supports_audio_output": true,
- "supports_system_messages": true,
- "supports_tool_choice": true
- },
- "gpt-4o-mini": {
- "max_tokens": 16384,
- "max_input_tokens": 128000,
- "max_output_tokens": 16384,
- "input_cost_per_token": 1.5e-07,
- "output_cost_per_token": 6e-07,
- "input_cost_per_token_batches": 7.5e-08,
- "output_cost_per_token_batches": 3e-07,
- "cache_read_input_token_cost": 7.5e-08,
- "litellm_provider": "openai",
- "mode": "chat",
- "supports_pdf_input": true,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_response_schema": true,
- "supports_vision": true,
- "supports_prompt_caching": true,
- "supports_system_messages": true,
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@@ -2574,43 +885,34 @@
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@@ -2623,2209 +925,2284 @@
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+ ]
+ }
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+ "max_tokens": 1000000,
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+ }
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+ "max_tokens": 262144,
+ "mode": "chat",
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+ ]
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+ 252000.0
+ ]
+ }
+ ]
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+ "litellm_provider": "dashscope",
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+ "max_output_tokens": 8192,
+ "max_tokens": 131072,
+ "mode": "chat",
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+ "litellm_provider": "databricks",
+ "max_input_tokens": 512,
+ "max_tokens": 512,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "embedding",
+ "output_cost_per_token": 0.0,
+ "output_dbu_cost_per_token": 0.0,
+ "output_vector_size": 1024,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
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+ "input_dbu_cost_per_token": 3.571e-05,
+ "litellm_provider": "databricks",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "max_tokens": 200000,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Claude 3.7 conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 1.7857e-05,
+ "output_db_cost_per_token": 0.000214286,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_assistant_prefill": true,
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true
+ },
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+ "input_dbu_cost_per_token": 1.857e-06,
+ "litellm_provider": "databricks",
+ "max_input_tokens": 8192,
+ "max_tokens": 8192,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "embedding",
+ "output_cost_per_token": 0.0,
+ "output_dbu_cost_per_token": 0.0,
+ "output_vector_size": 1024,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
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+ "input_dbu_cost_per_token": 7.143e-06,
+ "litellm_provider": "databricks",
+ "max_input_tokens": 4096,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-06,
+ "output_dbu_cost_per_token": 2.1429e-05,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "databricks/databricks-llama-4-maverick": {
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+ "input_dbu_cost_per_token": 7.143e-05,
+ "litellm_provider": "databricks",
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "metadata": {
+ "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "output_dbu_cost_per_token": 0.00021429,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
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+ "input_dbu_cost_per_token": 7.1429e-05,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 1.500002e-05,
+ "output_db_cost_per_token": 0.000214286,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "databricks/databricks-meta-llama-3-3-70b-instruct": {
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+ "input_dbu_cost_per_token": 1.4286e-05,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 2.99999e-06,
+ "output_dbu_cost_per_token": 4.2857e-05,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
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+ "input_dbu_cost_per_token": 1.4286e-05,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 2.99999e-06,
+ "output_dbu_cost_per_token": 4.2857e-05,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "databricks/databricks-mixtral-8x7b-instruct": {
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+ "input_dbu_cost_per_token": 7.143e-06,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 9.9902e-07,
+ "output_dbu_cost_per_token": 1.4286e-05,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "databricks/databricks-mpt-30b-instruct": {
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+ "input_dbu_cost_per_token": 1.4286e-05,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 9.9902e-07,
+ "output_dbu_cost_per_token": 1.4286e-05,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "databricks/databricks-mpt-7b-instruct": {
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+ "input_dbu_cost_per_token": 7.143e-06,
+ "litellm_provider": "databricks",
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+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "metadata": {
+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+ },
+ "mode": "chat",
+ "output_cost_per_token": 0.0,
+ "output_dbu_cost_per_token": 0.0,
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+ "supports_tool_choice": true
+ },
+ "davinci-002": {
+ "input_cost_per_token": 2e-06,
+ "litellm_provider": "text-completion-openai",
+ "max_input_tokens": 16384,
+ "max_output_tokens": 4096,
+ "max_tokens": 16384,
+ "mode": "completion",
+ "output_cost_per_token": 2e-06
+ },
+ "deepgram/base": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-conversationalai": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-finance": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-general": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-meeting": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-phonecall": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-video": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/base-voicemail": {
+ "input_cost_per_second": 0.00020833,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+ "original_pricing_per_minute": 0.0125
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/enhanced": {
+ "input_cost_per_second": 0.00024167,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+ "original_pricing_per_minute": 0.0145
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/enhanced-finance": {
+ "input_cost_per_second": 0.00024167,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+ "original_pricing_per_minute": 0.0145
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/enhanced-general": {
+ "input_cost_per_second": 0.00024167,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+ "original_pricing_per_minute": 0.0145
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/enhanced-meeting": {
+ "input_cost_per_second": 0.00024167,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+ "original_pricing_per_minute": 0.0145
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/enhanced-phonecall": {
+ "input_cost_per_second": 0.00024167,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+ "original_pricing_per_minute": 0.0145
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2-atc": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2-automotive": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2-conversationalai": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2-drivethru": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
+ "deepgram/nova-2-finance": {
+ "input_cost_per_second": 7.167e-05,
+ "litellm_provider": "deepgram",
+ "metadata": {
+ "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+ "original_pricing_per_minute": 0.0043
+ },
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0,
+ "source": "https://deepgram.com/pricing",
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
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+ "max_input_tokens": 16384,
+ "max_output_tokens": 4096,
+ "max_tokens": 16384,
+ "mode": "completion",
+ "output_cost_per_token": 4e-07,
+ "output_cost_per_token_batches": 2e-07
+ },
+ "ft:davinci-002": {
+ "input_cost_per_token": 2e-06,
+ "input_cost_per_token_batches": 1e-06,
+ "litellm_provider": "text-completion-openai",
+ "max_input_tokens": 16384,
+ "max_output_tokens": 4096,
+ "max_tokens": 16384,
+ "mode": "completion",
+ "output_cost_per_token": 2e-06,
+ "output_cost_per_token_batches": 1e-06
+ },
+ "ft:gpt-3.5-turbo": {
+ "input_cost_per_token": 3e-06,
+ "input_cost_per_token_batches": 1.5e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 16385,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "mode": "chat",
+ "output_cost_per_token": 6e-06,
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+ "supports_tool_choice": true
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+ "litellm_provider": "openai",
+ "max_input_tokens": 16385,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "mode": "chat",
+ "output_cost_per_token": 6e-06,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
+ "ft:gpt-3.5-turbo-0613": {
+ "input_cost_per_token": 3e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 4096,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "mode": "chat",
+ "output_cost_per_token": 6e-06,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
+ "ft:gpt-3.5-turbo-1106": {
+ "input_cost_per_token": 3e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 16385,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "mode": "chat",
+ "output_cost_per_token": 6e-06,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
+ "ft:gpt-4-0613": {
+ "input_cost_per_token": 3e-05,
+ "litellm_provider": "openai",
+ "max_input_tokens": 8192,
+ "max_output_tokens": 4096,
+ "max_tokens": 4096,
+ "mode": "chat",
+ "output_cost_per_token": 6e-05,
+ "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing",
+ "supports_function_calling": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true
+ },
+ "ft:gpt-4o-2024-08-06": {
+ "input_cost_per_token": 3.75e-06,
+ "input_cost_per_token_batches": 1.875e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "max_tokens": 16384,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "output_cost_per_token_batches": 7.5e-06,
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
+ },
+ "ft:gpt-4o-2024-11-20": {
+ "cache_creation_input_token_cost": 1.875e-06,
+ "input_cost_per_token": 3.75e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "max_tokens": 16384,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
+ },
+ "ft:gpt-4o-mini-2024-07-18": {
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+ "input_cost_per_token": 3e-07,
+ "input_cost_per_token_batches": 1.5e-07,
+ "litellm_provider": "openai",
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "max_tokens": 16384,
+ "mode": "chat",
+ "output_cost_per_token": 1.2e-06,
+ "output_cost_per_token_batches": 6e-07,
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
+ },
+ "gemini-1.0-pro": {
+ "input_cost_per_character": 1.25e-07,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_token": 5e-07,
+ "input_cost_per_video_per_second": 0.002,
+ "litellm_provider": "vertex_ai-language-models",
+ "max_input_tokens": 32760,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_character": 3.75e-07,
+ "output_cost_per_token": 1.5e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
"supports_tool_choice": true
},
"gemini-1.0-pro-001": {
- "max_tokens": 8192,
+ "deprecation_date": "2025-04-09",
+ "input_cost_per_character": 1.25e-07,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_token": 5e-07,
+ "input_cost_per_video_per_second": 0.002,
+ "litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_image": 0.0025,
- "input_cost_per_video_per_second": 0.002,
- "input_cost_per_token": 5e-07,
- "input_cost_per_character": 1.25e-07,
- "output_cost_per_token": 1.5e-06,
- "output_cost_per_character": 3.75e-07,
- "litellm_provider": "vertex_ai-language-models",
+ "max_tokens": 8192,
"mode": "chat",
- "supports_function_calling": true,
+ "output_cost_per_character": 3.75e-07,
+ "output_cost_per_token": 1.5e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "deprecation_date": "2025-04-09",
- "supports_tool_choice": true,
- "supports_parallel_function_calling": true
- },
- "gemini-1.0-ultra": {
- "max_tokens": 8192,
- "max_input_tokens": 8192,
- "max_output_tokens": 2048,
- "input_cost_per_image": 0.0025,
- "input_cost_per_video_per_second": 0.002,
- "input_cost_per_token": 5e-07,
- "input_cost_per_character": 1.25e-07,
- "output_cost_per_token": 1.5e-06,
- "output_cost_per_character": 3.75e-07,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
"supports_function_calling": true,
- "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_tool_choice": true,
- "supports_parallel_function_calling": true
- },
- "gemini-1.0-ultra-001": {
- "max_tokens": 8192,
- "max_input_tokens": 8192,
- "max_output_tokens": 2048,
- "input_cost_per_image": 0.0025,
- "input_cost_per_video_per_second": 0.002,
- "input_cost_per_token": 5e-07,
- "input_cost_per_character": 1.25e-07,
- "output_cost_per_token": 1.5e-06,
- "output_cost_per_character": 3.75e-07,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_function_calling": true,
- "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_tool_choice": true,
- "supports_parallel_function_calling": true
+ "supports_parallel_function_calling": true,
+ "supports_tool_choice": true
},
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- "max_tokens": 8192,
+ "deprecation_date": "2025-04-09",
+ "input_cost_per_character": 1.25e-07,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_token": 5e-07,
+ "input_cost_per_video_per_second": 0.002,
+ "litellm_provider": "vertex_ai-language-models",
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_image": 0.0025,
- "input_cost_per_video_per_second": 0.002,
- "input_cost_per_token": 5e-07,
- "input_cost_per_character": 1.25e-07,
- "output_cost_per_token": 1.5e-06,
+ "max_tokens": 8192,
+ "mode": "chat",
"output_cost_per_character": 3.75e-07,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_function_calling": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "deprecation_date": "2025-04-09",
- "supports_tool_choice": true,
- "supports_parallel_function_calling": true
- },
- "gemini-1.5-pro": {
- "max_tokens": 8192,
- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_image": 0.00032875,
- "input_cost_per_audio_per_second": 3.125e-05,
- "input_cost_per_video_per_second": 0.00032875,
- "input_cost_per_token": 1.25e-06,
- "input_cost_per_character": 3.125e-07,
- "input_cost_per_image_above_128k_tokens": 0.0006575,
- "input_cost_per_video_per_second_above_128k_tokens": 0.0006575,
- "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05,
- "input_cost_per_token_above_128k_tokens": 2.5e-06,
- "input_cost_per_character_above_128k_tokens": 6.25e-07,
- "output_cost_per_token": 5e-06,
- "output_cost_per_character": 1.25e-06,
- "output_cost_per_token_above_128k_tokens": 1e-05,
- "output_cost_per_character_above_128k_tokens": 2.5e-06,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_vision": true,
- "supports_pdf_input": true,
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_parallel_function_calling": true
- },
- "gemini-1.5-pro-002": {
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- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_image": 0.00032875,
- "input_cost_per_audio_per_second": 3.125e-05,
- "input_cost_per_video_per_second": 0.00032875,
- "input_cost_per_token": 1.25e-06,
- "input_cost_per_character": 3.125e-07,
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- "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05,
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- "input_cost_per_character_above_128k_tokens": 6.25e-07,
- "output_cost_per_token": 5e-06,
- "output_cost_per_character": 1.25e-06,
- "output_cost_per_token_above_128k_tokens": 1e-05,
- "output_cost_per_character_above_128k_tokens": 2.5e-06,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_vision": true,
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-pro",
- "deprecation_date": "2025-09-24",
- "supports_parallel_function_calling": true
- },
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- "input_cost_per_image": 0.00032875,
- "input_cost_per_audio_per_second": 3.125e-05,
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- "input_cost_per_character": 3.125e-07,
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- "litellm_provider": "vertex_ai-language-models",
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- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
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- },
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- "output_cost_per_character_above_128k_tokens": 2.5e-06,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_parallel_function_calling": true
- },
- "gemini-1.5-pro-preview-0215": {
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- "input_cost_per_audio_per_second": 3.125e-05,
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- "input_cost_per_character": 3.125e-07,
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- "litellm_provider": "vertex_ai-language-models",
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- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
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- "supports_parallel_function_calling": true
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- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
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- "supports_parallel_function_calling": true
- },
- "gemini-1.5-flash": {
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- "max_input_tokens": 1000000,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
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- "input_cost_per_audio_per_second": 2e-06,
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- "input_cost_per_character": 1.875e-08,
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- "output_cost_per_token": 3e-07,
- "output_cost_per_character": 7.5e-08,
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- "output_cost_per_character_above_128k_tokens": 1.5e-07,
- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_tool_choice": true,
- "supports_parallel_function_calling": true
- },
- "gemini-1.5-flash-exp-0827": {
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- "max_input_tokens": 1000000,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
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- "max_pdf_size_mb": 30,
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- "input_cost_per_image_above_128k_tokens": 4e-05,
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- "litellm_provider": "vertex_ai-language-models",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
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- "supports_parallel_function_calling": true
- },
- "gemini-1.5-flash-002": {
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- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
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- "supports_parallel_function_calling": true
+ "supports_function_calling": true,
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},
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+ "supports_system_messages": true,
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+ "supports_system_messages": true,
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+ },
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@@ -8603,33 +8673,120 @@
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@@ -9615,11386 +10237,11104 @@
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+ "max_videos_per_prompt": 10,
+ "mode": "chat",
+ "output_cost_per_token": 1e-05,
+ "output_cost_per_token_above_200k_tokens": 1.5e-05,
+ "rpm": 10000,
+ "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-pro-preview",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "audio"
+ ],
+ "supports_audio_output": false,
+ "supports_function_calling": true,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "supports_web_search": true,
+ "tpm": 10000000
},
"gemini/gemini-exp-1114": {
- "max_tokens": 8192,
- "max_input_tokens": 1048576,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
- "max_video_length": 1,
- "max_audio_length_hours": 8.4,
- "max_audio_per_prompt": 1,
- "max_pdf_size_mb": 30,
"input_cost_per_token": 0,
"input_cost_per_token_above_128k_tokens": 0,
- "output_cost_per_token": 0,
- "output_cost_per_token_above_128k_tokens": 0,
"litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing",
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_images_per_prompt": 3000,
+ "max_input_tokens": 1048576,
+ "max_output_tokens": 8192,
+ "max_pdf_size_mb": 30,
+ "max_tokens": 8192,
+ "max_video_length": 1,
+ "max_videos_per_prompt": 10,
"metadata": {
"notes": "Rate limits not documented for gemini-exp-1114. Assuming same as gemini-1.5-pro.",
"supports_tool_choice": true
- }
- },
- "gemini/gemini-exp-1206": {
- "max_tokens": 8192,
- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
- "max_video_length": 1,
- "max_audio_length_hours": 8.4,
- "max_audio_per_prompt": 1,
- "max_pdf_size_mb": 30,
- "input_cost_per_token": 0,
- "input_cost_per_token_above_128k_tokens": 0,
+ },
+ "mode": "chat",
"output_cost_per_token": 0,
"output_cost_per_token_above_128k_tokens": 0,
- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_tool_choice": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "tpm": 4000000,
"rpm": 1000,
"source": "https://ai.google.dev/pricing",
+ "supports_function_calling": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tpm": 4000000
+ },
+ "gemini/gemini-exp-1206": {
+ "input_cost_per_token": 0,
+ "input_cost_per_token_above_128k_tokens": 0,
+ "litellm_provider": "gemini",
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_images_per_prompt": 3000,
+ "max_input_tokens": 2097152,
+ "max_output_tokens": 8192,
+ "max_pdf_size_mb": 30,
+ "max_tokens": 8192,
+ "max_video_length": 1,
+ "max_videos_per_prompt": 10,
"metadata": {
"notes": "Rate limits not documented for gemini-exp-1206. Assuming same as gemini-1.5-pro.",
"supports_tool_choice": true
- }
- },
- "gemini/gemini-1.5-flash-exp-0827": {
- "max_tokens": 8192,
- "max_input_tokens": 1048576,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
- "max_video_length": 1,
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- "max_audio_per_prompt": 1,
- "max_pdf_size_mb": 30,
- "input_cost_per_token": 0,
- "input_cost_per_token_above_128k_tokens": 0,
+ },
+ "mode": "chat",
"output_cost_per_token": 0,
"output_cost_per_token_above_128k_tokens": 0,
- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 2000,
- "source": "https://ai.google.dev/pricing",
- "supports_tool_choice": true
- },
- "gemini/gemini-1.5-flash-8b-exp-0827": {
- "max_tokens": 8192,
- "max_input_tokens": 1000000,
- "max_output_tokens": 8192,
- "max_images_per_prompt": 3000,
- "max_videos_per_prompt": 10,
- "max_video_length": 1,
- "max_audio_length_hours": 8.4,
- "max_audio_per_prompt": 1,
- "max_pdf_size_mb": 30,
- "input_cost_per_token": 0,
- "input_cost_per_token_above_128k_tokens": 0,
- "output_cost_per_token": 0,
- "output_cost_per_token_above_128k_tokens": 0,
- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 4000,
- "source": "https://ai.google.dev/pricing",
- "supports_tool_choice": true
- },
- "gemini/gemini-pro": {
- "max_tokens": 8192,
- "max_input_tokens": 32760,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-07,
- "input_cost_per_token_above_128k_tokens": 7e-07,
- "output_cost_per_token": 1.05e-06,
- "output_cost_per_token_above_128k_tokens": 2.1e-06,
- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_function_calling": true,
- "rpd": 30000,
- "tpm": 120000,
- "rpm": 360,
- "source": "https://ai.google.dev/gemini-api/docs/models/gemini",
- "supports_tool_choice": true
- },
- "gemini/gemini-1.5-pro": {
- "max_tokens": 8192,
- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-06,
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- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing"
- },
- "gemini/gemini-1.5-pro-002": {
- "max_tokens": 8192,
- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-06,
- "input_cost_per_token_above_128k_tokens": 7e-06,
- "output_cost_per_token": 1.05e-05,
- "output_cost_per_token_above_128k_tokens": 2.1e-05,
- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
- "supports_tool_choice": true,
- "supports_response_schema": true,
- "supports_prompt_caching": true,
- "tpm": 4000000,
"rpm": 1000,
"source": "https://ai.google.dev/pricing",
- "deprecation_date": "2025-09-24"
- },
- "gemini/gemini-1.5-pro-001": {
- "max_tokens": 8192,
- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-06,
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- "output_cost_per_token": 1.05e-05,
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- "litellm_provider": "gemini",
- "mode": "chat",
- "supports_system_messages": true,
"supports_function_calling": true,
- "supports_vision": true,
- "supports_tool_choice": true,
"supports_response_schema": true,
- "supports_prompt_caching": true,
- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing",
- "deprecation_date": "2025-05-24"
- },
- "gemini/gemini-1.5-pro-exp-0801": {
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- "max_input_tokens": 2097152,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-06,
- "input_cost_per_token_above_128k_tokens": 7e-06,
- "output_cost_per_token": 1.05e-05,
- "output_cost_per_token_above_128k_tokens": 2.1e-05,
- "litellm_provider": "gemini",
- "mode": "chat",
"supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
"supports_tool_choice": true,
- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing"
- },
- "gemini/gemini-1.5-pro-exp-0827": {
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- "max_input_tokens": 2097152,
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- "input_cost_per_token": 0,
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- "litellm_provider": "gemini",
- "mode": "chat",
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- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing"
- },
- "gemini/gemini-1.5-pro-latest": {
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- "max_input_tokens": 1048576,
- "max_output_tokens": 8192,
- "input_cost_per_token": 3.5e-06,
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- "litellm_provider": "gemini",
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- "supports_system_messages": true,
- "supports_function_calling": true,
- "supports_vision": true,
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- "supports_response_schema": true,
- "tpm": 4000000,
- "rpm": 1000,
- "source": "https://ai.google.dev/pricing"
- },
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- "input_cost_per_token_above_128k_tokens": 7e-07,
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- "rpm": 360,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_tool_choice": true
+ "tpm": 4000000
},
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- "max_output_tokens": 8192,
"input_cost_per_token": 3.5e-07,
- "output_cost_per_token": 1.05e-06,
"litellm_provider": "gemini",
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
"mode": "chat",
- "supports_function_calling": true,
- "supports_vision": true,
+ "output_cost_per_token": 1.05e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
- "supports_tool_choice": true
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
},
"gemini/gemini-gemma-2-9b-it": {
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- "max_output_tokens": 8192,
"input_cost_per_token": 3.5e-07,
- "output_cost_per_token": 1.05e-06,
"litellm_provider": "gemini",
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+ "max_tokens": 8192,
"mode": "chat",
- "supports_function_calling": true,
- "supports_vision": true,
+ "output_cost_per_token": 1.05e-06,
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+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
},
- "gemini/imagen-4.0-generate-001": {
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+ "gemini/gemini-pro": {
+ "input_cost_per_token": 3.5e-07,
+ "input_cost_per_token_above_128k_tokens": 7e-07,
+ "litellm_provider": "gemini",
+ "max_input_tokens": 32760,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 1.05e-06,
+ "output_cost_per_token_above_128k_tokens": 2.1e-06,
+ "rpd": 30000,
+ "rpm": 360,
+ "source": "https://ai.google.dev/gemini-api/docs/models/gemini",
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "tpm": 120000
+ },
+ "gemini/gemini-pro-vision": {
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+ "input_cost_per_token_above_128k_tokens": 7e-07,
+ "litellm_provider": "gemini",
+ "max_input_tokens": 30720,
+ "max_output_tokens": 2048,
+ "max_tokens": 2048,
+ "mode": "chat",
+ "output_cost_per_token": 1.05e-06,
+ "output_cost_per_token_above_128k_tokens": 2.1e-06,
+ "rpd": 30000,
+ "rpm": 360,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models",
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tpm": 120000
+ },
+ "gemini/gemma-3-27b-it": {
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+ "input_cost_per_audio_per_second_above_128k_tokens": 0,
+ "input_cost_per_character": 0,
+ "input_cost_per_character_above_128k_tokens": 0,
+ "input_cost_per_image": 0,
+ "input_cost_per_image_above_128k_tokens": 0,
+ "input_cost_per_token": 0,
+ "input_cost_per_token_above_128k_tokens": 0,
+ "input_cost_per_video_per_second": 0,
+ "input_cost_per_video_per_second_above_128k_tokens": 0,
+ "litellm_provider": "gemini",
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+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
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+ "output_cost_per_character_above_128k_tokens": 0,
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+ "output_cost_per_token_above_128k_tokens": 0,
+ "source": "https://aistudio.google.com",
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+ "supports_function_calling": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_vision": true
+ },
+ "gemini/imagen-3.0-fast-generate-001": {
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"mode": "image_generation",
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- },
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- },
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- },
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- "litellm_provider": "gemini",
- "mode": "image_generation",
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
},
"gemini/imagen-3.0-generate-001": {
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"litellm_provider": "gemini",
"mode": "image_generation",
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
},
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+ "litellm_provider": "gemini",
+ "mode": "image_generation",
+ "output_cost_per_image": 0.04,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
+ },
+ "gemini/imagen-4.0-fast-generate-001": {
+ "litellm_provider": "gemini",
+ "mode": "image_generation",
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},
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- },
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- "max_output_tokens": 4096,
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- },
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- "supported_modalities": ["text"],
+ "mode": "chat",
+ "supported_endpoints": [
+ "/v1/chat/completions"
+ ],
+ "supported_modalities": [
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+ ],
"supports_tool_choice": false
},
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"litellm_provider": "gradient_ai",
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"mode": "chat",
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- "supported_endpoints": ["/v1/chat/completions"],
- "supported_modalities": ["text"],
+ "output_cost_per_token": 8e-06,
+ "supported_endpoints": [
+ "/v1/chat/completions"
+ ],
+ "supported_modalities": [
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+ ],
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},
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- "output_cost_per_token": 2.9e-07,
- "litellm_provider": "nscale",
+ "gradient_ai/openai-o3-mini": {
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+ "litellm_provider": "gradient_ai",
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"mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
+ "output_cost_per_token": 4.4e-06,
+ "supported_endpoints": [
+ "/v1/chat/completions"
+ ],
+ "supported_modalities": [
+ "text"
+ ],
+ "supports_tool_choice": false
},
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- "output_cost_per_token": 3e-08,
- "litellm_provider": "nscale",
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+ "litellm_provider": "groq",
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
"mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
+ "output_cost_per_token": 9.9e-07,
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true
},
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- "output_cost_per_token": 3e-08,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
+ "groq/distil-whisper-large-v3-en": {
+ "input_cost_per_second": 5.56e-06,
+ "litellm_provider": "groq",
+ "mode": "audio_transcription",
+ "output_cost_per_second": 0.0
},
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- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
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- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models"
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- "litellm_provider": "nscale",
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- "notes": "Pricing listed as $0.75/1M tokens total. Assumed 50/50 split for input/output."
- }
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- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.05/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B": {
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- "output_cost_per_token": 9e-08,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.18/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B": {
- "input_cost_per_token": 2e-07,
- "output_cost_per_token": 2e-07,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.40/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": {
+ "groq/gemma-7b-it": {
+ "deprecation_date": "2024-12-18",
"input_cost_per_token": 7e-08,
+ "litellm_provider": "groq",
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
"output_cost_per_token": 7e-08,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.14/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B": {
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- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.30/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/mistralai/mixtral-8x22b-instruct-v0.1": {
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- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $1.20/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/meta-llama/Llama-3.1-8B-Instruct": {
- "input_cost_per_token": 3e-08,
- "output_cost_per_token": 3e-08,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.06/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/meta-llama/Llama-3.3-70B-Instruct": {
- "input_cost_per_token": 2e-07,
- "output_cost_per_token": 2e-07,
- "litellm_provider": "nscale",
- "mode": "chat",
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models",
- "metadata": {
- "notes": "Pricing listed as $0.40/1M tokens total. Assumed 50/50 split for input/output."
- }
- },
- "nscale/black-forest-labs/FLUX.1-schnell": {
- "mode": "image_generation",
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- "output_cost_per_pixel": 0.0,
- "litellm_provider": "nscale",
- "supported_endpoints": [
- "/v1/images/generations"
- ],
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models"
- },
- "nscale/stabilityai/stable-diffusion-xl-base-1.0": {
- "mode": "image_generation",
- "input_cost_per_pixel": 3e-09,
- "output_cost_per_pixel": 0.0,
- "litellm_provider": "nscale",
- "supported_endpoints": [
- "/v1/images/generations"
- ],
- "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models"
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- "max_input_tokens": 32768,
- "max_output_tokens": 4096,
- "litellm_provider": "featherless_ai",
- "mode": "chat"
- },
- "featherless_ai/featherless-ai/Qwerky-QwQ-32B": {
- "max_tokens": 32768,
- "max_input_tokens": 32768,
- "max_output_tokens": 4096,
- "litellm_provider": "featherless_ai",
- "mode": "chat"
- },
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- "mode": "audio_transcription",
- "input_cost_per_second": 7.167e-05,
- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-3-general": {
- "mode": "audio_transcription",
- "input_cost_per_second": 7.167e-05,
- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
- "original_pricing_per_minute": 0.0043,
- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-3-medical": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)"
- }
- },
- "deepgram/nova-2": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
- "original_pricing_per_minute": 0.0043,
- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-general": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
- "original_pricing_per_minute": 0.0043,
- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-meeting": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-phonecall": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-voicemail": {
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-finance": {
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- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-conversationalai": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
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- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-video": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
- "original_pricing_per_minute": 0.0043,
- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-drivethru": {
- "mode": "audio_transcription",
- "input_cost_per_second": 7.167e-05,
- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
- "original_pricing_per_minute": 0.0043,
- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-automotive": {
- "mode": "audio_transcription",
- "input_cost_per_second": 7.167e-05,
- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-2-atc": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova": {
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- "litellm_provider": "deepgram",
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- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/nova-general": {
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- "litellm_provider": "deepgram",
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- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
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- "mode": "audio_transcription",
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- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
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- "source": "https://deepgram.com/pricing",
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- "calculation": "$0.0043/60 seconds = $0.00007167 per second"
- }
- },
- "deepgram/enhanced": {
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- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
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- "calculation": "$0.0145/60 seconds = $0.00024167 per second"
- }
- },
- "deepgram/enhanced-general": {
- "mode": "audio_transcription",
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- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
- "/v1/audio/transcriptions"
- ],
- "source": "https://deepgram.com/pricing",
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- "calculation": "$0.0145/60 seconds = $0.00024167 per second"
- }
- },
- "deepgram/enhanced-meeting": {
- "mode": "audio_transcription",
- "input_cost_per_second": 0.00024167,
- "output_cost_per_second": 0.0,
- "litellm_provider": "deepgram",
- "supported_endpoints": [
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- ],
- "source": "https://deepgram.com/pricing",
- "metadata": {
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- "calculation": "$0.0145/60 seconds = $0.00024167 per second"
- }
- },
- "deepgram/enhanced-phonecall": {
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- "litellm_provider": "deepgram",
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- "source": "https://deepgram.com/pricing",
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- }
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- }
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- "source": "https://deepgram.com/pricing",
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+ "supports_tool_choice": true
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+ "litellm_provider": "sambanova",
+ "max_input_tokens": 16384,
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+ "max_tokens": 16384,
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+ "mode": "chat"
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+ "max_output_tokens": 8192,
+ "max_tokens": 128000,
+ "mode": "chat"
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+ "snowflake/mixtral-8x7b": {
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+ "max_output_tokens": 8192,
+ "max_tokens": 32000,
+ "mode": "chat"
+ },
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+ "max_output_tokens": 8192,
+ "max_tokens": 32000,
+ "mode": "chat"
+ },
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+ "max_output_tokens": 8192,
+ "max_tokens": 100000,
+ "mode": "chat"
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+ "max_tokens": 77,
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+ "litellm_provider": "openai",
+ "mode": "image_generation",
+ "output_cost_per_pixel": 0.0
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+ "litellm_provider": "openai",
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+ "output_cost_per_token": 1.25e-07,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
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+ "litellm_provider": "vertex_ai-text-models",
+ "max_input_tokens": 8192,
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+ "mode": "completion",
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+ "max_tokens": 1024,
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+ "output_cost_per_character": 5e-07,
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+ "input_cost_per_token": 0.0,
+ "litellm_provider": "text-completion-codestral",
+ "max_input_tokens": 32000,
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+ "max_tokens": 8191,
+ "mode": "completion",
+ "output_cost_per_token": 0.0,
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
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+ "text-completion-codestral/codestral-latest": {
+ "input_cost_per_token": 0.0,
+ "litellm_provider": "text-completion-codestral",
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "max_tokens": 8191,
+ "mode": "completion",
+ "output_cost_per_token": 0.0,
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
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+ "input_cost_per_token": 1e-07,
+ "litellm_provider": "vertex_ai-embedding-models",
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+ "max_tokens": 2048,
+ "mode": "embedding",
+ "output_cost_per_token": 0,
+ "output_vector_size": 768,
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+ "input_cost_per_token_batch_requests": 5e-09,
+ "litellm_provider": "vertex_ai-embedding-models",
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+ "litellm_provider": "vertex_ai-text-models",
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+ "text-unicorn@001": {
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+ "litellm_provider": "vertex_ai-text-models",
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+ "mode": "completion",
+ "output_cost_per_token": 2.8e-05,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
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+ "textembedding-gecko": {
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+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 131072,
+ "max_output_tokens": 131072,
+ "max_tokens": 131072,
+ "mode": "chat",
+ "output_cost_per_token": 4e-06,
+ "source": "https://x.ai/api#pricing",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_response_schema": false,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-3-mini-fast-latest": {
+ "input_cost_per_token": 6e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 131072,
+ "max_output_tokens": 131072,
+ "max_tokens": 131072,
+ "mode": "chat",
+ "output_cost_per_token": 4e-06,
+ "source": "https://x.ai/api#pricing",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_response_schema": false,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-3-mini-latest": {
+ "input_cost_per_token": 3e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 131072,
+ "max_output_tokens": 131072,
+ "max_tokens": 131072,
+ "mode": "chat",
+ "output_cost_per_token": 5e-07,
+ "source": "https://x.ai/api#pricing",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_response_schema": false,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-4": {
+ "input_cost_per_token": 3e-06,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-4-0709": {
+ "input_cost_per_token": 3e-06,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-4-latest": {
+ "input_cost_per_token": 3e-06,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true,
+ "supports_web_search": true
+ },
+ "xai/grok-beta": {
+ "input_cost_per_token": 5e-06,
+ "litellm_provider": "xai",
+ "max_input_tokens": 131072,
+ "max_output_tokens": 131072,
+ "max_tokens": 131072,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "supports_web_search": true
+ },
+ "xai/grok-code-fast": {
+ "cache_read_input_token_cost": 2e-08,
+ "input_cost_per_token": 2e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-06,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true
+ },
+ "xai/grok-code-fast-1": {
+ "cache_read_input_token_cost": 2e-08,
+ "input_cost_per_token": 2e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-06,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true
+ },
+ "xai/grok-code-fast-1-0825": {
+ "cache_read_input_token_cost": 2e-08,
+ "input_cost_per_token": 2e-07,
+ "litellm_provider": "xai",
+ "max_input_tokens": 256000,
+ "max_output_tokens": 256000,
+ "max_tokens": 256000,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-06,
+ "source": "https://docs.x.ai/docs/models",
+ "supports_function_calling": true,
+ "supports_reasoning": true,
+ "supports_tool_choice": true
+ },
+ "xai/grok-vision-beta": {
+ "input_cost_per_image": 5e-06,
+ "input_cost_per_token": 5e-06,
+ "litellm_provider": "xai",
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "max_tokens": 8192,
+ "mode": "chat",
+ "output_cost_per_token": 1.5e-05,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "supports_web_search": true
}
-}
+}
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
index a075de13fb..6afed97fe9 100644
--- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
+++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
@@ -28,12 +28,16 @@ class MCPRequestHandler:
LITELLM_MCP_SERVERS_HEADER_NAME = SpecialHeaders.mcp_servers.value
LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME = SpecialHeaders.mcp_access_groups.value
-
+
# MCP Protocol Version header
MCP_PROTOCOL_VERSION_HEADER_NAME = "MCP-Protocol-Version"
@staticmethod
- async def process_mcp_request(scope: Scope) -> Tuple[UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]]:
+ async def process_mcp_request(
+ scope: Scope,
+ ) -> Tuple[
+ UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]]
+ ]:
"""
Process and validate MCP request headers from the ASGI scope.
This includes:
@@ -49,7 +53,6 @@ class MCPRequestHandler:
mcp_auth_header: Optional[str] MCP auth header to be passed to the MCP server (deprecated)
mcp_servers: Optional[List[str]] List of MCP servers and access groups to use
mcp_server_auth_headers: Optional[Dict[str, str]] Server-specific auth headers in format {server_alias: auth_value}
- mcp_protocol_version: Optional[str] MCP protocol version from request header
Raises:
HTTPException: If headers are invalid or missing required headers
@@ -58,39 +61,50 @@ class MCPRequestHandler:
litellm_api_key = (
MCPRequestHandler.get_litellm_api_key_from_headers(headers) or ""
)
-
+
# Get the old mcp_auth_header for backward compatibility
mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers)
-
- # Get the new server-specific auth headers
- mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers)
- # Get MCP protocol version from header
- mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME)
+ # Get the new server-specific auth headers
+ mcp_server_auth_headers = (
+ MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers)
+ )
# Parse MCP servers from header
- mcp_servers_header = headers.get(MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME)
+ mcp_servers_header = headers.get(
+ MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME
+ )
verbose_logger.debug(f"Raw MCP servers header: {mcp_servers_header}")
mcp_servers = None
if mcp_servers_header is not None:
try:
- mcp_servers = [s.strip() for s in mcp_servers_header.split(",") if s.strip()]
+ mcp_servers = [
+ s.strip() for s in mcp_servers_header.split(",") if s.strip()
+ ]
verbose_logger.debug(f"Parsed MCP servers: {mcp_servers}")
except Exception as e:
verbose_logger.debug(f"Error parsing mcp_servers header: {e}")
mcp_servers = None
- if mcp_servers_header == "" or (mcp_servers is not None and len(mcp_servers) == 0):
+ if mcp_servers_header == "" or (
+ mcp_servers is not None and len(mcp_servers) == 0
+ ):
mcp_servers = []
# Create a proper Request object with mock body method to avoid ASGI receive channel issues
request = Request(scope=scope)
+
async def mock_body():
return b"{}"
+
request.body = mock_body # type: ignore
validated_user_api_key_auth = await user_api_key_auth(
api_key=litellm_api_key, request=request
)
- return validated_user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version
-
+ return (
+ validated_user_api_key_auth,
+ mcp_auth_header,
+ mcp_servers,
+ mcp_server_auth_headers,
+ )
@staticmethod
def _get_mcp_auth_header_from_headers(headers: Headers) -> Optional[str]:
@@ -104,10 +118,12 @@ class MCPRequestHandler:
Support this auth: https://docs.litellm.ai/docs/mcp#using-your-mcp-with-client-side-credentials
If you want to use a different header name, you can set the `LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME` in the secret manager or `mcp_client_side_auth_header_name` in the general settings.
-
+
DEPRECATED: This method is deprecated in favor of server-specific auth headers using the format x-mcp-{{server_alias}}-{{header_name}} instead.
"""
- mcp_client_side_auth_header_name: str = MCPRequestHandler._get_mcp_client_side_auth_header_name()
+ mcp_client_side_auth_header_name: str = (
+ MCPRequestHandler._get_mcp_client_side_auth_header_name()
+ )
auth_header = headers.get(mcp_client_side_auth_header_name)
if auth_header:
verbose_logger.warning(
@@ -115,42 +131,49 @@ class MCPRequestHandler:
f"Please use server-specific auth headers in the format 'x-mcp-{{server_alias}}-{{header_name}}' instead."
)
return auth_header
-
+
@staticmethod
def _get_mcp_server_auth_headers_from_headers(headers: Headers) -> Dict[str, str]:
"""
Parse server-specific MCP auth headers from the request headers.
-
+
Looks for headers in the format: x-mcp-{server_alias}-{header_name}
Examples:
- x-mcp-github-authorization: Bearer token123
- x-mcp-zapier-x-api-key: api_key_456
- x-mcp-deepwiki-authorization: Basic base64_encoded_creds
-
+
Returns:
Dict[str, str]: Mapping of server alias to auth value
"""
server_auth_headers = {}
prefix = "x-mcp-"
-
+
for header_name, header_value in headers.items():
if header_name.lower().startswith(prefix):
# Skip the access groups header as it's not a server auth header
- if header_name.lower() == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() or header_name.lower() == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower():
+ if (
+ header_name.lower()
+ == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower()
+ or header_name.lower()
+ == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower()
+ ):
continue
-
+
# Extract server_alias and header_name from x-mcp-{server_alias}-{header_name}
- remaining = header_name[len(prefix):].lower()
- if '-' in remaining:
+ remaining = header_name[len(prefix) :].lower()
+ if "-" in remaining:
# Split on the last dash to separate server_alias from header_name
- parts = remaining.rsplit('-', 1)
+ parts = remaining.rsplit("-", 1)
if len(parts) == 2:
server_alias, auth_header_name = parts
server_auth_headers[server_alias] = header_value
- verbose_logger.debug(f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}...")
-
+ verbose_logger.debug(
+ f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}..."
+ )
+
return server_auth_headers
-
+
@staticmethod
def _get_mcp_client_side_auth_header_name() -> str:
"""
@@ -162,13 +185,21 @@ class MCPRequestHandler:
"""
from litellm.proxy.proxy_server import general_settings
from litellm.secret_managers.main import get_secret_str
- MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME
- if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None:
- MCP_CLIENT_SIDE_AUTH_HEADER_NAME = get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME
- elif general_settings.get("mcp_client_side_auth_header_name") is not None:
- MCP_CLIENT_SIDE_AUTH_HEADER_NAME = general_settings.get("mcp_client_side_auth_header_name") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME
- return MCP_CLIENT_SIDE_AUTH_HEADER_NAME
+ MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = (
+ MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME
+ )
+ if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None:
+ MCP_CLIENT_SIDE_AUTH_HEADER_NAME = (
+ get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME")
+ or MCP_CLIENT_SIDE_AUTH_HEADER_NAME
+ )
+ elif general_settings.get("mcp_client_side_auth_header_name") is not None:
+ MCP_CLIENT_SIDE_AUTH_HEADER_NAME = (
+ general_settings.get("mcp_client_side_auth_header_name")
+ or MCP_CLIENT_SIDE_AUTH_HEADER_NAME
+ )
+ return MCP_CLIENT_SIDE_AUTH_HEADER_NAME
@staticmethod
def get_litellm_api_key_from_headers(headers: Headers) -> Optional[str]:
@@ -229,10 +260,14 @@ class MCPRequestHandler:
try:
allowed_mcp_servers: List[str] = []
allowed_mcp_servers_for_key = (
- await MCPRequestHandler._get_allowed_mcp_servers_for_key(user_api_key_auth)
+ await MCPRequestHandler._get_allowed_mcp_servers_for_key(
+ user_api_key_auth
+ )
)
allowed_mcp_servers_for_team = (
- await MCPRequestHandler._get_allowed_mcp_servers_for_team(user_api_key_auth)
+ await MCPRequestHandler._get_allowed_mcp_servers_for_team(
+ user_api_key_auth
+ )
)
#########################################################
@@ -274,7 +309,9 @@ class MCPRequestHandler:
try:
key_object_permission = (
await prisma_client.db.litellm_objectpermissiontable.find_unique(
- where={"object_permission_id": user_api_key_auth.object_permission_id},
+ where={
+ "object_permission_id": user_api_key_auth.object_permission_id
+ },
)
)
if key_object_permission is None:
@@ -282,17 +319,21 @@ class MCPRequestHandler:
# Get direct MCP servers
direct_mcp_servers = key_object_permission.mcp_servers or []
-
+
# Get MCP servers from access groups
- access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups(
- key_object_permission.mcp_access_groups or []
+ access_group_servers = (
+ await MCPRequestHandler._get_mcp_servers_from_access_groups(
+ key_object_permission.mcp_access_groups or []
+ )
)
-
+
# Combine both lists
all_servers = direct_mcp_servers + access_group_servers
return list(set(all_servers))
except Exception as e:
- verbose_logger.warning(f"Failed to get allowed MCP servers for key: {str(e)}")
+ verbose_logger.warning(
+ f"Failed to get allowed MCP servers for key: {str(e)}"
+ )
return []
@staticmethod
@@ -318,10 +359,10 @@ class MCPRequestHandler:
return []
try:
- team_obj: Optional[LiteLLM_TeamTable] = (
- await prisma_client.db.litellm_teamtable.find_unique(
- where={"team_id": user_api_key_auth.team_id},
- )
+ team_obj: Optional[
+ LiteLLM_TeamTable
+ ] = await prisma_client.db.litellm_teamtable.find_unique(
+ where={"team_id": user_api_key_auth.team_id},
)
if team_obj is None:
verbose_logger.debug("team_obj is None")
@@ -333,21 +374,27 @@ class MCPRequestHandler:
# Get direct MCP servers
direct_mcp_servers = object_permissions.mcp_servers or []
-
+
# Get MCP servers from access groups
- access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups(
- object_permissions.mcp_access_groups or []
+ access_group_servers = (
+ await MCPRequestHandler._get_mcp_servers_from_access_groups(
+ object_permissions.mcp_access_groups or []
+ )
)
-
+
# Combine both lists
all_servers = direct_mcp_servers + access_group_servers
return list(set(all_servers))
except Exception as e:
- verbose_logger.warning(f"Failed to get allowed MCP servers for team: {str(e)}")
+ verbose_logger.warning(
+ f"Failed to get allowed MCP servers for team: {str(e)}"
+ )
return []
@staticmethod
- def _get_config_server_ids_for_access_groups(config_mcp_servers, access_groups: List[str]) -> Set[str]:
+ def _get_config_server_ids_for_access_groups(
+ config_mcp_servers, access_groups: List[str]
+ ) -> Set[str]:
"""
Helper to get server_ids from config-loaded servers that match any of the given access groups.
"""
@@ -359,7 +406,9 @@ class MCPRequestHandler:
return server_ids
@staticmethod
- async def _get_db_server_ids_for_access_groups(prisma_client, access_groups: List[str]) -> Set[str]:
+ async def _get_db_server_ids_for_access_groups(
+ prisma_client, access_groups: List[str]
+ ) -> Set[str]:
"""
Helper to get server_ids from DB servers that match any of the given access groups.
"""
@@ -367,21 +416,19 @@ class MCPRequestHandler:
if access_groups and prisma_client is not None:
try:
mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many(
- where={
- "mcp_access_groups": {
- "hasSome": access_groups
- }
- }
+ where={"mcp_access_groups": {"hasSome": access_groups}}
)
for server in mcp_servers:
server_ids.add(server.server_id)
except Exception as e:
- verbose_logger.debug(f"Error getting MCP servers from access groups: {e}")
+ verbose_logger.debug(
+ f"Error getting MCP servers from access groups: {e}"
+ )
return server_ids
@staticmethod
async def _get_mcp_servers_from_access_groups(
- access_groups: List[str]
+ access_groups: List[str],
) -> List[str]:
"""
Resolve MCP access groups to server IDs by querying BOTH the MCP server table (DB) AND config-loaded servers
@@ -390,22 +437,28 @@ class MCPRequestHandler:
try:
# Import here to avoid circular import
- from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager
-
+ from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+ global_mcp_server_manager,
+ )
+
# Use the new helper for config-loaded servers
server_ids = MCPRequestHandler._get_config_server_ids_for_access_groups(
global_mcp_server_manager.config_mcp_servers, access_groups
)
# Use the new helper for DB servers
- db_server_ids = await MCPRequestHandler._get_db_server_ids_for_access_groups(
- prisma_client, access_groups
+ db_server_ids = (
+ await MCPRequestHandler._get_db_server_ids_for_access_groups(
+ prisma_client, access_groups
+ )
)
server_ids.update(db_server_ids)
return list(server_ids)
except Exception as e:
- verbose_logger.warning(f"Failed to get MCP servers from access groups: {str(e)}")
+ verbose_logger.warning(
+ f"Failed to get MCP servers from access groups: {str(e)}"
+ )
return []
@staticmethod
@@ -418,8 +471,8 @@ class MCPRequestHandler:
from typing import List
access_groups: List[str] = []
- access_groups_for_key = (
- await MCPRequestHandler._get_mcp_access_groups_for_key(user_api_key_auth)
+ access_groups_for_key = await MCPRequestHandler._get_mcp_access_groups_for_key(
+ user_api_key_auth
)
access_groups_for_team = (
await MCPRequestHandler._get_mcp_access_groups_for_team(user_api_key_auth)
@@ -482,10 +535,10 @@ class MCPRequestHandler:
verbose_logger.debug("prisma_client is None")
return []
- team_obj: Optional[LiteLLM_TeamTable] = (
- await prisma_client.db.litellm_teamtable.find_unique(
- where={"team_id": user_api_key_auth.team_id},
- )
+ team_obj: Optional[
+ LiteLLM_TeamTable
+ ] = await prisma_client.db.litellm_teamtable.find_unique(
+ where={"team_id": user_api_key_auth.team_id},
)
if team_obj is None:
verbose_logger.debug("team_obj is None")
@@ -502,10 +555,14 @@ class MCPRequestHandler:
"""
Extract and parse the x-mcp-access-groups header as a list of strings.
"""
- mcp_access_groups_header = headers.get(MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME)
+ mcp_access_groups_header = headers.get(
+ MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME
+ )
if mcp_access_groups_header is not None:
try:
- return [s.strip() for s in mcp_access_groups_header.split(",") if s.strip()]
+ return [
+ s.strip() for s in mcp_access_groups_header.split(",") if s.strip()
+ ]
except Exception:
return None
return None
@@ -516,4 +573,4 @@ class MCPRequestHandler:
Extract and parse the x-mcp-access-groups header from an ASGI scope.
"""
headers = MCPRequestHandler._safe_get_headers_from_scope(scope)
- return MCPRequestHandler.get_mcp_access_groups_from_headers(headers)
\ No newline at end of file
+ return MCPRequestHandler.get_mcp_access_groups_from_headers(headers)
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index b35b3ad93d..d0eadb36ba 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -34,8 +34,6 @@ from litellm.proxy._experimental.mcp_server.utils import (
from litellm.proxy._types import (
LiteLLM_MCPServerTable,
MCPAuthType,
- MCPSpecVersion,
- MCPSpecVersionType,
MCPTransport,
MCPTransportType,
UserAPIKeyAuth,
@@ -70,38 +68,6 @@ def _deserialize_env_dict(env_data: Any) -> Optional[Dict[str, str]]:
return env_data
-def _convert_protocol_version_to_enum(
- protocol_version: Optional[str | MCPSpecVersionType],
-) -> MCPSpecVersionType:
- """
- Convert string protocol version to MCPSpecVersion enum.
-
- Args:
- protocol_version: String protocol version, enum, or None
-
- Returns:
- MCPSpecVersionType: The enum value
- """
- if not protocol_version:
- return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025)
-
- # If it's already an MCPSpecVersion enum, return it
- if isinstance(protocol_version, MCPSpecVersion):
- return cast(MCPSpecVersionType, protocol_version)
-
- # If it's a string, try to match it to enum values
- if isinstance(protocol_version, str):
- for version in MCPSpecVersion:
- if version.value == protocol_version:
- return cast(MCPSpecVersionType, version)
-
- # If no match found, return default
- verbose_logger.warning(
- f"Unknown protocol version '{protocol_version}', using default"
- )
- return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025)
-
-
class MCPServerManager:
def __init__(self):
self.registry: Dict[str, MCPServer] = {}
@@ -113,8 +79,7 @@ class MCPServerManager:
"name": "zapier_mcp_server",
"url": "https://actions.zapier.com/mcp/sk-ak-2ew3bofIeQIkNoeKIdXrF1Hhhp/sse"
"transport": "sse",
- "auth_type": "api_key",
- "spec_version": "2025-03-26"
+ "auth_type": "api_key"
},
"uuid-2": {
"name": "google_drive_mcp_server",
@@ -223,7 +188,6 @@ class MCPServerManager:
server_name=server_name,
url=server_config.get("url", None) or "",
transport=server_config.get("transport", MCPTransport.http),
- spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025),
auth_type=server_config.get("auth_type", None),
alias=alias,
)
@@ -239,7 +203,6 @@ class MCPServerManager:
env=server_config.get("env", None) or {},
# TODO: utility fn the default values
transport=server_config.get("transport", MCPTransport.http),
- spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025),
auth_type=server_config.get("auth_type", None),
authentication_token=server_config.get(
"authentication_token", server_config.get("auth_value", None)
@@ -287,7 +250,6 @@ class MCPServerManager:
server_name=getattr(mcp_server, "server_name", None),
url=mcp_server.url,
transport=cast(MCPTransportType, mcp_server.transport),
- spec_version=_convert_protocol_version_to_enum(mcp_server.spec_version),
auth_type=cast(MCPAuthType, mcp_server.auth_type),
mcp_info=MCPInfo(
server_name=mcp_server.server_name or mcp_server.server_id,
@@ -350,7 +312,6 @@ class MCPServerManager:
user_api_key_auth: Optional[UserAPIKeyAuth] = None,
mcp_auth_header: Optional[str] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
) -> List[MCPTool]:
"""
List all tools available across all MCP Servers.
@@ -390,7 +351,6 @@ class MCPServerManager:
tools = await self._get_tools_from_server(
server=server,
mcp_auth_header=server_auth_header,
- mcp_protocol_version=mcp_protocol_version,
)
list_tools_result.extend(tools)
verbose_logger.info(
@@ -414,7 +374,6 @@ class MCPServerManager:
self,
server: MCPServer,
mcp_auth_header: Optional[str] = None,
- protocol_version: Optional[str] = None,
) -> MCPClient:
"""
Create an MCPClient instance for the given server.
@@ -422,18 +381,12 @@ class MCPServerManager:
Args:
server (MCPServer): The server configuration
mcp_auth_header: MCP auth header to be passed to the MCP server. This is optional and will be used if provided.
- protocol_version: Optional MCP protocol version to use. If not provided, uses server's default.
Returns:
MCPClient: Configured MCP client instance
"""
transport = server.transport or MCPTransport.sse
- # Convert protocol version string to enum
- protocol_version_enum = _convert_protocol_version_to_enum(
- protocol_version or server.spec_version
- )
-
# Handle stdio transport
if transport == MCPTransport.stdio:
# For stdio, we need to get the stdio config from the server
@@ -450,7 +403,6 @@ class MCPServerManager:
auth_value=mcp_auth_header or server.authentication_token,
timeout=60.0,
stdio_config=stdio_config,
- protocol_version=protocol_version_enum,
)
else:
# For HTTP/SSE transports
@@ -461,14 +413,12 @@ class MCPServerManager:
auth_type=server.auth_type,
auth_value=mcp_auth_header or server.authentication_token,
timeout=60.0,
- protocol_version=protocol_version_enum,
)
async def _get_tools_from_server(
self,
server: MCPServer,
mcp_auth_header: Optional[str] = None,
- mcp_protocol_version: Optional[str] = None,
) -> List[MCPTool]:
"""
Helper method to get tools from a single MCP server with prefixed names.
@@ -483,22 +433,18 @@ class MCPServerManager:
verbose_logger.debug(f"Connecting to url: {server.url}")
verbose_logger.info(f"_get_tools_from_server for {server.name}...")
- protocol_version = (
- mcp_protocol_version if mcp_protocol_version else server.spec_version
- )
client = None
try:
client = self._create_mcp_client(
server=server,
mcp_auth_header=mcp_auth_header,
- protocol_version=protocol_version,
)
tools = await self._fetch_tools_with_timeout(client, server.name)
-
+
prefixed_tools = self._create_prefixed_tools(tools, server)
-
+
return prefixed_tools
except Exception as e:
@@ -530,7 +476,7 @@ class MCPServerManager:
async def _list_tools_task():
try:
await client.connect()
-
+
tools = await client.list_tools()
verbose_logger.debug(f"Tools from {server_name}: {tools}")
return tools
@@ -609,7 +555,6 @@ class MCPServerManager:
user_api_key_auth: Optional[UserAPIKeyAuth] = None,
mcp_auth_header: Optional[str] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
) -> CallToolResult:
"""
@@ -660,32 +605,54 @@ class MCPServerManager:
"arguments": arguments,
"server_name": server_name_from_prefix,
"user_api_key_auth": user_api_key_auth,
- "user_api_key_user_id": getattr(user_api_key_auth, 'user_id', None) if user_api_key_auth else None,
- "user_api_key_team_id": getattr(user_api_key_auth, 'team_id', None) if user_api_key_auth else None,
- "user_api_key_end_user_id": getattr(user_api_key_auth, 'end_user_id', None) if user_api_key_auth else None,
- "user_api_key_hash": getattr(user_api_key_auth, 'api_key_hash', None) if user_api_key_auth else None,
+ "user_api_key_user_id": getattr(user_api_key_auth, "user_id", None)
+ if user_api_key_auth
+ else None,
+ "user_api_key_team_id": getattr(user_api_key_auth, "team_id", None)
+ if user_api_key_auth
+ else None,
+ "user_api_key_end_user_id": getattr(
+ user_api_key_auth, "end_user_id", None
+ )
+ if user_api_key_auth
+ else None,
+ "user_api_key_hash": getattr(user_api_key_auth, "api_key_hash", None)
+ if user_api_key_auth
+ else None,
}
-
+
# Create MCP request object for processing
- mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs(pre_hook_kwargs)
-
+ mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs(
+ pre_hook_kwargs
+ )
+
# Convert to LLM format for existing guardrail compatibility
- synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(mcp_request_obj, pre_hook_kwargs)
-
+ synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(
+ mcp_request_obj, pre_hook_kwargs
+ )
+
try:
# Use standard pre_call_hook with call_type="mcp_call"
modified_data = await proxy_logging_obj.pre_call_hook(
- user_api_key_dict=user_api_key_auth, #type: ignore
+ user_api_key_dict=user_api_key_auth, # type: ignore
data=synthetic_llm_data,
- call_type="mcp_call" #type: ignore
+ call_type="mcp_call", # type: ignore
)
if modified_data:
# Convert response back to MCP format and apply modifications
- modified_kwargs = proxy_logging_obj._convert_mcp_hook_response_to_kwargs(modified_data, pre_hook_kwargs)
+ modified_kwargs = (
+ proxy_logging_obj._convert_mcp_hook_response_to_kwargs(
+ modified_data, pre_hook_kwargs
+ )
+ )
if modified_kwargs.get("arguments") != arguments:
arguments = modified_kwargs["arguments"]
-
- except (BlockedPiiEntityError, GuardrailRaisedException, HTTPException) as e:
+
+ except (
+ BlockedPiiEntityError,
+ GuardrailRaisedException,
+ HTTPException,
+ ) as e:
# Re-raise guardrail exceptions to properly fail the MCP call
verbose_logger.error(
f"Guardrail blocked MCP tool call pre call: {str(e)}"
@@ -706,11 +673,9 @@ class MCPServerManager:
client = self._create_mcp_client(
server=mcp_server,
mcp_auth_header=server_auth_header,
- protocol_version=mcp_protocol_version,
)
async with client:
-
# Use the original tool name (without prefix) for the actual call
call_tool_params = MCPCallToolRequestParams(
name=original_tool_name,
@@ -721,7 +686,7 @@ class MCPServerManager:
# Create synthetic LLM data for during hook processing
from litellm.types.llms.base import HiddenParams
from litellm.types.mcp import MCPDuringCallRequestObject
-
+
request_obj = MCPDuringCallRequestObject(
tool_name=name,
arguments=arguments,
@@ -729,28 +694,29 @@ class MCPServerManager:
start_time=start_time.timestamp() if start_time else None,
hidden_params=HiddenParams(),
)
-
+
during_hook_kwargs = {
"name": name,
"arguments": arguments,
"server_name": server_name_from_prefix,
"user_api_key_auth": user_api_key_auth,
}
-
- synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(request_obj, during_hook_kwargs)
-
+
+ synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(
+ request_obj, during_hook_kwargs
+ )
+
during_hook_task = asyncio.create_task(
proxy_logging_obj.during_call_hook(
user_api_key_dict=user_api_key_auth,
data=synthetic_llm_data,
- call_type="mcp_call" #type: ignore
+ call_type="mcp_call", # type: ignore
)
)
tasks.append(during_hook_task)
tasks.append(asyncio.create_task(client.call_tool(call_tool_params)))
try:
-
mcp_responses = await asyncio.gather(*tasks)
# If proxy_logging_obj is None, the tool call result is at index 0
@@ -839,19 +805,21 @@ class MCPServerManager:
)
verbose_logger.info("Loading MCP servers from database into registry...")
-
+
# perform authz check to filter the mcp servers user has access to
prisma_client = get_prisma_client_or_throw(
"Database not connected. Connect a database to your proxy"
)
db_mcp_servers = await get_all_mcp_servers(prisma_client)
verbose_logger.info(f"Found {len(db_mcp_servers)} MCP servers in database")
-
+
# ensure the global_mcp_server_manager is up to date with the db
for server in db_mcp_servers:
- verbose_logger.debug(f"Adding server to registry: {server.server_id} ({server.server_name})")
+ verbose_logger.debug(
+ f"Adding server to registry: {server.server_id} ({server.server_name})"
+ )
self.add_update_server(server)
-
+
verbose_logger.info(f"Registry now contains {len(self.get_registry())} servers")
def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]:
@@ -869,7 +837,6 @@ class MCPServerManager:
server_name: str,
url: str,
transport: str,
- spec_version: str,
auth_type: Optional[str] = None,
alias: Optional[str] = None,
) -> str:
@@ -885,7 +852,6 @@ class MCPServerManager:
server_name: Name of the server
url: Server URL
transport: Transport type (sse, http, etc.)
- spec_version: MCP spec version
auth_type: Authentication type (optional)
alias: Server alias (optional)
@@ -893,7 +859,9 @@ class MCPServerManager:
A deterministic server ID string
"""
# Create a string from all the identifying parameters
- params_string = f"{server_name}|{url}|{transport}|{spec_version}|{auth_type or ''}|{alias or ''}"
+ params_string = (
+ f"{server_name}|{url}|{transport}|{auth_type or ''}|{alias or ''}"
+ )
# Generate SHA-256 hash
hash_object = hashlib.sha256(params_string.encode("utf-8"))
@@ -1050,11 +1018,12 @@ class MCPServerManager:
alias=_server_config.alias,
url=_server_config.url,
transport=_server_config.transport,
- spec_version=_server_config.spec_version,
auth_type=_server_config.auth_type,
created_at=datetime.datetime.now(),
updated_at=datetime.datetime.now(),
- description=_server_config.mcp_info.get("description") if _server_config.mcp_info else None,
+ description=_server_config.mcp_info.get("description")
+ if _server_config.mcp_info
+ else None,
mcp_info=_server_config.mcp_info,
mcp_access_groups=_server_config.access_groups or [],
# Stdio-specific fields
@@ -1111,7 +1080,6 @@ class MCPServerManager:
description=server.description,
url=server.url,
transport=server.transport,
- spec_version=server.spec_version,
auth_type=server.auth_type,
created_at=server.created_at,
created_by=server.created_by,
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index 048b25fa35..2a9174717d 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -23,7 +23,6 @@ router = APIRouter(
if MCP_AVAILABLE:
from litellm.experimental_mcp_client.client import MCPTool
from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
- _convert_protocol_version_to_enum,
global_mcp_server_manager,
)
from litellm.proxy._experimental.mcp_server.server import (
@@ -34,18 +33,24 @@ if MCP_AVAILABLE:
########################################################
############ MCP Server REST API Routes #################
def _get_server_auth_header(
- server, mcp_server_auth_headers: Optional[Dict[str, str]], mcp_auth_header: Optional[str]
+ server,
+ mcp_server_auth_headers: Optional[Dict[str, str]],
+ mcp_auth_header: Optional[str],
) -> Optional[str]:
"""Helper function to get server-specific auth header with case-insensitive matching."""
if mcp_server_auth_headers and server.alias:
normalized_server_alias = server.alias.lower()
- normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()}
+ normalized_headers = {
+ k.lower(): v for k, v in mcp_server_auth_headers.items()
+ }
server_auth = normalized_headers.get(normalized_server_alias)
if server_auth is not None:
return server_auth
elif mcp_server_auth_headers and server.server_name:
normalized_server_name = server.server_name.lower()
- normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()}
+ normalized_headers = {
+ k.lower(): v for k, v in mcp_server_auth_headers.items()
+ }
server_auth = normalized_headers.get(normalized_server_name)
if server_auth is not None:
return server_auth
@@ -63,12 +68,11 @@ if MCP_AVAILABLE:
for tool in tools
]
- async def _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version):
+ async def _get_tools_for_single_server(server, server_auth_header):
"""Helper function to get tools for a single server."""
tools = await global_mcp_server_manager._get_tools_from_server(
server=server,
mcp_auth_header=server_auth_header,
- mcp_protocol_version=mcp_protocol_version,
)
return _create_tool_response_objects(tools, server.mcp_info)
@@ -104,17 +108,20 @@ if MCP_AVAILABLE:
from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
MCPRequestHandler,
)
-
+
try:
# Extract auth headers from request
headers = request.headers
- mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers)
- mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers)
- mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME)
-
+ mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(
+ headers
+ )
+ mcp_server_auth_headers = (
+ MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers)
+ )
+
list_tools_result = []
error_message = None
-
+
# If server_id is specified, only query that specific server
if server_id:
server = global_mcp_server_manager.get_mcp_server_by_id(server_id)
@@ -122,49 +129,67 @@ if MCP_AVAILABLE:
return {
"tools": [],
"error": "server_not_found",
- "message": f"Server with id {server_id} not found"
+ "message": f"Server with id {server_id} not found",
}
-
- server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header)
-
+
+ server_auth_header = _get_server_auth_header(
+ server, mcp_server_auth_headers, mcp_auth_header
+ )
+
try:
- list_tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version)
+ list_tools_result = await _get_tools_for_single_server(
+ server, server_auth_header
+ )
except Exception as e:
- verbose_logger.exception(f"Error getting tools from {server.name}: {e}")
+ verbose_logger.exception(
+ f"Error getting tools from {server.name}: {e}"
+ )
return {
"tools": [],
"error": "server_error",
- "message": f"Failed to get tools from server {server.name}: {str(e)}"
+ "message": f"Failed to get tools from server {server.name}: {str(e)}",
}
else:
# Query all servers
errors = []
for server in global_mcp_server_manager.get_registry().values():
- server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header)
-
+ server_auth_header = _get_server_auth_header(
+ server, mcp_server_auth_headers, mcp_auth_header
+ )
+
try:
- tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version)
+ tools_result = await _get_tools_for_single_server(
+ server, server_auth_header
+ )
list_tools_result.extend(tools_result)
except Exception as e:
- verbose_logger.exception(f"Error getting tools from {server.name}: {e}")
+ verbose_logger.exception(
+ f"Error getting tools from {server.name}: {e}"
+ )
errors.append(f"{server.name}: {str(e)}")
continue
-
+
if errors and not list_tools_result:
- error_message = "Failed to get tools from servers: " + "; ".join(errors)
-
+ error_message = "Failed to get tools from servers: " + "; ".join(
+ errors
+ )
+
return {
"tools": list_tools_result,
"error": "partial_failure" if error_message else None,
- "message": error_message if error_message else "Successfully retrieved tools"
+ "message": error_message
+ if error_message
+ else "Successfully retrieved tools",
}
-
+
except Exception as e:
- verbose_logger.exception("Unexpected error in list_tool_rest_api: %s", str(e))
+ verbose_logger.exception(
+ "Unexpected error in list_tool_rest_api: %s", str(e)
+ )
return {
"tools": [],
"error": "unexpected_error",
- "message": f"An unexpected error occurred: {str(e)}"
+ "message": f"An unexpected error occurred: {str(e)}",
}
@router.post("/tools/call", dependencies=[Depends(user_api_key_auth)])
@@ -196,9 +221,9 @@ if MCP_AVAILABLE:
detail={
"error": "blocked_pii_entity",
"message": str(e),
- "entity_type": getattr(e, 'entity_type', None),
- "guardrail_name": getattr(e, 'guardrail_name', None)
- }
+ "entity_type": getattr(e, "entity_type", None),
+ "guardrail_name": getattr(e, "guardrail_name", None),
+ },
)
except GuardrailRaisedException as e:
verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}")
@@ -207,8 +232,8 @@ if MCP_AVAILABLE:
detail={
"error": "guardrail_violation",
"message": str(e),
- "guardrail_name": getattr(e, 'guardrail_name', None)
- }
+ "guardrail_name": getattr(e, "guardrail_name", None),
+ },
)
except HTTPException as e:
# Re-raise HTTPException as-is to preserve status code and detail
@@ -220,10 +245,10 @@ if MCP_AVAILABLE:
status_code=500,
detail={
"error": "internal_server_error",
- "message": f"An unexpected error occurred: {str(e)}"
- }
+ "message": f"An unexpected error occurred: {str(e)}",
+ },
)
-
+
########################################################
# MCP Connection testing routes
# /health -> Test if we can connect to the MCP server
@@ -234,15 +259,15 @@ if MCP_AVAILABLE:
from litellm.proxy.management_endpoints.mcp_management_endpoints import (
NewMCPServerRequest,
)
-
+
async def _execute_with_mcp_client(request: NewMCPServerRequest, operation):
"""
Common helper to create MCP client, execute operation, and ensure proper cleanup.
-
+
Args:
request: MCP server configuration
operation: Async function that takes a client and returns the operation result
-
+
Returns:
Operation result or error response
"""
@@ -254,15 +279,14 @@ if MCP_AVAILABLE:
name=request.alias or request.server_name or "",
url=request.url,
transport=request.transport,
- spec_version=_convert_protocol_version_to_enum(request.spec_version),
auth_type=request.auth_type,
mcp_info=request.mcp_info,
),
mcp_auth_header=None,
)
-
+
return await operation(client)
-
+
except Exception as e:
verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True)
return {"status": "error", "message": "An internal error has occurred."}
@@ -273,6 +297,7 @@ if MCP_AVAILABLE:
await client.disconnect()
except Exception as e:
verbose_logger.warning(f"Error disconnecting MCP client: {e}")
+
@router.post("/test/connection")
async def test_connection(
request: NewMCPServerRequest,
@@ -280,13 +305,13 @@ if MCP_AVAILABLE:
"""
Test if we can connect to the provided MCP server before adding it
"""
+
async def _test_connection_operation(client):
await client.connect()
return {"status": "ok"}
-
+
return await _execute_with_mcp_client(request, _test_connection_operation)
-
-
+
@router.post("/test/tools/list")
async def test_tools_list(
request: NewMCPServerRequest,
@@ -295,13 +320,16 @@ if MCP_AVAILABLE:
"""
Preview tools available from MCP server before adding it
"""
+
async def _list_tools_operation(client):
list_tools_result: List[MCPTool] = await client.list_tools()
- model_dumped_tools: List[dict] = [tool.model_dump() for tool in list_tools_result]
+ model_dumped_tools: List[dict] = [
+ tool.model_dump() for tool in list_tools_result
+ ]
return {
"tools": model_dumped_tools,
"error": None,
- "message": "Successfully retrieved tools"
+ "message": "Successfully retrieved tools",
}
-
+
return await _execute_with_mcp_client(request, _list_tools_operation)
diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py
index 51c19beb78..2cf91c84dc 100644
--- a/litellm/proxy/_experimental/mcp_server/server.py
+++ b/litellm/proxy/_experimental/mcp_server/server.py
@@ -130,7 +130,9 @@ if MCP_AVAILABLE:
await _sse_session_manager_cm.__aenter__()
_SESSION_MANAGERS_INITIALIZED = True
- verbose_logger.info("MCP Server started with StreamableHTTP and SSE session managers!")
+ verbose_logger.info(
+ "MCP Server started with StreamableHTTP and SSE session managers!"
+ )
async def shutdown_session_managers():
"""Shutdown the session managers."""
@@ -171,11 +173,18 @@ if MCP_AVAILABLE:
"""
try:
# Get user authentication from context variable
- user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = (
- get_auth_context()
+ (
+ user_api_key_auth,
+ mcp_auth_header,
+ mcp_servers,
+ mcp_server_auth_headers,
+ ) = get_auth_context()
+ verbose_logger.debug(
+ f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}"
+ )
+ verbose_logger.debug(
+ f"MCP list_tools - MCP servers from context: {mcp_servers}"
)
- verbose_logger.debug(f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}")
- verbose_logger.debug(f"MCP list_tools - MCP servers from context: {mcp_servers}")
verbose_logger.debug(
f"MCP list_tools - MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}"
)
@@ -186,9 +195,10 @@ if MCP_AVAILABLE:
mcp_auth_header=mcp_auth_header,
mcp_servers=mcp_servers,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
)
- verbose_logger.info(f"MCP list_tools - Successfully returned {len(tools)} tools")
+ verbose_logger.info(
+ f"MCP list_tools - Successfully returned {len(tools)} tools"
+ )
return tools
except Exception as e:
verbose_logger.exception(f"Error in list_tools endpoint: {str(e)}")
@@ -220,9 +230,16 @@ if MCP_AVAILABLE:
from litellm.proxy.proxy_server import proxy_config
# Validate arguments
- user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context()
+ (
+ user_api_key_auth,
+ mcp_auth_header,
+ _,
+ mcp_server_auth_headers,
+ ) = get_auth_context()
- verbose_logger.debug(f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}")
+ verbose_logger.debug(
+ f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}"
+ )
try:
# Create a body date for logging
body_data = {"name": name, "arguments": arguments}
@@ -249,17 +266,22 @@ if MCP_AVAILABLE:
user_api_key_auth=user_api_key_auth,
mcp_auth_header=mcp_auth_header,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
**data, # for logging
)
except BlockedPiiEntityError as e:
verbose_logger.error(f"BlockedPiiEntityError in MCP tool call: {str(e)}")
# Return error as text content for MCP protocol
- return [TextContent(text=f"Error: Blocked PII entity detected - {str(e)}", type="text")]
+ return [
+ TextContent(
+ text=f"Error: Blocked PII entity detected - {str(e)}", type="text"
+ )
+ ]
except GuardrailRaisedException as e:
verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}")
# Return error as text content for MCP protocol
- return [TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")]
+ return [
+ TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")
+ ]
except HTTPException as e:
verbose_logger.error(f"HTTPException in MCP tool call: {str(e)}")
# Return error as text content for MCP protocol
@@ -287,6 +309,7 @@ if MCP_AVAILABLE:
Get the filtered MCP servers from the MCP server names
"""
from typing import Set
+
filtered_server_ids: Set[str] = set()
# Filter servers based on mcp_servers parameter if provided
if mcp_servers is not None:
@@ -297,7 +320,11 @@ if MCP_AVAILABLE:
server = global_mcp_server_manager.get_mcp_server_by_id(server_id)
if server:
- match_list = [s.lower() for s in [server.alias, server.server_name, server_id] if s is not None]
+ match_list = [
+ s.lower()
+ for s in [server.alias, server.server_name, server_id]
+ if s is not None
+ ]
if server_or_group.lower() in match_list:
filtered_server_ids.add(server_id)
@@ -306,19 +333,23 @@ if MCP_AVAILABLE:
if not server_name_matched:
try:
- access_group_server_ids = await MCPRequestHandler._get_mcp_servers_from_access_groups(
- [server_or_group]
+ access_group_server_ids = (
+ await MCPRequestHandler._get_mcp_servers_from_access_groups(
+ [server_or_group]
+ )
)
# Only include servers that the user has access to
for server_id in access_group_server_ids:
if server_id in allowed_mcp_servers:
filtered_server_ids.add(server_id)
except Exception as e:
- verbose_logger.debug(f"Could not resolve '{server_or_group}' as access group: {e}")
+ verbose_logger.debug(
+ f"Could not resolve '{server_or_group}' as access group: {e}"
+ )
if filtered_server_ids:
allowed_mcp_servers = list(filtered_server_ids)
-
+
return allowed_mcp_servers
async def _get_tools_from_mcp_servers(
@@ -326,7 +357,6 @@ if MCP_AVAILABLE:
mcp_auth_header: Optional[str],
mcp_servers: Optional[List[str]],
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
) -> List[MCPTool]:
"""
Helper method to fetch tools from MCP servers based on server filtering criteria.
@@ -344,7 +374,9 @@ if MCP_AVAILABLE:
return []
# Get allowed MCP servers based on user permissions
- allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth)
+ allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(
+ user_api_key_auth
+ )
if mcp_servers is not None:
allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names(
@@ -352,7 +384,6 @@ if MCP_AVAILABLE:
allowed_mcp_servers=allowed_mcp_servers,
)
-
# Get tools from each allowed server
all_tools = []
for server_id in allowed_mcp_servers:
@@ -375,15 +406,20 @@ if MCP_AVAILABLE:
tools = await global_mcp_server_manager._get_tools_from_server(
server=server,
mcp_auth_header=server_auth_header,
- mcp_protocol_version=mcp_protocol_version,
)
all_tools.extend(tools)
- verbose_logger.debug(f"Successfully fetched {len(tools)} tools from server {server.name}")
+ verbose_logger.debug(
+ f"Successfully fetched {len(tools)} tools from server {server.name}"
+ )
except Exception as e:
- verbose_logger.exception(f"Error getting tools from server {server.name}: {str(e)}")
+ verbose_logger.exception(
+ f"Error getting tools from server {server.name}: {str(e)}"
+ )
# Continue with other servers instead of failing completely
- verbose_logger.info(f"Successfully fetched {len(all_tools)} tools total from all MCP servers")
+ verbose_logger.info(
+ f"Successfully fetched {len(all_tools)} tools total from all MCP servers"
+ )
return all_tools
async def _list_mcp_tools(
@@ -391,7 +427,6 @@ if MCP_AVAILABLE:
mcp_auth_header: Optional[str] = None,
mcp_servers: Optional[List[str]] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
) -> List[MCPTool]:
"""
List all available MCP tools.
@@ -415,11 +450,14 @@ if MCP_AVAILABLE:
mcp_auth_header=mcp_auth_header,
mcp_servers=mcp_servers,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
)
- verbose_logger.debug(f"Successfully fetched {len(managed_tools)} tools from managed MCP servers")
+ verbose_logger.debug(
+ f"Successfully fetched {len(managed_tools)} tools from managed MCP servers"
+ )
except Exception as e:
- verbose_logger.exception(f"Error getting tools from managed MCP servers: {str(e)}")
+ verbose_logger.exception(
+ f"Error getting tools from managed MCP servers: {str(e)}"
+ )
# Continue with empty managed tools list instead of failing completely
# Get tools from local registry
@@ -430,10 +468,16 @@ if MCP_AVAILABLE:
# Convert local tools to MCPTool format
for tool in local_tools_raw:
# Convert from litellm.types.mcp_server.tool_registry.MCPTool to mcp.types.Tool
- mcp_tool = MCPTool(name=tool.name, description=tool.description, inputSchema=tool.input_schema)
+ mcp_tool = MCPTool(
+ name=tool.name,
+ description=tool.description,
+ inputSchema=tool.input_schema,
+ )
local_tools.append(mcp_tool)
except Exception as e:
- verbose_logger.exception(f"Error getting tools from local registry: {str(e)}")
+ verbose_logger.exception(
+ f"Error getting tools from local registry: {str(e)}"
+ )
# Continue with empty local tools list instead of failing completely
# Combine all tools
@@ -448,7 +492,6 @@ if MCP_AVAILABLE:
user_api_key_auth: Optional[UserAPIKeyAuth] = None,
mcp_auth_header: Optional[str] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
**kwargs: Any,
) -> List[Union[TextContent, ImageContent, EmbeddedResource]]:
"""
@@ -456,35 +499,46 @@ if MCP_AVAILABLE:
"""
start_time = datetime.now()
if arguments is None:
- raise HTTPException(status_code=400, detail="Request arguments are required")
+ raise HTTPException(
+ status_code=400, detail="Request arguments are required"
+ )
# Remove prefix from tool name for logging and processing
- original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(name)
-
- standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = _get_standard_logging_mcp_tool_call(
- name=original_tool_name, # Use original name for logging
- arguments=arguments,
- server_name=server_name_from_prefix,
+ original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(
+ name
+ )
+
+ standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = (
+ _get_standard_logging_mcp_tool_call(
+ name=original_tool_name, # Use original name for logging
+ arguments=arguments,
+ server_name=server_name_from_prefix,
+ )
+ )
+ litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get(
+ "litellm_logging_obj", None
)
- litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None)
if litellm_logging_obj:
- litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = standard_logging_mcp_tool_call
+ litellm_logging_obj.model_call_details[
+ "mcp_tool_call_metadata"
+ ] = standard_logging_mcp_tool_call
litellm_logging_obj.model = f"MCP: {name}"
# Try managed server tool first (pass the full prefixed name)
# Primary and recommended way to use MCP servers
#########################################################
- mcp_server: Optional[MCPServer] = global_mcp_server_manager._get_mcp_server_from_tool_name(name)
+ mcp_server: Optional[
+ MCPServer
+ ] = global_mcp_server_manager._get_mcp_server_from_tool_name(name)
if mcp_server:
- standard_logging_mcp_tool_call["mcp_server_cost_info"] = (mcp_server.mcp_info or {}).get(
- "mcp_server_cost_info"
- )
+ standard_logging_mcp_tool_call["mcp_server_cost_info"] = (
+ mcp_server.mcp_info or {}
+ ).get("mcp_server_cost_info")
response = await _handle_managed_mcp_tool(
name=name, # Pass the full name (potentially prefixed)
arguments=arguments,
user_api_key_auth=user_api_key_auth,
mcp_auth_header=mcp_auth_header,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
litellm_logging_obj=litellm_logging_obj,
)
@@ -537,7 +591,6 @@ if MCP_AVAILABLE:
user_api_key_auth: Optional[UserAPIKeyAuth] = None,
mcp_auth_header: Optional[str] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
litellm_logging_obj: Optional[Any] = None,
) -> List[Union[TextContent, ImageContent, EmbeddedResource]]:
"""Handle tool execution for managed server tools"""
@@ -577,12 +630,39 @@ if MCP_AVAILABLE:
Get the MCP servers from the path
"""
import re
+
mcp_servers_from_path: Optional[List[str]] = None
- mcp_path_match = re.match(r"^/mcp/([^/]+/[^/]+|[^/]+)(/.*)?$", path)
+ # Match /mcp/
+ # Where servers can be comma-separated list of server names
+ # Server names can contain slashes (e.g., "custom_solutions/user_123")
+ mcp_path_match = re.match(r"^/mcp/([^?#]+)(?:\?.*)?(?:#.*)?$", path)
if mcp_path_match:
- mcp_servers_str = mcp_path_match.group(1)
- if mcp_servers_str:
- mcp_servers_from_path = [s.strip() for s in mcp_servers_str.split(",") if s.strip()]
+ servers_and_path = mcp_path_match.group(1)
+
+ if servers_and_path:
+ # Check if it contains commas (comma-separated servers)
+ if ',' in servers_and_path:
+ # For comma-separated, look for a path at the end
+ # Common patterns: /tools, /chat/completions, etc.
+ path_match = re.search(r'/([^/,]+(?:/[^/,]+)*)$', servers_and_path)
+ if path_match:
+ # Path found at the end, remove it from servers
+ path_part = '/' + path_match.group(1)
+ servers_part = servers_and_path[:-len(path_part)]
+ mcp_servers_from_path = [s.strip() for s in servers_part.split(',') if s.strip()]
+ else:
+ # No path, just comma-separated servers
+ mcp_servers_from_path = [s.strip() for s in servers_and_path.split(',') if s.strip()]
+ else:
+ # Single server case - use regex approach for server/path separation
+ # This handles cases like "custom_solutions/user_123/chat/completions"
+ # where we want to extract "custom_solutions/user_123" as the server name
+ single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", servers_and_path)
+ if single_server_match:
+ server_name = single_server_match.group(1)
+ mcp_servers_from_path = [server_name]
+ else:
+ mcp_servers_from_path = [servers_and_path]
return mcp_servers_from_path
async def extract_mcp_auth_context(scope, path):
@@ -597,7 +677,6 @@ if MCP_AVAILABLE:
mcp_auth_header,
_,
mcp_server_auth_headers,
- mcp_protocol_version,
) = await MCPRequestHandler.process_mcp_request(scope)
mcp_servers = mcp_servers_from_path
else:
@@ -606,11 +685,12 @@ if MCP_AVAILABLE:
mcp_auth_header,
mcp_servers,
mcp_server_auth_headers,
- mcp_protocol_version,
) = await MCPRequestHandler.process_mcp_request(scope)
- return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version
+ return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers
- async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None:
+ async def handle_streamable_http_mcp(
+ scope: Scope, receive: Receive, send: Send
+ ) -> None:
"""Handle MCP requests through StreamableHTTP."""
try:
path = scope.get("path", "")
@@ -619,20 +699,19 @@ if MCP_AVAILABLE:
mcp_auth_header,
mcp_servers,
mcp_server_auth_headers,
- mcp_protocol_version,
) = await extract_mcp_auth_context(scope, path)
- verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}")
+ verbose_logger.debug(
+ f"MCP request mcp_servers (header/path): {mcp_servers}"
+ )
verbose_logger.debug(
f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}"
)
- verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}")
# Set the auth context variable for easy access in MCP functions
set_auth_context(
user_api_key_auth=user_api_key_auth,
mcp_auth_header=mcp_auth_header,
mcp_servers=mcp_servers,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
)
# Ensure session managers are initialized
@@ -656,7 +735,9 @@ if MCP_AVAILABLE:
)
await error_response(scope, receive, send)
except Exception as response_error:
- verbose_logger.exception(f"Failed to send error response: {response_error}")
+ verbose_logger.exception(
+ f"Failed to send error response: {response_error}"
+ )
# If we can't send a proper response, re-raise the original error
raise e
@@ -669,19 +750,18 @@ if MCP_AVAILABLE:
mcp_auth_header,
mcp_servers,
mcp_server_auth_headers,
- mcp_protocol_version,
) = await extract_mcp_auth_context(scope, path)
- verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}")
+ verbose_logger.debug(
+ f"MCP request mcp_servers (header/path): {mcp_servers}"
+ )
verbose_logger.debug(
f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}"
)
- verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}")
set_auth_context(
user_api_key_auth=user_api_key_auth,
mcp_auth_header=mcp_auth_header,
mcp_servers=mcp_servers,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
)
if not _SESSION_MANAGERS_INITIALIZED:
@@ -703,7 +783,9 @@ if MCP_AVAILABLE:
)
await error_response(scope, receive, send)
except Exception as response_error:
- verbose_logger.exception(f"Failed to send error response: {response_error}")
+ verbose_logger.exception(
+ f"Failed to send error response: {response_error}"
+ )
# If we can't send a proper response, re-raise the original error
raise e
@@ -739,7 +821,6 @@ if MCP_AVAILABLE:
mcp_auth_header: Optional[str] = None,
mcp_servers: Optional[List[str]] = None,
mcp_server_auth_headers: Optional[Dict[str, str]] = None,
- mcp_protocol_version: Optional[str] = None,
) -> None:
"""
Set the UserAPIKeyAuth in the auth context variable.
@@ -755,13 +836,17 @@ if MCP_AVAILABLE:
mcp_auth_header=mcp_auth_header,
mcp_servers=mcp_servers,
mcp_server_auth_headers=mcp_server_auth_headers,
- mcp_protocol_version=mcp_protocol_version,
)
auth_context_var.set(auth_user)
- def get_auth_context() -> Tuple[
- Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]
- ]:
+ def get_auth_context() -> (
+ Tuple[
+ Optional[UserAPIKeyAuth],
+ Optional[str],
+ Optional[List[str]],
+ Optional[Dict[str, str]],
+ ]
+ ):
"""
Get the UserAPIKeyAuth from the auth context variable.
@@ -776,9 +861,8 @@ if MCP_AVAILABLE:
auth_user.mcp_auth_header,
auth_user.mcp_servers,
auth_user.mcp_server_auth_headers,
- auth_user.mcp_protocol_version,
)
- return None, None, None, None, None
+ return None, None, None, None
########################################################
############ End of Auth Context Functions #############
diff --git a/litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js b/litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
index 21cf536ca5..54b5be8263 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
@@ -1,2 +1,2 @@
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similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/220-8af5927d18414264.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/220-89d73a525e307735.js
index 22c341172f..57e9349d8f 100644
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n=t.graphicalItems,r=t.stackGroups,o=t.offset,a=t.updateId,l=t.dataStartIndex,s=t.dataEndIndex,c=e.barSize,u=e.layout,d=e.barGap,p=e.barCategoryGap,h=e.maxBarSize,m=ty(u),g=m.numericAxisName,v=m.cateAxisName,y=!!n&&!!n.length&&n.some(function(e){var t=(0,_.Gf)(e&&e.type);return t&&t.indexOf("Bar")>=0})&&(0,M.pt)({barSize:c,stackGroups:r}),b=[];return n.forEach(function(n,c){var m,x=tc(e.data,{graphicalItems:[n],dataStartIndex:l,dataEndIndex:s}),k=n.props,S=k.dataKey,E=k.maxBarSize,O=n.props["".concat(g,"Id")],C=n.props["".concat(v,"Id")],j=f.reduce(function(e,r){var o,i=t["".concat(r.axisType,"Map")],a=n.props["".concat(r.axisType,"Id")];i&&i[a]||"zAxis"===r.axisType||(0,w.Z)(!1);var l=i[a];return tt(tt({},e),{},(tn(o={},r.axisType,l),tn(o,"".concat(r.axisType,"Ticks"),(0,M.uY)(l)),o))},{}),P=j[v],T=j["".concat(v,"Ticks")],N=r&&r[O]&&r[O].hasStack&&(0,M.O3)(n,r[O].stackGroups),A=(0,_.Gf)(n.type).indexOf("Bar")>=0,I=(0,M.zT)(P,T),R=[];if(A){var D,L,z=i()(E)?h:E,Z=null!==(D=null!==(L=(0,M.zT)(P,T,!0))&&void 0!==L?L:z)&&void 0!==D?D:0;R=(0,M.qz)({barGap:d,barCategoryGap:p,bandSize:Z!==I?Z:I,sizeList:y[C],maxBarSize:z}),Z!==I&&(R=R.map(function(e){return tt(tt({},e),{},{position:tt(tt({},e.position),{},{offset:e.position.offset-Z/2})})}))}var B=n&&n.type&&n.type.getComposedData;B&&b.push({props:tt(tt({},B(tt(tt({},j),{},{displayedData:x,props:e,dataKey:S,item:n,bandSize:I,barPosition:R,offset:o,stackedData:N,layout:u,dataStartIndex:l,dataEndIndex:s}))),{},(tn(m={key:n.key||"item-".concat(c)},g,j[g]),tn(m,v,j[v]),tn(m,"animationId",a),m)),childIndex:(0,_.$R)(n,e.children),item:n})}),b},O=function(e,t){var r=e.props,i=e.dataStartIndex,a=e.dataEndIndex,l=e.updateId;if(!(0,_.TT)({props:r}))return null;var s=r.children,c=r.layout,u=r.stackOffset,d=r.data,p=r.reverseStackOrder,m=ty(c),g=m.numericAxisName,v=m.cateAxisName,b=(0,_.NN)(s,o),x=(0,M.wh)(d,b,"".concat(g,"Id"),"".concat(v,"Id"),u,p),w=f.reduce(function(e,t){var n="".concat(t.axisType,"Map");return tt(tt({},e),{},tn({},n,tm(r,tt(tt({},t),{},{graphicalItems:b,stackGroups:t.axisType===g&&x,dataStartIndex:i,dataEndIndex:a}))))},{}),k=tb(tt(tt({},w),{},{props:r,graphicalItems:b}),null==t?void 0:t.legendBBox);Object.keys(w).forEach(function(e){w[e]=h(r,w[e],k,e.replace("Map",""),n)});var S=tg(w["".concat(v,"Map")]),E=y(r,tt(tt({},w),{},{dataStartIndex:i,dataEndIndex:a,updateId:l,graphicalItems:b,stackGroups:x,offset:k}));return tt(tt({formattedGraphicalItems:E,graphicalItems:b,offset:k,stackGroups:x},S),w)};return t=function(e){(function(e,t){if("function"!=typeof t&&null!==t)throw TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),Object.defineProperty(e,"prototype",{writable:!1}),t&&e6(e,t)})(c,e);var t,o,a=(t=function(){if("undefined"==typeof Reflect||!Reflect.construct||Reflect.construct.sham)return!1;if("function"==typeof 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v?[C,null,null]:[C,null]}),tn(e3(s),"renderCustomized",function(e,t,n){return(0,r.cloneElement)(e,tt(tt({key:"recharts-customized-".concat(n)},s.props),s.state))}),tn(e3(s),"renderMap",{CartesianGrid:{handler:s.renderGrid,once:!0},ReferenceArea:{handler:s.renderReferenceElement},ReferenceLine:{handler:tl},ReferenceDot:{handler:s.renderReferenceElement},XAxis:{handler:tl},YAxis:{handler:tl},Brush:{handler:s.renderBrush,once:!0},Bar:{handler:s.renderGraphicChild},Line:{handler:s.renderGraphicChild},Area:{handler:s.renderGraphicChild},Radar:{handler:s.renderGraphicChild},RadialBar:{handler:s.renderGraphicChild},Scatter:{handler:s.renderGraphicChild},Pie:{handler:s.renderGraphicChild},Funnel:{handler:s.renderGraphicChild},Tooltip:{handler:s.renderCursor,once:!0},PolarGrid:{handler:s.renderPolarGrid,once:!0},PolarAngleAxis:{handler:s.renderPolarAxis},PolarRadiusAxis:{handler:s.renderPolarAxis},Customized:{handler:s.renderCustomized}}),s.clipPathId="".concat(null!==(t=e.id)&&void 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l=this.state.tooltipTicks[a]&&this.state.tooltipTicks[a].value,s=td(this.state,n,a,l),c=this.state.tooltipTicks[a].coordinate,u=(this.state.offset.top+r)/2,d="horizontal"===o?{x:c,y:u}:{y:c,x:u},f=this.state.formattedGraphicalItems.find(function(e){return"Scatter"===e.item.type.name});f&&(d=tt(tt({},d),f.props.points[a].tooltipPosition),s=f.props.points[a].tooltipPayload);var p={activeTooltipIndex:a,isTooltipActive:!0,activeLabel:l,activePayload:s,activeCoordinate:d};this.setState(p),this.renderCursor(i),this.accessibilityManager.setIndex(a)}}}},{key:"getSnapshotBeforeUpdate",value:function(e,t){if(!this.props.accessibilityLayer)return null;if(this.state.tooltipTicks!==t.tooltipTicks&&this.accessibilityManager.setDetails({coordinateList:this.state.tooltipTicks}),this.props.layout!==e.layout&&this.accessibilityManager.setDetails({layout:this.props.layout}),this.props.margin!==e.margin){var n,r;this.accessibilityManager.setDetails({offset:{left:null!==(n=this.props.margin.left)&&void 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u=(0,N.Kt)(s).scale,d=(0,N.Kt)(c).scale,f=u&&u.invert?u.invert(o.chartX):null,p=d&&d.invert?d.invert(o.chartY):null;return tt(tt({},o),{},{xValue:f,yValue:p})}var h=tf(this.state,this.props.data,this.props.layout,a);return h?tt(tt({},o),h):null}},{key:"inRange",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,r=this.props.layout,o=e/n,i=t/n;if("horizontal"===r||"vertical"===r){var a=this.state.offset;return o>=a.left&&o<=a.left+a.width&&i>=a.top&&i<=a.top+a.height?{x:o,y:i}:null}var l=this.state,s=l.angleAxisMap,c=l.radiusAxisMap;if(s&&c){var u=(0,N.Kt)(s);return(0,eP.z3)({x:o,y:i},u)}return null}},{key:"parseEventsOfWrapper",value:function(){var e=this.props.children,t=this.getTooltipEventType(),n=(0,_.sP)(e,E.u),r={};return 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