mirror of
https://github.com/tiennm99/litellm.git
synced 2026-08-19 04:23:44 +00:00
Merge branch 'BerriAI:main' into docs/elasticsearch-logging-tutorial
This commit is contained in:
Cole McIntosh
+26
-9
@@ -481,7 +481,7 @@ jobs:
|
||||
paths:
|
||||
- litellm_router_coverage.xml
|
||||
- litellm_router_coverage
|
||||
litellm_proxy_security_tests:
|
||||
litellm_security_tests:
|
||||
docker:
|
||||
- image: cimg/python:3.11
|
||||
auth:
|
||||
@@ -504,6 +504,23 @@ jobs:
|
||||
pip install "pytest-retry==1.6.3"
|
||||
pip install "pytest-asyncio==0.21.1"
|
||||
pip install "pytest-cov==5.0.0"
|
||||
- run:
|
||||
name: Install Trivy
|
||||
command: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install wget apt-transport-https gnupg lsb-release
|
||||
wget -qO - https://aquasecurity.github.io/trivy-repo/deb/public.key | sudo apt-key add -
|
||||
echo "deb https://aquasecurity.github.io/trivy-repo/deb $(lsb_release -sc) main" | sudo tee -a /etc/apt/sources.list.d/trivy.list
|
||||
sudo apt-get update
|
||||
sudo apt-get install trivy
|
||||
- run:
|
||||
name: Run Trivy scan on LiteLLM Docs
|
||||
command: |
|
||||
trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/
|
||||
- run:
|
||||
name: Run Trivy scan on LiteLLM UI
|
||||
command: |
|
||||
trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/
|
||||
- run:
|
||||
name: Run prisma ./docker/entrypoint.sh
|
||||
command: |
|
||||
@@ -522,16 +539,16 @@ jobs:
|
||||
- run:
|
||||
name: Rename the coverage files
|
||||
command: |
|
||||
mv coverage.xml litellm_proxy_security_tests_coverage.xml
|
||||
mv .coverage litellm_proxy_security_tests_coverage
|
||||
mv coverage.xml litellm_security_tests_coverage.xml
|
||||
mv .coverage litellm_security_tests_coverage
|
||||
# Store test results
|
||||
- store_test_results:
|
||||
path: test-results
|
||||
- persist_to_workspace:
|
||||
root: .
|
||||
paths:
|
||||
- litellm_proxy_security_tests_coverage.xml
|
||||
- litellm_proxy_security_tests_coverage
|
||||
- litellm_security_tests_coverage.xml
|
||||
- litellm_security_tests_coverage
|
||||
litellm_proxy_unit_testing: # Runs all tests with the "proxy", "key", "jwt" filenames
|
||||
docker:
|
||||
- image: cimg/python:3.11
|
||||
@@ -2419,7 +2436,7 @@ jobs:
|
||||
python -m venv venv
|
||||
. venv/bin/activate
|
||||
pip install coverage
|
||||
coverage combine llm_translation_coverage llm_responses_api_coverage mcp_coverage logging_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_proxy_security_tests_coverage guardrails_coverage
|
||||
coverage combine llm_translation_coverage llm_responses_api_coverage mcp_coverage logging_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage
|
||||
coverage xml
|
||||
- codecov/upload:
|
||||
file: ./coverage.xml
|
||||
@@ -2813,7 +2830,7 @@ workflows:
|
||||
only:
|
||||
- main
|
||||
- /litellm_.*/
|
||||
- litellm_proxy_security_tests:
|
||||
- litellm_security_tests:
|
||||
filters:
|
||||
branches:
|
||||
only:
|
||||
@@ -2972,7 +2989,7 @@ workflows:
|
||||
- litellm_router_testing
|
||||
- caching_unit_tests
|
||||
- litellm_proxy_unit_testing
|
||||
- litellm_proxy_security_tests
|
||||
- litellm_security_tests
|
||||
- langfuse_logging_unit_tests
|
||||
- local_testing
|
||||
- litellm_assistants_api_testing
|
||||
@@ -3042,7 +3059,7 @@ workflows:
|
||||
- db_migration_disable_update_check
|
||||
- e2e_ui_testing
|
||||
- litellm_proxy_unit_testing
|
||||
- litellm_proxy_security_tests
|
||||
- litellm_security_tests
|
||||
- installing_litellm_on_python
|
||||
- installing_litellm_on_python_3_13
|
||||
- proxy_logging_guardrails_model_info_tests
|
||||
|
||||
@@ -73,7 +73,7 @@ You can find [supported data regions litellm here](../docs/data_security#support
|
||||
Professional Support can assist with LLM/Provider integrations, deployment, upgrade management, and LLM Provider troubleshooting. We can’t solve your own infrastructure-related issues but we will guide you to fix them.
|
||||
|
||||
- 1 hour for Sev0 issues - 100% production traffic is failing
|
||||
- 6 hours for Sev1 - <100% production traffic is failing
|
||||
- 6 hours for Sev1 - < 100% production traffic is failing
|
||||
- 24h for Sev2-Sev3 between 7am – 7pm PT (Monday through Saturday) - setup issues e.g. Redis working on our end, but not on your infrastructure.
|
||||
- 72h SLA for patching vulnerabilities in the software.
|
||||
|
||||
|
||||
@@ -23,6 +23,9 @@ LiteLLM Proxy provides an MCP Gateway that allows you to use a fixed endpoint fo
|
||||
|
||||
## Adding your MCP
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="ui" label="LiteLLM UI">
|
||||
|
||||
On the LiteLLM UI, Navigate to "MCP Servers" and click "Add New MCP Server".
|
||||
|
||||
On this form, you should enter your MCP Server URL and the transport you want to use.
|
||||
@@ -36,6 +39,49 @@ LiteLLM supports the following MCP transports:
|
||||
style={{width: '80%', display: 'block', margin: '0'}}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="config" label="config.yaml">
|
||||
|
||||
Add your MCP servers directly in your `config.yaml` file:
|
||||
|
||||
```yaml title="config.yaml" showLineNumbers
|
||||
model_list:
|
||||
- model_name: gpt-4o
|
||||
litellm_params:
|
||||
model: openai/gpt-4o
|
||||
api_key: sk-xxxxxxx
|
||||
|
||||
mcp_servers:
|
||||
# HTTP Streamable Server
|
||||
deepwiki_mcp:
|
||||
url: "https://mcp.deepwiki.com/mcp"
|
||||
# SSE Server
|
||||
zapier_mcp:
|
||||
url: "https://actions.zapier.com/mcp/sk-akxxxxx/sse"
|
||||
|
||||
# Full configuration with all optional fields
|
||||
my_http_server:
|
||||
url: "https://my-mcp-server.com/mcp"
|
||||
transport: "http"
|
||||
description: "My custom MCP server"
|
||||
auth_type: "api_key"
|
||||
spec_version: "2025-03-26"
|
||||
```
|
||||
|
||||
**Configuration Options:**
|
||||
- **Server Name**: Use any descriptive name for your MCP server (e.g., `zapier_mcp`, `deepwiki_mcp`)
|
||||
- **URL**: The endpoint URL for your MCP server (required)
|
||||
- **Transport**: Optional transport type (defaults to `sse`)
|
||||
- `sse` - SSE (Server-Sent Events) transport
|
||||
- `http` - Streamable HTTP transport
|
||||
- **Description**: Optional description for the server
|
||||
- **Auth Type**: Optional authentication type
|
||||
- **Spec Version**: Optional MCP specification version (defaults to `2025-03-26`)
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
|
||||
|
||||
## Using your MCP
|
||||
|
||||
@@ -50,7 +50,7 @@ For further configuration, please refer to the [Argilla documentation](https://d
|
||||
## Usage
|
||||
|
||||
<Tabs>
|
||||
<Tab value="sdk" label="SDK">
|
||||
<TabItem value="sdk" label="SDK">
|
||||
|
||||
```python
|
||||
import os
|
||||
@@ -78,9 +78,9 @@ response = completion(
|
||||
)
|
||||
```
|
||||
|
||||
</Tab>
|
||||
</TabItem>
|
||||
|
||||
<Tab value="proxy" label="PROXY">
|
||||
<TabItem value="proxy" label="PROXY">
|
||||
|
||||
```yaml
|
||||
litellm_settings:
|
||||
@@ -90,7 +90,7 @@ litellm_settings:
|
||||
llm_output: "response"
|
||||
```
|
||||
|
||||
</Tab>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Example Output
|
||||
|
||||
@@ -1,3 +1,7 @@
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Image from '@theme/IdealImage';
|
||||
|
||||
# Custom API Server (Custom Format)
|
||||
|
||||
Call your custom torch-serve / internal LLM APIs via LiteLLM
|
||||
|
||||
@@ -168,7 +168,7 @@ The Nebius provider supports the following parameters:
|
||||
| max_tokens | integer | Maximum number of tokens to generate |
|
||||
| n | integer | Number of completions to generate |
|
||||
| presence_penalty | number | Penalizes tokens based on if they appear in the text so far |
|
||||
| response_format | object | Format of the response, e.g., {"type": "json"} |
|
||||
| response_format | object | Format of the response, e.g., `{"type": "json"}` |
|
||||
| seed | integer | Sampling seed for deterministic results |
|
||||
| stop | string/array | Sequences where the API will stop generating tokens |
|
||||
| stream | boolean | Whether to stream the response |
|
||||
|
||||
@@ -207,6 +207,50 @@ print(delete_response)
|
||||
|----------|---------------------|
|
||||
| `openai` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
|
||||
|
||||
### Reusable Prompts
|
||||
|
||||
Use the `prompt` parameter to reference a stored prompt template and optionally supply variables.
|
||||
|
||||
```python showLineNumbers title="Stored Prompt"
|
||||
import litellm
|
||||
|
||||
response = litellm.responses(
|
||||
model="openai/o1-pro",
|
||||
prompt={
|
||||
"id": "pmpt_abc123",
|
||||
"version": "2",
|
||||
"variables": {
|
||||
"customer_name": "Jane Doe",
|
||||
"product": "40oz juice box",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
print(response)
|
||||
```
|
||||
|
||||
The same parameter is supported when calling the LiteLLM proxy with the OpenAI SDK:
|
||||
|
||||
```python showLineNumbers title="Stored Prompt via Proxy"
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(base_url="http://localhost:4000", api_key="your-api-key")
|
||||
|
||||
response = client.responses.create(
|
||||
model="openai/o1-pro",
|
||||
prompt={
|
||||
"id": "pmpt_abc123",
|
||||
"version": "2",
|
||||
"variables": {
|
||||
"customer_name": "Jane Doe",
|
||||
"product": "40oz juice box",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Computer Use
|
||||
|
||||
<Tabs>
|
||||
|
||||
@@ -8,7 +8,7 @@ import TabItem from '@theme/TabItem';
|
||||
| Description | The Snowflake Cortex LLM REST API lets you access the COMPLETE function via HTTP POST requests|
|
||||
| Provider Route on LiteLLM | `snowflake/` |
|
||||
| Link to Provider Doc | [Snowflake ↗](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api) |
|
||||
| Base URL | [https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:complete/](https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:complete) |
|
||||
| Base URL | `https://{account-id}.snowflakecomputing.com/api/v2/cortex/inference:complete` |
|
||||
| Supported OpenAI Endpoints | `/chat/completions`, `/completions` |
|
||||
|
||||
|
||||
|
||||
@@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem';
|
||||
| Description | Vertex AI is a fully-managed AI development platform for building and using generative AI. |
|
||||
| Provider Route on LiteLLM | `vertex_ai/` |
|
||||
| Link to Provider Doc | [Vertex AI ↗](https://cloud.google.com/vertex-ai) |
|
||||
| Base URL | 1. Regional endpoints<br/>[https://{vertex_location}-aiplatform.googleapis.com/](https://{vertex_location}-aiplatform.googleapis.com/)<br/>2. Global endpoints (limited availability)<br/>[https://aiplatform.googleapis.com/](https://{aiplatform.googleapis.com/)|
|
||||
| Base URL | 1. Regional endpoints<br/>`https://{vertex_location}-aiplatform.googleapis.com/`<br/>2. Global endpoints (limited availability)<br/>`https://aiplatform.googleapis.com/`|
|
||||
| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models) |
|
||||
|
||||
|
||||
|
||||
@@ -50,6 +50,7 @@ GENERIC_AUTHORIZATION_ENDPOINT = "<your-okta-domain>/authorize" # https://dev-2k
|
||||
GENERIC_TOKEN_ENDPOINT = "<your-okta-domain>/token" # https://dev-2kqkcd6lx6kdkuzt.us.auth0.com/oauth/token
|
||||
GENERIC_USERINFO_ENDPOINT = "<your-okta-domain>/userinfo" # https://dev-2kqkcd6lx6kdkuzt.us.auth0.com/userinfo
|
||||
GENERIC_CLIENT_STATE = "random-string" # [OPTIONAL] REQUIRED BY OKTA, if not set random state value is generated
|
||||
GENERIC_SSO_HEADERS = "Content-Type=application/json, X-Custom-Header=custom-value" # [OPTIONAL] Comma-separated list of additional headers to add to the request - e.g. Content-Type=application/json, etc.
|
||||
```
|
||||
|
||||
You can get your domain specific auth/token/userinfo endpoints at `<YOUR-OKTA-DOMAIN>/.well-known/openid-configuration`
|
||||
|
||||
@@ -1,3 +1,7 @@
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Image from '@theme/IdealImage';
|
||||
|
||||
# Clientside LLM Credentials
|
||||
|
||||
|
||||
|
||||
@@ -333,6 +333,7 @@ router_settings:
|
||||
| AZURE_USERNAME | Username for Azure services, use in conjunction with AZURE_PASSWORD for azure ad token with basic username/password workflow
|
||||
| AZURE_PASSWORD | Password for Azure services, use in conjunction with AZURE_USERNAME for azure ad token with basic username/password workflow
|
||||
| AZURE_FEDERATED_TOKEN_FILE | File path to Azure federated token
|
||||
| AZURE_SCOPE | For EntraID Auth, Scope for Azure services, defaults to "https://cognitiveservices.azure.com/.default"
|
||||
| AZURE_KEY_VAULT_URI | URI for Azure Key Vault
|
||||
| AZURE_OPERATION_POLLING_TIMEOUT | Timeout in seconds for Azure operation polling
|
||||
| AZURE_STORAGE_ACCOUNT_KEY | The Azure Storage Account Key to use for Authentication to Azure Blob Storage logging
|
||||
@@ -446,6 +447,7 @@ router_settings:
|
||||
| GENERIC_CLIENT_ID | Client ID for generic OAuth providers
|
||||
| GENERIC_CLIENT_SECRET | Client secret for generic OAuth providers
|
||||
| GENERIC_CLIENT_STATE | State parameter for generic client authentication
|
||||
| GENERIC_SSO_HEADERS | Comma-separated list of additional headers to add to the request - e.g. Authorization=Bearer `<token>`, Content-Type=application/json, etc.
|
||||
| GENERIC_INCLUDE_CLIENT_ID | Include client ID in requests for OAuth
|
||||
| GENERIC_SCOPE | Scope settings for generic OAuth providers
|
||||
| GENERIC_TOKEN_ENDPOINT | Token endpoint for generic OAuth providers
|
||||
|
||||
@@ -1,3 +1,7 @@
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Image from '@theme/IdealImage';
|
||||
|
||||
# UI - Custom Root Path
|
||||
|
||||
💥 Use this when you want to serve LiteLLM on a custom base url path like `https://localhost:4000/api/v1`
|
||||
|
||||
@@ -892,7 +892,7 @@ litellm_settings:
|
||||
|
||||
This will default to claude-opus in case any model fails.
|
||||
|
||||
A model-specific fallbacks (e.g. {"gpt-3.5-turbo-small": ["claude-opus"]}) overrides default fallback.
|
||||
A model-specific fallbacks (e.g. `{"gpt-3.5-turbo-small": ["claude-opus"]}`) overrides default fallback.
|
||||
|
||||
### EU-Region Filtering (Pre-Call Checks)
|
||||
|
||||
|
||||
Generated
+7909
-13650
File diff suppressed because it is too large
Load Diff
@@ -14,21 +14,20 @@
|
||||
"write-heading-ids": "docusaurus write-heading-ids"
|
||||
},
|
||||
"dependencies": {
|
||||
"@docusaurus/core": "2.4.1",
|
||||
"@docusaurus/plugin-google-gtag": "^2.4.1",
|
||||
"@docusaurus/plugin-ideal-image": "^2.4.1",
|
||||
"@docusaurus/preset-classic": "2.4.1",
|
||||
"@mdx-js/react": "^1.6.22",
|
||||
"@docusaurus/core": "3.8.1",
|
||||
"@docusaurus/plugin-google-gtag": "3.8.1",
|
||||
"@docusaurus/plugin-ideal-image": "3.8.1",
|
||||
"@docusaurus/preset-classic": "3.8.1",
|
||||
"@mdx-js/react": "^3.0.0",
|
||||
"clsx": "^1.2.1",
|
||||
"docusaurus": "^1.14.7",
|
||||
"prism-react-renderer": "^1.3.5",
|
||||
"react": "^17.0.2",
|
||||
"react-dom": "^17.0.2",
|
||||
"react": "^18.0.0 || ^19.0.0",
|
||||
"react-dom": "^18.0.0 || ^19.0.0",
|
||||
"sharp": "^0.32.6",
|
||||
"uuid": "^9.0.1"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@docusaurus/module-type-aliases": "2.4.1"
|
||||
"@docusaurus/module-type-aliases": "3.8.1"
|
||||
},
|
||||
"browserslist": {
|
||||
"production": [
|
||||
@@ -44,5 +43,8 @@
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=16.14"
|
||||
},
|
||||
"overrides": {
|
||||
"webpack-dev-server": ">=5.2.1"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -57,7 +57,7 @@ Here's a Demo Instance to test changes:
|
||||
2. Bedrock Claude - fix tool calling transformation on invoke route. [Get Started](../../docs/providers/bedrock#usage---function-calling--tool-calling)
|
||||
3. Bedrock Claude - response_format support for claude on invoke route. [Get Started](../../docs/providers/bedrock#usage---structured-output--json-mode)
|
||||
4. Bedrock - pass `description` if set in response_format. [Get Started](../../docs/providers/bedrock#usage---structured-output--json-mode)
|
||||
5. Bedrock - Fix passing response_format: {"type": "text"}. [PR](https://github.com/BerriAI/litellm/commit/c84b489d5897755139aa7d4e9e54727ebe0fa540)
|
||||
5. Bedrock - Fix passing response_format: `{"type": "text"}`. [PR](https://github.com/BerriAI/litellm/commit/c84b489d5897755139aa7d4e9e54727ebe0fa540)
|
||||
6. OpenAI - Handle sending image_url as str to openai. [Get Started](https://docs.litellm.ai/docs/completion/vision)
|
||||
7. Deepseek - return 'reasoning_content' missing on streaming. [Get Started](https://docs.litellm.ai/docs/reasoning_content)
|
||||
8. Caching - Support caching on reasoning content. [Get Started](https://docs.litellm.ai/docs/proxy/caching)
|
||||
|
||||
@@ -407,6 +407,7 @@ BEDROCK_CONVERSE_MODELS = [
|
||||
"meta.llama3-70b-instruct-v1:0",
|
||||
"mistral.mistral-large-2407-v1:0",
|
||||
"mistral.mistral-large-2402-v1:0",
|
||||
"mistral.mistral-small-2402-v1:0",
|
||||
"meta.llama3-2-1b-instruct-v1:0",
|
||||
"meta.llama3-2-3b-instruct-v1:0",
|
||||
"meta.llama3-2-11b-instruct-v1:0",
|
||||
|
||||
@@ -40,7 +40,7 @@ class PrometheusLogger(CustomLogger):
|
||||
from prometheus_client import Counter, Gauge, Histogram
|
||||
|
||||
from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
|
||||
|
||||
|
||||
# Always initialize label_filters, even for non-premium users
|
||||
self.label_filters = self._parse_prometheus_config()
|
||||
|
||||
|
||||
@@ -4,15 +4,65 @@ Ollama /chat/completion calls handled in llm_http_handler.py
|
||||
[TODO]: migrate embeddings to a base handler as well.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import litellm
|
||||
from litellm.types.utils import EmbeddingResponse
|
||||
|
||||
# ollama wants plain base64 jpeg/png files as images. strip any leading dataURI
|
||||
# and convert to jpeg if necessary.
|
||||
def _prepare_ollama_embedding_payload(
|
||||
model: str,
|
||||
prompts: List[str],
|
||||
optional_params: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
|
||||
data: Dict[str, Any] = {"model": model, "input": prompts}
|
||||
special_optional_params = ["truncate", "options", "keep_alive"]
|
||||
|
||||
for k, v in optional_params.items():
|
||||
if k in special_optional_params:
|
||||
data[k] = v
|
||||
else:
|
||||
data.setdefault("options", {})
|
||||
if isinstance(data["options"], dict):
|
||||
data["options"].update({k: v})
|
||||
return data
|
||||
|
||||
def _process_ollama_embedding_response(
|
||||
response_json: dict,
|
||||
prompts: List[str],
|
||||
model: str,
|
||||
model_response: EmbeddingResponse,
|
||||
logging_obj: Any,
|
||||
encoding: Any
|
||||
) -> EmbeddingResponse:
|
||||
output_data = []
|
||||
embeddings: List[List[float]] = response_json["embeddings"]
|
||||
|
||||
for idx, emb in enumerate(embeddings):
|
||||
output_data.append({"object": "embedding", "index": idx, "embedding": emb})
|
||||
|
||||
input_tokens = response_json.get("prompt_eval_count", None)
|
||||
|
||||
if input_tokens is None:
|
||||
if encoding is not None:
|
||||
input_tokens = len(encoding.encode("".join(prompts)))
|
||||
if logging_obj:
|
||||
logging_obj.debug("Ollama response missing prompt_eval_count; estimated with encoding.")
|
||||
else:
|
||||
input_tokens = 0
|
||||
if logging_obj:
|
||||
logging_obj.warning("Missing prompt_eval_count and no encoding provided; defaulted to 0.")
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = "ollama/" + model
|
||||
model_response.usage = litellm.Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=input_tokens,
|
||||
prompt_tokens_details=None,
|
||||
completion_tokens_details=None,
|
||||
)
|
||||
return model_response
|
||||
|
||||
async def ollama_aembeddings(
|
||||
api_base: str,
|
||||
@@ -23,80 +73,45 @@ async def ollama_aembeddings(
|
||||
logging_obj: Any,
|
||||
encoding: Any,
|
||||
):
|
||||
if api_base.endswith("/api/embed"):
|
||||
url = api_base
|
||||
else:
|
||||
url = f"{api_base}/api/embed"
|
||||
if not api_base.endswith("/api/embed"):
|
||||
api_base += "/api/embed"
|
||||
|
||||
## Load Config
|
||||
config = litellm.OllamaConfig.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
|
||||
data = _prepare_ollama_embedding_payload(model, prompts, optional_params)
|
||||
|
||||
data: Dict[str, Any] = {"model": model, "input": prompts}
|
||||
special_optional_params = ["truncate", "options", "keep_alive"]
|
||||
response = await litellm.module_level_aclient.post(url=api_base, json=data)
|
||||
response_json = await response.json()
|
||||
|
||||
for k, v in optional_params.items():
|
||||
if k in special_optional_params:
|
||||
data[k] = v
|
||||
else:
|
||||
# Ensure "options" is a dictionary before updating it
|
||||
data.setdefault("options", {})
|
||||
if isinstance(data["options"], dict):
|
||||
data["options"].update({k: v})
|
||||
total_input_tokens = 0
|
||||
output_data = []
|
||||
|
||||
response = await litellm.module_level_aclient.post(url=url, json=data)
|
||||
|
||||
response_json = response.json()
|
||||
|
||||
embeddings: List[List[float]] = response_json["embeddings"]
|
||||
for idx, emb in enumerate(embeddings):
|
||||
output_data.append({"object": "embedding", "index": idx, "embedding": emb})
|
||||
|
||||
input_tokens = response_json.get("prompt_eval_count") or len(
|
||||
encoding.encode("".join(prompt for prompt in prompts))
|
||||
return _process_ollama_embedding_response(
|
||||
response_json=response_json,
|
||||
prompts=prompts,
|
||||
model=model,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
encoding=encoding
|
||||
)
|
||||
total_input_tokens += input_tokens
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = "ollama/" + model
|
||||
setattr(
|
||||
model_response,
|
||||
"usage",
|
||||
litellm.Usage(
|
||||
prompt_tokens=total_input_tokens,
|
||||
completion_tokens=total_input_tokens,
|
||||
total_tokens=total_input_tokens,
|
||||
prompt_tokens_details=None,
|
||||
completion_tokens_details=None,
|
||||
),
|
||||
)
|
||||
return model_response
|
||||
|
||||
|
||||
def ollama_embeddings(
|
||||
api_base: str,
|
||||
model: str,
|
||||
prompts: list,
|
||||
prompts: List[str],
|
||||
optional_params: dict,
|
||||
model_response: EmbeddingResponse,
|
||||
logging_obj: Any,
|
||||
encoding=None,
|
||||
encoding: Any = None,
|
||||
):
|
||||
return asyncio.run(
|
||||
ollama_aembeddings(
|
||||
api_base=api_base,
|
||||
model=model,
|
||||
prompts=prompts,
|
||||
model_response=model_response,
|
||||
optional_params=optional_params,
|
||||
logging_obj=logging_obj,
|
||||
encoding=encoding,
|
||||
)
|
||||
if not api_base.endswith("/api/embed"):
|
||||
api_base += "/api/embed"
|
||||
|
||||
data = _prepare_ollama_embedding_payload(model, prompts, optional_params)
|
||||
|
||||
response = litellm.module_level_client.post(url=api_base, json=data)
|
||||
response_json = response.json()
|
||||
|
||||
return _process_ollama_embedding_response(
|
||||
response_json=response_json,
|
||||
prompts=prompts,
|
||||
model=model,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
encoding=encoding
|
||||
)
|
||||
|
||||
@@ -38,6 +38,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
|
||||
"store",
|
||||
"background",
|
||||
"stream",
|
||||
"prompt",
|
||||
"temperature",
|
||||
"text",
|
||||
"tool_choice",
|
||||
|
||||
@@ -8381,6 +8381,24 @@
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.04,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-ultra-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.06,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-fast-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.02,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-3.0-generate-002": {
|
||||
"output_cost_per_image": 0.04,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
@@ -15002,4 +15020,4 @@
|
||||
"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1 +1 @@
|
||||
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@@ -1 +1 @@
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|
||||
if user_object and user_object.user_role == LitellmUserRoles.PROXY_ADMIN:
|
||||
is_proxy_admin = True
|
||||
else:
|
||||
is_proxy_admin = False
|
||||
|
||||
return JWTAuthBuilderResult(
|
||||
is_proxy_admin=False,
|
||||
is_proxy_admin=is_proxy_admin,
|
||||
team_id=team_id,
|
||||
team_object=team_object,
|
||||
user_id=user_id,
|
||||
|
||||
@@ -37,10 +37,6 @@ def get_provider_models(
|
||||
provider_models = get_valid_models(
|
||||
custom_llm_provider=provider, litellm_params=litellm_params
|
||||
)
|
||||
# provider_models = copy.deepcopy(litellm.models_by_provider[provider])
|
||||
for idx, _model in enumerate(provider_models):
|
||||
if provider not in _model:
|
||||
provider_models[idx] = f"{provider}/{_model}"
|
||||
return provider_models
|
||||
return None
|
||||
|
||||
@@ -189,26 +185,39 @@ def get_known_models_from_wildcard(
|
||||
wildcard_model: str, litellm_params: Optional[LiteLLM_Params] = None
|
||||
) -> List[str]:
|
||||
try:
|
||||
provider, model = wildcard_model.split("/", 1)
|
||||
wildcard_provider_prefix, wildcard_suffix = wildcard_model.split("/", 1)
|
||||
except ValueError: # safely fail
|
||||
return []
|
||||
|
||||
if litellm_params is None: # need litellm params to extract litellm model name
|
||||
return []
|
||||
|
||||
try:
|
||||
provider = litellm_params.model.split("/", 1)[0]
|
||||
except ValueError:
|
||||
provider = wildcard_provider_prefix
|
||||
|
||||
# get all known provider models
|
||||
wildcard_models = get_provider_models(
|
||||
provider=provider, litellm_params=litellm_params
|
||||
)
|
||||
if wildcard_models is None:
|
||||
return []
|
||||
if model == "*":
|
||||
return wildcard_models or []
|
||||
else:
|
||||
model_prefix = model.replace("*", "")
|
||||
if wildcard_suffix != "*":
|
||||
model_prefix = wildcard_suffix.replace("*", "")
|
||||
filtered_wildcard_models = [
|
||||
wc_model
|
||||
for wc_model in wildcard_models
|
||||
if wc_model.split("/")[1].startswith(model_prefix)
|
||||
if wc_model.startswith(model_prefix)
|
||||
]
|
||||
wildcard_models = filtered_wildcard_models
|
||||
|
||||
return filtered_wildcard_models
|
||||
suffix_appended_wildcard_models = []
|
||||
for model in wildcard_models:
|
||||
if not model.startswith(wildcard_provider_prefix):
|
||||
model = f"{wildcard_provider_prefix}/{model}"
|
||||
suffix_appended_wildcard_models.append(model)
|
||||
return suffix_appended_wildcard_models or []
|
||||
|
||||
|
||||
def _get_wildcard_models(
|
||||
|
||||
@@ -520,7 +520,11 @@ async def _user_api_key_auth_builder( # noqa: PLR0915
|
||||
team_object.rpm_limit if team_object is not None else None
|
||||
),
|
||||
team_models=team_object.models if team_object is not None else [],
|
||||
user_role=LitellmUserRoles.INTERNAL_USER,
|
||||
user_role=(
|
||||
LitellmUserRoles(user_object.user_role)
|
||||
if user_object is not None and user_object.user_role is not None
|
||||
else LitellmUserRoles.INTERNAL_USER
|
||||
),
|
||||
user_id=user_id,
|
||||
org_id=org_id,
|
||||
parent_otel_span=parent_otel_span,
|
||||
@@ -606,23 +610,23 @@ async def _user_api_key_auth_builder( # noqa: PLR0915
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
)
|
||||
if _end_user_object is not None:
|
||||
end_user_params["allowed_model_region"] = (
|
||||
_end_user_object.allowed_model_region
|
||||
)
|
||||
end_user_params[
|
||||
"allowed_model_region"
|
||||
] = _end_user_object.allowed_model_region
|
||||
if _end_user_object.litellm_budget_table is not None:
|
||||
budget_info = _end_user_object.litellm_budget_table
|
||||
if budget_info.tpm_limit is not None:
|
||||
end_user_params["end_user_tpm_limit"] = (
|
||||
budget_info.tpm_limit
|
||||
)
|
||||
end_user_params[
|
||||
"end_user_tpm_limit"
|
||||
] = budget_info.tpm_limit
|
||||
if budget_info.rpm_limit is not None:
|
||||
end_user_params["end_user_rpm_limit"] = (
|
||||
budget_info.rpm_limit
|
||||
)
|
||||
end_user_params[
|
||||
"end_user_rpm_limit"
|
||||
] = budget_info.rpm_limit
|
||||
if budget_info.max_budget is not None:
|
||||
end_user_params["end_user_max_budget"] = (
|
||||
budget_info.max_budget
|
||||
)
|
||||
end_user_params[
|
||||
"end_user_max_budget"
|
||||
] = budget_info.max_budget
|
||||
except Exception as e:
|
||||
if isinstance(e, litellm.BudgetExceededError):
|
||||
raise e
|
||||
|
||||
@@ -483,6 +483,23 @@ class LiteLLMProxyRequestSetup:
|
||||
tags = [tag.strip() for tag in _tags]
|
||||
elif isinstance(headers["x-litellm-tags"], list):
|
||||
tags = headers["x-litellm-tags"]
|
||||
if "user-agent" in headers:
|
||||
"""
|
||||
Allow tracking spend by cli tools like Claude Code - e.g. "claude-cli/1.0.25 (external, cli)"
|
||||
"""
|
||||
# add user-agent to tags
|
||||
if tags is None:
|
||||
tags = []
|
||||
user_agent = headers["user-agent"]
|
||||
if user_agent is not None:
|
||||
user_agent_part: Optional[str] = None
|
||||
if "/" in user_agent:
|
||||
user_agent_part = user_agent.split("/")[
|
||||
0
|
||||
] # extract "claude-cli" - enables spend tracking acrosss versions
|
||||
if user_agent_part is not None:
|
||||
tags.append(user_agent_part)
|
||||
tags.append(user_agent) # append full user-agent
|
||||
# Check request body for tags
|
||||
if "tags" in data and isinstance(data["tags"], list):
|
||||
tags = data["tags"]
|
||||
|
||||
@@ -260,9 +260,29 @@ async def get_generic_sso_response(
|
||||
scope=generic_scope,
|
||||
)
|
||||
verbose_proxy_logger.debug("calling generic_sso.verify_and_process")
|
||||
result = await generic_sso.verify_and_process(
|
||||
request, params={"include_client_id": generic_include_client_id}
|
||||
)
|
||||
additional_generic_sso_headers = os.getenv(
|
||||
"GENERIC_SSO_HEADERS", None
|
||||
) # Comma-separated list of headers to add to the request - e.g. Authorization=Bearer <token>, Content-Type=application/json, etc.
|
||||
additional_generic_sso_headers_dict = {}
|
||||
if additional_generic_sso_headers is not None:
|
||||
additional_generic_sso_headers_split = additional_generic_sso_headers.split(",")
|
||||
for header in additional_generic_sso_headers_split:
|
||||
header = header.strip()
|
||||
if header:
|
||||
key, value = header.split("=")
|
||||
additional_generic_sso_headers_dict[key] = value
|
||||
|
||||
try:
|
||||
result = await generic_sso.verify_and_process(
|
||||
request,
|
||||
params={"include_client_id": generic_include_client_id},
|
||||
headers=additional_generic_sso_headers_dict,
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.exception(
|
||||
f"Error verifying and processing generic SSO: {e}. Passed in headers: {additional_generic_sso_headers_dict}"
|
||||
)
|
||||
raise e
|
||||
verbose_proxy_logger.debug("generic result: %s", result)
|
||||
return result or {}
|
||||
|
||||
|
||||
@@ -48,6 +48,9 @@ class PassThroughEndpointLogging:
|
||||
AssemblyAIPassthroughLoggingHandler()
|
||||
)
|
||||
|
||||
# Langfuse
|
||||
self.TRACKED_LANGFUSE_ROUTES = ["/langfuse/"]
|
||||
|
||||
async def _handle_logging(
|
||||
self,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
@@ -103,9 +106,9 @@ class PassThroughEndpointLogging:
|
||||
standard_logging_response_object: Optional[
|
||||
PassThroughEndpointLoggingResultValues
|
||||
] = None
|
||||
logging_obj.model_call_details[
|
||||
"passthrough_logging_payload"
|
||||
] = passthrough_logging_payload
|
||||
logging_obj.model_call_details["passthrough_logging_payload"] = (
|
||||
passthrough_logging_payload
|
||||
)
|
||||
if self.is_vertex_route(url_route):
|
||||
vertex_passthrough_logging_handler_result = (
|
||||
VertexPassthroughLoggingHandler.vertex_passthrough_handler(
|
||||
@@ -181,6 +184,9 @@ class PassThroughEndpointLogging:
|
||||
**kwargs,
|
||||
)
|
||||
return
|
||||
elif self.is_langfuse_route(url_route):
|
||||
# Don't log langfuse pass-through requests
|
||||
return
|
||||
|
||||
if standard_logging_response_object is None:
|
||||
standard_logging_response_object = StandardPassThroughResponseObject(
|
||||
@@ -221,3 +227,10 @@ class PassThroughEndpointLogging:
|
||||
elif "/transcript" in parsed_url.path:
|
||||
return True
|
||||
return False
|
||||
|
||||
def is_langfuse_route(self, url_route: str):
|
||||
parsed_url = urlparse(url_route)
|
||||
for route in self.TRACKED_LANGFUSE_ROUTES:
|
||||
if route in parsed_url.path:
|
||||
return True
|
||||
return False
|
||||
|
||||
+24
-20
@@ -15,6 +15,7 @@ from litellm.responses.litellm_completion_transformation.handler import (
|
||||
)
|
||||
from litellm.responses.utils import ResponsesAPIRequestUtils
|
||||
from litellm.types.llms.openai import (
|
||||
PromptObject,
|
||||
Reasoning,
|
||||
ResponseIncludable,
|
||||
ResponseInputParam,
|
||||
@@ -96,6 +97,7 @@ async def aresponses(
|
||||
include: Optional[List[ResponseIncludable]] = None,
|
||||
instructions: Optional[str] = None,
|
||||
max_output_tokens: Optional[int] = None,
|
||||
prompt: Optional[PromptObject] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
parallel_tool_calls: Optional[bool] = None,
|
||||
previous_response_id: Optional[str] = None,
|
||||
@@ -141,6 +143,7 @@ async def aresponses(
|
||||
include=include,
|
||||
instructions=instructions,
|
||||
max_output_tokens=max_output_tokens,
|
||||
prompt=prompt,
|
||||
metadata=metadata,
|
||||
parallel_tool_calls=parallel_tool_calls,
|
||||
previous_response_id=previous_response_id,
|
||||
@@ -197,6 +200,7 @@ def responses(
|
||||
include: Optional[List[ResponseIncludable]] = None,
|
||||
instructions: Optional[str] = None,
|
||||
max_output_tokens: Optional[int] = None,
|
||||
prompt: Optional[PromptObject] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
parallel_tool_calls: Optional[bool] = None,
|
||||
previous_response_id: Optional[str] = None,
|
||||
@@ -255,11 +259,11 @@ def responses(
|
||||
)
|
||||
|
||||
# get provider config
|
||||
responses_api_provider_config: Optional[
|
||||
BaseResponsesAPIConfig
|
||||
] = ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=model,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
|
||||
ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=model,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
)
|
||||
)
|
||||
|
||||
local_vars.update(kwargs)
|
||||
@@ -449,11 +453,11 @@ def delete_responses(
|
||||
raise ValueError("custom_llm_provider is required but passed as None")
|
||||
|
||||
# get provider config
|
||||
responses_api_provider_config: Optional[
|
||||
BaseResponsesAPIConfig
|
||||
] = ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
|
||||
ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
)
|
||||
)
|
||||
|
||||
if responses_api_provider_config is None:
|
||||
@@ -628,11 +632,11 @@ def get_responses(
|
||||
raise ValueError("custom_llm_provider is required but passed as None")
|
||||
|
||||
# get provider config
|
||||
responses_api_provider_config: Optional[
|
||||
BaseResponsesAPIConfig
|
||||
] = ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
|
||||
ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
)
|
||||
)
|
||||
|
||||
if responses_api_provider_config is None:
|
||||
@@ -784,11 +788,11 @@ def list_input_items(
|
||||
if custom_llm_provider is None:
|
||||
raise ValueError("custom_llm_provider is required but passed as None")
|
||||
|
||||
responses_api_provider_config: Optional[
|
||||
BaseResponsesAPIConfig
|
||||
] = ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
|
||||
ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
)
|
||||
)
|
||||
|
||||
if responses_api_provider_config is None:
|
||||
|
||||
@@ -930,6 +930,19 @@ class ComputerToolParam(TypedDict, total=False):
|
||||
ALL_RESPONSES_API_TOOL_PARAMS = Union[ToolParam, ComputerToolParam]
|
||||
|
||||
|
||||
class PromptObject(TypedDict, total=False):
|
||||
"""Reference to a stored prompt template."""
|
||||
|
||||
id: Required[str]
|
||||
"""The unique identifier of the prompt template to use."""
|
||||
|
||||
variables: Optional[Dict]
|
||||
"""Variables to substitute into the prompt template."""
|
||||
|
||||
version: Optional[str]
|
||||
"""Optional version of the prompt template."""
|
||||
|
||||
|
||||
class ResponsesAPIOptionalRequestParams(TypedDict, total=False):
|
||||
"""TypedDict for Optional parameters supported by the responses API."""
|
||||
|
||||
@@ -950,6 +963,7 @@ class ResponsesAPIOptionalRequestParams(TypedDict, total=False):
|
||||
top_p: Optional[float]
|
||||
truncation: Optional[Literal["auto", "disabled"]]
|
||||
user: Optional[str]
|
||||
prompt: Optional[PromptObject]
|
||||
|
||||
|
||||
class ResponsesAPIRequestParams(ResponsesAPIOptionalRequestParams, total=False):
|
||||
|
||||
@@ -8381,6 +8381,24 @@
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.04,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-ultra-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.06,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-4.0-fast-generate-preview-06-06": {
|
||||
"output_cost_per_image": 0.02,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
"mode": "image_generation",
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
|
||||
},
|
||||
"vertex_ai/imagen-3.0-generate-002": {
|
||||
"output_cost_per_image": 0.04,
|
||||
"litellm_provider": "vertex_ai-image-models",
|
||||
@@ -15002,4 +15020,4 @@
|
||||
"notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Generated
+1
-1
@@ -4995,4 +4995,4 @@ utils = ["numpydoc"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.8.1,<4.0, !=3.9.7"
|
||||
content-hash = "55a9fa9dee2e3836205b692afb6429f0aa134fbfde15ea460bfb28f7dd0a85f1"
|
||||
content-hash = "fc4cdd244ca79be337519a95533bb3b85a8a2d6c192187bca38b327d0526949f"
|
||||
|
||||
+3
-3
@@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.72.6"
|
||||
version = "1.72.7"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
@@ -28,7 +28,7 @@ importlib-metadata = ">=6.8.0"
|
||||
tokenizers = "*"
|
||||
click = "*"
|
||||
jinja2 = "^3.1.2"
|
||||
aiohttp = "*"
|
||||
aiohttp = ">=3.10"
|
||||
pydantic = "^2.0.0"
|
||||
jsonschema = "^4.22.0"
|
||||
numpydoc = {version = "*", optional = true} # used in utils.py
|
||||
@@ -141,7 +141,7 @@ requires = ["poetry-core", "wheel"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.72.6"
|
||||
version = "1.72.7"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
||||
@@ -689,6 +689,80 @@ async def test_openai_responses_litellm_router_with_metadata():
|
||||
mock_post.assert_called_once()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_openai_responses_litellm_router_with_prompt():
|
||||
"""Test that prompt object is passed through the Router for responses API"""
|
||||
|
||||
prompt_obj = {
|
||||
"id": "pmpt_abc123",
|
||||
"version": "2",
|
||||
"variables": {"random_variable": "ishaan_from_litellm"},
|
||||
}
|
||||
|
||||
mock_response = {
|
||||
"id": "resp_123",
|
||||
"object": "response",
|
||||
"created_at": 1741476542,
|
||||
"status": "completed",
|
||||
"model": "gpt-4o",
|
||||
"output": [],
|
||||
"parallel_tool_calls": True,
|
||||
"usage": {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0},
|
||||
"text": {"format": {"type": "text"}},
|
||||
"error": None,
|
||||
"incomplete_details": None,
|
||||
"instructions": None,
|
||||
"metadata": {},
|
||||
"temperature": 1.0,
|
||||
"tool_choice": "auto",
|
||||
"tools": [],
|
||||
"top_p": 1.0,
|
||||
"max_output_tokens": None,
|
||||
"previous_response_id": None,
|
||||
"reasoning": {"effort": None, "summary": None},
|
||||
"truncation": "disabled",
|
||||
"user": None,
|
||||
}
|
||||
|
||||
class MockResponse:
|
||||
def __init__(self, json_data, status_code):
|
||||
self._json_data = json_data
|
||||
self.status_code = status_code
|
||||
self.text = str(json_data)
|
||||
|
||||
def json(self):
|
||||
return self._json_data
|
||||
|
||||
with patch(
|
||||
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
||||
new_callable=AsyncMock,
|
||||
) as mock_post:
|
||||
mock_post.return_value = MockResponse(mock_response, 200)
|
||||
|
||||
litellm._turn_on_debug()
|
||||
router = litellm.Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "gpt4o-special-alias",
|
||||
"litellm_params": {
|
||||
"model": "gpt-4o",
|
||||
"api_key": "fake-key",
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
await router.aresponses(
|
||||
model="gpt4o-special-alias",
|
||||
input="Hello",
|
||||
prompt=prompt_obj,
|
||||
)
|
||||
|
||||
request_body = mock_post.call_args.kwargs["json"]
|
||||
assert request_body["prompt"] == prompt_obj
|
||||
mock_post.assert_called_once()
|
||||
|
||||
|
||||
def test_bad_request_bad_param_error():
|
||||
"""Raise a BadRequestError when an invalid parameter value is provided"""
|
||||
try:
|
||||
|
||||
@@ -100,7 +100,7 @@ callback_class_str_to_classType = {
|
||||
"smtp_email": SMTPEmailLogger,
|
||||
"deepeval": DeepEvalLogger,
|
||||
"s3_v2": S3Logger,
|
||||
"langfuse_otel": LangfuseOtelLogger,
|
||||
"langfuse_otel": OpenTelemetry,
|
||||
}
|
||||
|
||||
expected_env_vars = {
|
||||
|
||||
@@ -472,13 +472,23 @@ def test_reading_openai_org_id_from_headers():
|
||||
@pytest.mark.parametrize(
|
||||
"headers, general_settings, expected_data",
|
||||
[
|
||||
({"X-OpenWebUI-User-Id": "ishaan3"}, {"user_header_name":"X-OpenWebUI-User-Id"}, "ishaan3"),
|
||||
({"x-openwebui-user-id": "ishaan3"}, {"user_header_name":"X-OpenWebUI-User-Id"}, "ishaan3"),
|
||||
(
|
||||
{"X-OpenWebUI-User-Id": "ishaan3"},
|
||||
{"user_header_name": "X-OpenWebUI-User-Id"},
|
||||
"ishaan3",
|
||||
),
|
||||
(
|
||||
{"x-openwebui-user-id": "ishaan3"},
|
||||
{"user_header_name": "X-OpenWebUI-User-Id"},
|
||||
"ishaan3",
|
||||
),
|
||||
({"X-OpenWebUI-User-Id": "ishaan3"}, {}, None),
|
||||
({}, None, None),
|
||||
],
|
||||
)
|
||||
def test_add_litellm_data_for_backend_llm_call(headers, general_settings, expected_data):
|
||||
def test_add_litellm_data_for_backend_llm_call(
|
||||
headers, general_settings, expected_data
|
||||
):
|
||||
import json
|
||||
from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
@@ -572,6 +582,7 @@ def test_update_internal_new_user_params_with_no_initial_role_set():
|
||||
== litellm.default_internal_user_params["budget_duration"]
|
||||
)
|
||||
|
||||
|
||||
def test_update_internal_new_user_params_with_user_defined_values():
|
||||
from litellm.proxy.management_endpoints.internal_user_endpoints import (
|
||||
_update_internal_new_user_params,
|
||||
@@ -585,14 +596,16 @@ def test_update_internal_new_user_params_with_user_defined_values():
|
||||
"user_role": "proxy_admin",
|
||||
}
|
||||
|
||||
data = NewUserRequest(user_email="krrish3@berri.ai", max_budget=1000, budget_duration="1mo")
|
||||
data = NewUserRequest(
|
||||
user_email="krrish3@berri.ai", max_budget=1000, budget_duration="1mo"
|
||||
)
|
||||
data_json = data.model_dump()
|
||||
updated_data_json = _update_internal_new_user_params(data_json, data)
|
||||
assert updated_data_json["user_email"] == "krrish3@berri.ai"
|
||||
assert updated_data_json["user_role"] == "proxy_admin"
|
||||
assert updated_data_json["max_budget"] == 1000
|
||||
assert updated_data_json["budget_duration"] == "1mo"
|
||||
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_proxy_config_update_from_db():
|
||||
@@ -831,9 +844,9 @@ async def test_add_litellm_data_to_request_duplicate_tags(
|
||||
{"service_account_settings": {"enforced_params": ["user"]}},
|
||||
UserAPIKeyAuth(
|
||||
api_key="test_api_key",
|
||||
user_id="test_user_id",
|
||||
user_id="test_user_id",
|
||||
org_id="test_org_id",
|
||||
metadata={"service_account_id": "test_service_account_id"}
|
||||
metadata={"service_account_id": "test_service_account_id"},
|
||||
),
|
||||
{},
|
||||
True,
|
||||
@@ -868,7 +881,7 @@ async def test_add_litellm_data_to_request_duplicate_tags(
|
||||
{"service_account_settings": {"enforced_params": ["user"]}},
|
||||
UserAPIKeyAuth(
|
||||
api_key="test_api_key",
|
||||
metadata={"service_account_id": "test_service_account_id"}
|
||||
metadata={"service_account_id": "test_service_account_id"},
|
||||
),
|
||||
{"user": "test_user"},
|
||||
False,
|
||||
@@ -1004,6 +1017,7 @@ def test_update_config_fields():
|
||||
assert team_config["langfuse_public_key"] == "my-fake-key"
|
||||
assert team_config["langfuse_secret"] == "my-fake-secret"
|
||||
|
||||
|
||||
def test_update_config_fields_default_internal_user_params(monkeypatch):
|
||||
from litellm.proxy.proxy_server import ProxyConfig
|
||||
|
||||
@@ -1011,7 +1025,6 @@ def test_update_config_fields_default_internal_user_params(monkeypatch):
|
||||
|
||||
monkeypatch.setattr(litellm, "default_internal_user_params", None)
|
||||
|
||||
|
||||
args = {
|
||||
"current_config": {},
|
||||
"param_name": "litellm_settings",
|
||||
@@ -1031,23 +1044,44 @@ def test_update_config_fields_default_internal_user_params(monkeypatch):
|
||||
"budget_duration": "1mo",
|
||||
}
|
||||
|
||||
monkeypatch.setattr(litellm, "default_internal_user_params", None) # reset to default
|
||||
monkeypatch.setattr(
|
||||
litellm, "default_internal_user_params", None
|
||||
) # reset to default
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"proxy_model_list,provider",
|
||||
"proxy_model_list,model_list,provider",
|
||||
[
|
||||
(["openai/*"], "openai"),
|
||||
(["bedrock/*"], "bedrock"),
|
||||
(["anthropic/*"], "anthropic"),
|
||||
(["cohere/*"], "cohere"),
|
||||
(
|
||||
["openai/*"],
|
||||
[{"model_name": "openai/*", "litellm_params": {"model": "openai/*"}}],
|
||||
"openai",
|
||||
),
|
||||
(
|
||||
["bedrock/*"],
|
||||
[{"model_name": "bedrock/*", "litellm_params": {"model": "bedrock/*"}}],
|
||||
"bedrock",
|
||||
),
|
||||
(
|
||||
["anthropic/*"],
|
||||
[{"model_name": "anthropic/*", "litellm_params": {"model": "anthropic/*"}}],
|
||||
"anthropic",
|
||||
),
|
||||
(
|
||||
["cohere/*"],
|
||||
[{"model_name": "cohere/*", "litellm_params": {"model": "cohere/*"}}],
|
||||
"cohere",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_get_complete_model_list(proxy_model_list, provider):
|
||||
def test_get_complete_model_list(proxy_model_list, model_list, provider):
|
||||
"""
|
||||
Test that get_complete_model_list correctly expands model groups like 'openai/*' into individual models with provider prefixes
|
||||
"""
|
||||
from litellm.proxy.auth.model_checks import get_complete_model_list
|
||||
from litellm import Router
|
||||
|
||||
llm_router = Router(model_list=model_list)
|
||||
|
||||
complete_list = get_complete_model_list(
|
||||
proxy_model_list=proxy_model_list,
|
||||
@@ -1055,6 +1089,7 @@ def test_get_complete_model_list(proxy_model_list, provider):
|
||||
team_models=[],
|
||||
user_model=None,
|
||||
infer_model_from_keys=False,
|
||||
llm_router=llm_router,
|
||||
)
|
||||
|
||||
# Check that we got a non-empty list back
|
||||
@@ -1565,6 +1600,7 @@ async def test_end_user_transactions_reset():
|
||||
end_user_list_transactions=end_user_list_transactions,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_spend_logs_cleanup_after_error():
|
||||
# Setup test data
|
||||
@@ -1665,22 +1701,34 @@ from litellm.proxy._types import LiteLLM_UserTable
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"wildcard_model, expected_models",
|
||||
"wildcard_model, litellm_params, expected_models",
|
||||
[
|
||||
(
|
||||
"anthropic/*",
|
||||
{"model": "anthropic/*"},
|
||||
["anthropic/claude-3-5-haiku-20241022", "anthropic/claude-3-opus-20240229"],
|
||||
),
|
||||
(
|
||||
"vertex_ai/gemini-*",
|
||||
{"model": "vertex_ai/gemini-*"},
|
||||
["vertex_ai/gemini-1.5-flash", "vertex_ai/gemini-1.5-pro"],
|
||||
),
|
||||
(
|
||||
"foo/*",
|
||||
{"model": "openai/*"},
|
||||
["foo/gpt-4o", "foo/gpt-4o-mini"],
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_get_known_models_from_wildcard(wildcard_model, expected_models):
|
||||
def test_get_known_models_from_wildcard(
|
||||
wildcard_model, litellm_params, expected_models
|
||||
):
|
||||
from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
|
||||
from litellm.types.router import LiteLLM_Params
|
||||
|
||||
wildcard_models = get_known_models_from_wildcard(wildcard_model=wildcard_model)
|
||||
wildcard_models = get_known_models_from_wildcard(
|
||||
wildcard_model=wildcard_model, litellm_params=LiteLLM_Params(**litellm_params)
|
||||
)
|
||||
# Check if all expected models are in the returned list
|
||||
print(f"wildcard_models: {wildcard_models}\n")
|
||||
for model in expected_models:
|
||||
@@ -1860,4 +1908,4 @@ async def test_get_admin_team_ids(
|
||||
where={"team_id": {"in": user_info.teams}}
|
||||
)
|
||||
else:
|
||||
mock_prisma_client.db.litellm_teamtable.find_many.assert_not_called()
|
||||
mock_prisma_client.db.litellm_teamtable.find_many.assert_not_called()
|
||||
|
||||
@@ -0,0 +1,102 @@
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
from litellm.types.utils import EmbeddingResponse
|
||||
|
||||
from litellm.llms.ollama.completion.handler import ollama_embeddings, ollama_aembeddings
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_response_data():
|
||||
return {
|
||||
"embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
|
||||
"prompt_eval_count": 5,
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_embedding_response():
|
||||
return EmbeddingResponse(object="", data=[], model="", usage=None)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_encoding():
|
||||
mock = MagicMock()
|
||||
mock.encode.return_value = [0] * 5
|
||||
return mock
|
||||
|
||||
|
||||
def test_ollama_embeddings(mock_response_data, mock_embedding_response, mock_encoding):
|
||||
with patch("litellm.module_level_client.post") as mock_post, \
|
||||
patch("litellm.OllamaConfig.get_config", return_value={"truncate": 512}):
|
||||
|
||||
mock_response = MagicMock()
|
||||
mock_response.json.return_value = mock_response_data
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
response = ollama_embeddings(
|
||||
api_base="http://localhost:11434",
|
||||
model="test-model",
|
||||
prompts=["hello", "world"],
|
||||
optional_params={},
|
||||
model_response=mock_embedding_response,
|
||||
logging_obj=None,
|
||||
encoding=mock_encoding,
|
||||
)
|
||||
|
||||
assert response.model == "ollama/test-model"
|
||||
assert response.object == "list"
|
||||
assert isinstance(response.data, list)
|
||||
assert response.usage.total_tokens == 5
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_ollama_aembeddings(mock_response_data, mock_embedding_response, mock_encoding):
|
||||
with patch("litellm.module_level_aclient.post", new_callable=AsyncMock) as mock_post, \
|
||||
patch("litellm.OllamaConfig.get_config", return_value={"truncate": 512}):
|
||||
|
||||
mock_post.return_value.json.return_value = mock_response_data
|
||||
|
||||
response = await ollama_aembeddings(
|
||||
api_base="http://localhost:11434",
|
||||
model="test-model",
|
||||
prompts=["hello", "world"],
|
||||
optional_params={},
|
||||
model_response=mock_embedding_response,
|
||||
logging_obj=None,
|
||||
encoding=mock_encoding,
|
||||
)
|
||||
|
||||
assert response.model == "ollama/test-model"
|
||||
assert response.object == "list"
|
||||
assert isinstance(response.data, list)
|
||||
assert response.usage.total_tokens == 5
|
||||
|
||||
|
||||
def test_prompt_eval_fallback_when_missing(mock_embedding_response, mock_encoding):
|
||||
response_data = {
|
||||
"embeddings": [[0.1, 0.2, 0.3]],
|
||||
# No "prompt_eval_count"
|
||||
}
|
||||
|
||||
with patch("litellm.module_level_client.post") as mock_post, \
|
||||
patch("litellm.OllamaConfig.get_config", return_value={}):
|
||||
|
||||
mock_response = MagicMock()
|
||||
mock_response.json.return_value = response_data
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
response = ollama_embeddings(
|
||||
api_base="http://localhost:11434",
|
||||
model="test-model",
|
||||
prompts=["only-prompt"],
|
||||
optional_params={},
|
||||
model_response=mock_embedding_response,
|
||||
logging_obj=None,
|
||||
encoding=mock_encoding,
|
||||
)
|
||||
|
||||
# Fallback should use encoding length (mocked to be 5)
|
||||
assert response.usage.prompt_tokens == 5
|
||||
assert response.usage.total_tokens == 5
|
||||
assert response.usage.completion_tokens == 0
|
||||
assert response.data[0]['embedding'] == [0.1, 0.2, 0.3]
|
||||
@@ -3,13 +3,16 @@ from unittest.mock import AsyncMock, patch
|
||||
import pytest
|
||||
|
||||
from litellm.proxy._types import (
|
||||
JWTAuthBuilderResult,
|
||||
LiteLLM_JWTAuth,
|
||||
LiteLLM_TeamTable,
|
||||
LiteLLM_UserTable,
|
||||
LitellmUserRoles,
|
||||
Member,
|
||||
ProxyErrorTypes,
|
||||
ProxyException,
|
||||
)
|
||||
from litellm.proxy.auth.handle_jwt import JWTAuthManager
|
||||
from litellm.proxy.auth.handle_jwt import JWTAuthManager, JWTHandler
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@@ -125,3 +128,171 @@ async def test_map_user_to_teams_null_inputs():
|
||||
|
||||
# Test with both null
|
||||
await JWTAuthManager.map_user_to_teams(user_object=None, team_object=None)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_auth_builder_proxy_admin_user_role():
|
||||
"""Test that is_proxy_admin is True when user_object.user_role is PROXY_ADMIN"""
|
||||
# Setup test data
|
||||
api_key = "test_jwt_token"
|
||||
request_data = {"model": "gpt-4"}
|
||||
general_settings = {"enforce_rbac": False}
|
||||
route = "/chat/completions"
|
||||
|
||||
# Create user object with PROXY_ADMIN role
|
||||
user_object = LiteLLM_UserTable(
|
||||
user_id="test_user_1", user_role=LitellmUserRoles.PROXY_ADMIN
|
||||
)
|
||||
|
||||
# Create mock JWT handler
|
||||
jwt_handler = JWTHandler()
|
||||
jwt_handler.litellm_jwtauth = LiteLLM_JWTAuth()
|
||||
|
||||
# Mock all the dependencies and method calls
|
||||
with patch.object(
|
||||
jwt_handler, "auth_jwt", new_callable=AsyncMock
|
||||
) as mock_auth_jwt, patch.object(
|
||||
JWTAuthManager, "check_rbac_role", new_callable=AsyncMock
|
||||
) as mock_check_rbac, patch.object(
|
||||
jwt_handler, "get_rbac_role", return_value=None
|
||||
) as mock_get_rbac, patch.object(
|
||||
jwt_handler, "get_scopes", return_value=[]
|
||||
) as mock_get_scopes, patch.object(
|
||||
jwt_handler, "get_object_id", return_value=None
|
||||
) as mock_get_object_id, patch.object(
|
||||
JWTAuthManager,
|
||||
"get_user_info",
|
||||
new_callable=AsyncMock,
|
||||
return_value=("test_user_1", "test@example.com", True),
|
||||
) as mock_get_user_info, patch.object(
|
||||
jwt_handler, "get_org_id", return_value=None
|
||||
) as mock_get_org_id, patch.object(
|
||||
jwt_handler, "get_end_user_id", return_value=None
|
||||
) as mock_get_end_user_id, patch.object(
|
||||
JWTAuthManager, "check_admin_access", new_callable=AsyncMock, return_value=None
|
||||
) as mock_check_admin, patch.object(
|
||||
JWTAuthManager,
|
||||
"find_and_validate_specific_team_id",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(None, None),
|
||||
) as mock_find_team, patch.object(
|
||||
JWTAuthManager, "get_all_team_ids", return_value=set()
|
||||
) as mock_get_all_team_ids, patch.object(
|
||||
JWTAuthManager,
|
||||
"find_team_with_model_access",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(None, None),
|
||||
) as mock_find_team_access, patch.object(
|
||||
JWTAuthManager,
|
||||
"get_objects",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(user_object, None, None),
|
||||
) as mock_get_objects, patch.object(
|
||||
JWTAuthManager, "map_user_to_teams", new_callable=AsyncMock
|
||||
) as mock_map_user, patch.object(
|
||||
JWTAuthManager, "validate_object_id", return_value=True
|
||||
) as mock_validate_object:
|
||||
# Set up the mock return values
|
||||
mock_auth_jwt.return_value = {"sub": "test_user_1", "scope": ""}
|
||||
|
||||
# Call the auth_builder method
|
||||
result = await JWTAuthManager.auth_builder(
|
||||
api_key=api_key,
|
||||
jwt_handler=jwt_handler,
|
||||
request_data=request_data,
|
||||
general_settings=general_settings,
|
||||
route=route,
|
||||
prisma_client=None,
|
||||
user_api_key_cache=None,
|
||||
parent_otel_span=None,
|
||||
proxy_logging_obj=None,
|
||||
)
|
||||
|
||||
# Verify that is_proxy_admin is True
|
||||
assert result["is_proxy_admin"] is True
|
||||
assert result["user_object"] == user_object
|
||||
assert result["user_id"] == "test_user_1"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_auth_builder_non_proxy_admin_user_role():
|
||||
"""Test that is_proxy_admin is False when user_object.user_role is not PROXY_ADMIN"""
|
||||
# Setup test data
|
||||
api_key = "test_jwt_token"
|
||||
request_data = {"model": "gpt-4"}
|
||||
general_settings = {"enforce_rbac": False}
|
||||
route = "/chat/completions"
|
||||
|
||||
# Create user object with regular USER role
|
||||
user_object = LiteLLM_UserTable(
|
||||
user_id="test_user_1", user_role=LitellmUserRoles.INTERNAL_USER
|
||||
)
|
||||
|
||||
# Create mock JWT handler
|
||||
jwt_handler = JWTHandler()
|
||||
jwt_handler.litellm_jwtauth = LiteLLM_JWTAuth()
|
||||
|
||||
# Mock all the dependencies and method calls
|
||||
with patch.object(
|
||||
jwt_handler, "auth_jwt", new_callable=AsyncMock
|
||||
) as mock_auth_jwt, patch.object(
|
||||
JWTAuthManager, "check_rbac_role", new_callable=AsyncMock
|
||||
) as mock_check_rbac, patch.object(
|
||||
jwt_handler, "get_rbac_role", return_value=None
|
||||
) as mock_get_rbac, patch.object(
|
||||
jwt_handler, "get_scopes", return_value=[]
|
||||
) as mock_get_scopes, patch.object(
|
||||
jwt_handler, "get_object_id", return_value=None
|
||||
) as mock_get_object_id, patch.object(
|
||||
JWTAuthManager,
|
||||
"get_user_info",
|
||||
new_callable=AsyncMock,
|
||||
return_value=("test_user_1", "test@example.com", True),
|
||||
) as mock_get_user_info, patch.object(
|
||||
jwt_handler, "get_org_id", return_value=None
|
||||
) as mock_get_org_id, patch.object(
|
||||
jwt_handler, "get_end_user_id", return_value=None
|
||||
) as mock_get_end_user_id, patch.object(
|
||||
JWTAuthManager, "check_admin_access", new_callable=AsyncMock, return_value=None
|
||||
) as mock_check_admin, patch.object(
|
||||
JWTAuthManager,
|
||||
"find_and_validate_specific_team_id",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(None, None),
|
||||
) as mock_find_team, patch.object(
|
||||
JWTAuthManager, "get_all_team_ids", return_value=set()
|
||||
) as mock_get_all_team_ids, patch.object(
|
||||
JWTAuthManager,
|
||||
"find_team_with_model_access",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(None, None),
|
||||
) as mock_find_team_access, patch.object(
|
||||
JWTAuthManager,
|
||||
"get_objects",
|
||||
new_callable=AsyncMock,
|
||||
return_value=(user_object, None, None),
|
||||
) as mock_get_objects, patch.object(
|
||||
JWTAuthManager, "map_user_to_teams", new_callable=AsyncMock
|
||||
) as mock_map_user, patch.object(
|
||||
JWTAuthManager, "validate_object_id", return_value=True
|
||||
) as mock_validate_object:
|
||||
# Set up the mock return values
|
||||
mock_auth_jwt.return_value = {"sub": "test_user_1", "scope": ""}
|
||||
|
||||
# Call the auth_builder method
|
||||
result = await JWTAuthManager.auth_builder(
|
||||
api_key=api_key,
|
||||
jwt_handler=jwt_handler,
|
||||
request_data=request_data,
|
||||
general_settings=general_settings,
|
||||
route=route,
|
||||
prisma_client=None,
|
||||
user_api_key_cache=None,
|
||||
parent_otel_span=None,
|
||||
proxy_logging_obj=None,
|
||||
)
|
||||
|
||||
# Verify that is_proxy_admin is False
|
||||
assert result["is_proxy_admin"] is False
|
||||
assert result["user_object"] == user_object
|
||||
assert result["user_id"] == "test_user_1"
|
||||
|
||||
@@ -690,3 +690,130 @@ async def test_check_and_update_if_proxy_admin_id_already_admin():
|
||||
# Assert
|
||||
assert updated_role == LitellmUserRoles.PROXY_ADMIN.value
|
||||
mock_prisma.db.litellm_usertable.update.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_generic_sso_response_with_additional_headers():
|
||||
"""
|
||||
Test that GENERIC_SSO_HEADERS environment variable is correctly processed
|
||||
and passed to generic_sso.verify_and_process
|
||||
"""
|
||||
from litellm.proxy.management_endpoints.ui_sso import get_generic_sso_response
|
||||
|
||||
# Arrange
|
||||
mock_request = MagicMock(spec=Request)
|
||||
mock_jwt_handler = MagicMock(spec=JWTHandler)
|
||||
mock_jwt_handler.get_team_ids_from_jwt.return_value = []
|
||||
|
||||
generic_client_id = "test_client_id"
|
||||
redirect_url = "http://test.com/callback"
|
||||
|
||||
# Mock response from verify_and_process
|
||||
mock_sso_response = {
|
||||
"sub": "test_user_123",
|
||||
"email": "test@example.com",
|
||||
"preferred_username": "testuser",
|
||||
}
|
||||
|
||||
# Set up environment variables including GENERIC_SSO_HEADERS
|
||||
test_env_vars = {
|
||||
"GENERIC_CLIENT_SECRET": "test_secret",
|
||||
"GENERIC_AUTHORIZATION_ENDPOINT": "https://auth.example.com/auth",
|
||||
"GENERIC_TOKEN_ENDPOINT": "https://auth.example.com/token",
|
||||
"GENERIC_USERINFO_ENDPOINT": "https://auth.example.com/userinfo",
|
||||
"GENERIC_SSO_HEADERS": "Authorization=Bearer token123, Content-Type=application/json, X-Custom-Header=custom-value",
|
||||
}
|
||||
|
||||
# Expected headers dictionary
|
||||
expected_headers = {
|
||||
"Authorization": "Bearer token123",
|
||||
"Content-Type": "application/json",
|
||||
"X-Custom-Header": "custom-value",
|
||||
}
|
||||
|
||||
# Mock the SSO provider and its methods
|
||||
mock_sso_instance = MagicMock()
|
||||
mock_sso_instance.verify_and_process = AsyncMock(return_value=mock_sso_response)
|
||||
|
||||
mock_sso_class = MagicMock(return_value=mock_sso_instance)
|
||||
|
||||
with patch.dict(os.environ, test_env_vars):
|
||||
with patch("fastapi_sso.sso.base.DiscoveryDocument") as mock_discovery:
|
||||
with patch(
|
||||
"fastapi_sso.sso.generic.create_provider", return_value=mock_sso_class
|
||||
) as mock_create_provider:
|
||||
# Act
|
||||
result = await get_generic_sso_response(
|
||||
request=mock_request,
|
||||
jwt_handler=mock_jwt_handler,
|
||||
generic_client_id=generic_client_id,
|
||||
redirect_url=redirect_url,
|
||||
)
|
||||
|
||||
# Assert
|
||||
# Verify verify_and_process was called with the correct headers
|
||||
mock_sso_instance.verify_and_process.assert_called_once_with(
|
||||
mock_request,
|
||||
params={"include_client_id": False},
|
||||
headers=expected_headers,
|
||||
)
|
||||
|
||||
# Verify the result is returned correctly
|
||||
assert result == mock_sso_response
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_generic_sso_response_with_empty_headers():
|
||||
"""
|
||||
Test that when GENERIC_SSO_HEADERS is not set, an empty headers dict is passed
|
||||
"""
|
||||
from litellm.proxy.management_endpoints.ui_sso import get_generic_sso_response
|
||||
|
||||
# Arrange
|
||||
mock_request = MagicMock(spec=Request)
|
||||
mock_jwt_handler = MagicMock(spec=JWTHandler)
|
||||
mock_jwt_handler.get_team_ids_from_jwt.return_value = []
|
||||
|
||||
generic_client_id = "test_client_id"
|
||||
redirect_url = "http://test.com/callback"
|
||||
|
||||
mock_sso_response = {
|
||||
"sub": "test_user_123",
|
||||
"email": "test@example.com",
|
||||
"preferred_username": "testuser",
|
||||
}
|
||||
|
||||
# Set up environment variables without GENERIC_SSO_HEADERS
|
||||
test_env_vars = {
|
||||
"GENERIC_CLIENT_SECRET": "test_secret",
|
||||
"GENERIC_AUTHORIZATION_ENDPOINT": "https://auth.example.com/auth",
|
||||
"GENERIC_TOKEN_ENDPOINT": "https://auth.example.com/token",
|
||||
"GENERIC_USERINFO_ENDPOINT": "https://auth.example.com/userinfo",
|
||||
}
|
||||
|
||||
# Mock the SSO provider and its methods
|
||||
mock_sso_instance = MagicMock()
|
||||
mock_sso_instance.verify_and_process = AsyncMock(return_value=mock_sso_response)
|
||||
|
||||
mock_sso_class = MagicMock(return_value=mock_sso_instance)
|
||||
|
||||
with patch.dict(os.environ, test_env_vars):
|
||||
with patch("fastapi_sso.sso.base.DiscoveryDocument") as mock_discovery:
|
||||
with patch(
|
||||
"fastapi_sso.sso.generic.create_provider", return_value=mock_sso_class
|
||||
) as mock_create_provider:
|
||||
# Act
|
||||
result = await get_generic_sso_response(
|
||||
request=mock_request,
|
||||
jwt_handler=mock_jwt_handler,
|
||||
generic_client_id=generic_client_id,
|
||||
redirect_url=redirect_url,
|
||||
)
|
||||
|
||||
# Assert
|
||||
# Verify verify_and_process was called with empty headers dict
|
||||
mock_sso_instance.verify_and_process.assert_called_once_with(
|
||||
mock_request, params={"include_client_id": False}, headers={}
|
||||
)
|
||||
|
||||
assert result == mock_sso_response
|
||||
|
||||
@@ -19,6 +19,9 @@ from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
|
||||
HttpPassThroughEndpointHelpers,
|
||||
pass_through_request,
|
||||
)
|
||||
from litellm.proxy.pass_through_endpoints.success_handler import (
|
||||
PassThroughEndpointLogging,
|
||||
)
|
||||
|
||||
|
||||
# Test is_multipart
|
||||
@@ -178,3 +181,90 @@ async def test_pass_through_request_failure_handler():
|
||||
call_args["original_exception"], TypeError
|
||||
) # Now expecting TypeError
|
||||
assert "traceback_str" in call_args
|
||||
|
||||
|
||||
def test_is_langfuse_route():
|
||||
"""
|
||||
Test that the is_langfuse_route method correctly identifies Langfuse routes
|
||||
"""
|
||||
handler = PassThroughEndpointLogging()
|
||||
|
||||
# Test positive cases
|
||||
assert (
|
||||
handler.is_langfuse_route("http://localhost:4000/langfuse/api/public/traces")
|
||||
== True
|
||||
)
|
||||
assert (
|
||||
handler.is_langfuse_route(
|
||||
"https://proxy.example.com/langfuse/api/public/sessions"
|
||||
)
|
||||
== True
|
||||
)
|
||||
assert handler.is_langfuse_route("/langfuse/api/public/ingestion") == True
|
||||
assert handler.is_langfuse_route("http://localhost:4000/langfuse/") == True
|
||||
|
||||
# Test negative cases
|
||||
assert (
|
||||
handler.is_langfuse_route("https://api.openai.com/v1/chat/completions") == False
|
||||
)
|
||||
assert (
|
||||
handler.is_langfuse_route("http://localhost:4000/anthropic/v1/messages")
|
||||
== False
|
||||
)
|
||||
assert handler.is_langfuse_route("https://example.com/other") == False
|
||||
assert handler.is_langfuse_route("") == False
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_langfuse_passthrough_no_logging():
|
||||
"""
|
||||
Test that langfuse pass-through requests skip logging by returning early
|
||||
"""
|
||||
from datetime import datetime
|
||||
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.types.passthrough_endpoints.pass_through_endpoints import (
|
||||
PassthroughStandardLoggingPayload,
|
||||
)
|
||||
|
||||
handler = PassThroughEndpointLogging()
|
||||
|
||||
# Mock the logging object
|
||||
mock_logging_obj = MagicMock(spec=LiteLLMLoggingObj)
|
||||
mock_logging_obj.model_call_details = {}
|
||||
|
||||
# Mock httpx response for langfuse request
|
||||
mock_response = MagicMock(spec=httpx.Response)
|
||||
mock_response.text = '{"status": "success"}'
|
||||
|
||||
# Create langfuse URL
|
||||
langfuse_url = "http://localhost:4000/langfuse/api/public/traces"
|
||||
|
||||
passthrough_logging_payload = PassthroughStandardLoggingPayload(
|
||||
url=langfuse_url,
|
||||
request_body={"test": "data"},
|
||||
request_method="POST",
|
||||
)
|
||||
|
||||
# Call the success handler with langfuse route
|
||||
result = await handler.pass_through_async_success_handler(
|
||||
httpx_response=mock_response,
|
||||
response_body={"status": "success"},
|
||||
logging_obj=mock_logging_obj,
|
||||
url_route=langfuse_url,
|
||||
result="",
|
||||
start_time=datetime.now(),
|
||||
end_time=datetime.now(),
|
||||
cache_hit=False,
|
||||
request_body={"test": "data"},
|
||||
passthrough_logging_payload=passthrough_logging_payload,
|
||||
)
|
||||
|
||||
# Should return None (early return) and not proceed with logging
|
||||
assert result is None
|
||||
|
||||
# Verify that the passthrough_logging_payload was still set (this happens before the langfuse check)
|
||||
assert (
|
||||
mock_logging_obj.model_call_details["passthrough_logging_payload"]
|
||||
== passthrough_logging_payload
|
||||
)
|
||||
|
||||
@@ -10,6 +10,7 @@ from fastapi import Request
|
||||
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
from litellm.proxy.litellm_pre_call_utils import (
|
||||
LiteLLMProxyRequestSetup,
|
||||
_get_enforced_params,
|
||||
add_litellm_data_to_request,
|
||||
check_if_token_is_service_account,
|
||||
@@ -215,3 +216,83 @@ async def test_add_litellm_data_to_request_audio_transcription_multipart():
|
||||
"jobID:214590dsff09fds",
|
||||
"taskName:run_page_classification",
|
||||
]
|
||||
|
||||
|
||||
def test_add_request_tag_to_metadata_user_agent_parsing():
|
||||
"""
|
||||
Test that user agent parsing works correctly in add_request_tag_to_metadata
|
||||
"""
|
||||
|
||||
# Test case 1: User agent with version (contains "/")
|
||||
headers_with_version = {"user-agent": "claude-cli/1.0.25 (external, cli)"}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_version,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["claude-cli", "claude-cli/1.0.25 (external, cli)"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 2: User agent without version (no "/")
|
||||
headers_without_version = {"user-agent": "my-custom-client"}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_without_version,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["my-custom-client"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 3: No user agent header
|
||||
headers_no_user_agent = {}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_no_user_agent,
|
||||
data=data,
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 4: User agent with existing x-litellm-tags
|
||||
headers_with_existing_tags = {
|
||||
"user-agent": "postman/7.36.1",
|
||||
"x-litellm-tags": "existing-tag1, existing-tag2",
|
||||
}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_existing_tags,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["existing-tag1", "existing-tag2", "postman", "postman/7.36.1"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 5: User agent with tags in request body (body tags override header tags)
|
||||
headers_with_user_agent = {"user-agent": "curl/7.68.0"}
|
||||
data = {"tags": ["body-tag1", "body-tag2"]}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_user_agent,
|
||||
data=data,
|
||||
)
|
||||
# When tags exist in data, they override everything else
|
||||
expected_tags = ["body-tag1", "body-tag2"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 6: Complex user agent string
|
||||
headers_complex = {
|
||||
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
|
||||
}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_complex,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = [
|
||||
"Mozilla",
|
||||
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
|
||||
]
|
||||
assert result == expected_tags
|
||||
|
||||
@@ -25,7 +25,11 @@ class TestResponsesAPIRequestUtils:
|
||||
model = "gpt-4o"
|
||||
config = OpenAIResponsesAPIConfig()
|
||||
optional_params = ResponsesAPIOptionalRequestParams(
|
||||
{"temperature": 0.7, "max_output_tokens": 100}
|
||||
{
|
||||
"temperature": 0.7,
|
||||
"max_output_tokens": 100,
|
||||
"prompt": {"id": "pmpt_123"},
|
||||
}
|
||||
)
|
||||
|
||||
# Execute
|
||||
@@ -41,6 +45,8 @@ class TestResponsesAPIRequestUtils:
|
||||
assert result["temperature"] == 0.7
|
||||
assert "max_output_tokens" in result
|
||||
assert result["max_output_tokens"] == 100
|
||||
assert "prompt" in result
|
||||
assert result["prompt"] == {"id": "pmpt_123"}
|
||||
|
||||
def test_get_optional_params_responses_api_unsupported_param(self):
|
||||
"""Test that unsupported parameters raise an error"""
|
||||
@@ -68,6 +74,7 @@ class TestResponsesAPIRequestUtils:
|
||||
params = {
|
||||
"temperature": 0.7,
|
||||
"max_output_tokens": 100,
|
||||
"prompt": {"id": "pmpt_456"},
|
||||
"invalid_param": "value",
|
||||
"model": "gpt-4o", # This is not in ResponsesAPIOptionalRequestParams
|
||||
}
|
||||
@@ -84,6 +91,7 @@ class TestResponsesAPIRequestUtils:
|
||||
assert "model" not in result
|
||||
assert result["temperature"] == 0.7
|
||||
assert result["max_output_tokens"] == 100
|
||||
assert result["prompt"] == {"id": "pmpt_456"}
|
||||
|
||||
def test_decode_previous_response_id_to_original_previous_response_id(self):
|
||||
"""Test decoding a LiteLLM encoded previous_response_id to the original previous_response_id"""
|
||||
|
||||
Generated
+48
-55
@@ -24,7 +24,7 @@
|
||||
"jwt-decode": "^4.0.0",
|
||||
"lucide-react": "^0.513.0",
|
||||
"moment": "^2.30.1",
|
||||
"next": "^14.2.26",
|
||||
"next": "^14.2.30",
|
||||
"openai": "^4.93.0",
|
||||
"papaparse": "^5.5.2",
|
||||
"react": "^18",
|
||||
@@ -425,9 +425,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/env": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.26.tgz",
|
||||
"integrity": "sha512-vO//GJ/YBco+H7xdQhzJxF7ub3SUwft76jwaeOyVVQFHCi5DCnkP16WHB+JBylo4vOKPoZBlR94Z8xBxNBdNJA==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.30.tgz",
|
||||
"integrity": "sha512-KBiBKrDY6kxTQWGzKjQB7QirL3PiiOkV7KW98leHFjtVRKtft76Ra5qSA/SL75xT44dp6hOcqiiJ6iievLOYug==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@next/eslint-plugin-next": {
|
||||
@@ -440,9 +440,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-darwin-arm64": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.26.tgz",
|
||||
"integrity": "sha512-zDJY8gsKEseGAxG+C2hTMT0w9Nk9N1Sk1qV7vXYz9MEiyRoF5ogQX2+vplyUMIfygnjn9/A04I6yrUTRTuRiyQ==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.30.tgz",
|
||||
"integrity": "sha512-EAqfOTb3bTGh9+ewpO/jC59uACadRHM6TSA9DdxJB/6gxOpyV+zrbqeXiFTDy9uV6bmipFDkfpAskeaDcO+7/g==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -456,9 +456,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-darwin-x64": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.26.tgz",
|
||||
"integrity": "sha512-U0adH5ryLfmTDkahLwG9sUQG2L0a9rYux8crQeC92rPhi3jGQEY47nByQHrVrt3prZigadwj/2HZ1LUUimuSbg==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.30.tgz",
|
||||
"integrity": "sha512-TyO7Wz1IKE2kGv8dwQ0bmPL3s44EKVencOqwIY69myoS3rdpO1NPg5xPM5ymKu7nfX4oYJrpMxv8G9iqLsnL4A==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -472,9 +472,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-linux-arm64-gnu": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.26.tgz",
|
||||
"integrity": "sha512-SINMl1I7UhfHGM7SoRiw0AbwnLEMUnJ/3XXVmhyptzriHbWvPPbbm0OEVG24uUKhuS1t0nvN/DBvm5kz6ZIqpg==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.30.tgz",
|
||||
"integrity": "sha512-I5lg1fgPJ7I5dk6mr3qCH1hJYKJu1FsfKSiTKoYwcuUf53HWTrEkwmMI0t5ojFKeA6Vu+SfT2zVy5NS0QLXV4Q==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -488,9 +488,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-linux-arm64-musl": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.26.tgz",
|
||||
"integrity": "sha512-s6JaezoyJK2DxrwHWxLWtJKlqKqTdi/zaYigDXUJ/gmx/72CrzdVZfMvUc6VqnZ7YEvRijvYo+0o4Z9DencduA==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.30.tgz",
|
||||
"integrity": "sha512-8GkNA+sLclQyxgzCDs2/2GSwBc92QLMrmYAmoP2xehe5MUKBLB2cgo34Yu242L1siSkwQkiV4YLdCnjwc/Micw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -504,9 +504,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-linux-x64-gnu": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.26.tgz",
|
||||
"integrity": "sha512-FEXeUQi8/pLr/XI0hKbe0tgbLmHFRhgXOUiPScz2hk0hSmbGiU8aUqVslj/6C6KA38RzXnWoJXo4FMo6aBxjzg==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.30.tgz",
|
||||
"integrity": "sha512-8Ly7okjssLuBoe8qaRCcjGtcMsv79hwzn/63wNeIkzJVFVX06h5S737XNr7DZwlsbTBDOyI6qbL2BJB5n6TV/w==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -520,9 +520,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-linux-x64-musl": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.26.tgz",
|
||||
"integrity": "sha512-BUsomaO4d2DuXhXhgQCVt2jjX4B4/Thts8nDoIruEJkhE5ifeQFtvW5c9JkdOtYvE5p2G0hcwQ0UbRaQmQwaVg==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.30.tgz",
|
||||
"integrity": "sha512-dBmV1lLNeX4mR7uI7KNVHsGQU+OgTG5RGFPi3tBJpsKPvOPtg9poyav/BYWrB3GPQL4dW5YGGgalwZ79WukbKQ==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -536,9 +536,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-win32-arm64-msvc": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.26.tgz",
|
||||
"integrity": "sha512-5auwsMVzT7wbB2CZXQxDctpWbdEnEW/e66DyXO1DcgHxIyhP06awu+rHKshZE+lPLIGiwtjo7bsyeuubewwxMw==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.30.tgz",
|
||||
"integrity": "sha512-6MMHi2Qc1Gkq+4YLXAgbYslE1f9zMGBikKMdmQRHXjkGPot1JY3n5/Qrbg40Uvbi8//wYnydPnyvNhI1DMUW1g==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -552,12 +552,13 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-win32-ia32-msvc": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.26.tgz",
|
||||
"integrity": "sha512-GQWg/Vbz9zUGi9X80lOeGsz1rMH/MtFO/XqigDznhhhTfDlDoynCM6982mPCbSlxJ/aveZcKtTlwfAjwhyxDpg==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.30.tgz",
|
||||
"integrity": "sha512-pVZMnFok5qEX4RT59mK2hEVtJX+XFfak+/rjHpyFh7juiT52r177bfFKhnlafm0UOSldhXjj32b+LZIOdswGTg==",
|
||||
"cpu": [
|
||||
"ia32"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"win32"
|
||||
@@ -567,9 +568,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@next/swc-win32-x64-msvc": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.26.tgz",
|
||||
"integrity": "sha512-2rdB3T1/Gp7bv1eQTTm9d1Y1sv9UuJ2LAwOE0Pe2prHKe32UNscj7YS13fRB37d0GAiGNR+Y7ZcW8YjDI8Ns0w==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.30.tgz",
|
||||
"integrity": "sha512-4KCo8hMZXMjpTzs3HOqOGYYwAXymXIy7PEPAXNEcEOyKqkjiDlECumrWziy+JEF0Oi4ILHGxzgQ3YiMGG2t/Lg==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -5050,12 +5051,12 @@
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/next": {
|
||||
"version": "14.2.26",
|
||||
"resolved": "https://registry.npmjs.org/next/-/next-14.2.26.tgz",
|
||||
"integrity": "sha512-b81XSLihMwCfwiUVRRja3LphLo4uBBMZEzBBWMaISbKTwOmq3wPknIETy/8000tr7Gq4WmbuFYPS7jOYIf+ZJw==",
|
||||
"version": "14.2.30",
|
||||
"resolved": "https://registry.npmjs.org/next/-/next-14.2.30.tgz",
|
||||
"integrity": "sha512-+COdu6HQrHHFQ1S/8BBsCag61jZacmvbuL2avHvQFbWa2Ox7bE+d8FyNgxRLjXQ5wtPyQwEmk85js/AuaG2Sbg==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@next/env": "14.2.26",
|
||||
"@next/env": "14.2.30",
|
||||
"@swc/helpers": "0.5.5",
|
||||
"busboy": "1.6.0",
|
||||
"caniuse-lite": "^1.0.30001579",
|
||||
@@ -5070,15 +5071,15 @@
|
||||
"node": ">=18.17.0"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@next/swc-darwin-arm64": "14.2.26",
|
||||
"@next/swc-darwin-x64": "14.2.26",
|
||||
"@next/swc-linux-arm64-gnu": "14.2.26",
|
||||
"@next/swc-linux-arm64-musl": "14.2.26",
|
||||
"@next/swc-linux-x64-gnu": "14.2.26",
|
||||
"@next/swc-linux-x64-musl": "14.2.26",
|
||||
"@next/swc-win32-arm64-msvc": "14.2.26",
|
||||
"@next/swc-win32-ia32-msvc": "14.2.26",
|
||||
"@next/swc-win32-x64-msvc": "14.2.26"
|
||||
"@next/swc-darwin-arm64": "14.2.30",
|
||||
"@next/swc-darwin-x64": "14.2.30",
|
||||
"@next/swc-linux-arm64-gnu": "14.2.30",
|
||||
"@next/swc-linux-arm64-musl": "14.2.30",
|
||||
"@next/swc-linux-x64-gnu": "14.2.30",
|
||||
"@next/swc-linux-x64-musl": "14.2.30",
|
||||
"@next/swc-win32-arm64-msvc": "14.2.30",
|
||||
"@next/swc-win32-ia32-msvc": "14.2.30",
|
||||
"@next/swc-win32-x64-msvc": "14.2.30"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@opentelemetry/api": "^1.1.0",
|
||||
@@ -5695,9 +5696,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/prismjs": {
|
||||
"version": "1.29.0",
|
||||
"resolved": "https://registry.npmjs.org/prismjs/-/prismjs-1.29.0.tgz",
|
||||
"integrity": "sha512-Kx/1w86q/epKcmte75LNrEoT+lX8pBpavuAbvJWRXar7Hz8jrtF+e3vY751p0R8H9HdArwaCTNDDzHg/ScJK1Q==",
|
||||
"version": "1.30.0",
|
||||
"resolved": "https://registry.npmjs.org/prismjs/-/prismjs-1.30.0.tgz",
|
||||
"integrity": "sha512-DEvV2ZF2r2/63V+tK8hQvrR2ZGn10srHbXviTlcv7Kpzw8jWiNTqbVgjO3IY8RxrrOUF8VPMQQFysYYYv0YZxw==",
|
||||
"engines": {
|
||||
"node": ">=6"
|
||||
}
|
||||
@@ -6637,14 +6638,6 @@
|
||||
"url": "https://github.com/sponsors/wooorm"
|
||||
}
|
||||
},
|
||||
"node_modules/refractor/node_modules/prismjs": {
|
||||
"version": "1.27.0",
|
||||
"resolved": "https://registry.npmjs.org/prismjs/-/prismjs-1.27.0.tgz",
|
||||
"integrity": "sha512-t13BGPUlFDR7wRB5kQDG4jjl7XeuH6jbJGt11JHPL96qwsEHNX2+68tFXqc1/k+/jALsbSWJKUOT/hcYAZ5LkA==",
|
||||
"engines": {
|
||||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/regenerator-runtime": {
|
||||
"version": "0.14.1",
|
||||
"resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.14.1.tgz",
|
||||
|
||||
@@ -25,7 +25,7 @@
|
||||
"jwt-decode": "^4.0.0",
|
||||
"lucide-react": "^0.513.0",
|
||||
"moment": "^2.30.1",
|
||||
"next": "^14.2.26",
|
||||
"next": "^14.2.30",
|
||||
"openai": "^4.93.0",
|
||||
"papaparse": "^5.5.2",
|
||||
"react": "^18",
|
||||
@@ -52,5 +52,8 @@
|
||||
"prettier": "3.2.5",
|
||||
"tailwindcss": "^3.4.1",
|
||||
"typescript": "5.3.3"
|
||||
},
|
||||
"overrides": {
|
||||
"prismjs": ">=1.30.0"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -225,21 +225,21 @@ const PROVIDER_CREDENTIAL_FIELDS: Record<Providers, ProviderCredentialField[]> =
|
||||
key: "aws_access_key_id",
|
||||
label: "AWS Access Key ID",
|
||||
type: "password",
|
||||
required: true,
|
||||
required: false,
|
||||
tooltip: "You can provide the raw key or the environment variable (e.g. `os.environ/MY_SECRET_KEY`)."
|
||||
},
|
||||
{
|
||||
key: "aws_secret_access_key",
|
||||
label: "AWS Secret Access Key",
|
||||
type: "password",
|
||||
required: true,
|
||||
required: false,
|
||||
tooltip: "You can provide the raw key or the environment variable (e.g. `os.environ/MY_SECRET_KEY`)."
|
||||
},
|
||||
{
|
||||
key: "aws_region_name",
|
||||
label: "AWS Region Name",
|
||||
placeholder: "us-east-1",
|
||||
required: true,
|
||||
required: false,
|
||||
tooltip: "You can provide the raw key or the environment variable (e.g. `os.environ/MY_SECRET_KEY`)."
|
||||
}
|
||||
],
|
||||
|
||||
@@ -1074,6 +1074,7 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
|
||||
// Trigger a refresh to update UI
|
||||
handleRefreshClick();
|
||||
}}
|
||||
modelAccessGroups={availableModelAccessGroups}
|
||||
/>
|
||||
) : (
|
||||
<TabGroup className="gap-2 p-8 h-[75vh] w-full mt-2">
|
||||
|
||||
@@ -36,6 +36,7 @@ interface ModelInfoViewProps {
|
||||
setEditModalVisible: (visible: boolean) => void;
|
||||
setSelectedModel: (model: any) => void;
|
||||
onModelUpdate?: (updatedModel: any) => void;
|
||||
modelAccessGroups: string[] | null;
|
||||
}
|
||||
|
||||
export default function ModelInfoView({
|
||||
@@ -48,7 +49,8 @@ export default function ModelInfoView({
|
||||
editModel,
|
||||
setEditModalVisible,
|
||||
setSelectedModel,
|
||||
onModelUpdate
|
||||
onModelUpdate,
|
||||
modelAccessGroups
|
||||
}: ModelInfoViewProps) {
|
||||
const [form] = Form.useForm();
|
||||
const [localModelData, setLocalModelData] = useState<any>(null);
|
||||
@@ -147,6 +149,13 @@ export default function ModelInfoView({
|
||||
let updatedModelInfo;
|
||||
try {
|
||||
updatedModelInfo = values.model_info ? JSON.parse(values.model_info) : modelData.model_info;
|
||||
// Update access_groups from the form
|
||||
if (values.model_access_group) {
|
||||
updatedModelInfo = {
|
||||
...updatedModelInfo,
|
||||
access_groups: values.model_access_group
|
||||
};
|
||||
}
|
||||
} catch (e) {
|
||||
message.error("Invalid JSON in Model Info");
|
||||
return;
|
||||
@@ -365,6 +374,7 @@ export default function ModelInfoView({
|
||||
(localModelData.litellm_params.output_cost_per_token * 1_000_000) : localModelData.model_info?.output_cost_per_token * 1_000_000 || null,
|
||||
cache_control: localModelData.litellm_params?.cache_control_injection_points ? true : false,
|
||||
cache_control_injection_points: localModelData.litellm_params?.cache_control_injection_points || [],
|
||||
model_access_group: Array.isArray(localModelData.model_info?.access_groups) ? localModelData.model_info.access_groups : [],
|
||||
}}
|
||||
layout="vertical"
|
||||
onValuesChange={() => setIsDirty(true)}
|
||||
@@ -527,6 +537,45 @@ export default function ModelInfoView({
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<Text className="font-medium">Model Access Groups</Text>
|
||||
{isEditing ? (
|
||||
<Form.Item name="model_access_group" className="mb-0">
|
||||
<Select
|
||||
mode="tags"
|
||||
showSearch
|
||||
placeholder="Select existing groups or type to create new ones"
|
||||
optionFilterProp="children"
|
||||
tokenSeparators={[',']}
|
||||
maxTagCount="responsive"
|
||||
allowClear
|
||||
style={{ width: '100%' }}
|
||||
options={modelAccessGroups?.map((group) => ({
|
||||
value: group,
|
||||
label: group
|
||||
}))}
|
||||
/>
|
||||
</Form.Item>
|
||||
) : (
|
||||
<div className="mt-1 p-2 bg-gray-50 rounded">
|
||||
{localModelData.model_info?.access_groups ? (
|
||||
Array.isArray(localModelData.model_info.access_groups) ? (
|
||||
localModelData.model_info.access_groups.length > 0 ? (
|
||||
<div className="flex flex-wrap gap-1">
|
||||
{localModelData.model_info.access_groups.map((group: string, index: number) => (
|
||||
<span key={index} className="inline-flex items-center px-2 py-1 rounded-full text-xs font-medium bg-blue-100 text-blue-800">
|
||||
{group}
|
||||
</span>
|
||||
))}
|
||||
</div>
|
||||
) : "No groups assigned"
|
||||
) : localModelData.model_info.access_groups
|
||||
) : "Not Set"}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
|
||||
{/* Cache Control Section */}
|
||||
{isEditing ? (
|
||||
<CacheControlSettings
|
||||
|
||||
@@ -260,9 +260,8 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
|
||||
);
|
||||
|
||||
return (
|
||||
<div className="w-full p-6">
|
||||
<div className="flex items-center justify-between mb-4">
|
||||
<h1 className="text-xl font-semibold">Users</h1>
|
||||
<div className="w-full p-6 mx-4">
|
||||
<div className="flex items-center justify-between mb-4 mt-4">
|
||||
<div className="flex space-x-3">
|
||||
<CreateUser
|
||||
userID={userID}
|
||||
|
||||
Reference in New Issue
Block a user