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[Feat] Proxy - New LLM API Routes /v1/vector_stores and /v1/vector_stores/vs_abc123/search (#12699)
* add Search + Create vector stores * add new vector_stores routes * add avector_store_search routes * docs vector stores * docs vector stores * Create Vector Store * fix - refactor * add vector_store_router * fix imports * fix ruff
This commit is contained in:
Ishaan Jaff
@@ -0,0 +1,314 @@
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# /vector_stores - Create Vector Store
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Create a vector store which can be used to store and search document chunks for retrieval-augmented generation (RAG) use cases.
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## Overview
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| Feature | Supported | Notes |
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|---------|-----------|-------|
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| Cost Tracking | ✅ | Tracked per vector store operation |
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| Logging | ✅ | Works across all integrations |
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| End-user Tracking | ✅ | |
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| Support LLM Providers | **OpenAI, Azure OpenAI, Bedrock, Vertex RAG Engine** | Full vector stores API support across providers |
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## Usage
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### LiteLLM Python SDK
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<Tabs>
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<TabItem value="basic" label="Basic Usage">
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#### Non-streaming example
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```python showLineNumbers title="Create Vector Store - Basic"
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import litellm
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response = await litellm.vector_stores.acreate(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"]
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)
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print(response)
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```
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#### Synchronous example
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```python showLineNumbers title="Create Vector Store - Sync"
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import litellm
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response = litellm.vector_stores.create(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"]
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)
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print(response)
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```
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</TabItem>
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<TabItem value="advanced" label="Advanced Configuration">
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#### With expiration and chunking strategy
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```python showLineNumbers title="Create Vector Store - Advanced"
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import litellm
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response = await litellm.vector_stores.acreate(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"],
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expires_after={
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"anchor": "last_active_at",
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"days": 7
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},
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chunking_strategy={
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"type": "static",
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"static": {
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"max_chunk_size_tokens": 800,
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"chunk_overlap_tokens": 400
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}
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},
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metadata={
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"project": "rag-system",
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"environment": "production"
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}
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)
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print(response)
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```
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</TabItem>
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<TabItem value="openai-provider" label="OpenAI Provider">
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#### Using OpenAI provider explicitly
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```python showLineNumbers title="Create Vector Store - OpenAI Provider"
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import litellm
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import os
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# Set API key
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os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
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response = await litellm.vector_stores.acreate(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"],
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custom_llm_provider="openai"
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)
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print(response)
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```
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</TabItem>
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</Tabs>
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### LiteLLM Proxy Server
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<Tabs>
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<TabItem value="proxy-setup" label="Setup & Usage">
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1. Setup config.yaml
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```yaml
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model_list:
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- model_name: gpt-4o
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litellm_params:
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model: openai/gpt-4o
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api_key: os.environ/OPENAI_API_KEY
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general_settings:
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# Vector store settings can be added here if needed
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```
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2. Start proxy
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```bash
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litellm --config /path/to/config.yaml
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```
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3. Test it with OpenAI SDK!
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```python showLineNumbers title="OpenAI SDK via LiteLLM Proxy"
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from openai import OpenAI
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# Point OpenAI SDK to LiteLLM proxy
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client = OpenAI(
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base_url="http://0.0.0.0:4000",
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api_key="sk-1234", # Your LiteLLM API key
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)
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vector_store = client.beta.vector_stores.create(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"]
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)
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print(vector_store)
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```
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</TabItem>
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<TabItem value="curl-proxy" label="curl">
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```bash showLineNumbers title="Create Vector Store via curl"
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curl -L -X POST 'http://0.0.0.0:4000/v1/vector_stores' \
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-H 'Content-Type: application/json' \
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-H 'Authorization: Bearer sk-1234' \
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-d '{
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"name": "My Document Store",
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"file_ids": ["file-abc123", "file-def456"],
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"expires_after": {
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"anchor": "last_active_at",
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"days": 7
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},
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"chunking_strategy": {
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"type": "static",
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"static": {
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"max_chunk_size_tokens": 800,
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"chunk_overlap_tokens": 400
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}
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},
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"metadata": {
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"project": "rag-system",
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"environment": "production"
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}
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}'
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```
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</TabItem>
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</Tabs>
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### OpenAI SDK (Standalone)
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<Tabs>
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<TabItem value="openai-direct" label="Direct OpenAI Usage">
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```python showLineNumbers title="OpenAI SDK Direct"
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from openai import OpenAI
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client = OpenAI(api_key="your-openai-api-key")
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vector_store = client.beta.vector_stores.create(
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name="My Document Store",
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file_ids=["file-abc123", "file-def456"]
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)
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print(vector_store)
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```
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</TabItem>
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</Tabs>
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## Request Format
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The request body follows OpenAI's vector stores API format.
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#### Example request body
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```json
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{
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"name": "My Document Store",
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"file_ids": ["file-abc123", "file-def456"],
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"expires_after": {
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"anchor": "last_active_at",
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"days": 7
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},
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"chunking_strategy": {
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"type": "static",
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"static": {
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"max_chunk_size_tokens": 800,
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"chunk_overlap_tokens": 400
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}
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},
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"metadata": {
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"project": "rag-system",
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"environment": "production"
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}
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}
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```
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#### Optional Fields
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- **name** (string): The name of the vector store.
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- **file_ids** (array of strings): A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files.
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- **expires_after** (object): The expiration policy for the vector store.
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- **anchor** (string): Anchor timestamp after which the expiration policy applies. Supported anchors: `last_active_at`.
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- **days** (integer): The number of days after the anchor time that the vector store will expire.
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- **chunking_strategy** (object): The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy.
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- **type** (string): Always `static`.
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- **static** (object): The static chunking strategy.
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- **max_chunk_size_tokens** (integer): The maximum number of tokens in each chunk. The default value is `800`. The minimum value is `100` and the maximum value is `4096`.
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- **chunk_overlap_tokens** (integer): The number of tokens that overlap between chunks. The default value is `400`.
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- **metadata** (object): Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long.
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## Response Format
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#### Example Response
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```json
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{
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"id": "vs_abc123",
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"object": "vector_store",
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"created_at": 1699061776,
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"name": "My Document Store",
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"bytes": 139920,
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"file_counts": {
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"in_progress": 0,
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"completed": 2,
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"failed": 0,
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"cancelled": 0,
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"total": 2
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},
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"status": "completed",
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"expires_after": {
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"anchor": "last_active_at",
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"days": 7
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},
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"expires_at": null,
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"last_active_at": 1699061776,
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"metadata": {
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"project": "rag-system",
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"environment": "production"
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}
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}
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```
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#### Response Fields
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- **id** (string): The identifier, which can be referenced in API endpoints.
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- **object** (string): The object type, which is always `vector_store`.
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- **created_at** (integer): The Unix timestamp (in seconds) for when the vector store was created.
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- **name** (string): The name of the vector store.
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- **bytes** (integer): The total number of bytes used by the files in the vector store.
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- **file_counts** (object): The file counts for the vector store.
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- **in_progress** (integer): The number of files that are currently being processed.
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- **completed** (integer): The number of files that have been successfully processed.
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- **failed** (integer): The number of files that failed to process.
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- **cancelled** (integer): The number of files that were cancelled.
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- **total** (integer): The total number of files.
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- **status** (string): The status of the vector store, which can be either `expired`, `in_progress`, or `completed`. A status of `completed` indicates that the vector store is ready for use.
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- **expires_after** (object or null): The expiration policy for the vector store.
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- **expires_at** (integer or null): The Unix timestamp (in seconds) for when the vector store will expire.
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- **last_active_at** (integer or null): The Unix timestamp (in seconds) for when the vector store was last active.
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- **metadata** (object or null): Set of 16 key-value pairs that can be attached to an object.
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## Mock Response Testing
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For testing purposes, you can use mock responses:
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```python showLineNumbers title="Mock Response Example"
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import litellm
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# Mock response for testing
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mock_response = {
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"id": "vs_mock123",
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"object": "vector_store",
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"created_at": 1699061776,
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"name": "Mock Vector Store",
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"bytes": 0,
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"file_counts": {
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"in_progress": 0,
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"completed": 0,
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"failed": 0,
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"cancelled": 0,
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"total": 0
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},
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"status": "completed"
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}
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response = await litellm.vector_stores.acreate(
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name="Test Store",
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mock_response=mock_response
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)
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print(response)
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```
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@@ -0,0 +1,368 @@
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# /vector_stores/{vector_store_id}/search - Search Vector Store
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Search a vector store for relevant chunks based on a query and file attributes filter. This is useful for retrieval-augmented generation (RAG) use cases.
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## Overview
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|
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| Feature | Supported | Notes |
|
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|---------|-----------|-------|
|
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| Cost Tracking | ✅ | Tracked per search operation |
|
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| Logging | ✅ | Works across all integrations |
|
||||
| End-user Tracking | ✅ | |
|
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| Support LLM Providers | **OpenAI, Azure OpenAI, Bedrock, Vertex RAG Engine** | Full vector stores API support across providers |
|
||||
|
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## Usage
|
||||
|
||||
### LiteLLM Python SDK
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="basic" label="Basic Usage">
|
||||
|
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#### Non-streaming example
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```python showLineNumbers title="Search Vector Store - Basic"
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import litellm
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response = await litellm.vector_stores.asearch(
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vector_store_id="vs_abc123",
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query="What is the capital of France?"
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)
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print(response)
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```
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#### Synchronous example
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```python showLineNumbers title="Search Vector Store - Sync"
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import litellm
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response = litellm.vector_stores.search(
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vector_store_id="vs_abc123",
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query="What is the capital of France?"
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)
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print(response)
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```
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</TabItem>
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<TabItem value="advanced" label="Advanced Configuration">
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#### With filters and ranking options
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```python showLineNumbers title="Search Vector Store - Advanced"
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import litellm
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response = await litellm.vector_stores.asearch(
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vector_store_id="vs_abc123",
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query="What is the capital of France?",
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filters={
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"file_ids": ["file-abc123", "file-def456"]
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},
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max_num_results=5,
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ranking_options={
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"score_threshold": 0.7
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},
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rewrite_query=True
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)
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print(response)
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```
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</TabItem>
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<TabItem value="multiple-queries" label="Multiple Queries">
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#### Searching with multiple queries
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```python showLineNumbers title="Search Vector Store - Multiple Queries"
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import litellm
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response = await litellm.vector_stores.asearch(
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vector_store_id="vs_abc123",
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query=[
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"What is the capital of France?",
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"What is the population of Paris?"
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],
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max_num_results=10
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)
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print(response)
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```
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</TabItem>
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<TabItem value="openai-provider" label="OpenAI Provider">
|
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|
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#### Using OpenAI provider explicitly
|
||||
```python showLineNumbers title="Search Vector Store - OpenAI Provider"
|
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import litellm
|
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import os
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|
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# Set API key
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os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
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|
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response = await litellm.vector_stores.asearch(
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vector_store_id="vs_abc123",
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query="What is the capital of France?",
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custom_llm_provider="openai"
|
||||
)
|
||||
print(response)
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
### LiteLLM Proxy Server
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="proxy-setup" label="Setup & Usage">
|
||||
|
||||
1. Setup config.yaml
|
||||
|
||||
```yaml
|
||||
model_list:
|
||||
- model_name: gpt-4o
|
||||
litellm_params:
|
||||
model: openai/gpt-4o
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
general_settings:
|
||||
# Vector store settings can be added here if needed
|
||||
```
|
||||
|
||||
2. Start proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
```
|
||||
|
||||
3. Test it with OpenAI SDK!
|
||||
|
||||
```python showLineNumbers title="OpenAI SDK via LiteLLM Proxy"
|
||||
from openai import OpenAI
|
||||
|
||||
# Point OpenAI SDK to LiteLLM proxy
|
||||
client = OpenAI(
|
||||
base_url="http://0.0.0.0:4000",
|
||||
api_key="sk-1234", # Your LiteLLM API key
|
||||
)
|
||||
|
||||
search_results = client.beta.vector_stores.search(
|
||||
vector_store_id="vs_abc123",
|
||||
query="What is the capital of France?",
|
||||
max_num_results=5
|
||||
)
|
||||
print(search_results)
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="curl-proxy" label="curl">
|
||||
|
||||
```bash showLineNumbers title="Search Vector Store via curl"
|
||||
curl -L -X POST 'http://0.0.0.0:4000/v1/vector_stores/vs_abc123/search' \
|
||||
-H 'Content-Type: application/json' \
|
||||
-H 'Authorization: Bearer sk-1234' \
|
||||
-d '{
|
||||
"query": "What is the capital of France?",
|
||||
"filters": {
|
||||
"file_ids": ["file-abc123", "file-def456"]
|
||||
},
|
||||
"max_num_results": 5,
|
||||
"ranking_options": {
|
||||
"score_threshold": 0.7
|
||||
},
|
||||
"rewrite_query": true
|
||||
}'
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
### OpenAI SDK (Standalone)
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="openai-direct" label="Direct OpenAI Usage">
|
||||
|
||||
```python showLineNumbers title="OpenAI SDK Direct"
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(api_key="your-openai-api-key")
|
||||
|
||||
search_results = client.beta.vector_stores.search(
|
||||
vector_store_id="vs_abc123",
|
||||
query="What is the capital of France?",
|
||||
max_num_results=5
|
||||
)
|
||||
print(search_results)
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Request Format
|
||||
|
||||
The request body follows OpenAI's vector stores search API format.
|
||||
|
||||
#### Example request body
|
||||
|
||||
```json
|
||||
{
|
||||
"query": "What is the capital of France?",
|
||||
"filters": {
|
||||
"file_ids": ["file-abc123", "file-def456"]
|
||||
},
|
||||
"max_num_results": 5,
|
||||
"ranking_options": {
|
||||
"score_threshold": 0.7
|
||||
},
|
||||
"rewrite_query": true
|
||||
}
|
||||
```
|
||||
|
||||
#### Required Fields
|
||||
- **query** (string or array of strings): A query string or array for the search. The query is used to find relevant chunks in the vector store.
|
||||
|
||||
#### Optional Fields
|
||||
- **filters** (object): Optional filter to apply based on file attributes.
|
||||
- **file_ids** (array of strings): Filter chunks based on specific file IDs.
|
||||
- **max_num_results** (integer): Maximum number of results to return. Must be between 1 and 50. Default is 10.
|
||||
- **ranking_options** (object): Optional ranking options for search.
|
||||
- **score_threshold** (number): Minimum similarity score threshold for results.
|
||||
- **rewrite_query** (boolean): Whether to rewrite the natural language query for vector search optimization. Default is true.
|
||||
|
||||
## Response Format
|
||||
|
||||
#### Example Response
|
||||
|
||||
```json
|
||||
{
|
||||
"object": "vector_store.search_results.page",
|
||||
"search_query": "What is the capital of France?",
|
||||
"data": [
|
||||
{
|
||||
"score": 0.95,
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "Paris is the capital and most populous city of France. With an official estimated population of 2,102,650 residents as of 1 January 2023 in an area of more than 105 km², Paris is the fourth-most populated city in the European Union and the 30th most densely populated city in the world in 2022."
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"score": 0.87,
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "France, officially the French Republic, is a country located primarily in Western Europe. Its capital is Paris, one of the most important cultural and economic centers in Europe."
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Response Fields
|
||||
|
||||
- **object** (string): The object type, which is always `vector_store.search_results.page`.
|
||||
- **search_query** (string): The query that was used for the search.
|
||||
- **data** (array): An array of search result objects.
|
||||
- **score** (number): The similarity score of the search result, typically between 0 and 1, where 1 is the most similar.
|
||||
- **content** (array): Array of content objects containing the retrieved text.
|
||||
- **type** (string): The type of content, typically `text`.
|
||||
- **text** (string): The actual text content that was retrieved from the vector store.
|
||||
|
||||
## Mock Response Testing
|
||||
|
||||
For testing purposes, you can use mock responses:
|
||||
|
||||
```python showLineNumbers title="Mock Response Example"
|
||||
import litellm
|
||||
|
||||
# Mock response for testing
|
||||
mock_results = [
|
||||
{
|
||||
"score": 0.95,
|
||||
"content": [
|
||||
{
|
||||
"text": "Paris is the capital of France.",
|
||||
"type": "text"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"score": 0.87,
|
||||
"content": [
|
||||
{
|
||||
"text": "France is a country in Western Europe.",
|
||||
"type": "text"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
response = await litellm.vector_stores.asearch(
|
||||
vector_store_id="vs_abc123",
|
||||
query="What is the capital of France?",
|
||||
mock_response=mock_results
|
||||
)
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
Common errors you might encounter:
|
||||
|
||||
```python showLineNumbers title="Error Handling Example"
|
||||
import litellm
|
||||
|
||||
try:
|
||||
response = await litellm.vector_stores.asearch(
|
||||
vector_store_id="vs_invalid",
|
||||
query="What is the capital of France?"
|
||||
)
|
||||
except litellm.NotFoundError as e:
|
||||
print(f"Vector store not found: {e}")
|
||||
except litellm.RateLimitError as e:
|
||||
print(f"Rate limit exceeded: {e}")
|
||||
except Exception as e:
|
||||
print(f"Unexpected error: {e}")
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Query Optimization**: Use clear, specific queries for better search results.
|
||||
2. **Result Filtering**: Use file_ids filter to limit search scope when needed.
|
||||
3. **Score Thresholds**: Set appropriate score thresholds to filter out irrelevant results.
|
||||
4. **Batch Queries**: Use array queries when searching for multiple related topics.
|
||||
5. **Error Handling**: Always implement proper error handling for production use.
|
||||
|
||||
```python showLineNumbers title="Best Practices Example"
|
||||
import litellm
|
||||
|
||||
async def search_documents(vector_store_id: str, user_query: str):
|
||||
"""
|
||||
Search documents with best practices applied
|
||||
"""
|
||||
try:
|
||||
response = await litellm.vector_stores.asearch(
|
||||
vector_store_id=vector_store_id,
|
||||
query=user_query,
|
||||
max_num_results=5,
|
||||
ranking_options={
|
||||
"score_threshold": 0.7 # Filter out low-relevance results
|
||||
},
|
||||
rewrite_query=True # Optimize query for vector search
|
||||
)
|
||||
|
||||
# Filter results by score for additional quality control
|
||||
high_quality_results = [
|
||||
result for result in response.data
|
||||
if result.score >= 0.8
|
||||
]
|
||||
|
||||
return high_quality_results
|
||||
|
||||
except Exception as e:
|
||||
print(f"Search failed: {e}")
|
||||
return []
|
||||
|
||||
# Usage
|
||||
results = await search_documents("vs_abc123", "What is the capital of France?")
|
||||
```
|
||||
@@ -278,6 +278,14 @@ const sidebars = {
|
||||
"text_to_speech",
|
||||
]
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "/vector_stores",
|
||||
items: [
|
||||
"vector_stores/create",
|
||||
"vector_stores/search",
|
||||
]
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Pass-through Endpoints (Anthropic SDK, etc.)",
|
||||
|
||||
@@ -11,7 +11,7 @@ All /vector_store management endpoints
|
||||
import copy
|
||||
from typing import List
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from fastapi import APIRouter, Depends, HTTPException, Request, Response
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_proxy_logger
|
||||
@@ -27,7 +27,9 @@ from litellm.vector_stores.vector_store_registry import VectorStoreRegistry
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
########################################################
|
||||
# Management Endpoints
|
||||
########################################################
|
||||
@router.post(
|
||||
"/vector_store/new",
|
||||
tags=["vector store management"],
|
||||
|
||||
@@ -273,6 +273,8 @@ class ProxyBaseLLMRequestProcessing:
|
||||
"agenerate_content",
|
||||
"agenerate_content_stream",
|
||||
"allm_passthrough_route",
|
||||
"avector_store_search",
|
||||
"avector_store_create",
|
||||
],
|
||||
version: Optional[str] = None,
|
||||
user_model: Optional[str] = None,
|
||||
@@ -357,6 +359,8 @@ class ProxyBaseLLMRequestProcessing:
|
||||
"agenerate_content",
|
||||
"agenerate_content_stream",
|
||||
"allm_passthrough_route",
|
||||
"avector_store_search",
|
||||
"avector_store_create",
|
||||
],
|
||||
proxy_logging_obj: ProxyLogging,
|
||||
general_settings: dict,
|
||||
|
||||
@@ -0,0 +1,129 @@
|
||||
from fastapi import APIRouter, Depends, Request, Response
|
||||
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
|
||||
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
|
||||
|
||||
router = APIRouter()
|
||||
########################################################
|
||||
# OpenAI Compatible Endpoints
|
||||
########################################################
|
||||
|
||||
@router.post("/v1/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
|
||||
@router.post("/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
|
||||
async def vector_store_search(
|
||||
request: Request,
|
||||
vector_store_id: str,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
"""
|
||||
Search a vector store.
|
||||
|
||||
API Reference:
|
||||
https://platform.openai.com/docs/api-reference/vector-stores/search
|
||||
"""
|
||||
from litellm.proxy.proxy_server import (
|
||||
_read_request_body,
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
|
||||
|
||||
data = await _read_request_body(request=request)
|
||||
if "vector_store_id" not in data:
|
||||
data["vector_store_id"] = vector_store_id
|
||||
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||||
try:
|
||||
return await processor.base_process_llm_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type="avector_store_search",
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
llm_router=llm_router,
|
||||
general_settings=general_settings,
|
||||
proxy_config=proxy_config,
|
||||
select_data_generator=select_data_generator,
|
||||
model=None,
|
||||
user_model=user_model,
|
||||
user_temperature=user_temperature,
|
||||
user_request_timeout=user_request_timeout,
|
||||
user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base,
|
||||
version=version,
|
||||
)
|
||||
except Exception as e:
|
||||
raise await processor._handle_llm_api_exception(
|
||||
e=e,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
version=version,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@router.post("/v1/vector_stores", dependencies=[Depends(user_api_key_auth)])
|
||||
@router.post("/vector_stores", dependencies=[Depends(user_api_key_auth)])
|
||||
async def vector_store_create(
|
||||
request: Request,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
"""
|
||||
Create a vector store.
|
||||
|
||||
API Reference:
|
||||
https://platform.openai.com/docs/api-reference/vector-stores/create
|
||||
"""
|
||||
from litellm.proxy.proxy_server import (
|
||||
_read_request_body,
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
|
||||
|
||||
data = await _read_request_body(request=request)
|
||||
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||||
try:
|
||||
return await processor.base_process_llm_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type="avector_store_create",
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
llm_router=llm_router,
|
||||
general_settings=general_settings,
|
||||
proxy_config=proxy_config,
|
||||
select_data_generator=select_data_generator,
|
||||
model=None,
|
||||
user_model=user_model,
|
||||
user_temperature=user_temperature,
|
||||
user_request_timeout=user_request_timeout,
|
||||
user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base,
|
||||
version=version,
|
||||
)
|
||||
except Exception as e:
|
||||
raise await processor._handle_llm_api_exception(
|
||||
e=e,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
version=version,
|
||||
)
|
||||
@@ -207,6 +207,9 @@ from litellm.proxy.common_utils.openai_endpoint_utils import (
|
||||
from litellm.proxy.common_utils.proxy_state import ProxyState
|
||||
from litellm.proxy.common_utils.reset_budget_job import ResetBudgetJob
|
||||
from litellm.proxy.common_utils.swagger_utils import ERROR_RESPONSES
|
||||
from litellm.proxy.common_utils.vector_store_endpoints.endpoints import (
|
||||
router as vector_store_router,
|
||||
)
|
||||
from litellm.proxy.credential_endpoints.endpoints import router as credential_router
|
||||
from litellm.proxy.db.db_transaction_queue.spend_log_cleanup import SpendLogCleanup
|
||||
from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler
|
||||
@@ -8708,6 +8711,7 @@ app.include_router(public_endpoints_router)
|
||||
app.include_router(rerank_router)
|
||||
app.include_router(image_router)
|
||||
app.include_router(fine_tuning_router)
|
||||
app.include_router(vector_store_router)
|
||||
app.include_router(credential_router)
|
||||
app.include_router(llm_passthrough_router)
|
||||
app.include_router(mcp_management_router)
|
||||
|
||||
@@ -76,6 +76,8 @@ async def route_request(
|
||||
"agenerate_content",
|
||||
"agenerate_content_stream",
|
||||
"allm_passthrough_route",
|
||||
"avector_store_search",
|
||||
"avector_store_create",
|
||||
],
|
||||
):
|
||||
"""
|
||||
|
||||
+27
-1
@@ -783,6 +783,26 @@ class Router:
|
||||
litellm.allm_passthrough_route, call_type="allm_passthrough_route"
|
||||
)
|
||||
|
||||
#########################################################
|
||||
# Vector Store routes
|
||||
#########################################################
|
||||
from litellm.vector_stores.main import acreate, asearch, create, search
|
||||
|
||||
# async routes
|
||||
self.avector_store_search = self.factory_function(
|
||||
asearch, call_type="avector_store_search"
|
||||
)
|
||||
self.avector_store_create = self.factory_function(
|
||||
acreate, call_type="avector_store_create"
|
||||
)
|
||||
# sync routes
|
||||
self.vector_store_search = self.factory_function(
|
||||
search, call_type="vector_store_search"
|
||||
)
|
||||
self.vector_store_create = self.factory_function(
|
||||
create, call_type="vector_store_create"
|
||||
)
|
||||
|
||||
#########################################################
|
||||
# Gemini Native routes
|
||||
#########################################################
|
||||
@@ -3290,6 +3310,10 @@ class Router:
|
||||
"generate_content",
|
||||
"agenerate_content_stream",
|
||||
"generate_content_stream",
|
||||
"avector_store_search",
|
||||
"avector_store_create",
|
||||
"vector_store_search",
|
||||
"vector_store_create",
|
||||
] = "assistants",
|
||||
):
|
||||
"""
|
||||
@@ -3300,7 +3324,7 @@ class Router:
|
||||
- An asynchronous function for asynchronous call types
|
||||
"""
|
||||
# Handle synchronous call types
|
||||
if call_type in ("responses", "generate_content", "generate_content_stream"):
|
||||
if call_type in ("responses", "generate_content", "generate_content_stream", "vector_store_search", "vector_store_create"):
|
||||
|
||||
def sync_wrapper(
|
||||
custom_llm_provider: Optional[
|
||||
@@ -3346,6 +3370,8 @@ class Router:
|
||||
"aimage_edit",
|
||||
"agenerate_content",
|
||||
"agenerate_content_stream",
|
||||
"avector_store_search",
|
||||
"avector_store_create",
|
||||
):
|
||||
return await self._ageneric_api_call_with_fallbacks(
|
||||
original_function=original_function,
|
||||
|
||||
Reference in New Issue
Block a user