/vector_stores/search - 搜尋向量儲存

依據查詢與檔案屬性篩選條件搜尋向量儲存中相關的區塊。這對檢索增強生成（RAG）使用案例很有幫助。

概覽

功能	支援	備註
成本追蹤	✅	依每次搜尋操作追蹤
記錄	✅	可跨所有整合運作
最終使用者追蹤	✅
支援 LLM 提供者	OpenAI、Azure OpenAI、Bedrock、Vertex RAG Engine、Azure AI、Milvus、Gemini	跨提供者完整支援向量儲存 API

如需透過 HTTP 進行 retrieve、list、update 與 delete（包含 custom_llm_provider / model 路由），請參閱建立向量儲存。

用法

LiteLLM Python SDK

基本用法

非串流範例

Search Vector Store - Basic

import litellm

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query="What is the capital of France?"
)
print(response)

同步範例

Search Vector Store - Sync

import litellm

response = litellm.vector_stores.search(
    vector_store_id="vs_abc123",
    query="What is the capital of France?"
)
print(response)

進階設定

使用篩選與排序選項

Search Vector Store - Advanced

import litellm

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    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
)
print(response)

多重查詢

使用多個查詢進行搜尋

Search Vector Store - Multiple Queries

import litellm

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query=[
        "What is the capital of France?",
        "What is the population of Paris?"
    ],
    max_num_results=10
)
print(response)

OpenAI 提供者

明確使用 OpenAI 提供者

Search Vector Store - OpenAI Provider

import litellm
import os

# Set API key
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query="What is the capital of France?",
    custom_llm_provider="openai"
)
print(response)

Azure AI 提供者

使用 Azure AI Search

Search Vector Store - Azure AI Provider

import litellm
import os

# Set credentials
os.environ["AZURE_SEARCH_API_KEY"] = "your-search-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="my-vector-index",
    query="What is the capital of France?",
    custom_llm_provider="azure_ai",
    azure_search_service_name="your-search-service",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
    },
    api_key=os.getenv("AZURE_SEARCH_API_KEY"),
)
print(response)

請參閱完整的 Azure AI 向量儲存文件

Milvus 提供者

使用 Milvus

Search Vector Store - Milvus Provider

import litellm
import os

# Set credentials
os.environ["MILVUS_API_KEY"] = "your-milvus-api-key"
os.environ["MILVUS_API_BASE"] = "https://your-milvus-instance.milvus.io"

response = await litellm.vector_stores.asearch(
    vector_store_id="my-collection-name",
    query="What is the capital of France?",
    custom_llm_provider="milvus",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
    },
    milvus_text_field="book_intro",
    api_key=os.getenv("MILVUS_API_KEY"),
)
print(response)

請參閱完整的 Milvus 向量儲存文件

Gemini 提供者

使用 Gemini File Search

Search Vector Store - Gemini Provider

import litellm
import os

# Set credentials
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="fileSearchStores/your-store-id",
    query="What is the capital of France?",
    custom_llm_provider="gemini",
    max_num_results=5
)
print(response)

搭配中繼資料篩選：

Search with Metadata Filter

response = await litellm.vector_stores.asearch(
    vector_store_id="fileSearchStores/your-store-id",
    query="What is LiteLLM?",
    custom_llm_provider="gemini",
    filters={"author": "John Doe", "category": "documentation"},
    max_num_results=5
)
print(response)

請參閱完整的 Gemini File Search 文件

LiteLLM Proxy Server

設定與用法

1. 設定 config.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. 啟動 proxy

litellm --config /path/to/config.yaml

3. 使用 OpenAI SDK 測試它！

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)

curl

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
}'

設定向量儲存

若要使用向量儲存搜尋，請在 vector_store_registry 中設定您的向量儲存。請參閱向量儲存設定指南以了解：

• 提供者特定設定（Bedrock、OpenAI、Azure、Vertex AI、PG Vector）
• Python SDK 與 Proxy 設定範例
• 驗證與憑證管理

在 Chat Completions 中使用向量儲存

在 chat completion 請求中傳入 vector_store_ids，即可自動擷取相關脈絡。請參閱在 Chat Completions 中使用向量儲存以了解實作細節。