---
title: "Bedrock 知識庫"
url: "/docs/providers/bedrock_vector_store"
canonical_url: "https://docs.litellm.ai/docs/providers/bedrock_vector_store"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/providers/aws_polly"
  - "/docs/providers/bedrock_mantle"
---
# Bedrock 知識庫

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


AWS Bedrock Knowledge Bases 可讓您將 LLM 連接至您組織的資料，讓模型擷取並參照與您業務相關的資訊。

| 屬性 | 詳細資訊 |
|----------|---------|
| 說明 | Bedrock Knowledge Bases 將您的資料連接至 LLM，讓它們能在回應中擷取並參照您組織的資訊。 |
| LiteLLM 提供者路由 | `bedrock` 於 litellm vector_store_registry 中 |
| 提供者文件 | [AWS Bedrock Knowledge Bases ↗](https://aws.amazon.com/bedrock/knowledge-bases/) |

## 快速開始 {#quick-start}

### LiteLLM Python SDK {#litellm-python-sdk}

```python showLineNumbers title="Example using LiteLLM Python SDK"
import os
import litellm

from litellm.vector_stores.vector_store_registry import VectorStoreRegistry, LiteLLM_ManagedVectorStore

# Init vector store registry with your Bedrock Knowledge Base
litellm.vector_store_registry = VectorStoreRegistry(
    vector_stores=[
        LiteLLM_ManagedVectorStore(
            vector_store_id="YOUR_KNOWLEDGE_BASE_ID",  # KB ID from AWS Bedrock
            custom_llm_provider="bedrock"
        )
    ]
)

# Make a completion request using your Knowledge Base
response = await litellm.acompletion(
    model="anthropic/claude-sonnet-5", 
    messages=[{"role": "user", "content": "What does our company policy say about remote work?"}],
    tools=[
        {
            "type": "file_search",
            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
        }
    ],
)

print(response.choices[0].message.content)
```

### LiteLLM Proxy {#litellm-proxy}

#### 1. 設定您的 vector_store_registry {#1-configure-your-vector_store_registry}

**config.yaml**

```yaml
model_list:
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY

vector_store_registry:
  - vector_store_name: "bedrock-company-docs"
    litellm_params:
      vector_store_id: "YOUR_KNOWLEDGE_BASE_ID"
      custom_llm_provider: "bedrock"
      vector_store_description: "Bedrock Knowledge Base for company documents"
      vector_store_metadata:
        source: "Company internal documentation"
```

**LiteLLM UI**

在 LiteLLM UI 中，前往 Experimental > Vector Stores > Create Vector Store。在此頁面中，您可以建立具有名稱、vector store id 和憑證的 vector store。

#### 2. 使用 vector_store_ids 參數發出請求 {#2-make-a-request-with-vector_store_ids-parameter}

**Curl**

```bash
curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [{"role": "user", "content": "What does our company policy say about remote work?"}],
    "tools": [
        {
            "type": "file_search",
            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
        }
    ]
  }'
```

**OpenAI Python SDK**

```python
from openai import OpenAI

# Initialize client with your LiteLLM proxy URL
client = OpenAI(
    base_url="http://localhost:4000",
    api_key="your-litellm-api-key"
)

# Make a completion request with vector_store_ids parameter
response = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "What does our company policy say about remote work?"}],
    tools=[
        {
            "type": "file_search",
            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
        }
    ]
)

print(response.choices[0].message.content)
```

## 篩選結果 {#filter-results}

依中繼資料屬性篩選。

**運算子**（OpenAI 樣式，自動轉譯）：
- `eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`

**AWS 運算子**（直接使用）：
- `equals`, `notEquals`, `greaterThan`, `greaterThanOrEquals`, `lessThan`, `lessThanOrEquals`, `in`, `notIn`, `startsWith`, `listContains`, `stringContains`

**單一篩選器**

```python
response = await litellm.acompletion(
    model="anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "What are the latest updates?"}],
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"],
        "filters": {
            "key": "category",
            "value": "updates",
            "operator": "eq"
        }
    }]
)
```

**AND**

```python
response = await litellm.acompletion(
    model="anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "What are the policies?"}],
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"],
        "filters": {
            "and": [
                {"key": "category", "value": "policy", "operator": "eq"},
                {"key": "year", "value": 2024, "operator": "gte"}
            ]
        }
    }]
)
```

**OR**

```python
response = await litellm.acompletion(
    model="anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "Show me technical docs"}],
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"],
        "filters": {
            "or": [
                {"key": "category", "value": "api", "operator": "eq"},
                {"key": "category", "value": "sdk", "operator": "eq"}
            ]
        }
    }]
)
```

**AWS 運算子**

```python
response = await litellm.acompletion(
    model="anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "Find docs"}],
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"],
        "filters": {
            "and": [
                {"key": "title", "value": "Guide", "operator": "stringContains"},
                {"key": "tags", "value": "important", "operator": "listContains"}
            ]
        }
    }]
)
```

**Proxy**

```bash
curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [{"role": "user", "content": "What are our policies?"}],
    "tools": [{
        "type": "file_search",
        "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"],
        "filters": {
            "and": [
                {"key": "department", "value": "engineering", "operator": "eq"},
                {"key": "type", "value": "policy", "operator": "eq"}
            ]
        }
    }]
  }'
```

## 將結果限制為某位使用者 {#restrict-results-to-a-user}

具有存取控制的 Knowledge Bases（Kendra GenAI index 或具有文件層級 ACL 的資料來源）只會回傳特定使用者可見的區塊。將該身分作為 Bedrock 的 `userContext` 傳入，LiteLLM 會在 Retrieve 請求中轉送它。該值會原樣送出，因此 Bedrock 會驗證它：`userId` 必須是字串。LiteLLM 不會將該身分與 proxy key 進行比對，因此只有您信任其可為使用者命名的呼叫端，才能搜尋具有 ACL 的 store。

**LiteLLM Python SDK**

```python
import litellm

response = litellm.vector_stores.search(
    vector_store_id="YOUR_KNOWLEDGE_BASE_ID",
    custom_llm_provider="bedrock",
    query="What does our company policy say about remote work?",
    extra_body={"userContext": {"userId": "alice@example.com"}},
)
```

**Proxy (OpenAI SDK)**

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:4000", api_key="your-litellm-api-key")

response = client.vector_stores.search(
    vector_store_id="YOUR_KNOWLEDGE_BASE_ID",
    query="What does our company policy say about remote work?",
    extra_body={"userContext": {"userId": "alice@example.com"}},
)
```

**Proxy (curl)**

```bash
curl http://localhost:4000/v1/vector_stores/YOUR_KNOWLEDGE_BASE_ID/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "query": "What does our company policy say about remote work?",
    "userContext": {"userId": "alice@example.com"}
  }'
```

**Proxy (config.yaml)**

在 store 上設定 `user_context`，即可將單一身分套用到對它的每次搜尋。於請求中送出的 `userContext` 會覆寫它。

```yaml
vector_store_registry:
  - vector_store_name: "bedrock-company-docs"
    litellm_params:
      vector_store_id: "YOUR_KNOWLEDGE_BASE_ID"
      custom_llm_provider: "bedrock"
      user_context:
        userId: "service-account@example.com"
```

## 存取搜尋結果 {#accessing-search-results}

了解如何在回應中存取 vector store 搜尋結果：
- [存取搜尋結果（非串流與串流）](../completion/knowledgebase#accessing-search-results-citations)

## 延伸閱讀 {#further-reading}

Vector Stores：
- [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model)
- [在 litellm proxy 上列出可用的 vector stores](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores)
- [LiteLLM Vector Stores 的運作方式](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works)

## Related pages

- [AWS Polly 文字轉語音 (tts)](https://docs.litellm.ai/docs/providers/aws_polly.md)
- [Amazon Bedrock Mantle](https://docs.litellm.ai/docs/providers/bedrock_mantle.md)