> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Bedrock (knowledge bases) 集成

> 使用 LangChain Python 集成 Bedrock (knowledge bases) 检索器。

This guide will help you get started with the AWS Knowledge Bases [retriever](/oss/python/langchain/retrieval).

[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.

Implementing `RAG` requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.

With `Knowledge Bases for Amazon Bedrock`, simply point to the location of your data in `Amazon S3`, and `Knowledge Bases for Amazon Bedrock` takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you. For retrievals, use the LangChain - Amazon Bedrock integration via the Retrieve API to retrieve relevant results for a user query from knowledge bases.

### 集成详情

<ItemTable category="document_retrievers" item="AmazonKnowledgeBasesRetriever" />

## 设置

Knowledge Bases can be configured through [AWS Console](https://aws.amazon.com/console/) or by using [AWS SDKs](https://aws.amazon.com/developer/tools/). We will need the `knowledge_base_id` to instantiate the retriever.

如果您希望从单个查询中获得自动追踪功能，还可以通过取消注释以下代码来设置 [LangSmith](/langsmith/home) API 密钥：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

### 安装

此检索器位于 `langchain-aws` 包中：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-aws
```

## 实例化

现在我们可以实例化检索器：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(
    knowledge_base_id="PUIJP4EQUA",
    retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
)
```

## 用法

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
query = "What did the president say about Ketanji Brown?"

retriever.invoke(query)
```

## 在链中使用

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from botocore.client import Config
from langchain_classic.chains import RetrievalQA
from langchain_aws import Bedrock

model_kwargs_claude = {"temperature": 0, "top_k": 10, "max_tokens_to_sample": 3000}

llm = Bedrock(model_id="anthropic.claude-v2", model_kwargs=model_kwargs_claude)

qa = RetrievalQA.from_chain_type(
    llm=llm, retriever=retriever, return_source_documents=True
)

qa(query)
```

***

## API 参考

For detailed documentation of all `AmazonKnowledgeBasesRetriever` features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-aws/retrievers/bedrock/AmazonKnowledgeBasesRetriever).

***

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