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.

Knowledge Bases for Amazon Bedrock 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

设置

Knowledge Bases can be configured through AWS Console or by using AWS SDKs. 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 API 密钥：

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：

pip install -qU langchain-aws

实例化

现在我们可以实例化检索器：

from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever

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

用法

query = "What did the president say about Ketanji Brown?"

retriever.invoke(query)

在链中使用

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)

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API 参考

For detailed documentation of all AmazonKnowledgeBasesRetriever features and configurations head to the API reference.

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