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Bedrock (Knowledge Bases) Retriever

Overview​

This guide will help you getting 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.

Integration details​

RetrieverSelf-hostCloud offeringPackage
AmazonKnowledgeBasesRetriever❌✅langchain_aws

Setup​

Knowledge Bases can be configured through AWS Console or by using AWS SDKs. We will need the knowledge_base_id to instantiate the retriever.

If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installation​

This retriever lives in the langchain-aws package:

%pip install -qU langchain-aws

Instantiation​

Now we can instantiate our retriever:

from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever

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

Usage​

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

retriever.invoke(query)

Use within a chain​

from botocore.client import Config
from langchain.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 Reference:RetrievalQA

API reference​

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

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