Bedrock (Knowledge Bases)

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.

Knowledge base can be configured through AWS Console or by using AWS SDKs.

Using the Knowledge Bases Retriever

%pip install --upgrade --quiet  boto3

from langchain_community.retrievers import AmazonKnowledgeBasesRetriever

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

API Reference:

• AmazonKnowledgeBasesRetriever

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

retriever.invoke(query)

Using in a QA Chain

from botocore.client import Config
from langchain.chains import RetrievalQA
from langchain_community.llms 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
• Bedrock