LlamaIndex Retrievers Integration: 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.

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

Installation

pip install llama-index-retrievers-bedrock

Usage

from llama_index.retrievers.bedrock import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(
    knowledge_base_id="<knowledge-base-id>",
    retrieval_config={
        "vectorSearchConfiguration": {
            "numberOfResults": 4,
            "overrideSearchType": "HYBRID",
            "filter": {"equals": {"key": "tag", "value": "space"}},
        }
    },
)

query = "How big is Milky Way as compared to the entire universe?"
retrieved_results = retriever.retrieve(query)

# Prints the first retrieved result
print(retrieved_results[0].get_content())

Notebook

Explore the retriever using Notebook present at: https://docs.llamaindex.ai/en/latest/examples/retrievers/bedrock_retriever/