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.

In this notebook, we introduce AmazonKnowledgeBasesRetriever - Amazon Bedrock integration in Llama Index via the Retrieve API to retrieve relevant results for a user query from knowledge bases.

Using the Knowledge Base Retriever¶

In [ ]:

%pip install --upgrade --quiet  boto3 botocore
%pip install llama-index
%pip install llama-index-retrievers-bedrock

%pip install --upgrade --quiet boto3 botocore %pip install llama-index %pip install llama-index-retrievers-bedrock

For more information about the supported parameters for retrieval_config, please check the boto3 documentation: link

To use filters in the retrieval_config you will need to set up metadata.json file for your data source. For more info, see: link

In [ ]:

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"}},
        }
    },
)

from llama_index.retrievers.bedrock import AmazonKnowledgeBasesRetriever retriever = AmazonKnowledgeBasesRetriever( knowledge_base_id="", retrieval_config={ "vectorSearchConfiguration": { "numberOfResults": 4, "overrideSearchType": "HYBRID", "filter": {"equals": {"key": "tag", "value": "space"}}, } }, )

In [ ]:

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())

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())

Use the retriever to query with Bedrock LLMs¶

In [ ]:

%pip install llama-index-llms-bedrock

%pip install llama-index-llms-bedrock

In [ ]:

from llama_index.core import get_response_synthesizer
from llama_index.llms.bedrock.base import Bedrock

llm = Bedrock(model="anthropic.claude-v2", temperature=0, max_tokens=3000)
response_synthesizer = get_response_synthesizer(
    response_mode="compact", llm=llm
)
response_obj = response_synthesizer.synthesize(query, retrieved_results)
print(response_obj)

from llama_index.core import get_response_synthesizer from llama_index.llms.bedrock.base import Bedrock llm = Bedrock(model="anthropic.claude-v2", temperature=0, max_tokens=3000) response_synthesizer = get_response_synthesizer( response_mode="compact", llm=llm ) response_obj = response_synthesizer.synthesize(query, retrieved_results) print(response_obj)