Regarding Knowledge Bases and Lambda in Bedrock Agents

To extract information from Knowledge Bases and pass it to Lambda in Bedrock Agents, you can use the Retrieve API provided by Amazon Bedrock. This API allows you to query your knowledge base and retrieve relevant information directly.

Here's how you can implement this process:

1. Use the Retrieve API to query your knowledge base. This API will convert your query into embeddings, search the knowledge base, and return the relevant results.

2. In your Lambda function, you can then process these retrieved results. This gives you the flexibility to evaluate their relevance, perform additional processing, or develop custom workflows based on the retrieved information.

3. You can integrate this workflow into your Bedrock Agent by creating a custom action group that includes your Lambda function. When the agent determines it needs specific information from the knowledge base, it can trigger this action group.

4. Your Lambda function can then use the Retrieve API to query the knowledge base, process the results, and return the formatted information back to the agent.

5. The agent can then use this processed information to generate its response or take further actions.

This approach allows you to have more control over how the knowledge base information is used and processed, rather than relying solely on the built-in RetrieveAndGenerate API. It's particularly useful if you need to perform custom processing on the retrieved information before using it in your agent's response.

Remember to handle any necessary error cases and ensure your Lambda function is optimized for performance, especially if you're dealing with large amounts of data from the knowledge base.

Sources
Knowledge bases for Amazon Bedrock - AWS Prescriptive Guidance
How to get a specific formatted output from a Bedrock Agent that uses a KB? | AWS re:Post

Using Amazon Bedrock + RAG (Retrieval-Augmented Generation) Use vector databases (e.g., OpenSearch, Pinecone) to retrieve relevant chunks from a knowledge base and generate answers with an LLM.

Flow: Embed user query → search vector DB.

Retrieve context → top chunks relevant to the query.

Pass context + query to Bedrock model.

Lambda receives response and does further processing.

This can be orchestrated either:

Manually in a Lambda function.

Or using an Agent for Amazon Bedrock.

You can invoke Lambda from:

API Gateway (frontend)

Bedrock Agent (through action group)

Step Functions (for workflow)

EventBridge (scheduled queries)

If you maintain a custom KB (e.g., JSON files, SQL DB, Confluence, etc.), you can:

Fetch content in Lambda.

Or pre-index into a vector DB and use RAG.