Exploring RAG Workflow Using Knowledge Bases for Amazon Bedrock in Drug Discovery Use Case

With the rapid advancement of technology in the field of Artificial Intelligence (AI), the possibilities of what can be achieved are endless. Amazon Bedrock is one such platform that provides a wide range of models from Amazon and third-party providers for various AI use cases. In this blog post, we will focus on how Knowledge Bases for Amazon Bedrock can be utilized to build a powerful Retrieval Augmented Generation (RAG) workflow for a drug discovery use case.

Knowledge Bases for Amazon Bedrock supports a variety of file types, including .txt, .docx, .pdf, .csv, and more. It allows for efficient retrieval from private data by splitting documents into manageable chunks, generating vector embeddings, and storing them in a vector index. This process includes synchronization of data from an Amazon Simple Storage Service (Amazon S3) bucket and intelligent diffing.

RAG is a technique that combines private data with large language models to retrieve relevant documents and generate responses for user queries. By using Knowledge Bases for Amazon Bedrock, you can easily set up a workflow that retrieves relevant documents based on user queries and utilizes language models to generate responses effectively.

Building a knowledge base with Amazon Bedrock involves creating a knowledge base via the console, setting up data sources, embeddings models, and vector databases, and syncing the data to generate vector embeddings. By following the steps outlined in the console, you can create a knowledge base tailored to your specific use case.

Querying the knowledge base involves using Amazon Bedrock’s Retrieve and RetrieveAndGenerate APIs to obtain answers to user queries. By utilizing these APIs and integrating them with your workflow, you can efficiently fetch relevant information and generate responses with source attribution for traceability.

In the drug discovery use case demo, we showcased how to generate questions and answers using Amazon Bedrock, retrieve relevant information based on user queries, and create an end-to-end Q&A application using LangChain integration.

In conclusion, Amazon Bedrock and Knowledge Bases provide a powerful set of tools to build scalable and efficient RAG applications. By leveraging these technologies, businesses can analyze data more effectively, extract valuable insights, and deliver accurate responses to user queries. The possibilities are endless, and the future looks bright for AI solutions built on platforms like Amazon Bedrock.