Use LangChain and vector search on Amazon DocumentDB to build a generative AI chatbot

This article discusses building a generative AI chatbot using LangChain and vector search with Amazon DocumentDB. It shows how to leverage Amazon DocumentDB's support for vector search along with AWS AI services like Amazon Bedrock and SageMaker, as well as third-party tools like LangChain and OpenAI.

Specifically, the article covers:

• How to use Retrieval Augmented Generation (RAG) with large language models (LLMs) to retrieve relevant data from a knowledge base and augment prompts
• Creating an HNSW index on an Amazon DocumentDB collection for vector search
• Loading and embedding documents from a PDF using LangChain and Amazon Titan Text Embeddings
• Initializing a reasoning agent like Anthropic Claude from Amazon Bedrock
• Using the RetrievalQA chain to query the vector index and generate answers
• The benefits of combining DocumentDB's JSON flexibility with vector search for AI applications