Learn how to set up Amazon Bedrock to access top-tier AI models (like Amazon Titan) and integrate it with LangChain to power your RAG application.

PostgreSQL insights for every developer journey. Get tips and tutorials on database performance, query optimization, time-series data, and more—from real-time analytics and IoT data management to events and vector embeddings. 


TigerData (Creators of TimescaleDB)

Learn how to implement RAG (retrieval-augmented generation) with Amazon Bedrock and LangChain. Set up Amazon Bedrock, interact with language models, understand pricing, load and split datasets with LangChain, store embeddings, and use a retriever. Discover how to construct a chatbot using Chains and prompts.

How to Implement RAG With Amazon Bedrock and LangChain

Getting Started With Amazon Bedrock for RAG Applications

Vector Database: Embedding Storage and Search