AI Infrastructure · GitHub ·228 ★

rag-using-langchain-amazon-bedrock-and-opensearch

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

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Details

Author
aws-samples
Category
AI Infrastructure
Platform
GitHub
Framework
langchain
Language
python
Stars
228
First indexed
2026-05-15
Last active
2025-01-07
Directory sync
2026-05-15
@aws-samples

Overview

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

Quick start

git

git clone https://github.com/aws-samples/rag-using-langchain-amazon-bedrock-and-opensearch

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What rag-using-langchain-amazon-bedrock-and-opensearch can do

embeddingrag
  • Embedding — Computes vector embeddings for semantic search.
  • Rag — Retrieves grounded context before answering.

Frequently asked questions

What is rag-using-langchain-amazon-bedrock-and-opensearch?
RAG with langchain using Amazon Bedrock and Amazon OpenSearch
How do I install rag-using-langchain-amazon-bedrock-and-opensearch?
Use git: `git clone https://github.com/aws-samples/rag-using-langchain-amazon-bedrock-and-opensearch`. Full setup details on the source page linked above.
Is rag-using-langchain-amazon-bedrock-and-opensearch open source?
rag-using-langchain-amazon-bedrock-and-opensearch is published on GitHub.
What are alternatives to rag-using-langchain-amazon-bedrock-and-opensearch?
Comparable agents include awesome, openclaw, AutoGPT. Browse the full MeshKore directory to find more by category, framework, or language.

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Profile data for rag-using-langchain-amazon-bedrock-and-opensearch is sourced from GitHub, published by aws-samples.

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