amazon-bedrock-rag and rag-using-langchain-amazon-bedrock-and-opensearch
These are complements: the first uses Bedrock's managed Knowledge Bases service for turnkey RAG, while the second provides a flexible, open-source alternative using LangChain to orchestrate Bedrock LLMs with self-managed OpenSearch vector storage.
About amazon-bedrock-rag
aws-samples/amazon-bedrock-rag
Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock
This project helps you build a custom chatbot that can answer questions using your own private documents or website content. You provide your proprietary information, and the chatbot generates accurate answers, citing its sources from your data, instead of relying solely on generic internet knowledge. This is ideal for knowledge managers, customer support leads, or anyone needing to make internal company data or specific domain knowledge easily searchable and consumable through a conversational AI.
About rag-using-langchain-amazon-bedrock-and-opensearch
aws-samples/rag-using-langchain-amazon-bedrock-and-opensearch
RAG with langchain using Amazon Bedrock and Amazon OpenSearch
This project helps developers and AI engineers build more accurate and context-aware AI applications. It takes your proprietary documents or datasets, transforms them into numerical representations, and stores them in a search engine. When users ask questions, the system retrieves relevant information from your documents to provide the large language model with specific context, resulting in more precise answers.
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