Examples of using different retrievers in LangChain, including Wikipedia, Vector Store, MMR, MultiQuery, and Contextual Compression retrievers. Demonstrates how to fetch relevant context for semantic search, Q&A, summarization, and retrieval-augmented generation (RAG).

This repository implements Retrieval-Augmented Generation (RAG) for Query Translation and Semantic Search. It processes documents from a given URL, splits and indexes them, and uses different retrieval methods (multi-query, fusion, and decomposition) to accurately answer user queries.

LangChain