Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
-
Updated
Sep 29, 2024 - Python
Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
Contextual RAG over webinar videos using Pinecone, Claude and AWS.
It is a case study of an intelligent agent for Ocean.
RAG-Ingest: A tool for converting PDFs to markdown and indexing them for enhanced Retrieval Augmented Generation (RAG) capabilities.
Enhance your RAG with Contextual Retrieval
ContextualRetriever enhances document retrieval accuracy by leveraging Voyage AI models for embedding & reranking models, and the GEMINI model for context and retrieval generation.
Chatbot based on Contextual RAG with Hybrid Search and Reranking with short conversation history awareness, fully OpenSource.
A LangChain-powered application that parses PDF resumes, converts them into semantic chunks using FAISS, and enables intelligent querying via RAG and Anthropic models. Designed to assist in resume screening through contextual and multi-query retrieval.
A powerful toolkit for text chunking and semantic search using OpenSearch. This toolkit provides various text chunking strategies and embedding capabilities for efficient document retrieval.
Add a description, image, and links to the contextual-retrieval topic page so that developers can more easily learn about it.
To associate your repository with the contextual-retrieval topic, visit your repo's landing page and select "manage topics."