Pinecone Hybrid Search with LangChain and HuggingFace

This project demonstrates the implementation of a hybrid search application using LangChain, Pinecone, and Hugging Face embeddings. It combines dense vector embeddings with sparse BM25 encoding for enhanced search accuracy and relevance. The hybrid approach enables users to query textual data with greater precision, accounting for both semantic and keyword-based matching.

Key Features:

1. Pinecone Integration for Hybrid Search: Utilizes Pinecone's HybridSearchRetriever to support both dense embeddings (for semantic similarity) and sparse matrix encoding (for keyword-based matching).
2. Hugging Face Embeddings: Embeds text using the all-MiniLM-L6-v2 model, which creates dense vector representations for nuanced semantic retrieval.
3. BM25 Sparse Encoding: Uses BM25 encoding for token-level keyword matches, providing a more granular approach to search accuracy in hybrid retrieval.
4. Efficient Indexing: Texts are indexed within a Pinecone environment, making search queries fast, efficient, and suitable for large-scale applications.

Ideal for applications requiring high-precision search, this project combines multiple retrieval methods, making it well-suited for content-heavy platforms, recommendation systems, and knowledge bases.