A framework for benchmarking embedding models in hybrid search scenarios (BM25 + vector search) using Weaviate.
Simple RAG system powered by Milvus.
A 3-stage Information Retrieval engine leveraging Elasticsearch, BERT embeddings, and RRF to implement a robust Hybrid Search strategy for EU Research Projects.
🔍 Benchmark embedding models in hybrid search with Weaviate. Evaluate MRR@K, Hit@K, latency, and memory using your data or MTEB datasets.
A production-ready Retrieval-Augmented Generation (RAG) pipeline combining BM25 lexical search and Pinecone vector search to deliver high-accuracy hybrid retrieval. Built using LangChain, Pinecone, and CrewAI, this project demonstrates an end-to-end workflow for document ingestion, embedding generation, hybrid querying.
Hybrid BM25-powered search & DB management toolkit for PostgreSQL/pgvector with LangChain integration
A production-ready Retrieval-Augmented Generation (RAG) system for semantic search over product reviews.
A simple hybrid search Flask application that integrates traditional full-text (lexical/BM25) search with semantic (neural sparse embeddings) search. It also provides autocomplete functionality for an improved search experience.
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