A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
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Updated
Oct 31, 2024 - Rust
A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Postgres for Search and Analytics
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
Distributed vector search for AI-native applications
Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.
A cutting-edge search engine project tailored specifically for the AI product
The codebase for the book "AI-Powered Search" (Manning Publications, 2024)
Website for the Weaviate vector database
Hybrid Search with Postgres and Ecto
Semantic Search + Keyword Search + Hybrid Search + Filtering + Faceting on 300K HN Comments
A LLM RAG system runs on your laptop.
Swfit library for fuzzy search. No dependencies lib.
Lite weight wrapper for the independent implementation of SPLADE++ models for search & retrieval pipelines. Models and Library created by Prithivi Da, For PRs and Collaboration checkout the readme.
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite
This project provides an example of consolidating Milvus (vector search engine) and PostgreSQL (relational database) to carry out the hybrid search of vectors and structured data.
OpenAI chatGPT hybrid search and retrieval augmented generation
This repository contains a Google Colab notebook that demonstrates how to set up and use a hybrid search Retrieval-Augmented Generation (RAG) system using LangChain and Pinecone. The hybrid search combines vector embeddings and sparse (BM25) encodings to provide efficient and accurate information retrieval.
Building a multi-agent RAG system with advanced RAG methods
Hybrid Search (BM25 & Vector) with SQLite
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