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

Distributed vector search for AI-native applications

The AI-native database built for LLM applications, providing incredibly fast full-text and vector search

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.

Website for the Weaviate vector database

Semantic Search + Keyword Search + Hybrid Search + Filtering + Faceting on 300K HN Comments

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.

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

Semantic Search on Langchain Github Issues with Weaviate

Doraemon Himitsu Dogu Japanese hybrid search based on Elascticsearch ANN x multi match

An example of Named-entity Recognition and relation mapping using an LLM and Vector Database. Also includes a Branching Hybrid-Search Chatbot to utilize extracted relations.

This project implements hybrid search of vectors and structured data based on Milvus Partitions.

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