vector-database

A distributed Key-Value Storage using Raft

Shared Memory Storage for Multi-Agent Systems

Memory for AI that works like yours—local, instant, persistent. 13x faster than Pinecone, 5x leaner than RAG. Finds what RAG misses. Zero cloud, zero cost.

图片搜索引擎，很简单。三步构建属于你自己的图片搜索引擎，掌握向量数据库和以图搜图、文本搜索图片。

Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.

Fast similarity search using DuckDB

A python package for developing AI applications with local LLMs.

A low-latency, billion-scale, and updatable graph-based vector store on SSD.

A Blazor Web App and Minimal API for performing RAG (Retrieval Augmented Generation) and vector search using the native VECTOR type in Azure SQL Database and Azure OpenAI.

A curated list of open source repositories for AI Engineers

Official repository of the Seismic library.

Memory that remembers the story not just the facts. File System Memory and Three layer sentence graph for AI agents -> Facts, Episodes, raw Sentences. One DB. Zero config.

VTS is a tool designed for the transformation and transportation of vectors and unstructured data.

A Vector Store written in Go - Supports hybrid retrieval over BM25, Flat, HNSW, IVF, PQ and IVFPQ Index with Quantization, Metadata Filtering, Reranking, Reciprocal Rank Fusion, Soft Deletes, Index Rebuilds and much much more

A demo ChatBot application developed using Amazon Bedrock service's KnowledgeBase, Agent and other AWS's serveless GenAI solution.

Lightweight graph database with relational, vector, and MCP support, designed to power knowledge and artificial intelligence persistence and retrieval.

High-performance Knowledge Graph engine for AI, LLMs, and GraphRAG — built for the next generation of intelligent applications.

A complete collection of RAG interview questions, answers (548 questions & 41 RAG types), system design scenarios, architecture patterns, and production-ready concepts.

Timescale Vector Cookbook. A collection of recipes to build applications with LLMs using PostgreSQL and Timescale Vector.

Use local files or public GitHub repository as a source and ask questions through ChatGPT about it