vector-database

A context harness for AI agents: all your scattered context — code, memory, docs, databases, SaaS — in one searchable, browsable, file-like interface.

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

The Kubernetes Operator of Milvus.

DocumentGPT is a web application that allows you to chat over your research document using OpenAI's chat API and perform semantic search using vector databases. This tool provides a seamless interface for interacting with your research document, exploring search results, and engaging in a conversation with an AI chatbot.

The documentation for SurrealDB, powered by Astro

Advanced document extraction and chunking techniques for retrieval augmented generation that is aware of the layout of documents. Increases knowledge retrieval accuracy and provides control for retrieved knowledge context management

A cognition aware database engine for AI agent memory. Purpose built in Rust with WAL, HNSW, knowledge graphs, and speculative context pre assembly. Not a wrapper, a ground up storage engine that thinks.

Pinecone.io client with excellent TypeScript support.

Using LlamaIndex, Redis, and OpenAI to chat with PDF documents. Supplementary material for blog post on Microsoft Developer Blog

Shared memory MCP server — persistent, searchable, cross-client Claude, Opencode

AI Engineering Specially Topics- Agentic AI & GenAI Explanation

A handy lib for smooth interaction with large language models (LLMs) and crafting AI apps.

Shared memory + orchestration for your coding agents — one MCP server, persistent vector memory, agent registry

Build super simple end-to-end data & ETL pipelines for your vector databases and Generative AI applications

Source Code of NeurIPS21 and T-PAMI24 paper: Recognizing Vector Graphics without Rasterization

Simple Graph Memory for AI applications

A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.

GibRAM is an in-memory knowledge graph server designed for retrieval augmented generation (RAG / GraphRAG) workflows.

Self improving agents through iterations

MSVBASE is a system that efficiently supports complex queries of both approximate similarity search and relational operators. It integrates high-dimensional vector indices into PostgreSQL, a relational database to facilitate complex approximate similarity queries.