vector-database

Embeddable vector database for Rust and Python. HNSW indexing, metadata filtering, hybrid search, and snapshots. The SQLite of vector search.

This project implements RAG using OpenAI's embedding models and LangChain's Python library

The in-browser vector store that remembers — a Rust→WASM HNSW engine that persists to OPFS and stays consistent across tabs. Private, offline semantic search in 3 lines.

Rules you can enforce, state you can derive, context you can query: plain files in your repo, under whichever coding agent you use.

🔒 Privacy-first MCP server for Claude using PostgreSQL + Ollama. Local alternative to cloud-based code context with full data sovereignty. No API keys, no external calls, 100% local.

A clean, modular implementation of an Agentic RAG (Retrieval-Augmented Generation) system built with a production-ready architecture.

Miscellaneous codes and writings for MLOps

LLM-powered financial analyst using LoRA-tuned Llama-3 and RAG pipeline to answer complex queries over SEC 10-K filings with contextual accuracy.

Local RAG app with zero-config Docker setup. FastAPI + Streamlit + Qdrant + Ollama. Just run `docker-compose up --build`! 🚀

🧠 Personal knowledge MCP server with vector database for Opencode. Store and retrieve knowledge using semantic search, powered by local embeddings.

This tool provides a fast and efficient way to convert text into vector embeddings and store them in the Qdrant search engine. Built with Rust, this tool is designed to handle large datasets and deliver lightning-fast search results.

Find the images in your dataset most similar to a query image from URL or drag-and-drop, with FiftyOne!

Implements a framework to build Generative AI applications.

Universal memory layer for AI applications. Self-host in minutes. Open source.

A curated list of awesome Pinecone resources, libraries, tools and applications

CLI tool for OpenAI compatible APIs

LangGraphRAG: A terminal-based Retrieval-Augmented Generation system using LangGraph. Features include message history caching, query transformation, and vector database retrieval. Ideal for NLP researchers and developers working on advanced conversational AI and information retrieval systems.

High-Ratio Vector Quantization

Carter is a streamlined platform designed to help users effortlessly organize, share, and manage their links. With a secure interface, it allows users to save, categorize, and retrieve links in a controlled environment. Emphasizing privacy, Carter keeps links accessible and shareable as needed, empowering users with full control.

The Ultimate Vectors Library for Java