Private & local AI personal knowledge management app for high entropy people.

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling

A LLM RAG system runs on your laptop. 大模型检索增强生成系统，可以轻松部署在笔记本电脑上，实现本地知识库智能问答。

Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.

RAG on codebases using treesitter and LanceDB

A full-stack serverless RAG workflow. This is thought for running PoCs, prototypes and bootstrap your MVP.

Vector search over tweets from the tweet archive using OpenAI embeddings and LanceDB

Graph RAG workshop using Kùzu and LanceDB for hybrid RAG

Benchmark study on LanceDB, an embedded vector DB, for full-text search and vector search

Lightweight representation engineering dataflow operations for agent developers.

Code from the ODSC Agentic Graph RAG workshop combining vector, FTS & graph retrieval for RAG. Includes observability and guardrails for evaluating outputs.

spaCy pipeline component for ANN Entity Linking using LanceDB

Interactive Social Media Simulation of Believable Human Proxies

Semantic search knowledge base for MCP-enabled AI assistants. Index local files or GitHub repos, query with natural language. Built on LanceDB vector storage. Works with Claude Desktop, Cursor, and other MCP clients.

🤖 Next.js AI Chatbot - Chat with any website

This repository contains comprehensive documentation for implementing and optimizing the A-MEM (Associative Memory) system utilizing both vector and graph storage for dynamic memory organization.

The missing index for the Heritage Foundation's Project 2025

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

An implementation of Retrieval-Augmented Generation (RAG) indexing using Rust and LanceDB. It leverages the power of Rust for efficient and safe performance, combined with LanceDB for advanced indexing capabilities.