chromadb

A Node.js RESTful server for a Retrieval-Augmented Generation (RAG) system using ChromaDB and Ollama!

LLM, Fine Tuning, Llama 2, Gemma, Mixtral, vLLM, LangChain, RAG, ChromaDB, FAISS

A documentation querying engine, useful for scanning the docs in a question-answer like manner

A-MEM Agentic Memory System - MCP Server for IDE Integration (Cursor, VSCode) | Dual-Storage: ChromaDB + NetworkX DiGraph with explicit typed edges | Based on Zettelkasten

A semantic file retrieval application based on LLM（一个轻量级基于大模型解析的多模态文件语义检索工具，不同于传统基于文件名或metadata检索的方式，该工具可实现基于文件内容的语义检索，支持各类主流格式文档、图片、音频、视频。)

Local-first AI assistant — 9 specialized agents (code, web, debug, security…), 10M-token vector memory, multimodal (vision + local image generation), two-way voice (dictation + read-aloud), slash commands & codebase RAG, agentic VS Code extension, mobile relay via secure tunnel. 100% local with Ollama. Your data, your control.

A PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.

This project aims to provide an efficient and scalable API for embedding and storing vectors, leveraging the power of FastAPI, Langchain framework and a vector database such as Chroma.

Production-style Retrieval-Augmented Generation document Q&A: LangChain ingestion, Chroma/pgvector store, hybrid retrieval, citation-grounded answers and a streaming chat API.