rag

3DCF / doc2dataset: token-efficient document layer with NumGuard numeric integrity and multi-framework exports for RAG & fine-tuning.

Cookbooks and tutorials on Literal AI

Build a simple RAG chatbot with LangChain and Streamlit

Vector Database with support for late interaction and token level embeddings.

A Web app stack written in FastAPI, Qdrant, and React for creating AI projects

Self-contained offline environment providing local AI chat, offline Wikipedia/content archives, IRC communication, audio streaming, file server, and development tools. Designed for zero internet dependency - download once, run anywhere. Perfect for remote areas, emergency scenarios, or escaping surveillance capitalism.

(Let's start with a) Scalable question-answering system utilizing FastAPI, LangChain (LCEL), and PGVector, featuring an ingestion pipeline. Deployed on GCP Cloud Run via Terraform.

AI-powered learning platform with 6 specialized agents for personalized education. Features adaptive roadmaps, quizzes, tutoring, RAG document Q&A, and learning style adaptation. Built with Phidata, Streamlit, and LangChain.

Python SDK for interacting with the Moorcheh Semantic Search API v1. Moorcheh provides ultra-fast, highly accurate vector similarity search and analysis capabilities based on information-theoretic principles.

Kubernetes operator for Qdrant

My attempt at implementing retreival augmented generation on Ollama and other LLM services using chromadb and langchain while also providing an easy to understand, clean code for others since nobody else does

A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI capabilities for answering questions about Ultimate Frisbee rules and strategies. This project showcases how to build a production-ready RAG system using cutting-edge technologies.

Local LLM-maintained personal wiki, built on Karpathy's LLM-Wiki pattern. Runs on Ollama + Qwen3 + QMD.

CustomGPT.ai’s RAG API’s Starter Kit, including multi-instance embedded widgets, floating buttons, and standalone application.

AI-powered Python library that converts any document (PDF, Word, Excel, PowerPoint, HTML) to clean Markdown while preserving complex tables and layouts using AI-Powered OCR technology.

Turn PostgreSQL into your search engine in a Pythonic way.

专利侵权分析系统 —— 输入专利公开号，产出竞品侵权分析报告；同时打包成 skill，可被任意 agent（codex，claude code 等） 调用。

Simple RAG tutorials that can be run locally or using Google Colab (only Pro version).

Free AI/ML course with 950+ Jupyter notebooks — Python, deep learning, LLMs, RAG, agents, prompt engineering, fine-tuning, MLOps

🦜 🔗 Query and obtain data from Markdown documents with LangChain's RAG system