A minimal Agentic RAG built with LangGraph — learn Retrieval-Augmented Agents in minutes.
We believe that every SOTA result is only valid on its own dataset. RAGView provides a unified evaluation platform to benchmark different RAG methods on your specific data.
A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.
Generate & Ship UI with minimal effort - Open Source Generative UI with natural language
RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).
Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.
Quickest way to production grade RAG UI.
AI-powered mock interview platform using Next.js, Gemini AI, JSON, Drizzle, NeonDB, API routes and Clerk for dynamic questions, feedback & session recording, plus Dockerized & deployed microservices.
A python module library that simplifies RAG through abstraction
The implementation of Test Time Diffusion paper by Google with some tweaks to run on 24gb gpu
Vectrain is a high-performance, modular, plug-and-play RAG pipeline that ingests data, generates vector embeddings, and stores them in vector databases for semantic search, recommendations, and analytics.
Build a RAG preprocessing pipeline
A multi-provider AI coding agent with the persona of a Tech-Priest
An intelligent customer support system powered by LangGraph and LangChain that uses Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses to customer queries. Built with FastAPI, FAISS, and multi-stage validation for production-ready deployment.
Production-ready Chainlit RAG application with Pinecone pipeline offering all Groq and OpenAI Models, to chat with your documents.
The Audited Context Generation (ACG) Protocol prevents AI hallucinations with a dual-layer system. The UGVP layer links every fact to a precise source for verification. The RSVP layer audits the AI's logical reasoning when combining facts. This creates a fully transparent, machine-auditable trail for both source and logical integrity.
Search for a holiday and get destination advice from an LLM. Observability by Dynatrace.
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection - LDK 2025
Token-Oriented Object Notation (TOON) is an LLM-optimized data serialization format implemented in Python.
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