Langfuse is an open-source LLM observability and evaluation platform. It provides tracing, prompt management, evaluations, datasets, experiments, monitors, and a public API for integrating with AI applications. The architecture is a monorepo with pnpm workspaces and Turborepo, consisting of a Next.js frontend (web), a background worker (Express + BullMQ), a shared package with types and server utilities, and an enterprise edition. Data flows from ingestion (REST API, OpenTelemetry, SDKs) into ClickHouse for high-volume analytics and PostgreSQL for relational metadata. Background jobs handle evaluations, batch exports, cloud billing, and integrations. The codebase is well-structured with extensive testing, a custom ESLint plugin, and reusable UI components. It is actively maintained with frequent migrations and feature additions.
This repository is a comprehensive collection of examples, tutorials, and applications demonstrating how to use LanceDB (a vector database) for various AI/ML applications. It showcases patterns for retrieval-augmented generation (RAG), multimodal search, chatbots, recommendation systems, and AI agents. The repository contains both Python and JavaScript/TypeScript implementations, with many examples featuring web interfaces using Streamlit, Gradio, or React. While it serves as a valuable resource for learning LanceDB integration patterns, the quality varies across examples, and some content is marked as archived.
This repository is a comprehensive, educational collection of 42+ Jupyter notebooks and runnable scripts demonstrating a wide array of Retrieval-Augmented Generation (RAG) techniques. It is primarily a data_research and educational resource, not a deployable application or library. The codebase serves as a practical cookbook for engineers and researchers, covering the full RAG stack from basic chunking and retrieval (e.g., simple RAG, BM25) to advanced methods like GraphRAG, Self-RAG, HyDE, RAPTOR, and agentic RAG. It also includes a dedicated evaluation framework with notebooks and reusable scripts for measuring faithfulness, relevance, and correctness using tools like DeepEval and custom LLM judges. The architecture is modular, with a shared helper_functions.py providing common utilities for PDF loading, embedding, and retrieval, and an evaluation/ module offering reusable evaluation pipelines. The runnable scripts in all_rag_techniques_runnable_scripts/ provide cleaner, class-based implementations of the techniques that are more suitable for integration into other projects than the notebooks themselves. While the code is well-structured for an educational project and demonstrates high craftsmanship in its reusable components, it is not designed for production deployment. Reproducibility is limited by the lack of a pinned dependency file and reliance on external API keys. The repository is actively maintained and highly influential (29k+ stars), making it a valuable reference for anyone building or evaluating RAG systems.
This repository is a comprehensive Chinese-language educational tutorial and codebase for Retrieval-Augmented Generation (RAG) systems, created by Datawhale. It systematically covers the full RAG stack from fundamentals (data loading, chunking, embeddings) to advanced techniques (hybrid search, query construction, Text2SQL, reranking, agentic RAG, and Graph RAG). The code is organized by chapter, with runnable Python scripts demonstrating each concept using popular frameworks like LangChain, LlamaIndex, and LangGraph, alongside vector databases (Milvus, FAISS) and graph databases (Neo4j). While primarily an educational resource, the repository contains several well-crafted, reusable components such as a minimal Text2SQL agent framework, a clean LangGraph-based agentic RAG pattern, and a custom ColBERT reranker. The practical application is a Chinese recipe Q&A system ('尝尝咸淡'), which serves as a consistent domain for demonstrating various RAG techniques. The codebase is hybrid in nature, blending structured educational content with functional demo applications. The repository is actively maintained with a large community following. However, users should be aware of technical drift risks including vendored model code, hardcoded API dependencies, and the presence of AI-generated code sections. The core value lies in its structured curriculum and reusable RAG patterns rather than as a production-ready system.