evidently
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Retrieval-augmented generation (RAG) makes a language model answer from your own documents. The text is split into chunks, turned into vectors and stored in an index, and the most relevant chunks are added to the prompt when a question comes in. The projects here cover the whole pipeline or one part of it: document parsing, vector search, or a complete question-answering app.
Answer quality in RAG depends mostly on retrieval, not on the model. Compare how each project handles difficult documents such as PDFs, tables and scans, whether it supports hybrid search that combines keywords with vectors and re-ranking, and whether it shows which sources an answer came from.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
每个人都能看懂的大模型知识分享,LLMs春/秋招大模型面试前必看,让你和面试官侃侃而谈
LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your knowledge base. Register here 👇🏼
可私有部署的多租户知识智能体平台:统一 RAG、知识图谱、多智能体、MCP/Skills、沙盒与权限管理。Self-hosted knowledge agent platform for RAG, knowledge graphs and multi-agent workflows.
Memory library for building stateful agents
PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!
The system of action for AI-native cybersecurity—where intent becomes governed execution, evidence becomes operational memory, and every operation improves the next.
Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML
🔥 基于大模型和 RAG 的智能问数系统,对话式数据分析神器。Text-to-SQL Generation via LLMs using RAG.
Turn your PC, Mac, or Linux box into an AI server. LLM inference, chat UI, voice, agents, workflows, RAG, and image generation.
《大模型与智能体》电子书与 6 个编程任务:Transformer、mini-GPT、SFT/DPO、RAG、工具调用与编程智能体。
↥ ↥ ↥ Follow for updates An RBAC permission management system based on Spring Cloud 2025, Spring Boot 4, and OAuth2.
Open-source context retrieval layer for AI agents. Open-source context retrieval layer for AI agents and RAG systems.
Open-source framework for building agentic apps in JavaScript, Go, Dart, and Python, built and used in production by Google
The live data layer for apps and AI agents. Create up-to-the-second views into your business, just using SQL
HelixDB is an OLTP graph database with native vector and full-text search built in Rust on Object Storage.
Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots.
📌 异步线程池框架,支持线程池动态变更&监控&报警,无需修改代码轻松引入。Asynchronous thread pool framework, support Thread Pool Dynamic Change & monitoring & Alarm, no need to modify the code easily introduced.
An automated document analyzer for Paperless-ngx using OpenAI API, Ollama, Deepseek-r1, Azure and all OpenAI API compatible Services to automatically analyze and tag your documents.
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles, unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
🐢 Open-Source Evaluation & Testing library for LLM Agents
A visual playground for agentic workflows: Iterate over your agents 10x faster