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RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
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🦄🦄🦄AI赋能股票分析：自选股行情获取，成本盈亏展示，涨跌报警推送。目前已支持A股，港股，美股，未来计划加入基金，ETF等支持。支持市场整体/个股情绪分析，K线分析等。数据全部保留在本地。支持DeepSeek，OpenAI， Ollama，LMStudio，AnythingLLM，硅基流动，火山方舟，阿里云百炼等平台或模型。
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
No fortress, purely open ground. OpenManus is Coming.
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
"LightRAG: Simple and Fast Retrieval-Augmented Generation"
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
[KDD 2024]this is project for training explicit graph reasoning large language models.
A modular graph-based Retrieval-Augmented Generation (RAG) system
100 % FREE, Private (No Internet) DeepSeek’s Advanced RAG: Boost Your RAG Chatbot: Hybrid Retrieval (BM25 + FAISS) + Neural Reranking + HyDe🚀
Finetune Qwen3, Llama 4, TTS, DeepSeek-R1 & Gemma 3 LLMs 2x faster with 70% less memory! 🦥
A Langchain app that allows you to chat with multiple PDFs
PyGWalker: Turn your dataframe into an interactive UI for visual analysis
real time face swap and one-click video deepfake with only a single image
Extrapolating knowledge graphs from unstructured text using GPT-3 🕵️♂️
A collection of AWESOME things about Graph-Related LLMs.
Data Apps & Dashboards for Python. No JavaScript Required.
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
Turns Data and AI algorithms into production-ready web applications in no time.
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐