RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months.
A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.
DeepRetrieval - 🔥 Training Search Agent with Retrieval Outcomes via Reinforcement Learning
A sample app for the Multimodal Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power Q&A experiences.
A RAG (Retrieval-Augmented Generation) solution Based on Advanced Pre-generated QA Pairs. 基于高级 QA 问答对预生成的 RAG 知识库解决方案
Claude Plus is an advanced AI-powered development assistant that combines the capabilities of Anthropic's Claude AI with a suite of development tools.
A happy medium between AI search and GPT Agents
PostgreSQL-native semantic search engine with multi-modal capabilities. Add AI-powered search to your existing database without separate vector databases, vendor fees, or complex setup. Features text + image search using CLIP embeddings, native SQL joins, and 10-minute Docker deployment.
Remove information gap with AI assistant.
Seamless web search integration for LLM agents. Transforms natural language queries into structured web data, enabling AI models to access real-time information through a clean API.
Ai driven - command line tool to perform goal-based web searches
Type @deepseek in browser address bar to get answers from DeepSeek AI
OpenDeepSearch tool configured for Ollama, SearXNG, Reranking, Custom prompt - fully local and free.
🌐 Open-Source Web Search Backend Framework via Plug-and-Play Configuration
Self-adaptive Planning Agent。自适应规划代理的多模态检索增强生成技术。
将语雀知识库接入大语言模型，实现基于 RAG（检索增强生成）的智能问答系统，支持FastAPI，兼容OpenAI API与本地Ollama模型。
Web research automation with hierarchical citation trees. A more deterministic alternative to agent-based systems (like OpenAI Deep Research) that builds verifiable reasoning chains, works with smaller LLMs, and prioritizes transparency over black-box agents. Engineered for reliability and cost efficiency.
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