{"id":30194133,"url":"https://github.com/gavinhome/langgraph_adaptive_rag","last_synced_at":"2025-08-13T02:50:40.973Z","repository":{"id":308806958,"uuid":"1034169340","full_name":"GavinHome/Langgraph_Adaptive_RAG","owner":"GavinHome","description":"Langgraph_Adaptive_RAG","archived":false,"fork":false,"pushed_at":"2025-08-08T01:59:07.000Z","size":711,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-08T03:08:30.968Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GavinHome.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-08T00:57:36.000Z","updated_at":"2025-08-08T01:59:10.000Z","dependencies_parsed_at":"2025-08-08T03:08:33.679Z","dependency_job_id":"77c406cd-5df4-4581-871f-babc5cbadb82","html_url":"https://github.com/GavinHome/Langgraph_Adaptive_RAG","commit_stats":null,"previous_names":["gavinhome/langgraph_adaptive_rag"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/GavinHome/Langgraph_Adaptive_RAG","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GavinHome%2FLanggraph_Adaptive_RAG","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GavinHome%2FLanggraph_Adaptive_RAG/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GavinHome%2FLanggraph_Adaptive_RAG/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GavinHome%2FLanggraph_Adaptive_RAG/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GavinHome","download_url":"https://codeload.github.com/GavinHome/Langgraph_Adaptive_RAG/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GavinHome%2FLanggraph_Adaptive_RAG/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270170928,"owners_count":24539363,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-13T02:00:09.904Z","response_time":66,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-08-13T02:50:36.889Z","updated_at":"2025-08-13T02:50:40.925Z","avatar_url":"https://github.com/GavinHome.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LangGraph Adaptive RAG\n\nThis is a demo project that demonstrates how to build an Adaptive RAG (Retrieval-Augmented Generation) system using LangGraph. The project includes a React frontend and a Python (FastAPI) backend.\n\n## Project Structure\n\n```text\n.\n├── LICENSE\n├── README.md\n├── README.zh.md\n├── backend/\n│   ├── README.md\n│   ├── adaptive_rag.py\n│   ├── api.py\n│   └── requirements.txt\n└── frontend/\n    ├── README.md\n    ├── package.json\n    ├── public/\n    │   └── index.html\n    └── src/\n        ├── App.js\n        └── index.js\n```\n\n## Backend\n\nThe backend is implemented using Python, FastAPI, and LangGraph to create the Adaptive RAG flow.\n\nFor detailed setup and running instructions, please refer to [backend/README.md](./backend/README.md).\n\n### Quick Start\n\n1.  **Navigate to the backend directory**\n    ```bash\n    cd backend\n    ```\n\n2.  **Set up the environment and install dependencies**\n    ```bash\n    # Create and activate a virtual environment\n    python3 -m venv rag\n    source rag/bin/activate\n\n    # Install dependencies\n    pip install -r requirements.txt\n    ```\n\n3.  **Set API Keys**\n    ```bash\n    export OPENAI_API_KEY=\"your_openai_api_key\"\n    export TAVILY_API_KEY=\"your_tavily_api_key\"\n    ```\n\n4.  **Start the API Server**\n    ```bash\n    python api.py\n    ```\n\n## Frontend\n\nThe frontend is built with React to provide a user-friendly interface for interacting with the backend RAG system.\n\n### Quick Start\n\n1.  **Navigate to the frontend directory**\n    ```bash\n    cd frontend\n    ```\n\n2.  **Install dependencies**\n    ```bash\n    npm install\n    ```\n\n3.  **Start the development server**\n    ```bash\n    npm start\n    ```\n    The application will run at [http://localhost:3000](http://localhost:3000).\n\n4.  **Environment Variables**\n    The frontend application can be configured with environment variables via `.env` files. For example, `REACT_APP_API_BASE_URL` can be set when the backend service is deployed at a different address. If left blank in the development environment, it will be proxied to port 8000, which can be configured in `package.json`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgavinhome%2Flanggraph_adaptive_rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgavinhome%2Flanggraph_adaptive_rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgavinhome%2Flanggraph_adaptive_rag/lists"}