{"id":24983990,"url":"https://github.com/sureshbeekhani/rag_with_knowledge_graph","last_synced_at":"2026-04-25T11:36:51.241Z","repository":{"id":275288459,"uuid":"920722122","full_name":"SURESHBEEKHANI/RAG_With_Knowledge_Graph","owner":"SURESHBEEKHANI","description":"RAG_With_Knowledge_Graph enhances customer support using Retrieval-Augmented Generation (RAG) and a knowledge graph. It leverages Neo4j for structured data, LangChain for retrieval, and Google Generative AI for intelligent responses, ensuring efficient query resolution.","archived":false,"fork":false,"pushed_at":"2025-02-02T09:04:17.000Z","size":2446,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-29T10:15:25.451Z","etag":null,"topics":["ai-customer-support","chatbot","chatbots","fastapi","fastapi-docker","generativeai","neo4j-database"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SURESHBEEKHANI.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2025-01-22T16:58:09.000Z","updated_at":"2025-02-02T09:14:20.000Z","dependencies_parsed_at":"2025-02-02T09:20:01.743Z","dependency_job_id":null,"html_url":"https://github.com/SURESHBEEKHANI/RAG_With_Knowledge_Graph","commit_stats":null,"previous_names":["sureshbeekhani/graphrag-llm","sureshbeekhani/rag_with_knowledge_graph"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2FRAG_With_Knowledge_Graph","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2FRAG_With_Knowledge_Graph/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2FRAG_With_Knowledge_Graph/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2FRAG_With_Knowledge_Graph/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SURESHBEEKHANI","download_url":"https://codeload.github.com/SURESHBEEKHANI/RAG_With_Knowledge_Graph/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246168107,"owners_count":20734390,"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","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":["ai-customer-support","chatbot","chatbots","fastapi","fastapi-docker","generativeai","neo4j-database"],"created_at":"2025-02-04T09:41:42.704Z","updated_at":"2026-04-25T11:36:46.204Z","avatar_url":"https://github.com/SURESHBEEKHANI.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RAG_With_Knowledge_Graph\n\n**RAG_With_Knowledge_Graph** is an advanced AI-driven customer support system that integrates LangChain, Neo4j, and Google Generative AI to deliver efficient and dependable customer assistance. The application features a FastAPI backend and a Streamlit frontend.\n\n## Key Features\n\n- AI-Powered Assistance\n- 24/7 Support Availability\n- Comprehensive Customer Query Resolution\n\n## Installation\n\n1. Clone the repository:\n   ```sh\n   git clone https://github.com/SURESHBEEKHANI/RAG_With_Knowledge_Graph.git\n   cd RAG_With_Knowledge_Graph\n   ```\n\n2. Set up a virtual environment and activate it:\n   ```sh\n   python -m venv venv\n   source venv/bin/activate  # Use `venv\\Scripts\\activate` on Windows\n   ```\n\n3. Install required dependencies:\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n4. Configure environment variables:\n   ```sh\n   export NEO4J_URI=\"your_neo4j_uri\"\n   export NEO4J_USERNAME=\"your_neo4j_username\"\n   export NEO4J_PASSWORD=\"your_neo4j_password\"\n   export GROQ_API_KEY=\"your_groq_api_key\"\n   export GEMINI_API_KEY=\"your_gemini_api_key\"\n   ```\n\n## Running the Application\n\n### Backend\n\n1. Navigate to the backend directory:\n   ```sh\n   cd backend\n   ```\n\n2. Launch the FastAPI application:\n   ```sh\n   uvicorn backend:app --host 127.0.0.1 --port 9999\n   ```\n\n### Frontend\n\n1. Navigate to the frontend directory:\n   ```sh\n   cd ../frontend\n   ```\n\n2. Start the Streamlit application:\n   ```sh\n   streamlit run app.py\n   ```\n\n## Usage\n\n1. Open your web browser and go to `http://127.0.0.1:8501` to access the Streamlit frontend.\n2. Interact with the chatbot by entering your queries into the input box.\n3. The chatbot will respond with AI-generated answers based on context and data retrieved from the Neo4j graph database.\n\n## Project Structure\n\n- `backend.py`: Implementation of the FastAPI backend.\n- `app.py`: Implementation of the Streamlit frontend.\n- `Graprag.py`: Core logic for query processing and data retrieval.\n\n## Video Demonstration\n\nCheck out the video demonstration of the project:\n\n[Video Demonstration](notebook/Customer%20Support%20LangChain.mp4)\n\n## License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsureshbeekhani%2Frag_with_knowledge_graph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsureshbeekhani%2Frag_with_knowledge_graph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsureshbeekhani%2Frag_with_knowledge_graph/lists"}