{"id":24144497,"url":"https://github.com/piyush-sri11/qa-chatbot","last_synced_at":"2026-05-08T15:02:24.455Z","repository":{"id":271614998,"uuid":"913990684","full_name":"Piyush-sri11/QA-chatbot","owner":"Piyush-sri11","description":"A Question-Answering chatbot built using RAG (Retrieval-Augmented Generation) with conversation memory. 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This project uses LangChain, various LLM options, and vector stores to create an intelligent chatbot that can answer questions about Jessup Cellars winery.\n\n## Features\n\n- RAG-based question answering\n- Conversation memory to maintain context\n- Support for multiple LLM options (Groq, OpenAI)\n- Vector store options (Pinecone, FAISS)\n- Environment variable configuration\n- Flexible embedding models (HuggingFace, OpenAI)\n\n## Installation\n\n#### 1.Clone the repository:\n```bash\ngit clone https://github.com/Piyush-sri11/QA-chatbot.git\n```\n\n#### 2.Create and activate a virtual environment:\n```bash\nvirtualenv env\nenv/Scripts/activate\n```\n\n#### 3.Install required packages:\n```bash\npip install -r requirements.txt\n```\n\n## Configuration\n\n### 1.Create a `.env` file in the project root and add your API keys:\n\n```bash\nLANGCHAIN_API_KEY=\"your_langchain_api_key\"\nLANGCHAIN_PROJECT=\"RAG QA Chatbot with Memory\"\nOPENAI_API_KEY=\"your_openai_api_key\"\nGOOGLE_API_KEY=\"your_google_api_key\"\nGROQ_API_KEY=\"your_groq_api_key\"\nHUGGING_FACE_TOKEN=\"your_huggingface_token\"\nPINECONE_API_KEY=\"your_pinecone_api_key\"\n```\n\n\n### For Google Colab, use secrets to store API keys:\n\n```bash\nfrom google.colab import userdata\nos.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\nos.environ['GROQ_API_KEY'] = userdata.get('GROQ_API_KEY')\n# Add other API keys as needed\n```\n\n# LLM Options\n\n## Default Configuration (Open Source)\n\nThe project uses Groq's Llama3-70b-8192 model by default, as it's a powerful open-source alternative:\n\n```bash\nllm = ChatGroq(\n    groq_api_key=groq_api_key,\n    model_name=\"Llama3-70b-8192\",\n    temperature=0\n)\n```\n## OpenAI Configuration (Commented Out)\n\nTo use OpenAI's models (requires API credits):\n\n```bash\nllm = ChatOpenAI(\n    model=\"gpt-4\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=4,\n    api_key=openai_api_key,\n)\n```\n## Vector Store Options\n\n### Pinecone (Default)\nThe project uses Pinecone as the default vector store for production use.\n\n### FAISS (Local Alternative)\nFor local development or testing, uncomment the FAISS implementation in the code.\n\n## Running the Project\n\n- 1.Ensure all configurations are set up properly\n- 2.Run the main script:\n\n```bash\npython yard.py\n```\n- 3.Start asking questions about Jessup Cellars.\n- 4.Type 'exit' to end the session.\n\n\n## Sample Usage\n\n```bash\nEnter your question: What makes Jessup Cellars wines special?\n[Response will appear here]\nResponse time: [time in seconds]\n##################################################\nEnter your question:\n```\n## Note on LLM Choice\n- This project was developed using open-source LLMs (Groq's Llama3-70b-8192) due to OpenAI API credit limitations. The code includes commented sections for OpenAI integration if you have API credits available.\n\n## Corpus Information\n\n- See `corpus_info.md` for detailed information about the Jessup Cellars knowledge base used in this project.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiyush-sri11%2Fqa-chatbot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpiyush-sri11%2Fqa-chatbot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiyush-sri11%2Fqa-chatbot/lists"}