{"id":19517292,"url":"https://github.com/bhxvxshh/promptquest","last_synced_at":"2026-04-28T16:02:52.816Z","repository":{"id":262116483,"uuid":"886259502","full_name":"bhxvxshh/PromptQuest","owner":"bhxvxshh","description":" PromptQuest is an AI-powered platform for generating essays and answering questions using large language models. Built with FastAPI and LangChain, it offers fast, accurate responses based on custom prompts and document retrieval. 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Built with FastAPI and LangChain, it offers fast, accurate responses based on custom prompts and document retrieval. Ideal for academic, business, or research use, PromptQuest delivers real-time, context-aware results.\n\nFeatures\nEssay Generation: Generate essays based on any topic.\nPolicy Q\u0026A: Answer questions based on provided documents (e.g., company policies).\nCustom Prompts: Tailor prompts for different use cases using LangChain.\nDocument Retrieval: Efficiently search and retrieve information from large text documents using Chroma and Ollama embeddings.\nInteractive UI: Streamlit interface for seamless interaction.\nTechnologies Used\nFastAPI: Web framework for building the backend API.\nLangChain: Framework for creating advanced language models and prompts.\nOllama: LLM integration for text generation and document-based retrieval.\nChroma: Vector store for efficient document embedding and retrieval.\nStreamlit: Frontend interface for user interaction.\nUvicorn: ASGI server for running the FastAPI app.\ndotenv: For managing environment variables.\n\nInstallation \u0026 Setup\n1. Clone the Repository\nbash\nCopy code\ngit clone https://github.com/bhxvxshh/PromptQuest.git\ncd PromptQuest\n2. Create a Virtual Environment\nbash\nCopy code\npython -m venv venv\nsource venv/bin/activate   # On Windows, use `venv\\Scripts\\activate`\n3. Install Dependencies\nbash\nCopy code\npip install -r requirements.txt\n4. Set Up Environment Variables\nCreate a .env file in the root of the project with the following variables:\n\nenv\nCopy code\nLANGCHAIN_API_KEY=your_langchain_api_key\n5. Run the Backend API\nbash\nCopy code\nuvicorn app:app --reload\n6. Run the Streamlit Frontend\nbash\nCopy code\nstreamlit run client.py\nHow It Works\nFastAPI Backend: The backend exposes two primary routes:\n\n/essay: Takes a topic as input and generates a 100-word essay.\n/gemma2:2b: Handles policy-related queries and retrieves relevant answers from documents.\nStreamlit Frontend: The user enters a topic for essay generation or asks a policy-related question. The frontend sends the input to the backend API, receives the response, and displays it.\n\nDocument-Based Querying: When users ask policy questions, the backend uses Chroma to retrieve relevant information from provided documents (e.g., policies) and returns the answer.\nLicense\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\nAcknowledgements\nLangChain and Ollama for providing powerful tools for building language models and document retrieval.\nStreamlit for easy-to-use frontend development.\nUvicorn for serving the FastAPI application.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhxvxshh%2Fpromptquest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbhxvxshh%2Fpromptquest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhxvxshh%2Fpromptquest/lists"}