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https://github.com/bhxvxshh/promptquest

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. Ideal for academic, business, or research use, PromptQuest delivers real-time, context-aware results.
https://github.com/bhxvxshh/promptquest

chroma dotenv fastapi langchain-python ollama python streamlit uvicorn

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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. Ideal for academic, business, or research use, PromptQuest delivers real-time, context-aware results.

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README 

        
# PromptQuest

 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. Ideal for academic, business, or research use, PromptQuest delivers real-time, context-aware results.



Features

Essay Generation: Generate essays based on any topic.

Policy Q&A: Answer questions based on provided documents (e.g., company policies).

Custom Prompts: Tailor prompts for different use cases using LangChain.

Document Retrieval: Efficiently search and retrieve information from large text documents using Chroma and Ollama embeddings.

Interactive UI: Streamlit interface for seamless interaction.

Technologies Used

FastAPI: Web framework for building the backend API.

LangChain: Framework for creating advanced language models and prompts.

Ollama: LLM integration for text generation and document-based retrieval.

Chroma: Vector store for efficient document embedding and retrieval.

Streamlit: Frontend interface for user interaction.

Uvicorn: ASGI server for running the FastAPI app.

dotenv: For managing environment variables.



Installation & Setup

1. Clone the Repository

bash

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git clone https://github.com/bhxvxshh/PromptQuest.git

cd PromptQuest

2. Create a Virtual Environment

bash

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python -m venv venv

source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

bash

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pip install -r requirements.txt

4. Set Up Environment Variables

Create a .env file in the root of the project with the following variables:



env

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LANGCHAIN_API_KEY=your_langchain_api_key

5. Run the Backend API

bash

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uvicorn app:app --reload

6. Run the Streamlit Frontend

bash

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streamlit run client.py

How It Works

FastAPI Backend: The backend exposes two primary routes:



/essay: Takes a topic as input and generates a 100-word essay.

/gemma2:2b: Handles policy-related queries and retrieves relevant answers from documents.

Streamlit 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.



Document-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.

License

This project is licensed under the MIT License - see the LICENSE file for details.



Acknowledgements

LangChain and Ollama for providing powerful tools for building language models and document retrieval.

Streamlit for easy-to-use frontend development.

Uvicorn for serving the FastAPI application.