nhat117/bloomz-api

bloomz-api

BloomZ API

This project is a FastAPI-based application that allows users to upload PDF files, extract text from them using OCR, store the extracted text in a vector database, and interact with a fine-tuned language model. Users can also chat directly with the model.

Features

• Upload PDF files and associate them with a user ID.
• Extract text from PDF files using OCR.
• Store extracted text in a vector database for efficient retrieval.
• List all PDFs uploaded by a user.
• Interact with the model by asking questions or chatting directly.

Requirements

• Python 3.7+
• requirements.txt contains all the necessary dependencies.

Installation

1. Clone the repository:

   git clone https://github.com/nhat117/bloomz-api.git
   cd bloomz-api

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Install Tesseract and Poppler (for OCR):

   • On Ubuntu:

     sudo apt-get install tesseract-ocr
     sudo apt-get install poppler-utils

   • On macOS:

     brew install tesseract
     brew install poppler

Usage

1. Start the FastAPI server:

   uvicorn main:app --host 0.0.0.0 --port 8000 --reload

2. Upload a PDF:

   • Use curl:

     curl -X POST "http://127.0.0.1:8000/upload_pdf/" -F "user_id=12345" -F "file=@/path/to/your/file.pdf"

   • Use Postman:
     • Create a new POST request to http://127.0.0.1:8000/upload_pdf/
     • In the Body tab, select form-data.
     • Add a key user_id with the value as the user ID.
     • Add a key file with the type File and choose the file to upload.

3. List uploaded PDFs:

   • Use curl:

     curl -X GET "http://127.0.0.1:8000/list_pdfs/?user_id=12345"

   • Use Postman:
     • Create a new GET request to http://127.0.0.1:8000/list_pdfs/?user_id=12345.

4. Ask a question:

   • Use curl:

     curl -X POST "http://127.0.0.1:8000/predict" -H "Content-Type: application/json" -d '{"user_id": "12345", "question": "What is the capital of France?"}'

   • Use Postman:
     • Create a new POST request to http://127.0.0.1:8000/predict.
     • In the Body tab, select raw and set the type to JSON.
     • Provide the JSON payload: {"user_id": "12345", "question": "What is the capital of France?"}.

5. Chat with the model:

   • Use curl:

     curl -X POST "http://127.0.0.1:8000/chat" -H "Content-Type: application/json" -d '{"message": "Hello, how are you?"}'

   • Use Postman:
     • Create a new POST request to http://127.0.0.1:8000/chat.
     • In the Body tab, select raw and set the type to JSON.
     • Provide the JSON payload: {"message": "Hello, how are you?"}.

Project Structure

• main.py: FastAPI application with endpoints for uploading PDFs, listing uploaded PDFs, performing predictions, and direct chat functionality.
• model.py: Contains model and tokenizer initialization, text extraction, and vector database functions.
• schemas.py: Defines Pydantic models for request validation.
• requirements.txt: Lists all necessary dependencies.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.