readme.github.md

AI Project

A FastAPI-based AI application providing chat, file, image, and web data services with advanced streaming and tool-based capabilities.

Features

• Real-time chat streaming with AI (supports context from uploaded files)
• File upload, processing, and vector storage (PDF, DOCX, images, etc.)
• Vector store for semantic search and context retrieval
• Image generation using Stable Diffusion
• Web search and web content reading via integrated tools
• Tool-based AI interactions (extensible via utils/tools)
• CORS-enabled API endpoints
• Static file serving for generated outputs

Project Structure

src/
├── constants/         # System prompts and configuration
│   ├── system_prompts.py
│   └── file_type.py
├── models/           # Request/Response models
│   ├── requests/
│   └── responses/
├── routes/           # API route definitions
│   ├── chat_routes.py
│   ├── process_file_routes.py
│   └── vector_store_routes.py
├── services/         # Business logic
│   ├── chat_service.py
│   ├── image_service.py
│   ├── process_file_service.py
│   ├── web_data_service.py
│   └── vector_store_service.py
├── utils/            # Utility functions
│   ├── tools/        # Tool implementations (image, web search, etc.)
│   ├── client.py
│   ├── exception.py
│   ├── image_pipeline.py
│   └── timing.py
└── main.py           # Application entry point

API Endpoints

Chat API

• POST /api/v1/chat/stream: Stream chat responses in real-time (supports tool-based and context-aware interactions)
• POST /api/v1/chat: Non-streaming chat (batched response)

File Processing API

• POST /api/v1/process-file/upload-and-process-file: Upload and process files (PDF, DOCX, images, etc.), store in vector DB

Vector Store API

• GET /api/v1/vector-store/get-all-ids: List all vector store collection IDs
• POST /api/v1/vector-store/inspect-collection: Inspect a specific vector store collection

Tool-based AI

• Image Generation: Generate images from prompts using Stable Diffusion
• Web Search: Search the web using Brave Search API
• Web Content Reading: Fetch and summarize web pages using Jina API

Error Handling

• Custom exceptions and global error handling
• Standardized JSON error responses

Static Files

• Serves generated outputs (e.g., images) from /outputs at /outputs endpoint

Getting Started

Prerequisites

• Python 3.11
• CUDA 12.9.0 (for GPU acceleration)
• FastAPI 0.114.0
• Uvicorn 0.34.2

Installation

1. Clone the repository
2. Create a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Running the Application

Local Development

uvicorn main:app --reload --port 8080

Docker Deployment

# Build the Docker image
docker build -t ai-assistance-server .

# Run the container
docker run -p 7860:7860 --gpus all ai-assistance-server

The application will be available at:

• Local: http://localhost:7860
• Server: http://0.0.0.0:7860 or https://leonguyen101120-ai-assistance.hf.space

Development

Key Dependencies

• AI/ML:

  • diffusers 0.33.1
  • transformers 4.52.4
  • torch 2.7.0
  • accelerate 1.6.0

• File Processing (Optional):

  • beautifulsoup4 4.13.4
  • langchain_chroma 0.2.2
  • langchain_huggingface 0.1.2
  • langchain_community 0.3.19
  • chromadb 0.6.3
  • pymupdf 1.25.1

Environment Variables

The following environment variables are required for specific features:

• Brave Search API key (for web search)
• Jina API key (for web content reading)
• HuggingFace API key (for model access)

License

[Add your license information here]