Chat Bot Project

This project implements a chat bot using LangChain and LlamaIndex, with a FastAPI backend.

Project Structure

├── app
│   ├── chat_bot.py                 # Redmine data base assistant
│   ├── evaluators
│   │   └── dataset.ipynb           # creating eval datasets on langsmith
│   ├── langchainService
│   │   ├── database_retriver.py
│   │   ├── langchain_util.py
│   │   ├── prompts.py
│   │   ├── sql_agent_graph.py      # main file for Redmine db assistant
│   │   ├── summary_chain
│   │   └── test_nodebooks
│   ├── llamaIndex
│   │   ├── llamaIndexAgentDoc.py
│   │   ├── llamaIndexAgent.py
│   │   ├── llamaIndex_utils.py
│   │   └── rnd
│   ├── main.py                     # main fast api entry point
│   └── utils
│       ├── chat_bot_utils.py
│       ├── connections.py
│       ├── db_info.py
│       ├── models.py
│       └── settings.py
├── docker-compose.yml
├── Dockerfile
├── env_example.txt
├── poetry.lock
├── pyproject.toml
├── README.md

Prerequisites

• Python 3.10 or higher
• Poetry for dependency management
• Docker and Docker Compose (optional, for containerized deployment)

Setup

1. Clone the repository:

   git clone <repository-url>
   cd <project-directory>

2. Install Poetry if you haven't already:

   curl -sSL https://install.python-poetry.org | python3 -
   

3. Set up environment variables:

   • Copy env_example.txt to .env
   • Fill in the required environment variables in the .env file

4. Set up python environment

   python3 -m venv venv
   # Activate venvironment
   . venv/bin/activate

Running the Application

Using Poetry

1. If you've added new dependencies or updated existing ones, update the lock file:

   poetry lock
   

2. Install dependencies:

   poetry install
   

3. Run the FastAPI application:

   poetry run uvicorn app.main:app --reload --reload-delay 1 --log-level debug --reload-dir app/ --host 0.0.0.0 --port 8080
   

   Alternatively, you can run the application with:

   poetry run python app/main.py
   

4. To add new dependencies:

   poetry add <package-name>
   

5. To update dependencies:

   poetry update
   

Using Docker

1. Build and start the Docker containers:

   docker-compose up --build
   

Project Components

• app/main.py: Entry point of the FastAPI application
• app/chat_bot.py: Main chat bot logic
• app/langchainService/: LangChain related services and utilities
• app/llamaIndex/: LlamaIndex integration components
• app/utils/: Utility functions and configurations
• app/evaluators/: Evaluation scripts and notebooks

Configuration

Adjust the settings in app/utils/settings.py and environment variables as needed.

Redmine endpoint

@app.post("/ask/lang")
async def ask_lang_agent(request: userInput):
    response = ask_lang(query=request.question)
    return response["output"]