AI Expert Project

This project consists of a Python backend agent and a TypeScript/Node.js client for ALS (Agent Language Service). This README provides instructions on how to set up and run the project.

Project Structure

.
├── README.md
├── agent.py                  # Main Python agent file
├── pydantic_ai_expert.py     # Pydantic AI expert module
├── crawl_pydantic_ai_docs.py # Script to crawl Pydantic documentation
├── streamlit_ui.py           # Streamlit UI for the project
├── als_client/               # ALS client directory
│   ├── src/                  # TypeScript source files
│   └── agent.py              # Agent files
└── ...                       # Other project files

Prerequisites

• Python 3.10 or higher
• Node.js 16 or higher
• npm

Setup

1. Clone the Repository

git clone https://github.com/0xmukeshr/als_care.git
cd als_care

2. Python Environment Setup

Create and activate a virtual environment:

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

Install Python dependencies:

pip install -r requirements.txt

3. ALS Client Setup

Navigate to the ALS client directory and install dependencies:

cd als_client
npm install

Environment Configuration

1. Root Directory Environment

Create a .env file in the root directory with the following variables (adjust as needed):

# Get your Open AI API Key by following these instructions -
# https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
# You only need this environment variable set if you are using GPT (and not Ollama)
OPENAI_API_KEY=

# For the Supabase version (sample_supabase_agent.py), set your Supabase URL and Service Key.
# Get your SUPABASE_URL from the API section of your Supabase project settings -
# https://supabase.com/dashboard/project/<your project ID>/settings/api
SUPABASE_URL=

# Get your SUPABASE_SERVICE_KEY from the API section of your Supabase project settings -
# https://supabase.com/dashboard/project/<your project ID>/settings/api
# On this page it is called the service_role secret.
SUPABASE_SERVICE_KEY=

# The LLM you want to use from OpenAI. See the list of models here:
# https://platform.openai.com/docs/models
# Example: gpt-4o-mini
LLM_MODEL=

API_BASE_URL=

2. ALS Client Environment

Create a .env file in the als_client directory with the following variables (adjust as needed):

# API Configuration
TWITTER_USERNAME=
TWITTER_PASSWORD=
TWITTER_EMAIL=
SEARCH_INTERVAL_MINUTES=2
TWEET_INTERVAL_MINUTES=5
MAX_TWEETS_TO_FETCH=1
MAX_QUEUE_SIZE=50
OPENAI_API_KEY=

Running the Project

1. Start the Python Backend

In the root directory (with the virtual environment activated):

python3 agent.py

This will start the Python backend service.

2. Start the ALS Client

In a new terminal, navigate to the ALS client directory:

cd als_client
npm run dev

This will start the ALS client service in development mode.

Additional Components

Streamlit UI (Optional)

To run the Streamlit UI:

streamlit run streamlit_ui.py

Crawling Pydantic Documentation (Optional)

To update the crawled Pydantic documentation:

python crawl_pydantic_ai_docs.py

Troubleshooting

Common Issues

1. Port Already in Use

   • Change the port in the respective .env file

2. API Key Issues

   • Ensure all API keys are correctly set in the .env files

3. Module Not Found Errors

   • Make sure you've installed all dependencies with pip install -r requirements.txt
   • Check that you're running commands from the correct directory

4. ALS Client Build Issues

   • Try running npm clean-install in the als_client directory

Development

• Python files follow PEP 8 guidelines
• TypeScript files use ESLint with recommended rules
• Use npm run build in the als_client directory to build the production version