Project Setup Guide

Prerequisites

• Ensure you have Docker and Docker Compose installed on your machine.

• Python 3.7 or higher installed.

• Install Ollama: Follow the instructions to install Ollama on your machine. You can download it from Ollama's official website. For Linux users, you can run:

  curl -fsSL https://ollama.com/install.sh | sh

• Pull Models: After installing Ollama, you can pull the models you want to use. For example, to pull the Llama2 model, run:

  ollama pull llama3.1:8b
  ollama pull mxbai-embed-large

  You can check available models and their details on the Ollama model library.

Setting Up the Environment

1. Clone the Repository

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

2. Create a Python Virtual Environment

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

3. Install Requirements

   Install the required Python packages using the requirements.txt file.

   pip install -r requirements.txt

Setting Up PostgreSQL with Docker

1. Create a Docker Network (Optional)

   This step is optional but recommended for better organization.

   docker network create my_network

2. Run PostgreSQL Using Docker Compose

   Ensure your docker-compose.yml file is set up correctly. Then run:

   docker-compose up -d

   This command will start the PostgreSQL database in the background, with pgvector extension enabled and tables created.

   Table schema is defined in init.sql file, schema design is based on blog thenile.building_code_assistant

Configuring Environment Variables

1. Configure .env File for Docker

   Create a file named .env in the project root and add the following configurations:

   POSTGRES_DB=<your_database_name>
   POSTGRES_USER=<your_database_user>
   POSTGRES_PASSWORD=<your_database_password>
   POSTGRES_HOST_AUTH_METHOD=trust

   These variables will be used in the docker-compose.yml file to set up the PostgreSQL database.

2. Configure .env.chat File

   Configured .env.chat in the project root and add the following configurations:

   If you don't have a Gemini API key, you can use Ollama as the LLM.

   USE_OLLAMA=True
   GEMINI_API_KEY=<your_gemini_api_key>
   OLLAMA_BASE_URL=http://localhost:11434
   OLLAMA_MODEL=<ollama_model_name>

3. Create .env.embed File

   Create a file named .env.embed in the project root and add the following configurations:

   Ensure you have the mxbai-embed-large model installed in your ollama server.

   OLLAMA_BASE_URL=http://localhost:11434
   OLLAMA_MODEL=mxbai-embed-large
   DB_CONNECTION_STRING=<DB_CONNECTION_STRING>

Running the Application with Docker

1. Build the Docker Image

   To build the Docker image for the application, run:

   docker build -t code-assistant-ollama .

2. Run the Docker Container

   Start the application using Docker, exposing the necessary ports:

   docker run -p 8501:8501 -p 8502:8502 code-assistant-ollama

   This will run both the chat.py and embeddings.py applications, accessible at http://localhost:8501 and http://localhost:8502, respectively.

Running the Python Files

1. Run the Chat App

   To start the Streamlit application, run:

   streamlit run chat.py

2. Run the Embeddings App

   If you need to run the embeddings script, execute:

   streamlit run embeddings.py

Stopping the Services

To stop the PostgreSQL service, run:

docker-compose down