Sugar-AI Project

This document describes how to run Sugar-AI, test recent changes, and troubleshoot common issues.

Running Sugar-AI with Docker

Sugar-AI provides a Docker-based deployment option for an isolated and reproducible environment.

Build the Docker image

Open your terminal in the project's root directory and run:

docker build -t sugar-ai .

Run the Docker container

• With GPU (using NVIDIA Docker runtime):

  docker run --gpus all -it --rm sugar-ai

• CPU-only:

  docker run -it --rm sugar-ai

The container starts by executing main.py. To change the startup behavior, update the Dockerfile accordingly.

Testing the FastAPI App

The FastAPI server provides endpoints to interact with Sugar-AI.

Install dependencies

pip install -r requirements.txt

Run the server

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

Test API endpoints

• GET endpoint

  Access the root URL:
  http://localhost:8000/ to see the welcome message.

• POST endpoint for asking questions

  To submit a coding question, send a POST request to /ask with the question parameter. For example:

  curl -X POST "http://localhost:8000/ask?question=How%20do%20I%20create%20a%20Pygame%20window?"

  The API returns a JSON object with the answer.

• Additional POST endpoint (/ask-llm)

  An alternative endpoint /ask-llm is available in main.py, which provides similar functionality with an enhanced processing pipeline for LLM interactions. To use it, send your coding-related question using:

  curl -X POST "http://localhost:8000/ask-llm?question=How%20do%20I%20create%20a%20Pygame%20window?"

  The response format is JSON containing the answer generated by the language model.

• The RAG Agent has been updated with improvements for handling requests and retrieving relevant documents.

• The assistant now uses a more streamlined prompt and processing pipeline.

• You can choose to run the model with or without 4‑bit quantization using the --quantize flag.

Running the RAG Agent from the Command Line

To test the new RAG Agent directly from the CLI, execute:

python rag_agent.py --quantize

Remove the --quantize flag if you prefer running without 4‑bit quantization.

Testing the New Features

1. Verify Model Setup:

   • Confirm the selected model loads correctly by checking the terminal output for any errors.

2. Document Retrieval:

   • Place your documents (PDF or text files) in the directory specified in the default parameters or provide your paths using the --docs flag.
   • The vector store is rebuilt every time the agent starts. Ensure your documents are well placed to retrieve relevant content.

3. Question Handling:

   • After the agent starts, enter a sample coding-related question.
   • The assistant should respond by incorporating context from the loaded documents and answering your query.

4. API and Docker Route:

   • Optionally, combine these changes by deploying the updated version via Docker and testing the FastAPI endpoints as described above.

Troubleshooting CUDA Memory Issues

If you encounter CUDA out-of-memory errors, consider running the agent on CPU or adjust CUDA settings:

export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

Review the terminal output for further details and error messages.

Using the Streamlit App

Sugar-AI also provides a Streamlit-based interface for quick interactions and visualizations.

Running the Streamlit App

1. Install Streamlit:

   If you haven't already, install Streamlit:

   pip install streamlit

2. Make sure server is running using:

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

3. Start the App:

   Launch the Streamlit app by adding streamlit.py file.

   #./streamlit.py
   import streamlit as st
   import requests
   
   st.title("Sugar-AI Chat Interface")
   
   use_rag = st.checkbox("Use RAG (Retrieval-Augmented Generation)", value=True)
   
   st.subheader("Ask Sugar-AI")
   question = st.text_input("Enter your question:")
   
   if st.button("Submit"):
       if question:
           if use_rag:
               url = "http://localhost:8000/ask"
           else:
               url = "http://localhost:8000/ask-llm"
           params = {"question": question}
           try:
               response = requests.post(url, params=params)
               if response.status_code == 200:
                   result = response.json()
                   st.markdown("**Answer:** " + result["answer"])
               else:
                   st.error(f"Error {response.status_code}: {response.text}")
           except Exception as e:
               st.error(f"Error contacting the API: {e}")
       else:
           st.warning("Please enter a question.")

   streamlit run streamlit.py

4. Using the App:

   • The app provides a simple UI to input coding questions and displays the response using Sugar-AI.
   • Use the sidebar options to configure settings if available.
   • The app communicates with the FastAPI backend to process and retrieve answers.

Enjoy exploring Sugar-AI through both API endpoints and the interactive Streamlit interface!