llm-backend

This project provides a simple async interface to interact with an Ollama model and demonstrates basic tool usage. Chat histories are stored in a local SQLite database using Peewee. Histories are persisted per user and session so conversations can be resumed with context. One example tool is included:

execute_python – Executes Python code in a sandbox with selected built-ins and allows importing safe modules like math. The result is returned from a result variable or captured output.

The application now injects a system prompt that instructs the model to chain multiple tools when required. This prompt ensures the assistant can orchestrate tool calls in sequence to satisfy the user's request.

Usage

python run.py

The script will ask the model to compute an arithmetic expression and print the answer. Conversations are automatically persisted to chat.db and are now associated with a user and session.