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_terminal – Executes a shell command in a Linux VM with network access. Output from stdout and stderr is captured and returned.

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 instruct the model to run a simple shell command and print the result. Conversations are automatically persisted to chat.db and are now associated with a user and session.