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 inside a persistent Linux VM with network access. Use it to read uploaded documents under /data or run other commands. Output from stdout and stderr is captured and returned. The VM is created when a chat session starts and reused for all subsequent tool calls.

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.

Uploaded files are stored under the uploads directory and mounted inside the VM at /data. Call upload_document on the chat session to make a file available to the model:

async with ChatSession() as chat:
    path_in_vm = chat.upload_document("path/to/file.pdf")
    reply = await chat.chat(f"Summarize {path_in_vm}")

Docker

A Dockerfile is provided to run the Discord bot along with an Ollama server. The image installs Ollama, pulls the LLM and embedding models, and starts both the server and the bot.

Build the image:

docker build -t llm-discord-bot .

Run the container:

docker run -e DISCORD_TOKEN=your-token llm-discord-bot

The environment variables OLLAMA_MODEL and OLLAMA_EMBEDDING_MODEL can be set at build or run time to specify which models to download.