Implementing a local LLM chatbot that can run code and searches

A tutorial explains how to build a local LLM-based chatbot using Ollama, a GGUF-formatted Mistral Dolphin 7B model, and Python, beginning with installation, model configuration through a Modelfile, and a streamed command-line chat interface that preserves conversation history. It extends the chatbot with agent-like capabilities by directing the model, through a system prompt, to mark executable Python blocks with a custom `Python-exe` tag, detecting those blocks during streamed output, executing them with Python’s `exec`, and returning results or errors to the model for follow-up responses. The tutorial cautions that arbitrary code execution is unsafe for production because it can access the local environment and filesystem. It then introduces a modular tool framework that lets the model invoke either Python execution or a basic Google search through specially tagged code blocks, using regular expressions and a dispatcher function to select tools and feed results back into the conversation. The approach emphasizes clear prompting, limiting smaller models to only a few tools to reduce confusion and hallucinations, and notes that frameworks such as LangChain and ReAct can support more advanced agent workflows but add setup complexity.

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