Tutorial: Building an end-to-end LLM Chatbot

A tutorial explains how to build a locally run, voice-enabled chatbot using Ollama for LLM inference, Streamlit for a browser-based chat interface, and Deepgram for speech-to-text and text-to-speech services. It covers installing the required tools, selecting a GGUF-format open-source model such as Dolphin Mistral, assessing hardware memory and performance requirements, and configuring Ollama through a Modelfile containing the model path, prompt template, and optional system prompt. The chatbot preserves conversation history in Streamlit session state and sends it to Ollama’s local streaming API to generate multi-turn responses. Audio support is added through a recording widget and Deepgram transcription, while generated LLM replies can be converted into WAV audio using Deepgram voices and played in the interface. The tutorial also describes a bonus Apple Shortcuts modification that replaces an OpenAI-based Siri shortcut with calls to a local Ollama API, allowing Siri to relay responses from a locally hosted model.

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