Function Calling: Teaching Tools to the Assistant

This post is part of the Voice Assistant on Raspberry Pi series.

In article #5, we injected weather data into every conversation, even for questions like “what’s your name?”. That wastes tokens. Function calling fixes this: the LLM decides when it needs a tool and only calls it when the question actually warrants it.

The complete code for this article is available on GitHub.

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Real-Time Weather and Swapping to the Claude API

This post is part of the Voice Assistant on Raspberry Pi series.

The assistant responds well, but it has no idea what the weather is like outside. We wire up Open-Meteo, a free, key-less weather API. And while we’re at it, we swap Ollama for the Claude API: a single line in appsettings.json.

The complete code for this article is available on GitHub.

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Memory, Silence Detection, and systemd

This post is part of the Voice Assistant on Raspberry Pi series.

The assistant from article #3 works, but every exchange starts from scratch. We fix that in three steps: conversational memory, automatic silence detection, and auto-start at boot with systemd.

The complete code for this article is available on GitHub.

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Ollama Integration and Home Context

This post is part of the Voice Assistant on Raspberry Pi series.

Article #2 ended with a hardcoded response, which was enough to confirm the audio pipeline works. Now we swap that line for a real HTTP call to Ollama on the pi-cerveau, and add a system prompt to give the assistant a personality and some knowledge about your home.

The complete code for this article is available on GitHub.

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.NET 10 Worker Service and Audio Pipeline

This post is part of the Voice Assistant on Raspberry Pi series.

Both Pis are configured. Time to write some code. The goal: validate the full audio pipeline on the pi-client, from button press to spoken response, without a real LLM. We hardcode a reply for now. The LLM comes in article #3.

The complete code for this article is available on GitHub.

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Create a Blazor App with ML.NET

Learn how to integrate ML.NET into a Blazor Server app to build a sentiment analysis tool using binary classification. [Read More]
.NET  C#  Blazor  ML.NET  AI