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Python guide explains LLM function calling for structured data extraction

By PulseAugur Editorial · [1 sources] ·

This guide details how to implement function calling, also known as tool use, in Python to enable large language models to return structured data instead of free-form text. The process involves describing available tools (functions) to the model, which then returns a JSON object matching a defined schema. Developers then parse this JSON to execute the actual function, feeding the result back into the conversation. The guide covers both standard function calling and structured output modes, including chaining multiple tool calls in a conversational loop for more complex agentic behavior. AI

IMPACT Enables developers to build more sophisticated applications by allowing LLMs to interact with external tools and APIs.

RANK_REASON The item is a technical guide on implementing a feature of an existing LLM, not a new release or significant industry event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Python guide explains LLM function calling for structured data extraction

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20 / 100
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Tool
The item is a technical guide on implementing a feature of an existing LLM, not a new release or significant industry event.
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High
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ayi NEDJIMI ·

    How to Implement Function Calling with Structured Outputs

    <p>Most language models return free-form text. That works for chatbots, but it breaks down when you need your application to actually <em>do something</em> with the response — parse a price, trigger an API call, update a record. Function calling (also called tool use) solves this…

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