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Google's LangExtract library supports custom OpenAI-compatible APIs

By PulseAugur Editorial · [1 sources] ·

Google's LangExtract library, designed for extracting structured data from text, has been configured to work with custom OpenAI-compatible APIs. Initially, the library routes models based on their names, sending those starting with 'gpt' to OpenAI and others to Ollama. However, this default routing can be bypassed by explicitly specifying the provider and model configuration, allowing non-OpenAI models to be used with the OpenAI provider. When using a custom configuration, it's important to pass the API key explicitly, as environment variables may not be read correctly for non-OpenAI named models. AI

IMPACT Enables flexible integration of diverse LLMs with Google's LangExtract for structured data extraction tasks.

RANK_REASON The item describes a specific configuration and usage of an existing software library (LangExtract) with custom API endpoints, rather than a new release or major advancement.

Read on dev.to — LLM tag →

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

Google's LangExtract library supports custom OpenAI-compatible APIs

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24 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Tool
The item describes a specific configuration and usage of an existing software library (LangExtract) with custom API endpoints, rather than a new release or major advancement.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Aman Kumar ·

    Google's LangExtract on a Custom OpenAI-Compatible API: Model IDs That Route to Ollama, Strict Schemas and Silent Chunks

    <p>LangExtract is Google's Python library for pulling structured entities out of long text, with every extraction mapped back to its character position in the source. You describe the task, give one or two examples, and it chunks the document, sends each chunk to a model, parses …

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