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docs/api/models/groq.md

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# `pydantic_ai.models.groq`
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::: pydantic_ai.models.groq

docs/index.md

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## Why use PydanticAI
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* Built by the team behind Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, Instructor and many more)
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* Model-agnostic — currently both OpenAI and Gemini are supported, and Anthropic [is coming soon](https://github.com/pydantic/pydantic-ai/issues/63). And there is a simple interface to implement and customize support for other models.
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* Model-agnostic — currently both OpenAI, Gemini, and Groq are supported, Anthropic [is coming soon](https://github.com/pydantic/pydantic-ai/issues/63). And there is a simple interface to implement support for other models.
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* Type-safe
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* Control flow and composing agents is done with vanilla python, allowing you to make use of the same Python development best practices you'd use in any other (non-AI) project
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* [Structured response](results.md#structured-result-validation) validation with Pydantic

mkdocs.yml

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- api/models/openai.md
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- api/models/gemini.md
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- api/models/vertexai.md
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- api/models/groq.md
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- api/models/test.md
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- api/models/function.md
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pyproject.toml

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"httpx>=0.27.2",
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"logfire-api>=1.2.0",
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"openai>=1.54.3",
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"groq>=0.12.0",
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"pydantic>=2.10",
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]
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uv.lock

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