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========
AI / LLM
========

The AI module provides integration with large language models (LLMs) for tasks
such as classification, sentiment analysis, text extraction, translation,
summarization, PII masking, embedding generation, and vector search. Access
these functions through the ``df.llm`` accessor on a DataFrame.

Example::

    >>> import pyflink.dataframe as pf
    >>> pf.set_model_provider(pf.OpenAICompatProvider(
    ...     task="chat/completions", model="qwen3.6-plus"))
    >>> df = pf.from_dict({"text": ["Hello world", "Bonjour le monde"]})
    >>> df.llm.ai_translate(
    ...     "text", source_lang="auto", target_lang="en").collect()

Model Providers
---------------

Register and manage LLM providers.

.. currentmodule:: pyflink.dataframe

.. autosummary::
    :toctree: api/

    set_model_provider
    set_default_model_provider
    list_model_providers


Model Provider
--------------

Base class and built-in provider implementations.

.. currentmodule:: pyflink.dataframe

.. autosummary::
    :toctree: api/

    ModelProvider
    OpenAICompatProvider
    DashScopeProvider
    TritonProvider
    GenericProvider


LLM Accessor
------------

Accessed via the ``df.llm`` property on a DataFrame. Provides methods for
common AI tasks.

.. currentmodule:: pyflink.dataframe.ai.llm

.. autosummary::
    :toctree: api/

    LLMAccessor.vector_search
    LLMAccessor.predict
    LLMAccessor.ai_classify
    LLMAccessor.ai_sentiment
    LLMAccessor.ai_extract
    LLMAccessor.ai_translate
    LLMAccessor.ai_summarize
    LLMAccessor.ai_mask
    LLMAccessor.ai_embed