src.dackar.RCA.orchestrators.llm_clients
========================================

.. py:module:: src.dackar.RCA.orchestrators.llm_clients

.. autoapi-nested-parse::

   llm_clients — LLM client Protocol and concrete implementations.

   Extracted from rca_reasoning_orchestrator.py.  The parent module re-exports
   all three names for backward-compatible imports.



Attributes
----------

.. autoapisummary::

   src.dackar.RCA.orchestrators.llm_clients.JsonDict


Classes
-------

.. autoapisummary::

   src.dackar.RCA.orchestrators.llm_clients.LLMClient
   src.dackar.RCA.orchestrators.llm_clients.DummyLLMClient
   src.dackar.RCA.orchestrators.llm_clients.OllamaLLMClient


Module Contents
---------------

.. py:data:: JsonDict

.. py:class:: LLMClient

   Bases: :py:obj:`Protocol`


   Base class for protocol classes.

   Protocol classes are defined as::

       class Proto(Protocol):
           def meth(self) -> int:
               ...

   Such classes are primarily used with static type checkers that recognize
   structural subtyping (static duck-typing).

   For example::

       class C:
           def meth(self) -> int:
               return 0

       def func(x: Proto) -> int:
           return x.meth()

       func(C())  # Passes static type check

   See PEP 544 for details. Protocol classes decorated with
   @typing.runtime_checkable act as simple-minded runtime protocols that check
   only the presence of given attributes, ignoring their type signatures.
   Protocol classes can be generic, they are defined as::

       class GenProto[T](Protocol):
           def meth(self) -> T:
               ...


   .. py:method:: generate_json(model, prompt, temperature = 0.1)


.. py:class:: DummyLLMClient

   Development-only LLM client used by build_dev_orchestrator().

   Intentionally raises so the real synthesizer falls back to the
   deterministic template path.  This lets the pipeline run end-to-end
   before Ollama or another real LLM backend is wired in.


   .. py:method:: generate_json(model, prompt, temperature = 0.1)


.. py:class:: OllamaLLMClient(base_url = 'http://localhost:11434')

   .. py:attribute:: base_url
      :value: ''



   .. py:method:: generate_json(model, prompt, temperature = 0.1)