haive.agents.rag.agentic.agent
==============================

.. py:module:: haive.agents.rag.agentic.agent

.. autoapi-nested-parse::

   Agentic RAG Agent - ReAct + Retrieval with Proper Haive Patterns.

   This implementation follows the LangChain/LangGraph agentic RAG tutorial but uses
   proper Haive base agent infrastructure:
   - Inherits from ReActAgent for reasoning/acting patterns
   - Uses ToolRouteMixin for automatic tool routing
   - Proper Pydantic patterns (no __init__, model validators)
   - Generic type safety with bounds
   - Multiple engines (LLM + Retriever + Grader)



Classes
-------

.. autoapisummary::

   haive.agents.rag.agentic.agent.AgenticRAGAgent
   haive.agents.rag.agentic.agent.AgenticRAGState
   haive.agents.rag.agentic.agent.DocumentGrade
   haive.agents.rag.agentic.agent.QueryRewrite


Functions
---------

.. autoapisummary::

   haive.agents.rag.agentic.agent.create_agentic_rag_agent
   haive.agents.rag.agentic.agent.create_memory_aware_agentic_rag


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

.. py:class:: AgenticRAGAgent(/, **data)

   Bases: :py:obj:`haive.agents.react.agent.ReactAgent`, :py:obj:`haive.core.common.mixins.tool_route_mixin.ToolRouteMixin`


   Agentic RAG agent combining ReAct reasoning with intelligent retrieval.

   This agent can:
   - Decide when to retrieve vs respond directly (agentic behavior)
   - Grade retrieved documents for relevance
   - Rewrite queries when documents are not relevant
   - Loop until relevant documents are found or max attempts reached

   Key features:
   - Proper Pydantic patterns (no __init__)
   - Multiple engines with proper typing
   - Automatic tool routing via ToolRouteMixin
   - Generic type safety

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


   .. py:method:: from_documents(documents, llm_config, embedding_config = None, **kwargs)
      :classmethod:


      Create agentic RAG agent from documents using proper factory pattern.

      This follows Pydantic best practices by using a classmethod factory
      instead of complex __init__ logic.



   .. py:method:: model_post_init(__context)

      Set state schema after init.



   .. py:method:: setup_agent()

      Setup agentic RAG with multiple engines and tools.



.. py:class:: AgenticRAGState(/, **data)

   Bases: :py:obj:`pydantic.BaseModel`


   State schema for agentic RAG with retrieval metadata.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


.. py:class:: DocumentGrade(/, **data)

   Bases: :py:obj:`pydantic.BaseModel`


   Grade for document relevance.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


.. py:class:: QueryRewrite(/, **data)

   Bases: :py:obj:`pydantic.BaseModel`


   Rewritten query for better retrieval.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


.. py:function:: create_agentic_rag_agent(documents, llm_config, embedding_config = None, **kwargs)

   Create agentic RAG agent with sensible defaults.


.. py:function:: create_memory_aware_agentic_rag(documents, llm_config, memory_config = None, **kwargs)

   Create agentic RAG with long-term memory capabilities.