haive.agents.rag.agentic.agent

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

AgenticRAGAgent	Agentic RAG agent combining ReAct reasoning with intelligent retrieval.
AgenticRAGState	State schema for agentic RAG with retrieval metadata.
DocumentGrade	Grade for document relevance.
QueryRewrite	Rewritten query for better retrieval.

Functions

create_agentic_rag_agent(documents, llm_config[, ...])	Create agentic RAG agent with sensible defaults.
create_memory_aware_agentic_rag(documents, llm_config)	Create agentic RAG with long-term memory capabilities.

Module Contents

class haive.agents.rag.agentic.agent.AgenticRAGAgent(/, **data)

Bases: haive.agents.react.agent.ReactAgent, 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.

Parameters:

data (Any)

classmethod from_documents(documents, llm_config, embedding_config=None, **kwargs)

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.

Parameters:

• documents (list[langchain_core.documents.Document])

• llm_config (haive.core.models.llm.base.LLMConfig)

• embedding_config (Any | None)

Return type:

AgenticRAGAgent

model_post_init(__context)

Set state schema after init.

Parameters:

__context (Any)

Return type:

None

setup_agent()

Setup agentic RAG with multiple engines and tools.

Return type:

None

class haive.agents.rag.agentic.agent.AgenticRAGState(/, **data)

Bases: 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.

Parameters:

data (Any)

class haive.agents.rag.agentic.agent.DocumentGrade(/, **data)

Bases: 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.

Parameters:

data (Any)

class haive.agents.rag.agentic.agent.QueryRewrite(/, **data)

Bases: 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.

Parameters:

data (Any)

haive.agents.rag.agentic.agent.create_agentic_rag_agent(documents, llm_config, embedding_config=None, **kwargs)

Create agentic RAG agent with sensible defaults.

Parameters:

• documents (list[langchain_core.documents.Document])

• llm_config (haive.core.models.llm.base.LLMConfig)

• embedding_config (Any | None)

Return type:

AgenticRAGAgent

haive.agents.rag.agentic.agent.create_memory_aware_agentic_rag(documents, llm_config, memory_config=None, **kwargs)

Create agentic RAG with long-term memory capabilities.

Parameters:

• documents (list[langchain_core.documents.Document])

• llm_config (haive.core.models.llm.base.LLMConfig)

• memory_config (Any | None)

Return type:

AgenticRAGAgent