agents.rag.multi_query.agent¶

Multi-Query RAG Agent.

Improves recall through query diversification. Generates multiple query variations and retrieves from all.

Classes¶

`MultiQueryRAGAgent`	Multi-Query RAG with query expansion for improved recall.
`MultiRetrievalAgent`	Agent that performs parallel retrieval with multiple queries.
`QueryVariations`	Structured output for query variations.

class agents.rag.multi_query.agent.MultiQueryRAGAgent¶

Bases: haive.agents.multi.enhanced_sequential_agent.SequentialAgent

Multi-Query RAG with query expansion for improved recall.

classmethod from_documents(documents, llm_config=None, embedding_model=None, **kwargs)¶

Create Multi-Query RAG from documents.

Parameters:

documents (list[langchain_core.documents.Document]) – Documents to index
llm_config (haive.core.models.llm.base.LLMConfig | None) – Optional LLM configuration
embedding_model (str | None) – Optional embedding model for vector store
**kwargs – Additional arguments

Returns:

MultiQueryRAGAgent instance

class agents.rag.multi_query.agent.MultiRetrievalAgent¶

Bases: haive.agents.base.agent.Agent

Agent that performs parallel retrieval with multiple queries.

build_graph()¶

Build graph that retrieves with multiple queries in parallel.

class agents.rag.multi_query.agent.QueryVariations(/, **data)¶

Bases: pydantic.BaseModel

Structured output for query variations.

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.