Toolsets

haiku.rag exposes its RAG capabilities as haiku.skills skills. For most integrations, see Skills.

For lower-level access, haiku.rag.tools provides individual FunctionToolset factories that the skills themselves compose.

Low-Level Toolsets

For advanced use cases, individual toolset factories are available in haiku.rag.tools. These are the same primitives the rag and rag-analysis skills compose, and can be reused to build custom agents.

RAGDeps Protocol

All toolsets use the RAGDeps protocol for dependency injection:

from haiku.rag.tools import RAGDeps

class MyDeps:
    def __init__(self, client: HaikuRAG):
        self.client = client

Search Toolset

create_search_toolset() provides hybrid search with context expansion.

from haiku.rag.tools import create_search_toolset

search = create_search_toolset(config)

Parameter	Default	Description
`config`	required	`AppConfig`
`expand_context`	`True`	Expand results with surrounding chunks
`base_filter`	`None`	SQL WHERE clause applied to all searches
`tool_name`	`"search"`	Name of the tool exposed to the agent
`on_results`	`None`	Callback `(list[SearchResult]) -> None` invoked with results

Document Toolset

create_document_toolset() provides document browsing and retrieval.

from haiku.rag.tools import create_document_toolset

docs = create_document_toolset(config)

Parameter	Default	Description
`config`	required	`AppConfig`
`base_filter`	`None`	SQL WHERE clause for list operations

Tools:

list_documents(page?) — Paginated document listing (50 per page).
get_document(query) — Retrieve a document by title or URI.
summarize_document(query) — Generate an LLM summary of a document's content.

Filter Helpers

haiku.rag.tools.filters provides utilities for building SQL filters:

build_multi_document_filter(document_names) — Combines multiple document name filters with OR logic. Matches against both uri and title, case-insensitive.