RAG Skill

The RAG skill is the primary way to use haiku.rag tools. It bundles search, Q&A, document browsing, and research into a single skill with managed state.

`create_skill(db_path?, config?)`

from haiku.rag.skills.rag import create_skill

skill = create_skill(db_path=db_path, config=config)

Parameter	Default	Description
`db_path`	`None`	Path to LanceDB database. Falls back to `HAIKU_RAG_DB` env var, then config default.
`config`	`None`	`AppConfig` instance. If None, uses `get_config()`.

Tools

Tool	Purpose
`search(query, limit?)`	Hybrid search (vector + full-text) with context expansion
`list_documents(limit?, offset?, filter?)`	Paginated document listing
`get_document(query)`	Retrieve a document by ID, title, or URI
`ask(question)`	Q&A with citations via the QA agent
`research(question)`	Deep multi-agent research producing comprehensive reports

State

The skill manages a RAGState under the "rag" namespace:

class RAGState(BaseModel):
    citations: list[Citation] = []
    qa_history: list[QAHistoryEntry] = []
    document_filter: str | None = None
    searches: dict[str, list[SearchResult]] = {}
    documents: list[DocumentInfo] = []
    reports: list[ResearchEntry] = []

citations — Accumulated citations from ask calls, with sequential indexing across calls.
qa_history — Questions and answers from ask calls. Prior Q&A is used as context for follow-up questions when embeddings are similar.
document_filter — SQL WHERE clause applied to search, ask, and research calls. Set this to scope queries to specific documents.
searches — Search results keyed by query string.
documents — Documents seen via list_documents or get_document (deduplicated by ID).
reports — Research reports from research calls.