RAG Skill

The RAG skill is the primary way to use haiku.rag tools. It bundles search, document browsing, and citation management 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()`	List all documents in the knowledge base
`get_document(query)`	Retrieve a document by ID, title, or URI
`cite(chunk_ids)`	Register chunk IDs as citations for the current answer

State

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

class RAGState(BaseModel):
    citation_index: dict[str, Citation] = {}
    citations: list[list[str]] = []
    document_filter: str | None = None
    searches: dict[str, list[SearchResult]] = {}

citation_index — All citations indexed by chunk ID (deduplicated across turns).
citations — Per-turn lists of chunk IDs registered via the cite tool.
document_filter — SQL WHERE clause applied to search and list_documents calls. Set this to scope queries to specific documents.
searches — Search results keyed by query string.