42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
# RAG Skill
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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.
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## `create_skill(db_path?, config?)`
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```python
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from haiku.rag.skills.rag import create_skill
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skill = create_skill(db_path=db_path, config=config)
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```
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `db_path` | `None` | Path to LanceDB database. Falls back to `HAIKU_RAG_DB` env var, then config default. |
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| `config` | `None` | `AppConfig` instance. If None, uses `get_config()`. |
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## Tools
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| Tool | Purpose |
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|------|---------|
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| `search(query, limit?)` | Hybrid search (vector + full-text) with context expansion |
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| `list_documents()` | List all documents in the knowledge base |
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| `get_document(query)` | Retrieve a document by ID, title, or URI |
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| `cite(chunk_ids)` | Register chunk IDs as citations for the current answer |
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## State
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The skill manages a `RAGState` under the `"rag"` namespace:
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```python
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class RAGState(BaseModel):
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citation_index: dict[str, Citation] = {}
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citations: list[list[str]] = []
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document_filter: str | None = None
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searches: dict[str, list[SearchResult]] = {}
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```
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- **citation_index** — All citations indexed by chunk ID (deduplicated across turns).
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- **citations** — Per-turn lists of chunk IDs registered via the `cite` tool.
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- **document_filter** — SQL WHERE clause applied to `search` and `list_documents` calls. Set this to scope queries to specific documents.
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- **searches** — Search results keyed by query string.
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