47 lines
1.9 KiB
Markdown
47 lines
1.9 KiB
Markdown
# RAG Skill
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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.
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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(limit?, offset?, filter?)` | Paginated document listing |
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| `get_document(query)` | Retrieve a document by ID, title, or URI |
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| `ask(question)` | Q&A with citations via the QA agent |
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| `research(question)` | Deep multi-agent research producing comprehensive reports |
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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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citations: list[Citation] = []
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qa_history: list[QAHistoryEntry] = []
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document_filter: str | None = None
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searches: dict[str, list[SearchResult]] = {}
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documents: list[DocumentInfo] = []
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reports: list[ResearchEntry] = []
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```
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- **citations** — Accumulated citations from `ask` calls, with sequential indexing across calls.
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- **qa_history** — Questions and answers from `ask` calls. Prior Q&A is used as context for follow-up questions when embeddings are similar.
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- **document_filter** — SQL WHERE clause applied to `search`, `list_documents`, `ask`, and `research` calls. Set this to scope queries to specific documents.
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- **searches** — Search results keyed by query string.
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- **documents** — Documents seen via `list_documents` or `get_document` (deduplicated by ID).
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- **reports** — Research reports from `research` calls.
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