72 lines
2.1 KiB
Markdown
72 lines
2.1 KiB
Markdown
# Skills
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haiku.rag exposes its RAG capabilities as [haiku.skills](https://github.com/ggozad/haiku.skills) skills. Skills are self-contained units that bundle tools, instructions, and state — they can be composed into any pydantic-ai agent via `SkillToolset`.
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## Available Skills
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| Skill | Description |
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|-------|-------------|
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| [`rag`](rag.md) | Search, retrieve, and answer questions from the knowledge base |
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| [`rag-rlm`](rlm.md) | Computational analysis via code execution |
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## Discovery
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Skills are registered as Python entrypoints under `haiku.skills`. They are discovered automatically by `haiku.skills`:
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```bash
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haiku-skills list --use-entrypoints
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# rag — Search, retrieve and analyze documents using RAG.
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# rag-rlm — Analyze documents using code execution in a sandboxed interpreter.
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```
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## Usage
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```python
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from haiku.rag.skills.rag import create_skill
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from haiku.skills.agent import SkillToolset
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from pydantic_ai import Agent
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skill = create_skill(db_path=db_path, config=config)
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toolset = SkillToolset(skills=[skill])
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agent = Agent(
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"openai:gpt-4o",
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instructions=toolset.system_prompt,
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toolsets=[toolset],
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)
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result = await agent.run("What documents do we have?")
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```
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## Database Path Resolution
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Both skills resolve the database path in the same order:
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1. `db_path` argument passed to `create_skill()`
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2. `HAIKU_RAG_DB` environment variable
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3. Config default (`config.storage.data_dir / "haiku.rag.lancedb"`)
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## State Management
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Each skill manages its own state under a dedicated namespace. State is automatically synced via the AG-UI protocol when using `AGUIAdapter`.
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```python
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rag_state = toolset.get_namespace("rag")
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rlm_state = toolset.get_namespace("rlm")
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```
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See the individual skill pages for state model details.
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## AG-UI Streaming
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For web applications, use pydantic-ai's `AGUIAdapter` to stream tool calls, text, and state deltas:
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```python
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from pydantic_ai.ag_ui import AGUIAdapter
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adapter = AGUIAdapter(agent=agent, run_input=run_input)
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event_stream = adapter.run_stream()
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sse_event_stream = adapter.encode_stream(event_stream)
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```
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See the [Web Application](../apps.md#web-application) for a complete implementation.
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