70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
# RLM Skill
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The RLM (Reflexion Language Model) skill provides computational analysis via code execution. It writes and runs Python code in an isolated Docker sandbox to answer questions that require computation, aggregation, or data traversal.
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!!! warning "Requires Docker"
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The `analyze` tool executes code in a Docker sandbox. Docker must be running on the host machine. This skill is not suitable for Docker-deployed applications — use the [`rag`](rag.md) skill alone in those environments.
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## `create_skill(db_path?, config?)`
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```python
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from haiku.rag.skills.rlm 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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| `analyze(question, document?, filter?)` | Answer analytical questions using code execution |
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**Parameters:**
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- `question` — The analytical question to answer.
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- `document` — Optional document ID or title to pre-load for analysis.
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- `filter` — Optional SQL WHERE clause to filter documents.
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## State
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The skill manages an `RLMState` under the `"rlm"` namespace:
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```python
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class RLMState(BaseModel):
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analyses: list[AnalysisEntry] = []
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class AnalysisEntry(BaseModel):
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question: str
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answer: str
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program: str | None = None
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```
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Each `analyze` call appends an `AnalysisEntry` with the question, answer, and executed program.
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## Usage with RAG Skill
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Combine both skills to give the agent full RAG + analysis capabilities:
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```python
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from haiku.rag.skills.rag import create_skill as create_rag_skill
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from haiku.rag.skills.rlm import create_skill as create_rlm_skill
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from haiku.skills.agent import SkillToolset
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from pydantic_ai import Agent
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rag = create_rag_skill(db_path=db_path)
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rlm = create_rlm_skill(db_path=db_path)
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toolset = SkillToolset(skills=[rag, rlm])
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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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```
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See the [RLM Agent](../agents/rlm.md) documentation for details on how the underlying agent works.
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