141 lines
4.7 KiB
Python
141 lines
4.7 KiB
Python
from unittest.mock import AsyncMock
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from haiku.rag.agents.rlm.models import RLMResult
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from haiku.rag.client import HaikuRAG
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from haiku.rag.skills.rlm import (
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STATE_NAMESPACE,
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STATE_TYPE,
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RLMState,
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instructions,
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skill_metadata,
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state_metadata,
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)
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from haiku.skills.models import SkillMetadata, StateMetadata
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from .conftest import _get_tool, _make_ctx
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class TestRLMModuleAPI:
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def test_state_type_is_rlm_state(self):
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assert STATE_TYPE is RLMState
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def test_state_namespace(self):
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assert STATE_NAMESPACE == "rlm"
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def test_state_metadata_returns_state_metadata(self):
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result = state_metadata()
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assert isinstance(result, StateMetadata)
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assert result.namespace == "rlm"
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assert result.type is RLMState
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assert result.schema == RLMState.model_json_schema()
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def test_skill_metadata_returns_skill_metadata(self):
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result = skill_metadata()
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assert isinstance(result, SkillMetadata)
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assert result.name == "rag-rlm"
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def test_instructions_returns_string(self):
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result = instructions()
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assert isinstance(result, str)
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assert len(result) > 0
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def test_constants_match_create_skill(self, temp_db_path):
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from haiku.rag.skills.rlm import create_skill
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skill = create_skill(db_path=temp_db_path)
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assert skill.state_type is STATE_TYPE
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assert skill.state_namespace == STATE_NAMESPACE
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assert skill.metadata == skill_metadata()
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assert skill.instructions == instructions()
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class TestRLMSkillCreation:
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def test_create_skill_returns_valid_skill(self, temp_db_path):
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from haiku.rag.skills.rlm import create_skill
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skill = create_skill(db_path=temp_db_path)
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assert skill.metadata.name == "rag-rlm"
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assert skill.metadata.description
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assert skill.instructions
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def test_create_skill_has_expected_tools(self, temp_db_path):
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from haiku.rag.skills.rlm import create_skill
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skill = create_skill(db_path=temp_db_path)
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tool_names = {getattr(t, "__name__") for t in skill.tools if callable(t)}
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assert tool_names == {"analyze"}
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def test_create_skill_has_state(self, temp_db_path):
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from haiku.rag.skills.rlm import RLMState, create_skill
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skill = create_skill(db_path=temp_db_path)
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assert skill._state_type is RLMState
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assert skill._state_namespace == "rlm"
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def test_create_skill_from_env(self, monkeypatch, temp_db_path):
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monkeypatch.setenv("HAIKU_RAG_DB", str(temp_db_path))
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from haiku.rag.skills.rlm import create_skill
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skill = create_skill()
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assert skill.metadata.name == "rag-rlm"
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class TestAnalyzeTool:
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async def test_analyze_returns_result(self, rag_db, monkeypatch):
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from haiku.rag.skills.rlm import create_skill
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monkeypatch.setattr(
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HaikuRAG,
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"rlm",
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AsyncMock(return_value=RLMResult(answer="42", program="print(42)")),
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)
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skill = create_skill(db_path=rag_db)
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analyze = _get_tool(skill, "analyze")
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ctx = _make_ctx()
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result = await analyze(ctx, question="How many documents?")
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assert isinstance(result, str)
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assert "42" in result
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assert "print(42)" in result
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async def test_analyze_updates_state(self, rag_db, monkeypatch):
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from haiku.rag.skills.rlm import RLMState, create_skill
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monkeypatch.setattr(
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HaikuRAG,
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"rlm",
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AsyncMock(return_value=RLMResult(answer="42", program="print(42)")),
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)
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skill = create_skill(db_path=rag_db)
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analyze = _get_tool(skill, "analyze")
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state = RLMState()
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ctx = _make_ctx(state)
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await analyze(ctx, question="How many documents?")
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assert len(state.analyses) == 1
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assert state.analyses[0].question == "How many documents?"
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assert state.analyses[0].answer == "42"
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assert state.analyses[0].program == "print(42)"
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async def test_analyze_with_document_and_filter(self, rag_db, monkeypatch):
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from haiku.rag.skills.rlm import create_skill
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captured_kwargs = {}
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async def mock_rlm(self, question, **kwargs):
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captured_kwargs.update(kwargs)
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return RLMResult(answer="Result", program="code()")
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monkeypatch.setattr(HaikuRAG, "rlm", mock_rlm)
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skill = create_skill(db_path=rag_db)
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analyze = _get_tool(skill, "analyze")
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ctx = _make_ctx()
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await analyze(
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ctx,
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question="Count pages",
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document="AI Overview",
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filter="title = 'AI Overview'",
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)
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assert captured_kwargs.get("documents") == ["AI Overview"]
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assert captured_kwargs.get("filter") == "title = 'AI Overview'"
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