80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
import pytest
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from pydantic_ai.models.test import TestModel
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from haiku.rag.client import HaikuRAG
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from haiku.rag.graph.agui.stream import stream_graph
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from haiku.rag.graph.research.dependencies import ResearchContext
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from haiku.rag.graph.research.graph import build_research_graph
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from haiku.rag.graph.research.state import ResearchDeps, ResearchState
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def test_build_graph_and_state():
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graph = build_research_graph()
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assert graph is not None
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state = ResearchState(
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context=ResearchContext(
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original_question="What are the key features of haiku.rag?"
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),
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max_iterations=1,
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confidence_threshold=0.8,
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)
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assert state.iterations == 0
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assert state.context.sub_questions == []
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@pytest.mark.asyncio
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async def test_graph_end_to_end_with_test_model(monkeypatch, temp_db_path):
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"""Test research graph with mocked LLM using AG-UI events."""
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# Mock get_model to return TestModel which generates valid schema-compliant data
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def test_model_factory(_provider, _model, _config=None):
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return TestModel()
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# Patch all locations where get_model is imported
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monkeypatch.setattr("haiku.rag.utils.get_model", test_model_factory)
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monkeypatch.setattr("haiku.rag.graph.common.get_model", test_model_factory)
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monkeypatch.setattr("haiku.rag.graph.common.nodes.get_model", test_model_factory)
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monkeypatch.setattr("haiku.rag.graph.research.graph.get_model", test_model_factory)
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graph = build_research_graph()
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state = ResearchState(
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context=ResearchContext(original_question="What is haiku.rag?"),
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max_iterations=1,
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confidence_threshold=0.5,
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max_concurrency=2,
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)
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# Use real client but with TestModel for LLM calls
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client = HaikuRAG(temp_db_path, create=True)
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deps = ResearchDeps(client=client)
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events = []
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result = None
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async for event in stream_graph(graph, state, deps):
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events.append(event)
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if event["type"] == "RUN_FINISHED":
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result = event["result"]
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elif event["type"] == "RUN_ERROR":
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pytest.fail(f"Graph execution failed: {event['message']}")
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# TestModel will generate valid structured output for each node
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assert result is not None, (
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f"No result. Events collected: {[e['type'] for e in events]}"
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)
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# Result is serialized as dict in AG-UI events
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assert isinstance(result, dict)
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assert "title" in result
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assert isinstance(result["title"], str)
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assert "executive_summary" in result
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assert "main_findings" in result
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# Verify AG-UI events were emitted
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event_types = [e["type"] for e in events]
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assert "RUN_STARTED" in event_types
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assert "RUN_FINISHED" in event_types
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assert "STATE_SNAPSHOT" in event_types
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assert "STEP_STARTED" in event_types
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client.close()
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