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https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
"""Integration tests for the full Phase 8 pipeline."""
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from __future__ import annotations
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import pytest
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from auto_client_acquisition.pipeline import AcquisitionPipeline
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@pytest.mark.asyncio
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async def test_pipeline_runs_without_llm(sample_lead_payload, mock_router):
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"""Pipeline should complete even if LLM calls fail (graceful degradation)."""
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mock_router.run.return_value.content = (
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'{"pain_points":[], "urgency_score":0.5, "likely_offer":"Demo", '
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'"recommended_next_step":"Call", "key_phrases":[]}'
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)
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pipeline = AcquisitionPipeline()
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result = await pipeline.run(
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payload=sample_lead_payload,
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auto_book=False,
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auto_proposal=False,
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)
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assert result.lead.id
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assert result.lead.company_name == sample_lead_payload["company"]
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assert result.fit_score is not None
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assert result.fit_score.overall_score > 0.5 # Good fit for our sample
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@pytest.mark.asyncio
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async def test_pipeline_handles_missing_optional_fields(mock_router):
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mock_router.run.return_value.content = '{"ok": true}'
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pipeline = AcquisitionPipeline()
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result = await pipeline.run(
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payload={"company": "Mini Co", "name": "X"},
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auto_book=False,
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)
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assert result.lead.id
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# Should not crash even with sparse data
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assert result.fit_score is not None
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