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33faedf427
* Add Unstructured engagement report as uv workspace member Three-tier Plotly Dash app (Executive Brief, Engineering Team, Full Detail) with data in JSON, theme constants in theme.py, and Dash production improvements (Google Fonts, clientside callbacks, meta tags). Also: add .playwright-mcp/ to .gitignore, add reports/* ruff overrides, remove tracked .codeflash/observability/read-tracker. * Rewrite statusline to derive context from git state Detects active area from changed files (reports, packages, plugin, .codeflash, case-studies, evals), falls back to branch name convention (perf/*, feat/*, fix/*), shows dirty indicator. Uses whoami for cross-platform user detection. * Add pre-push lint rule to commit guidelines * Exclude .codeflash/ from ruff linting Benchmark and profiling scripts in .codeflash/ are scratch work, not package source. Excluding them prevents CI failures from ad-hoc scripts. * Run ruff format across packages, scripts, evals, and plugin refs * Fix github-app async test failures in CI Add asyncio_mode = "auto" to root pytest config so async tests are detected when running from the repo root via uv run pytest packages/.
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
from pipeline.formatter import format_results
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def test_format_basic():
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schema = {"version": "1.0", "env": "prod"}
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records = [
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{"id": 1, "category": "web", "value": 42},
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{"id": 2, "category": "api", "value": 10},
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]
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result = format_results(records, schema)
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assert len(result) == 2
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def test_format_large_batch():
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"""Large formatting job — too slow for 50k records."""
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schema = {
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"version": "2.0",
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"env": "production",
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"metadata": {
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"pipeline": "v3",
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"config": {
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"retry": {"count": 3, "backoff": [1, 2, 4, 8, 16]},
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"timeouts": {"connect": 5, "read": 30, "write": 10},
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"features": ["dedup", "enrich", "validate", "compress"],
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},
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},
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"tags": ["processed", "validated", "v2"],
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"audit": {
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"created_by": "pipeline-worker",
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"approved": True,
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"reviewers": ["system", "auto-qa"],
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},
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}
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records = []
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for i in range(50_000):
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records.append(
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{
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"id": i,
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"category": f"cat-{i % 100}",
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"value": float(i % 1000),
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"timestamp": f"2024-01-{(i % 28) + 1:02d}T{i % 24:02d}:00:00Z",
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"source": f"source-{i % 50}",
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}
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)
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result = format_results(records, schema)
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assert len(result) == 50_000
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