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Celes Renata 88ad1e8d99 feat: comprehensive docs, unit tests, docker-compose app services

- Add scheduler and ingestion unit tests (test_scheduler_unit.py, test_ingestion_unit.py)
- Add all 13 app services + dashboard to docker-compose.yml
- Add full documentation suite: API reference, Helm reference, Docker deployment guide,
  3 architecture diagrams (K8s, Docker Compose, data pipeline), AI agent guide,
  backup/restore guide, observability/metrics reference, per-service docs
- Add intelligence pipeline deep-dive docs with Mermaid diagrams
- Update README with documentation index and links
- Add specs for comprehensive-quality-docs, intelligence-pipeline-deep-dive,
  sanitized-pipeline-docs

2026-04-22 02:56:41 +00:00

2.1 KiB

Raw Blame History

Trend Accumulation and Escalation

flowchart TD
    subgraph Windows["Five Time Windows\nservices/aggregation/worker.py"]
        W1["intraday (12h)"]
        W2["1d (1 day)"]
        W3["7d (7 days)"]
        W4["30d (30 days)"]
        W5["90d (90 days)"]
    end

    W1 & W2 & W3 & W4 & W5 --> SIGNALS

    SIGNALS["Fetch signals per window\nEntity + Macro + Competitive\n→ WeightedSignal[]"]

    SIGNALS --> SENT["weighted_sentiment_average()\nCompute avg sentiment across signals"]

    SENT --> DIR

    subgraph DIR["derive_trend_direction()"]
        D1["avg_sentiment ≥ 0.15 → POSITIVE"]
        D2["avg_sentiment ≤ −0.15 → NEGATIVE"]
        D3["contradiction > 0.10\nAND |avg| < 0.30 → MIXED"]
        D4["otherwise → NEUTRAL"]
    end

    DIR --> CONF

    subgraph CONF["compute_trend_confidence()"]
        C1["Unique source count\ncaps at 15 → 0.8 contribution"]
        C2["Avg extraction credibility"]
        C3["Signal agreement ratio\ndampened by log₂(n+1)/log₂(8)\nsaturates ~7 unique sources"]
        C4["Contradiction penalty\n−0.4 × contradiction_score"]
        C5["confidence = 0.3×count + 0.3×credibility\n+ 0.4×agreement − penalty"]
    end

    CONF --> STRENGTH["trend_strength = |avg_sentiment|\nclamped to [0, 1]"]

    STRENGTH --> ESC

    subgraph ESC["Escalation Path\n(via eligibility thresholds)"]
        direction TB
        E1["NEUTRAL\nconfidence < 0.35\nOR strength < 0.10\nOR direction = neutral"]
        E2["OBSERVE\nstrength < 0.25\nAND confidence < 0.50"]
        E3["MONITOR\nstrength < 0.25\nAND confidence ≥ 0.50"]
        E4["ACT / DEFER\nstrength ≥ 0.25\nAND direction = positive/negative"]

        E1 -->|"More signals\nsame direction"| E2
        E2 -->|"Confidence grows\nmore unique sources"| E3
        E3 -->|"Strength exceeds 0.25\naccumulated evidence"| E4
    end

    ESC --> PERSIST

    subgraph PERSIST["Persistence"]
        P1["trend_windows\n(upserted each cycle)"]
        P2["trend_history\n(time-series snapshots)"]
        P3["trend_evidence\n(per-document rankings)"]
        P4["trend_projections\nservices/aggregation/projection.py"]
    end

2.1 KiB Raw Blame History Unescape Escape

Trend Accumulation and Escalation

2.1 KiB

Raw Blame History