mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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bf91167350
1. GTM API Routes: 12 endpoints at /api/v1/gtm/* - company-intelligence, score-target, outreach-pack - compliance-check, classify-reply, next-action - daily-command-pack, targets, approvals - approve-action, log-outcome All registered in router.py 2. Governance Module: 4 files - approval_queue.py: add/approve/reject/get_pending - action_policy.py: policy per action type - audit_log.py: log every proposed action - risk_flags.py: HIGH/LOW risk classification 3. Proof Module: 3 files - evidence.py: VERIFIED/INFERRED/UNVERIFIED/LOW_CONFIDENCE - claim_validator.py: blocks fake claims - source_quality.py: rates source reliability 4. Customer Delivery: 2 files - customer_workspace.py: Pydantic model with onboarding checklist - customer_delivery_pipeline.py: create workspace + weekly report 5. All verified: 9/9 new imports pass, 30/30 evals, dry-run works https://claude.ai/code/session_01W1rJthWDkasijTdXCfxVHs
21 lines
703 B
Python
21 lines
703 B
Python
"""Source quality — rates the reliability of evidence sources."""
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SOURCE_RATINGS = {
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"company_website": 0.8,
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"uploaded_file": 0.9,
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"google_search": 0.6,
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"tavily": 0.7,
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"manual_input": 1.0,
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"llm_inference": 0.4,
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"mock": 0.3,
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"unknown": 0.1,
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}
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def rate_source(source_type: str) -> float:
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return SOURCE_RATINGS.get(source_type, 0.2)
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def rate_sources(sources: list[str]) -> dict:
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if not sources:
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return {"average_quality": 0.0, "best_source": None, "count": 0}
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ratings = [rate_source(s) for s in sources]
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return {"average_quality": round(sum(ratings) / len(ratings), 2), "best_source": sources[ratings.index(max(ratings))], "count": len(sources)}
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