FastEmbed v2 Audit Report (Plan vs Current Codebase)

FastEmbed v2 Audit Report (Plan vs Current Codebase)

This audit compares fastembed-improvement-plan.md against the current implementation in:

• cascadeflow/routing/domain.py
• cascadeflow/quality/complexity.py
• tests/test_domain_detection.py

Scope exclusions (per request):

• OpenClaw integrations
• Skills integration

Executive Summary

• P0 items: ✅ Implemented.
• P1 items: ✅ Implemented.
• P2 item (SemanticAlignmentChecker): ⏭️ Not implemented in the audited files and not required for this session.
• No missing P0/P1 gaps found in the audited scope.

Plan Item Status Matrix

Detailed Findings

1) Enhanced Domain Exemplars (P0)

Implemented.

The plan called for richer exemplar coverage in weak domains. Current DOMAIN_EXEMPLARS contains expanded and targeted examples for:

• Domain.FINANCIAL (e.g., compound interest, ROI, tax implications, diversification, P/E ratio)
• Domain.CONVERSATION (greetings, chat prompts, social dialogue markers)
• Domain.FACTUAL (capital/country questions, historical fact checks, verification-style prompts)

Test coverage present:

• TestFastEmbedPlanEnhancements checks exemplar counts and representative prompts for these domains.

2) Smart Hybrid Mode Default (P0)

Implemented.

SemanticDomainDetector has use_hybrid=True by default and blends semantic + rule-based scoring in detect_with_scores.

Notes:

• Hybrid weighting is dynamic (70/30 when semantic confidence is high, else 50/50).
• This differs from the exact pseudo-logic in the plan but achieves the same goal (hybrid-first behavior with confidence-aware blending).

3) Domain-Specific Thresholds (P1)

Implemented.

DOMAIN_THRESHOLDS includes domain-specific cutoffs:

• Lower: CONVERSATION=0.50, FINANCIAL=0.55, FACTUAL=0.50
• Higher safety bars: MEDICAL=0.70, LEGAL=0.70
• Fallback: GENERAL=0.40

SemanticDomainDetector.detect_with_scores applies per-domain thresholding before fallback.

Test coverage present:

• Threshold-focused assertions exist and verify lower/higher threshold expectations.

4) Semantic Complexity Detection (P1)

Implemented.

complexity.py contains:

• COMPLEXITY_EXEMPLARS for each complexity level
• SemanticComplexityDetector using embeddings + cosine similarity
• Optional hybrid blending with rule-based detector

Notes:

• The plan named this concept SemanticComplexityBooster; the implementation name is SemanticComplexityDetector.
• Functional intent is satisfied.

5) Semantic Alignment Checker (P2)

Not in this audited scope.

No code changes required here for requested deliverables.

What’s Not Needed (for this session)

• No additional P0/P1 implementation work is required in the audited files because those items are already present.
• No OpenClaw or Skills integration work performed (explicitly skipped).
• No UI/frontend screenshot required (backend/test/docs-only changes).

Recommended Follow-ups (Optional)

1. Add dedicated tests for SemanticDomainDetector runtime behavior (with mocked embedder) to validate:
   • hybrid default behavior,
   • per-domain threshold fallback logic,
   • disagreement resolution between rule vs semantic scores.
2. Add/verify complexity semantic tests if not present in tests/ for SemanticComplexityDetector.
3. If alignment enhancement is still desired, audit cascadeflow/quality/alignment_scorer.py separately for FastEmbed parity.