# 2405 Fastembed Audit Report Dc0348b9

> FastEmbed v2 Audit Report (Plan vs Current Codebase)

- Skill: `tools-only/2405-fastembed-audit-report-dc0348b9` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/2405-fastembed-audit-report-dc0348b9`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/2405-fastembed-audit-report-dc0348b9/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/2405-fastembed-audit-report-dc0348b9

---

# 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

| Priority | Plan item | Status | Evidence | Action needed |
|---|---|---|---|---|
| P0 | Add domain exemplars (finance, conversation, factual) | ✅ Implemented | `DOMAIN_EXEMPLARS` includes expanded exemplar sets for `FINANCIAL`, `CONVERSATION`, and `FACTUAL`. | None |
| P0 | Enable hybrid mode by default | ✅ Implemented | `SemanticDomainDetector.__init__(..., use_hybrid: bool = True)` defaults hybrid on. | None |
| P1 | Domain-specific confidence thresholds | ✅ Implemented | `DOMAIN_THRESHOLDS` exists with lowered thresholds for conversation/financial/factual and stricter ones for medical/legal. | None |
| P1 | Add FastEmbed semantic layer to complexity detection | ✅ Implemented | `COMPLEXITY_EXEMPLARS` and `SemanticComplexityDetector` are present in `complexity.py`. | None |
| P2 | SemanticAlignmentChecker for query-response alignment | ⏭️ Not in audited files | No alignment checker implementation in reviewed files; likely handled elsewhere (`alignment_scorer.py` area mentioned in AGENTS context). | Not required for this task |

---

## 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.