---
name: test-embed-model
description: Tests an embedding model (BOR_LLM_EMBED_MODEL in .env) across three dimensions — dimension consistency, cosine accuracy on semantic pairs, and embedding speed — then records results in benchmarks/model_benchmarks.csv. Use when the user asks to test or benchmark an embedding model, check embedding quality, or compare embedding models (e.g. "test the embed model", "check embedding dimension", "benchmark embed speed").
---

# Test an Embedding Model (dimension + cosine + speed)

Tests the configured `BOR_LLM_EMBED_MODEL` (default `embed`) against
three independent checks:

1. **Dimension check** — output vector length matches `BOR_EMBEDDING_DIM`
2. **Cosine accuracy** — semantically similar text pairs have higher
   cosine similarity than dissimilar pairs (5 pairs tested)
3. **Speed** — vectors produced per second (50 vectors)

## Rules (non-negotiable)

- **Do not touch** the semantic pair texts — they are the controlled
  benchmark corpus. Changing them is a methodology change: flag it.
- **Do not edit app code** (`app/`, `tests/`). This skill tests a model,
  not the app. If the model exposes an app defect, report it — don't fix it.
- `.env` is gitignored and is **not committed** — the model switch stays
  a live dev setting, and the summary must say which model `.env` is left
  on (default: the tested model).

## Procedure

All commands run from the repo root with `uv run`.

### 1. Preconditions

```bash
podman compose up -d db
grep -E "BOR_LLM_(BASE_URL|API_KEY|EMBED_MODEL|EMBEDDING_DIM)" .env
```

### 2. Switch the model (optional)

Edit only `BOR_LLM_EMBED_MODEL` in `.env` (leave chat and summary alone):

```
BOR_LLM_EMBED_MODEL=<model>
```

### 3. Run the benchmark

```bash
# Single run (all three checks)
uv run python -m scripts.test_embed_model

# Specify a model explicitly
uv run python -m scripts.test_embed_model --model embed-v2

# Multiple runs for variance
uv run python -m scripts.test_embed_model --runs 3
```

Wall time: ~0.5–2 s per run (fast — the embedding endpoint is lightweight).

### 4. Read the verdicts

Each check prints `✓` (PASS) or `✗` (FAIL):

- **dimension**: `actual_dim == expected_dim` (from `BOR_EMBEDDING_DIM`, default 768)
- **cosine**: similar-pair margin ≥ 0.05 (similar > dissimilar by at least 0.05 cosine)
- **speed**: no NaN/Inf vectors in output; rate reported as vec/s

Gate: PASS if all three checks pass.

### 5. Record + commit

Results are automatically appended to `benchmarks/model_benchmarks.csv`
(one row per check per run). Commit:

```bash
git add benchmarks/model_benchmarks.csv
git commit --no-gpg-sign \
  -m "docs(agent): record the <model> embedding benchmark" \
  -m "<one-paragraph body: dimension match, cosine margin, speed, any failures>"
```

### 6. Summary

Table of dimension / cosine margin / speed per run, the one-line
conclusion, and a note that `.env` is now on `<model>`.

## Troubleshooting

- **dimension mismatch** — the model's output vectors are a different
  size than expected. This is a configuration error: check the model's
  docs for its embedding dimension and update `BOR_EMBEDDING_DIM`.
- **cosine margin < 0.05** — the embeddings don't separate similar from
  dissimilar texts well. The model may be a poor embedding model or
  the semantic pairs are too generic. Report the margin.
- **NaN/Inf vectors** — the model's embedding endpoint is broken or
  the input text is malformed. Check the endpoint directly.
- **very slow** — >100 vec/s is typical for a local endpoint; remote
  endpoints may be slower due to network latency. Report the rate.