bench

name: bench description: Run embedding benchmarks — R@5 code retrieval, timing, model comparison disable-model-invocation: false

Embedding Benchmarks

Run retrieval quality and performance benchmarks.

When to Use

• Evaluating embedding model changes
• Performance regression testing
• Comparing model candidates
• User says "benchmark", "bench", "test embeddings"

Steps

Step 1: Check Prerequisites

# Verify benchmark data exists
ls benchmarks/data/ 2>/dev/null || echo "No benchmark data"

# Verify current model
python -c "from mempalace.embeddings import get_embedder; e=get_embedder(); print(f'Model: {e.model_name}')"

Step 2: Code Retrieval Benchmark

Run the standard code retrieval benchmark:

python benchmarks/code_retrieval_bench.py --output benchmarks/results_$(date +%Y%m%d).json

Metrics collected:

• R@5: Recall at 5 (target: >= 0.95)
• R@10: Recall at 10 (target: 1.0)
• Embed time: Seconds to embed all chunks
• Query time: Milliseconds per query
• Index size: MB on disk

Step 3: Category Breakdown

If benchmark supports categories:

Step 4: Compare Models (optional)

If comparing multiple models:

# Test each candidate
for model in "all-MiniLM-L6-v2" "all-mpnet-base-v2"; do
  MEMPALACE_EMBED_MODEL=$model python benchmarks/code_retrieval_bench.py --output benchmarks/results_${model}_$(date +%Y%m%d).json
done

Step 5: Text Retrieval Gate (if changing models)

Per project policy, any embedding model change must also pass text retrieval benchmarks:

python benchmarks/text_retrieval_bench.py --dataset longmemeval

Target: Match or beat current model on LongMemEval R@5.

Output Format

## Benchmark Results

Model: all-MiniLM-L6-v2
Dataset: mempalace code retrieval (20 queries, N chunks)

| Metric | Value | Target | Status |
|--------|-------|--------|--------|
| R@5 | 0.950 | >= 0.95 | PASS |
| R@10 | 1.000 | 1.0 | PASS |
| Embed time | 15.2s | < 60s | PASS |
| Query time | 15.9ms | < 100ms | PASS |
| Index size | 17.0 MB | < 50 MB | PASS |

Category R@5:
- architecture: 0.800
- class_lookup: 1.000
- cross_file: 1.000
- function_lookup: 1.000

**Verdict: PASS** — Model meets all targets.

Model Comparison Table

When comparing models, produce:

| Model | R@5 | R@10 | Embed(s) | Query(ms) | Index(MB) |
|-------|-----|------|----------|-----------|-----------|
| all-MiniLM-L6-v2 | 0.950 | 1.000 | 15.2 | 15.9 | 17.0 |
| all-mpnet-base-v2 | 0.900 | 1.000 | 47.5 | 30.5 | 17.7 |

**Recommendation:** minilm remains default (better R@5, 3x faster).