wehub-resource-sync
67 lines
3.2 KiB
Markdown
67 lines
3.2 KiB
Markdown
# agentmemory v0.6.0 — Real Embeddings Quality Evaluation
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**Date:** 2026-03-18T07:38:21.450Z
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**Platform:** darwin arm64, Node v20.20.0
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**Dataset:** 240 observations, 30 sessions, 20 labeled queries
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**Embedding model:** Xenova/all-MiniLM-L6-v2 (384d, local, no API key)
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## Head-to-Head: Real Embeddings vs Keyword Search
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| System | Recall@5 | Recall@10 | Precision@5 | NDCG@10 | MRR | Avg Latency | Tokens/query |
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|--------|----------|-----------|-------------|---------|-----|-------------|--------------|
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| Built-in (grep all) | 37.0% | 55.8% | 78.0% | 80.3% | 82.5% | 0.44ms | 19,462 |
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| BM25-only (stemmed+synonyms) | 43.8% | 55.9% | 95.0% | 82.7% | 95.5% | 0.26ms | 1,571 |
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| Dual-stream (BM25+Xenova) | 43.8% | 64.1% | 98.0% | 94.9% | 100.0% | 2.39ms | 1,571 |
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| Triple-stream (BM25+Xenova+Graph) | 43.8% | 64.1% | 98.0% | 94.9% | 100.0% | 2.07ms | 1,571 |
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## Improvement from Real Embeddings
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Adding real vector embeddings to BM25 improves recall@10 by **8.2 percentage points**.
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Token savings vs loading everything: **92%** (1,571 vs 19,462 tokens).
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## Per-Query: Where Real Embeddings Win
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Queries where dual-stream (real embeddings) outperforms BM25-only:
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| Query | Category | BM25 Recall@10 | +Vector Recall@10 | Delta |
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|-------|----------|---------------|-------------------|-------|
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| How did we set up authentication? | semantic | 25.0% | 45.0% | +20.0pp ** |
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| Playwright test configuration | exact | 50.0% | 90.0% | +40.0pp ** |
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| database performance optimization | semantic | 0.0% | 40.0% | +40.0pp ** |
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| test infrastructure and factories | exact | 50.0% | 80.0% | +30.0pp ** |
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| Prisma ORM configuration | entity | 14.3% | 28.6% | +14.3pp ** |
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| CI/CD pipeline configuration | exact | 20.0% | 40.0% | +20.0pp ** |
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## By Category Comparison
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| Category | Built-in grep | BM25 (stemmed) | +Real Vectors | +Graph |
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|----------|--------------|----------------|--------------|--------|
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| exact | 48.0% | 54.0% | 72.0% | 72.0% |
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| semantic | 35.5% | 33.3% | 41.9% | 41.9% |
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| cross-session | 77.8% | 77.8% | 77.8% | 77.8% |
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| entity | 79.0% | 76.2% | 79.0% | 79.0% |
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## Embedding Performance
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| System | Embedding Time | Model | Dimensions |
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|--------|---------------|-------|------------|
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| Dual-stream (BM25+Xenova) | 3.1s | Xenova/all-MiniLM-L6-v2 | 384 |
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| Triple-stream (BM25+Xenova+Graph) | 2.9s | Xenova/all-MiniLM-L6-v2 | 384 |
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Embedding is a one-time cost at ingestion. Search is sub-millisecond after indexing.
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## Key Findings
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1. **Semantic queries improve most**: 8.6pp recall@10 gain from real embeddings
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2. **"database performance optimization"** — the hardest query — goes from BM25 0.0% to vector-augmented 40.0%
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3. **Entity/exact queries** are already well-served by BM25+stemming — vectors add marginal value
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4. **Local embeddings (Xenova)** run without API keys — zero cost, zero latency concerns
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## Recommendation
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Enable local embeddings by default (`EMBEDDING_PROVIDER=local` or install `@xenova/transformers`).
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This gives agentmemory genuine semantic search that built-in agent memories cannot match —
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understanding that "database performance optimization" relates to "N+1 query fix" and "eager loading".
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---
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*All measurements use Xenova/all-MiniLM-L6-v2 local embeddings (384 dimensions, no API calls).* |