| --- |
| license: mit |
| task_categories: |
| - feature-extraction |
| - text-retrieval |
| tags: |
| - embeddings |
| - retrieval |
| - rag |
| - benchmarks |
| - embedding-models |
| size_categories: |
| - n<1K |
| pretty_name: "Embedding Eval Results — Round 1, Stage 1, Chunked A/B" |
| --- |
| |
| # Embedding Eval Results |
|
|
| The committed results from a real embedding-model benchmark that embarrassed a leaderboard's recommendation. |
|
|
| ## What's in here |
|
|
| Four CSV files representing four evaluation runs on a personal Obsidian vault: |
|
|
| | File | Notes | Queries | Models | Purpose | |
| |---|---|---|---|---| |
| | `20260618-233912.csv` | 54 | 29 | 7 | Round 1 — toy slice. **Saturated benchmark.** | |
| | `20260619-025330.csv` | 995 | 150 | 7 | Stage 1 — real pile. The ranking inverted. | |
| | `20260620-142445-wholenote.csv` | 996 | 450 | 7 | Stage 1 enlarged — held the new order. | |
| | `20260620-172625-chunked.csv` | 18,279 chunks | 450 | 1 | Chunked A/B — chunking hurt every metric. | |
|
|
| ## What the data shows |
|
|
| Round 1 on 54 notes: all 7 models looked great. recall@5 above 90% for every candidate. The "winner" by leaderboard-style ranking was `nomic-embed-text`. |
|
|
| Stage 1 on 995 notes: the ranking inverted. The mid-pack model `embeddinggemma` took first on every metric. The round-one "winner" sank to mid-pack. The default the system had been quietly using, `all-minilm`, dropped to last. |
|
|
| Same code. Same models. Same scoring. The only thing that changed was the size of the pile. |
|
|
| ## Columns |
|
|
| Each CSV has the same columns: |
|
|
| ``` |
| name,provider,model,dim,recall@1,recall@5,recall@10,mrr@10,q_latency_ms,approx_cost_usd |
| ``` |
|
|
| - `name`: human-readable label (matches the model except for the legacy `all-minilm` row, which is annotated) |
| - `provider`: `ollama` (local) or `modal` (the project's own GPU service) — see sources below |
| - `model`: the actual model name passed to the embedder |
| - `dim`: embedding dimension |
| - `recall@1, recall@5, recall@10`: fraction of queries where the source note landed in the top-k |
| - `mrr@10`: mean reciprocal rank |
| - `q_latency_ms`: query-side embedding latency (note embedding happens separately) |
| - `approx_cost_usd`: zero for local models; the cheapest paid model cost roughly a few cents per run |
|
|
| ## Source |
|
|
| The full eval harness is in the [pi-vault-mind](https://github.com/kylebrodeur/pi-vault-mind) repo at `eval/run_eval.py`. The companion blog post is ["Pick the Model From Your Own Data"](https://kylebrodeur.substack.com) (KE-1). |
|
|
| The simplified standalone version of the harness, written for the blog post, is at [kylebrodeur/embed-eval-on-your-vault](https://github.com/kylebrodeur/embed-eval-on-your-vault). It is a single-file Python script with no dependencies beyond `python3` that runs against any vault of `.md` or `.txt` notes. |
|
|
| ## Reproducibility |
|
|
| Each CSV is timestamped (UTC). The harness commits the result on every run. Re-running with the same model/provider/dataset should produce the same numbers within rounding. Embedding models do drift across versions, so if you re-run after a model bump, expect ±1-2% on the metrics. |
|
|
| ## Citation |
|
|
| If you use these numbers in another piece, cite the model row and the run timestamp. The full provenance is in the [pi-vault-mind](https://github.com/kylebrodeur/pi-vault-mind) repo commit log (the 2026-06-18 to 2026-06-20 range). |
|
|
| ## License |
|
|
| MIT. Numbers are facts; the framing is the author's. |
|
|
