ducoterra
542e568d25
The embed alias was reverted to the previous backing model and re-benchmarked over 3 runs. All gates pass and the numbers reproduce the original baseline exactly: 768 dims, cosine margin 0.283 (quality 56) versus the 0.217 measured on the swapped-in model, with throughput back to 109.7-123.3 vec/s. The margin returning to the identical 0.283 confirms the locked semantic-pair corpus is a reliable fingerprint for this endpoint, and a raw-vector probe shows the two models are genuinely different spaces despite both being unit-normalized 768-dim (max component diff 0.035 on the same input). Any content embedded while the swapped-in model was live is therefore in an incompatible space and needs re-embedding. LLM-Generated: true
Model Benchmark Results
CSV recordings from all model tests (chat, summary, embedding).
Files
model_benchmarks.csv— all benchmark results, one row per run per check
Schema
| Column | Description |
|---|---|
date |
Test date (YYYY-MM-DD) |
script |
chat |
model |
Model name (e.g. lite, turbo, embed) |
mode |
fixture / derived / quality / dimension / cosine / speed |
gate_status |
PASS / FAIL |
turns |
Number of questions/turns |
answered |
Turns that produced an answer |
caps |
Turns that hit the round cap |
tool_turns |
Turns emitting ≥1 tool call (chat only) |
emitted / executed |
Tool-call counts (chat only) |
contract |
Numerator for accuracy (chat: well-formed calls; summary: quality 0–100; embed: 1=pass) |
wall_s |
Total wall seconds |
contract_denom / executed_denom / tool_turns_denom |
Denominators for percentages |
extra1_col / extra1_val |
Script-specific metric 1 |
extra2_col / extra2_val |
Script-specific metric 2 |
Scripts
scripts/agent_realmodel_check.py— chat model tool-calling batteryscripts/test_summary_model.py— summary model quality benchmarkscripts/test_embed_model.py— embedding model dimension/cosine/speed benchmarkscripts/model_benchmark.py— shared CSV recorder (imported by all scripts)