Commit Graph

5 Commits

Author SHA1 Message Date
56b8d9401a feat(dashboard): add decision board for strategy selection
Why:
- Compare is the wrong surface for two-stage family selection over the 10-doc set.

Changes:
- Add the Decision Tab; raise GET /experiments default/max so the board can load the grid client-side.

Impact:
- Operators pick fixed_size ±N vs semantic@Boundary from existing Experiments.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-17 14:22:21 +03:30
f562b91f83 feat(benchmarking): add model-scoped query and neighbor expansion
Why:
- Queries and Experiments must hit the Corpus Embedding Model's collections and optionally widen fixed_size context.

Changes:
- Resolve corpus model per request; apply Neighbor Expansion with Expansion Tree; persist and report expansion provenance.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-10 14:13:07 +03:30
a277672444 feat(benchmarking): add document filename and delete experiment endpoint
Why:
- Experiment list and detail responses showed raw IDs instead of filenames
- No way to delete experiments from the UI

Changes:
- Add document_filename field to ExperimentDetailResponse
- Enrich list endpoint with filenames and calculated best_strategy from aggregate_metrics
- Add DELETE /experiments/{id} endpoint

Impact:
- API responses now include document_filename for all experiment endpoints
- Frontend can display filenames instead of IDs
2026-07-29 17:54:07 +03:30
0bb3086289 feat(benchmarking): add benchmark models and routes with view parameter
Why:
- Need request/response models for benchmark endpoints
- Need routes for creating and retrieving benchmarks
- Need view parameter for managerial vs technical report views

Changes:
- models.py: Added BenchmarkRequest, BenchmarkResponse, StrategyMetrics, ExperimentDetailResponse
- routes.py: Added POST /benchmarks, GET /benchmarks/{id}, GET /experiments, view parameter for reports
2026-07-27 14:14:59 +03:30
496a58c62a feat(benchmarking): add query pipeline with logging and tracing
Why:
- Need query pipeline to ask questions against chunked documents
- Need comprehensive logging for debugging

Changes:
- Query service: embed question → vector search → LLM answer
- Query routes: POST /queries, GET /queries/{id}
- Query models: QueryRequest, QueryResponse, RetrievedChunk
- Added QueryError exception class
- Added STEP 1-6 logging for full traceability
2026-07-26 12:06:04 +03:30