search/find ranking: generic entities outrank project-specific memories (golden-query baseline)

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
48/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
ai, search

Línea de trabajo

Start by tracing the POST /api/v1/search/find ranking path and checking whether configuration already exposes a relevance or category-normalization option; the issue does not name implementation files or tests. Use the reported queries, open-bot deployment and plugin page server, as baseline cases, and compare top results against the open-bot golden-query harness at scripts/ov-eval.ts and scripts/ov-eval.golden.json. Done means project-specific memories rank ahead of unrelated generic entities while sorted and stable result checks still pass.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Summary

Semantic search/find ranking: generic provider entities outrank project-specific memories for project queries. Reporting with a repeatable golden-query baseline from a production open-bot instance (per-user OpenViking sidecar).

Baseline (2026-11-10, POST /api/v1/search/find, limit 5)

Query: open-bot deployment

  1. viking://user/agent/memories/entities/api/openai.md — 0.895 (generic API-provider entity)
  2. .../entities/promotion/openbot_promotion.md — 0.883
  3. viking://user/agent/memories/entities/modele_ia/clef.md — 0.865 (unrelated model entity)
  4. .../entities/promotion/openbot_promo_video.md — 0.862

Query: plugin page server

  1. .../events/2026/11/04/Chargement des plugins activés.md — 0.829
  2. .../events/2026/10/16/Migration et tests du plugin api-connectors.md — 0.828

Hard properties look good: scores are sorted descending and results are stable across repeated identical queries. The gap is relevance ordering: high-centrality generic entities (OpenAI, an unrelated model) beat memories that literally concern the queried project.

Harness

A repeatable golden-query eval lives in open-bot as scripts/ov-eval.ts + scripts/ov-eval.golden.json (records top-5 URIs/scores, latency, sorted/stable checks per query). Happy to extend the golden set or re-run against any candidate fix and share before/after.

Ask

Guidance or ranking work so project/topic-specific memories outrank generic entity pages for specific queries (e.g. lexical/entity-name match boost, or per-category score normalization). If a configuration knob already exists for this, pointing at it closes this just as well.

Related downstream reports (open-bot): shpaw415/open-bot#20, #21, #32, #71, #83.

Lenguaje dominante
Python
Estrellas
41.8k
Forks
3.4k
Merge medio
19 h 29 min
PR fusionados (30 d)
500

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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