docs(roadmap): video content vectorization brainstorm and roadmap#644
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docs(roadmap): video content vectorization brainstorm and roadmap#644
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… tickets Scene-level video embeddings for cross-film recommendations, starting with English-only prototype. Uses Gemini 2.5 Flash to describe scenes from extracted frames + transcript, then embeds descriptions via existing text-embedding-3-small pipeline into a separate pgvector scene_embeddings table. Adds: - Requirements doc with phased rollout, storage schema, cost model, and technology research (Spotify RecSys 2025 validates this approach) - Parent feature feat-037 plus 9 sub-tickets (feat-038 through feat-046) covering data audit, scene boundaries, descriptions, embeddings table, backfill worker, visual fusion, recommendation API, pipeline integration, and demo experience frontend - Updates feat-009 blocks to include feat-037 dependency Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Phase 1: en/es/fr to verify locale dedup - Use video segments via Gemini, not still frames - Extract: felt needs/themes, bible verses, content, tone, demographics - Locale-aware queries, no cross-locale bleed - No human tags, pure vector similarity for Phase 1 - Cost model ~$600-$900 for video segment approach - Schema: themes[], bible_verses[], demographics[] Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
scene_embeddingstable withthemes[],bible_verses[],demographics[]columnsTest plan
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