docs(roadmap): video content vectorization brainstorm and roadmap#638
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docs(roadmap): video content vectorization brainstorm and roadmap#638
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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) <[email protected]>
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🚅 Deployed to the forge-pr-638 environment in forge 4 services not affected by this PR
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Summary
Adds the complete planning artifacts for scene-level video content vectorization — enabling cross-film recommendations based on what's shown, not just what's said or tagged.
Architecture: Gemini 2.5 Flash describes scenes from Mux thumbnail frames + transcript, descriptions embedded via existing text-embedding-3-small pipeline into a separate
scene_embeddingspgvector table with HNSW index.Roadmap Tickets
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