ProjectIntent Over Keywords: From Stitched Embeddings to a Production Fashion Agent · Module 8: Windows, overlap and the audio surprise · part 10 of 26
Part 10 · Module 8: Windows, overlap and the audio surprise

Module 8: Windows, overlap and the audio surprise

23 min read·
About 50 minutes · Needs: Module 7 (one embedding space), Module 6 (the test video and its questions) · Code: part2-video-rag-unified/src/extractors/media.py, part2-video-rag-unified/src/ingestion/pipeline.py, part2-video-rag-unified/src/embedders/gemini_embedder.py

Where this fits

WHERE THIS FITS Intent Over Keywords this module PART 1: STITCHED VIDEO RAG three models, two spaces, RRF PART 2: UNIFIED VIDEO RAG one model, one space PART 3: STYLEME FastAPI, LangGraph, React QUALITY AND OPERATIONS evals, Docker, Cloud Run Frames ffmpeg, one per 5 s CLIP ViT-B/32 frames + text, 512 d Whisper base speech to text MiniLM 64-word chunks, 384 d Two collections p1_visual, p1_text Rank fusion RRF, k = 60 Gemini answer from transcript text API + Streamlit upload, ask, sources Windows ffmpeg, video + audio Embedding 2 Gemini, one vector One collection p2_unified, no fusion Notes + answer Gemini, cited times React web chat, photo, chips nginx one URL, /api proxy FastAPI /api/chat, limits Sessions in memory, cap of 3 Planner intent before retrieval Implementer batched embed, search Generator stylist reply, ids Grounding check no LLM, drops invented Gemini flash-lite, embedding 2 Qdrant styleme_products Catalogue ingest image + title vectors Langfuse optional traces Test video 26 questions, 4 kinds Video eval Part 1 vs Part 2 Agent evals intent, grounding Guards uploads, limits, secrets Containers compose profiles Cloud Run StyleMe only CI/CD GitHub Actions, WIF Original code v1, v2, fashion-agent
This module (highlighted): how Part 2 cuts a video into windows and what exactly it sends to the embedding model. Amber marks a model, blue our own code.

Module 7 chose one multimodal embedding model and one embedding space. This module is about what goes into that model: how long each piece of video is, how the pieces overlap, how they are cut, how their bytes reach Google, and, most of all, what the model really reads. It contains the most important measurement of Part 2: the original v2 believed it embedded picture and speech together, and it did not.

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