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Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.

4,276 starsPython

Local video search with Qwen3-VL: no API, runs on Apple Silicon, GPUs

by sohamrj·Mar 30, 2026·3 points·0 comments
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AI Analysis

●●SolidNiche GemBig Brain

Local Qwen3-VL video embeddings beat API costs for dashcam and security footage.

Strengths
  • •Offline Qwen3-VL backend eliminates API dependency and recurring costs
  • •Tesla metadata overlay renders speed and location onto matched clips
  • •ChromaDB storage with similarity threshold suppresses weak false matches
Weaknesses
  • •8B model requires 18GB RAM, limiting smaller machine deployment
  • •Video search category already has Twelve Labs and commercial APIs
Category
AI/ML
Target Audience

Security teams, dashcam users, video archivists

Similar To

Twelve Labs · Google Vertex AI Video Intelligence

Post Description

Last week, I posted SentrySearch, a CLI for semantic video search using Gemini's embedding API. The #1 request was local model support.

Turns out Qwen3-VL-Embedding can natively embed video into the same kind of vector space, no API, fully offline. Runs on Apple Silicon (MPS) and NVIDIA GPUs (CUDA). The 8B model needs ~18GB RAM, or use the 2B model on smaller machines.

sentrysearch index /path --backend local

Also added: similarity threshold to suppress weak matches, and a Tesla metadata overlay that renders speed/location onto matched clips.

Details on the README.

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