Semantic Video Search

Upload your video files to a cloud storage or local directory. Use a video processing library (e.g., FFmpeg) to extract keyframes at regular intervals (e.g., 1 frame per second) and transcribe audio to text using a speech-to-text API (e.g., Whisper). Store the transcripts and keyframe metadata in a searchable database (e.g., Elasticsearch) with timestamps.

Why NucliaDB: NucliaDB provides automated ingestion and indexing of multi-modal documents including video, which directly supports the need to ingest and index video content for semantic search.

Use a pre-trained multimodal embedding model (e.g., CLIP or Sentence-BERT) to convert each transcript segment and keyframe into a vector embedding. Store these embeddings in a vector database (e.g., Pinecone, Weaviate) alongside the original metadata. For user queries, embed the search text using the same model.

Why Weaviate: Weaviate directly supports vector search and semantic search, which are core requirements for generating and querying semantic embeddings.

Create a search endpoint that accepts natural language queries. Embed the query using the same model, then perform a nearest-neighbor search in the vector database to retrieve the top-K matching segments (text or keyframe). Return results with video ID, timestamp, and relevance score. Optionally, re-rank results using cross-encoder models for higher precision.

Why Cohere: Cohere provides semantic search and can serve as a reranking step, directly matching the need for a semantic search pipeline with optional reranking.

For each search result, extract a short video clip (e.g., 10 seconds around the matched timestamp) using FFmpeg. Generate a thumbnail from the keyframe and display the transcript snippet. Build a simple UI (e.g., Streamlit or React) that lists results with playable clips and links to the full video.

Why VideoHighlight: VideoHighlight provides automated clip extraction and semantic search within video, which directly supports presenting results with contextual video previews.

Collect user feedback on result relevance (e.g., thumbs up/down). Use this data to fine-tune the embedding model or adjust chunking strategy (e.g., smaller chunks for granularity). Optionally, add hybrid search (combining vector and keyword search) to improve recall for exact terms.

Why Elasticsearch AI: Elasticsearch AI provides semantic search and vector embedding storage, which are essential for hybrid search optimization and iterative quality improvement.