Batch processing historical video archives into a vector database involves three main stages: preprocessing, feature extraction, and database ingestion. First, you’ll need to decode and segment the video files into manageable chunks. For example, split a two-hour video into 10-minute clips or extract keyframes using tools like FFmpeg or OpenCV. This step ensures uniform processing and reduces memory overhead. Metadata (timestamps, resolution, etc.) should be logged and stored alongside the video segments. Parallelizing this step using frameworks like Apache Beam or Python’s multiprocessing library can speed up processing, especially with large archives.
Next, extract features from the video data using machine learning models. For visual content, convolutional neural networks (CNNs) like ResNet or Vision Transformers can generate embeddings from frames or clips. For audio, models like VGGish or Whisper can process soundtracks or spoken text. For instance, using PyTorch or TensorFlow, you could run a pre-trained model on each video segment to produce 512-dimensional vectors. If the videos include text (subtitles or OCR-derived text), combine these embeddings with text encoders like BERT. Batch inference tools like ONNX Runtime or NVIDIA Triton can optimize this step by processing multiple segments simultaneously on GPUs.
Finally, store the embeddings in a vector database optimized for similarity search. Popular choices include FAISS, Milvus, or Pinecone. Structure the data by associating each vector with its metadata (e.g., video ID, timestamp) to enable contextual queries. For example, after generating embeddings for 10,000 video clips, use FAISS’s index.add() method to load them in batches, ensuring the database is sharded or partitioned for scalability. Implement a pipeline to validate data consistency—check for missing embeddings or mismatched metadata. Once ingested, the database can support tasks like content search (finding similar scenes) or classification. Regularly update the pipeline to retrain models or adjust chunking strategies as the archive grows.