Several vector search platforms, including Milvus and Weaviate, can support video-based vector search, though their implementations require careful design. These platforms are primarily built for handling high-dimensional vectors, which makes them suitable for video data when combined with preprocessing steps. Video-based search typically involves breaking videos into frames or segments, extracting features (e.g., using CNNs or transformers), and storing those features as vectors. The platform then indexes these vectors for similarity searches. While none of these tools are explicitly designed only for video, their flexibility allows developers to adapt them for video use cases by integrating custom pipelines for frame extraction and feature generation.
Milvus is a strong candidate for video search due to its scalability and support for large-scale vector operations. It allows developers to store vectors representing video frames or clips and perform fast similarity searches using algorithms like IVF_FLAT or HNSW. For example, a video recommendation system could use Milvus to index embeddings from keyframes and retrieve visually similar content. Milvus also supports metadata filtering, which lets developers combine vector searches with contextual data (e.g., timestamps or video categories). However, Milvus doesn’t handle video preprocessing—developers must extract frames and generate vectors separately using tools like OpenCV or PyTorch before ingesting data into the database. Its distributed architecture makes it suitable for applications requiring real-time search across terabytes of video data.
Weaviate offers similar capabilities but emphasizes ease of integration with machine learning models. Developers can use its modules to automatically generate vectors from video frames using prebuilt or custom models. For instance, a video surveillance system might use Weaviate to index embeddings from motion detection algorithms and quickly search for similar activity patterns. Weaviate’s hybrid search (combining vector and keyword-based queries) could also help filter videos by both visual content and metadata like location or date. Like Milvus, Weaviate doesn’t process raw video directly; developers must design a pipeline to split videos into manageable chunks and convert them into vectors. Its RESTful API and built-in model support simplify integration with existing video processing workflows, making it accessible for teams without deep infrastructure expertise.
Other platforms like Qdrant and Elasticsearch can also be adapted for video search. Qdrant’s focus on low-latency search makes it viable for applications like live video analysis, where embeddings from a live feed are compared against indexed data in real time. Elasticsearch’s vector search features, combined with its robust text and metadata handling, could support hybrid video retrieval systems (e.g., finding clips that match both a visual pattern and a spoken keyword). However, all these platforms share the same limitation: they require developers to handle video segmentation, feature extraction, and vector generation externally. The choice between them often depends on secondary factors like deployment complexity, scalability needs, or existing infrastructure compatibility. For video-specific optimizations, pairing these tools with dedicated video processing libraries (e.g., FFmpeg) or cloud services (e.g., AWS Rekognition) is often necessary.