How does filtering by time or camera ID work in combination with vector search?

Filtering by time or camera ID in combination with vector search involves using metadata constraints to narrow down the dataset before or during the vector similarity search. This approach ensures that results are both contextually relevant (based on time or camera) and semantically similar to the query. For example, in a video surveillance system, you might search for “a red car” (vector similarity) but only want results from Camera 3 between 2:00 PM and 4:00 PM. The system first filters the dataset using the metadata (camera ID and timestamp) and then performs vector search on the subset, balancing efficiency and relevance.

To implement this, databases like Pinecone, Milvus, or Elasticsearch often store vectors alongside structured metadata (e.g., timestamps, camera IDs). During a query, the database applies the metadata filters first, reducing the search space. For instance, if you query for “a person wearing a hat,” the system might filter all vectors associated with Camera 5 and timestamps from the last hour. Only those filtered vectors are compared against the query vector using distance metrics like cosine similarity. This two-step process minimizes computational overhead, as vector comparisons are expensive and filtering first avoids scanning the entire dataset. Some databases optimize further by indexing metadata (e.g., B-tree for timestamps, hash indexes for camera IDs) and using approximate nearest neighbor (ANN) algorithms for vector search, ensuring fast retrieval even with large datasets.

Developers can implement this by structuring queries with both vector and filter parameters. For example, using Python with a vector database client, a query might look like:

results = index.query(
 vector=query_embedding,
 filter={
 "camera_id": {"$eq": 3},
 "timestamp": {"$gte": "2023-10-01T14:00:00", "$lte": "2023-10-01T16:00:00"}
 },
 top_k=10
)

Here, the database efficiently retrieves the top 10 vectors similar to query_embedding that also match the camera and time constraints. Challenges include ensuring metadata indexes are optimized (e.g., partitioning time-based data into hourly/daily chunks) and tuning the balance between filter strictness and recall. Proper indexing and partitioning strategies are critical to maintain low latency, especially when dealing with high-throughput systems like real-time video analysis or e-commerce product searches with time-sensitive promotions.