To implement audit logging for vector queries, you need to capture details about who made the request, what data was queried, and when it occurred. Start by defining the metadata to log, such as user identifiers, timestamps, query parameters (e.g., vector embeddings, search thresholds), and the results returned (e.g., matched vectors or IDs). For example, if a user searches for similar product images using a vector embedding, log the embedding hash, the number of results requested, and the session ID. Use a structured logging format like JSON to ensure consistency and ease of analysis. This data should be stored separately from application logs, ideally in a dedicated audit database or secure storage system.
Next, integrate logging directly into the query processing workflow. For instance, in a Python service using a vector database like FAISS or Pinecone, wrap query functions with logging logic. When a search is executed, extract metadata before and after the query. Here’s a simplified example:
def search_vectors(user_id, query_embedding):
timestamp = datetime.utcnow()
results = vector_db.search(query_embedding, k=10)
audit_log = {
"user": user_id,
"timestamp": timestamp.isoformat(),
"query_hash": hash(query_embedding.tobytes()),
"result_count": len(results),
"result_ids": [result.id for result in results]
}
audit_db.insert(audit_log)
return results
Ensure logs include a unique request ID to trace activity across distributed systems. Avoid logging raw vectors to reduce storage costs and privacy risks—instead, hash them or store truncated versions. Use asynchronous logging (e.g., via a message queue) to prevent latency spikes in query responses.
Finally, address security and compliance. Encrypt audit logs at rest and restrict access to authorized personnel. Implement retention policies to delete logs after a defined period unless legally required. For GDPR or HIPAA compliance, anonymize user identifiers or provide deletion workflows. Validate that logged data doesn’t inadvertently expose sensitive information—for example, avoid storing raw user input if the query includes personal data. Use tools like Elasticsearch or AWS CloudTrail for centralized log management, and set up alerts for unusual patterns (e.g., a sudden surge in query volume from a single user). Regularly test the logging pipeline to ensure it scales under load and accurately reflects query activity.