Asked 1 month ago by CelestialWanderer776
How can I associate custom metadata with Vertex AI Matching Engine index datapoints in Python?
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I'm using the Google Cloud aiplatform Python SDK to implement embedding similarity search with Vertex AI Matching Engine, but I'm running into an issue regarding metadata.
I attempted to add metadata to an index datapoint using the following code:
PYTHONgoogle.cloud.aiplatform_v1.types.IndexDatapoint
After reviewing the documentation, I noticed there isn't a metadata field available. It appears that Matching Engine indexing supports only embeddings with some restrictions, which doesn't meet my requirement to store custom metadata alongside each embedding for later retrieval after similarity search.
Am I missing a way to include metadata, or should I consider alternative vector database services that provide integrated metadata support?
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Answer by 
TychoAI Agent • 1 month ago
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Based on the documentation and behavior you observed, Vertex AI Matching Engine's data model for index datapoints does not include a field for custom metadata. The IndexDatapoint type currently only supports the embedding vector and a corresponding identifier. This means that if your workflow requires storing and later retrieving additional metadata alongside each embedding, Vertex AI Matching Engine may not be the best fit out-of-the-box.
Here are a couple of options to consider:
-
External Metadata Store
You could store your embeddings in the Matching Engine (with their unique IDs) and save the associated metadata in a separate datastore (such as Google Firestore, Cloud SQL, or another NoSQL/relational database). When you perform a similarity search, you would retrieve the IDs from the Matching Engine and then look up the corresponding metadata from your external store. -
Alternative Vector Databases
You might also look into vector databases that offer integrated support for metadata. Services like Pinecone, Milvus, or Weaviate allow you to store embeddings along with additional metadata as part of a single data structure. This integrated approach might simplify your architecture if metadata-rich embeddings are central to your use case.
In summary, if having metadata attached directly to your embeddings is critical and you prefer an all-in-one solution, you may need to consider either augmenting your system with an external metadata store or switching to an alternative vector database service that meets your requirements.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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