How can I store the private vector data in Opensearch per user?

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Hi!

I'm building a RAG approach AI feature. I did a test with AWS Bedrock creating a knowledgebase that gets the data from a S3 bucket. Then the data is sent to opensearch and store as vectors.

I would like to know if there is the possibility of storing that data in Opensearch in a private way, so later each user can get the response of his own data from the chatbot UI Im building. The user A will ask a question, and it should receive only the response based on his data, and then same should happen with the user B.

There is a way to achieve that? And is so how can I separate that data in Opensearch? I'm using Python with langchain.

Thank you guys!

1 Answer
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Accepted Answer

Hi,

Rather than OpenSearch, did you explore the pgvector extension of PostgreSQL: it is available on AWS RDS. See https://aws.amazon.com/about-aws/whats-new/2023/05/amazon-rds-postgresql-pgvector-ml-model-integration/ or https://aws.amazon.com/about-aws/whats-new/2023/10/amazon-rds-postgresql-pgvector-hnsw-indexing/

This blog post will also detail the approach: https://aws.amazon.com/blogs/database/building-ai-powered-search-in-postgresql-using-amazon-sagemaker-and-pgvector/

The ability to add columns to your relational tables identifying different users will allow you to obtain the separation that you want.

Best,

Didier

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answered 3 months ago
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reviewed 3 months ago
  • Hey! An update here. I used this approach and it worked as I needed, thank you!

  • Hi again! I was doing some tests and I loaded a very long excel file into my pgvector postgresql database. Then I did a query to get the embeddings by the user id, and got the response, but when calling bedrock I got an exception. ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Input is too long for requested model.

    How can I manage large volume of data with this approach using RAG?

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