Any examples using Vertex Agent Builder + a Tool with a Data Store containing structured CSV data?

[rawlingsj]

I'm attempting to prove out a RAG architecture using Vertex Agent Builder + a data store tool containing custom indexed structured CSV data.

I'm not sure if my approach is correct so looking for some guidance please.

I have created a new test Agent, in the instructions I am using a TOOL that is configured with a data store.  I'd like to enrich responses that have a similar match to the query passed into it.  Should this be possible and a correct use case?

I did start to go down the route of configuring the Tool with an Open AI spec that calls a GCP Cloud Function, so that can do the Augmentation step but I'm not sure if that is correct.  I was also having strange results.

Are there any recommendations on how to integrate my own custom structured data, into a Vertex Agent so I can describe Instructions on how to behave and return responses that include my custom data?

Here's some screenshots to show the basics that I'm trying, I'm probably holding it wrong but I've not been able to get any responses from the Pets TOOL that's configured with Pets_DS which is structured CSV:

Agent:

 Tool:

Data Store:

[dawnberdan]

Hi @rawlingsj,

Welcome to Google Cloud Community!

It sounds like you're on the right track with your RAG (Retrieval-Augmented Generation) architecture using Vertex Agent Builder. However, there are some key points to consider and adjustments you might need to ensure you're integrating your custom structured data effectively.

Here’s a step-by-step guide to help you:

1. Tool Configuration and Data Store:

• Tool Type: The "Tool" shown in your screenshots appears to be a "Generic Tool" with a Data Store. While this setup can work, it may be too broad. Consider developing a specialized tool specifically tailored for your structured CSV data, as this will enhance control and integration.
• Data Store Integration: Ensure that the "Pets_DS" Data Store is correctly linked to your tool and configured for access. If you stick with a "Generic Tool," make sure to specify the data store name and any required credentials.
• Data Store Schema: Verify that the structure of your CSV data matches the schema defined for your Data Store. This alignment is essential for accurate retrieval and processing.

2. Retrieval and Augmentation:

• Retrieval Method: Your "Generic Tool" may default to basic search queries. Consider a more refined method by specifying fields to query within your CSV, such as "Name," "Breed," or "Color." This will facilitate more targeted information retrieval.
• Augmentation vs. Response: The "Augmentation" step you mentioned is crucial. You want to enrich the Agent's responses with the retrieved data to make them more informative and contextually relevant. This is where the Cloud Function plays a key role.
• Cloud Function: Implementing a Cloud Function for data processing and augmentation is a smart move. It can transform the retrieved data from your Data Store into a format suitable for inclusion in the Agent's response. This may involve formatting, summarization, or applying a language model for more complex processing.

3. Agent Interaction:

• Tool Calls: Your agent will need to invoke the tool to fetch data from your custom data store. This requires providing the correct query parameters and the format expected by your tool.
• Response Integration: After the tool returns the data, your agent should integrate it into the final response. This could involve appending the retrieved information or using it to provide a more complete and informative answer to a user's question.

In addition, I found an article/blog on Architectural Blueprints for RAG Automation with Vertex AI Search that could help enhance your RAG implementation.

I hope the above information is helpful.