This is the full explainer, supplementing the Bigquery Demo Video below. This guide will show how to setup models, credentials, tasks, agents and apps to get the RAG chat app working.

1. Setup Models and Credentials

Go to the /models screen and add two models:

How to Setup Models in Agent Cloud

2. Setup Datasource

If running locally via Docker, during this process we reccommend running docker compose logs -f in your terminal to follow along and catch any errors if they occur. For Advanced debugging you can also open up apps like Qdrant or Airbyte to see progress as data passes through each system. Click Advanced Debugging for instructions on how to access these UIs.

Go to the /datasources screen, select New Connection and add a Bigquery data source:

How to Add Datasource in Agent Cloud

3. Setup A Tool

Go to the /tools screen and create a new tool

How to Create a RAG Tool in Agent Cloud

4. Setup an Agent

Go to the /agents screen and create a new Agent

How to Setup an Agent in Agent Cloud

5. Setup a Task

Go to the /tasks screen and create a new task with the following task description:

Have a back and forth conversation with the user.
Be clear in your answers always.
If you don't know the answer say "I do not know."

Set the Preferred Agent as the Conversational Agent you just created.

6. Create an App

Go to the /apps screen and create a new App

7. Have a chat!

Chat Interface at Agent Cloud

If you want to make sure the agent always uses the tool, you can update the agent prompt and tell it, ALWAYS use the … tool. Otherwise if you want to build an agent with a bit more autonomy to decide on multiple tools, you can keep the config light and let it infer which tool is required. For example in the video we prompted the tool by writing According to Elon… which helped guide the LLM to the correct tool.