Bringing data into BigQuery centralizes your information, but the real challenge is making that data accessible. Often, technical barriers separate the people with questions — from execs to analysts — from the answers they need.
With the Conversational Analytics API, powered by Gemini, you no longer need intricate systems to get insights. The API is engineered to help you build context-aware agents that can understand natural language, query your BigQuery data, and deliver answers in text, tables, and visual charts.
Now, you can build any solution that can interface with the API. For example, you can integrate it with the Agent Development Kit (ADK) to build a multi-agent systems, or to implement these data strategies:
- Self-service triage for operations: Give teams like Support and Sales an agent that answers data questions instantly. Instead of filing a ticket to ask, “Why did signups drop last week?”, they get the answer immediately.
- Differentiate your SaaS product: Differentiate your platform by embedding a powerful chat interface directly into your platform. Let your customers query and visualize their own usage data using plain English.
- Dynamic reporting: Move beyond static PDFs. Automate the core reporting function and enable stakeholders to ask nuanced, follow-up questions for deeper investigation, effectively replacing report versions with real-time conversation.
In this post, we’ll share ways to build a conversational agent in BigQuery using the Conversational Analytics API.
Step One: Configure and create the agent
The deployment of a Data Analytics Agent involves configuring its access, context, and environment before making the final creation call.
In our included example, the Python SDK is used, but the Conversational Analytics API supports many other languages, depending on your preference and environment.
Initialize the client and define BigQuery sources
Begin by instantiating the necessary client (DataAgentServiceClient) to interact with the API. This client is used in conjunction with explicit BigQueryTableReference objects, which authorize the agent’s access to specific tables (defined by project_id, dataset_id, and table_id). These individual references are then aggregated into a DatasourceReferences object under the bq field.
