Use Conversational Analytics API for natural language AI

Context retrieval: The key to unlocking relevant answers

For intelligent conversational data interactions, leveraging the context retrieval tool is essential. For BigQuery, the tool meticulously pulls schema information, detailed column, table descriptions from Dataplex. When interacting with Looker, it accesses the LookML model, retrieving field definitions, labels, and defined measures. This deep, accurate understanding of your data’s structure, relationships, and inherent business logic is critical as it enables the agent to become an expert in your data landscape. This ensures every response is firmly grounded, highly relevant, and ultimately, a trusted answer for your business, providing reliable and trusted answers.

NL2Query engine: Turning questions into queries

At the core of the Conversational Analytics API lies a robust NL2Query engine, designed to support both BigQuery and Looker data sources. This engine translates user-provided natural language questions into semantically equivalent and syntactically correct queries appropriate for the specified data source. 

For instance, a user could pose a question such as, “What is the average sales value per order item broken down by payment method?” and the engine would process this to generate and execute the necessary query, providing a precise answer without requiring the user to write any SQL. The NL2Query engine’s capability includes handling ambiguity and inferring the customer’s intent from natural language input, ensuring accurate mapping to the underlying dataset structure.