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Generative AI function GENERATE_TEXT in BigQuery

BigQuery’s GENERATE_TEXT function enables SQL-oriented data professionals to conduct NLP tasks like sentiment analysis and entity extraction in BigQuery. It uses Vertex AI’s LLM and requires knowledge of SQL and prompt structuring. The function supports various tasks and accommodates varied responses through parameters like temperature, max_output_tokens, top_k, and top_p. The post includes a hands-on guide for using the function with a mock dataset.

 Generative AI function GENERATE_TEXT in BigQuery

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Unlocking the Power of BigQuery’s GENERATE_TEXT Function

Unlock the Power of BigQuery for NLP with SQL

Transform your data analysis with BigQuery’s GENERATE_TEXT function!

Introduction

Google’s BigQuery has evolved, offering incredible features, including the ability to create machine learning models with SQL. This advancement means that with some machine learning and statistics knowledge, anyone familiar with SQL can now perform complex data tasks directly in BigQuery.

About the GENERATE_TEXT Function

The GENERATE_TEXT function is designed to help data professionals perform a variety of natural language tasks using simple SQL queries. This includes:

  • Classification
  • Sentiment Analysis
  • Entity Extraction
  • And more…

It leverages the Vertex AI text-bison natural language foundation model to process text stored in BigQuery tables.

Practical Steps to Use GENERATE_TEXT

Here’s a simplified 4-step methodology:

  1. Create a mockup dataset (e.g., using ChatGPT).
  2. Set up a BigQuery table linked to your dataset.
  3. Connect BigQuery to the Vertex AI service.
  4. Test the GENERATE_TEXT function with your data.

Value for Middle Managers

With GENERATE_TEXT, you can:

  • Automate Insights: Generate new data attributes from unstructured text.
  • Bridge Knowledge Gaps: Use SQL for NLP tasks without deep programming expertise.
  • Stay Competitive: Leverage the latest AI advancements in your data strategy.

Conclusion

This function simplifies the creation of machine learning models and NLP analysis within BigQuery using SQL, making it accessible to a broader range of professionals.

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