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How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.

Whether you’re an independent software vendor (ISV) or enterprise-sized company, you want the analytics software you invested in to enhance your users’ decision-making, open up greater access to key data, and improve operational performance for the long-term. However, continuously achieving these business outcomes requires a modern solution. Many organizations still rely on older business intelligence (BI) tools for reporting due to long-term licensing.

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

Using BigQuery DataFrames with CARTO visualization tools can help Python users access cloud-native analytics and generative AI services.