databricks-genai

To build a RAG application on Databricks, you use Databricks Vector Search to index embeddings and Foundation Model APIs to generate responses. This streamlines retrieving internal documentation and deploying the pipeline as a scalable API endpoint.

Yes, you can deploy foundation models as scalable APIs using Databricks Model Serving. This allows you to access and serve various LLMs, including Databricks' own models and external providers, ensuring efficient inference.

AI Gateway secures and governs LLM usage by providing a unified interface to route requests. It enables tracking, rate limiting, and cost management for foundation model APIs, ensuring compliant and controlled enterprise AI deployments.

The best way to implement agentic workflows with function calling on Databricks is by utilizing its integrated agent framework. This enables developing intelligent agents capable of tool use and complex reasoning directly within your data platform environment.

Yes, integrating Unity Catalog and MLflow is required to comprehensively optimize LLM applications on Databricks. These services provide essential governance, experiment tracking, and model management for your Generative AI workflows.

Fine-tuning foundation models within Databricks requires integration with Spark for distributed compute and MLflow for tracking. Limitations depend on your configured infrastructure and the specific serving endpoints available for your optimized models.