Generative AI and AI Agents

    Who this is for:

    Architecture / Concept Overview: Generative AI and AI Agents

    The Databricks Generative AI stack connects foundation models, retrieval, agent orchestration, and governance into a unified platform.

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    *Generative AI stack: documents feed vector search, foundation models power RAG chains, agents orchestrate tools, and governance wraps the entire surface.*

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    *Generative AI capability tree: foundation models, RAG, agents, and fine-tuning.*

    Key Terms

    Prerequisites and Setup

    • Databricks workspace (Premium or Enterprise) with Unity Catalog.
    • Foundation Model APIs enabled (available in supported regions).
    • Vector Search endpoints configured for RAG workloads.
    • Python SDK: databricks-agents, mlflow.

    Step-by-Step Implementation

      Configuration Reference

      Generative AI and AI Agents configuration options
      ParameterDefaultDescription
      max_tokens256Maximum tokens in LLM response
      temperature1.0Sampling temperature (lower = more deterministic)
      num_results (Vector Search)10Number of nearest neighbours to retrieve
      index_typeDELTA_SYNCVector index type: DELTA_SYNC or DIRECT_ACCESS
      pipeline_typeTRIGGEREDSync mode: TRIGGERED or CONTINUOUS
      embedding_model_endpoint_nameModel endpoint used for automatic embedding

      Monitoring, Cost, and Security Considerations

      Common Pitfalls and Recommended Patterns

        Frequently Asked Questions