Add AgentOps project type and vector search data preparation workflows

[veenaramesh]

Overview

This PR adds a new project type "AgentOps" to the existing MLOps Stacks template. Users can now select between two project types when initializing a stack:

• mlops - Existing template; traditional ML pipeline for model training and batch inference
• agentops - NEW template; agent-specific workflows for data ingestion + eventually agent development/deployment.

This PR also adds the vector search data ingestion pipeline for the AgentOps projects.

Features

AgentOps Template Updates

1. Project type selection

• Added input_project_type parameter to databricks_template_schema.json

  • Options: mlops (default) or agentops
  • First-order parameter in template initialization

• Updated minimum Databricks CLI version to v0.266.0 to support new features

• Default project name now reflects selected project type: my_{{ .input_project_type }}_project

• Other changes:

  • Reordered parameters with input_project_type as order 1
  • Updated all subsequent parameter orders
  • Conditional parameter display (e.g., input_include_models_in_unity_catalog skipped for agentops)
  • Updated default values to be project-type aware

2. Updating project structure layout

• Added conditional logic to generate appropriate project structure based on input_project_type to update_layout.tmpl

  • Ensures MLOps-specific files are only generated for MLOps projects

• Added conditional logic to certain files:

• Separate code structure sections for MLOps vs AgentOps, which conditionally renders based on input_project_type

  • requirements.txt.tmpl

    • Adds dependencies (e.g. vector search SDK)

  • README.md.tmpl

    • Adds basic documentation for agentops project

  • databricks.yml.tmpl

    • Extends bundle configuration to support agentops resources
    • Adds data preparation workflow targets

  • All CI/CD pipelines (more on this later)

3. Updating CI/CD workflows

• Extended CI/CD pipelines to handle AgentOps projects and test the correct workflows:

  • GitHub Actions (.github/workflows/{{.input_project_name}}-run-tests.yml.tmpl)
  • Azure DevOps (.azure/devops-pipelines/{{.input_project_name}}-tests-ci.yml.tmpl)
  • GitLab CI (.gitlab/pipelines/{{.input_project_name}}-bundle-ci.yml.tmpl)

Data preparation with vector search for AgentOps

1. Data preparation code

• Notebook: DataIngestion.py.tmpl
  • Processes raw documentation from data source URLs and stores data in UC
  • uses utility function fetch_data.py.tmpl for retrieval
• Notebook: DataPreprocessing.py.tmpl
  • Cleans and chunks documentation to prepare for vector search
  • uses utiltiy function create_chunk.py.tmpl for chunking logic
  • define configs for chunking in config.py.tmpl
• Notebook: VectorSearch.py.tmpl
  • Creates Vector Search endpoint and index using delta sync (TRIGGERED mode)
  • uses utility function vector_search_utils.py.tmpl for management + waiting for endpoint to be ready

2. Workflow resource configuration

• Defined the data preparation workflow in data-preparation-resource.yml.tmpl, which includes each notebook as a separate task (sequential execution)
  • Parameters for notebooks are given here
  • Scheduled for running everyday in the morning 5am
  • Severless environment and dependencies are also defined here
• Included resource in list of resources in databricks.yml.tmpl

3. Defined variables in databricks.yml

• Included variables (that will feed into data preparation workflow parameters)
  • catalog_name
    • Defined uniquely for each deployment target using template input (e.g.databricks_staging_workspace_host)
  • schema
    • Defined the same for each deployment target using template input input_schema_name
  • raw_data_table
    • Will automatically populate as "raw_documentation"
  • preprocessed_data_table
    • Will automatically populate as "databricks_documentation"
  • eval_table
    • Will automatically populate as "databricks_documentation_eval"
  • vector_search_endpoint
    • Will automatically populate as "ai_agent_endpoint"
  • vector_search_index
    • Will automatically populate as "databricks_documentation_vs_index"

What I have tested:

• Validated project generation for both mlops and agentops project types
• Tested original mlops-stacks project + confirmed that the default behavior is unchanged
• Validated data preparation pipeline works end-to-end
• Validated that bundle variables are used properly by resources