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dazense init

The dazense init command sets up your context repository with all necessary files and structure. Run dazense init 
dazense init
The command will guide you through an interactive setup: 1. Project Name 
What is the name of your project?
> my-analytics-agent
2. Database Connection (Optional) 
Do you want to connect a database? [y/N]
> y

Select your database type:
  1. Snowflake
  2. BigQuery
  3. Databricks
  4. PostgreSQL
  5. Redshift
  6. MySQL
If you select yes, you’ll be prompted for connection details specific to your database type. 3. Repository Context (Optional) 
Do you want to add a repository to your agent context? [y/N]
> y

Repository URL:
> https://github.com/your-org/dbt-project

Path within repo (optional):
> models/
4. LLM API Key (Optional) 
Do you want to add an LLM key? [y/N]
> y

Select your LLM provider:
  1. OpenAI
  2. Anthropic
  3. Azure OpenAI
  4. Other
5. Slack Integration (Optional) 
Do you want to setup a Slack connection? [y/N]
> y
You can skip any optional step and configure it later by editing dazense_config.yaml. What Gets Created After running dazense init, you’ll have a folder with the architecture of your context: 
my-analytics-agent/
├── dazense_config.yaml          # Main configuration file
├── RULES.md                 # Agent behavior rules
├── agent/                   # Agent tools and integrations
│   ├── mcps/               # Model Context Protocols
│   └── tools/              # Custom tools
├── databases/              # Database schemas (populated after sync)
├── docs/                   # Documentation files
├── semantics/              # semantic_model.yml and business_rules.yml
├── datasets/               # Dataset bundles for trusted modes
├── policies/               # Enforcement policy for trusted modes
├── contracts/              # Runtime contract logs (generated)
└── queries/                # Example queries

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dazense sync

Once initialized, populate your context with actual content: 
dazense sync
This will:
  • Connect to configured databases and pull schemas
  • Clone configured repositories
  • Generate structured context files
  • Index content for your agent

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dazense debug

Verify your configuration: 
dazense debug
This checks:
  • Configuration file syntax
  • Database connectivity
  • LLM API access
  • Environment variables
  • File permissions

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dazense_config.yaml

The dazense_config.yaml file is the central configuration for your analytics agent. You can always edit it and re-launch a sync with this configuration. Basic Structure 
project_name: my-analytics-agent

# Database Connections
databases:
  - name: bigquery-prod
    type: bigquery
    project_id: my-project
    dataset_id: analytics
    # Option 1: Use credentials_path for local files
    credentials_path: /path/to/credentials.json
    # Option 2: Use credentials_json for environment variables (recommended for cloud deployments)
    # credentials_json: {{ env('GCP_SERVICE_ACCOUNT_KEY_JSON') }}
    accessors:
      - columns
      - preview
      - description
      - profiling
    include: []
    exclude: []
    sso: false
    location: US

# Repository Integrations
repos:
  - name: dbt
    url: https://github.com/your-org/dbt-project.git
    branch: main

# LLM Configuration
llm:
  provider: anthropic
  api_key: {{ env('ANTHROPIC_API_KEY') }}

# Slack Integration (optional)
slack:
  bot_token: {{ env('SLACK_BOT_TOKEN') }}
  signing_secret: {{ env('SLACK_SIGNING_SECRET') }}
  post_message_url: https://slack.com/api/chat.postMessage
Environment Variables
Never commit sensitive credentials to Git! Always use environment variables for secrets.
Store sensitive values in environment variables: 
# .env file (add to .gitignore)
OPENAI_API_KEY=sk-...
SNOWFLAKE_USER=my_user
SNOWFLAKE_PASSWORD=my_password
SLACK_BOT_TOKEN=xoxb-...
SLACK_SIGNING_SECRET=...
Reference them in your config: 
api_key: {{ env('OPENAI_API_KEY') }}
user: {{ env('SNOWFLAKE_USER') }}
password: {{ env('SNOWFLAKE_PASSWORD') }}
Warehouse Credentials For Warehouse credentials, you can use either method: Method 1: credentials_path (local development) 
databases:
  - name: bigquery-prod
    type: bigquery
    project_id: my-project
    dataset_id: analytics
    credentials_path: /path/to/service-account.json
Method 2: credentials_json (cloud deployments) 
databases:
  - name: bigquery-prod
    type: bigquery
    project_id: my-project
    dataset_id: analytics
    credentials_json: {{ env('GCP_SERVICE_ACCOUNT_KEY_JSON') }}
Use credentials_json for cloud deployments (Cloud Run, GitHub Actions, etc.) where you store the full JSON content in an environment variable or secret manager. Use credentials_path for local development with credential files. When using credentials_json, the environment variable must contain the entire JSON content of your service account key file, not just the path.

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Trusted Analytics toggle points

When enabling Trusted Analytics V1/V2, configure:
  • datasets/<bundle_id>/dataset.yaml
  • policies/policy.yml
  • semantics/semantic_model.yml
  • semantics/business_rules.yml
For strict runtime gating: 
execution:
  require_contract: true
Next Steps
  • Context Synchronization - Learn how to sync and update your agent’s context
  • Context Principles - Learn how to optimize your context for reliability, speed, and cost