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Quick Start Guide

This guide will help you get up and running with Data Neuron in just a few minutes. You'll learn how to set up your database configuration, create a context, and start querying your data using natural language.

1. Initialize Database Configuration​

First, let's set up your database configuration:

dnn --db-init <database_type>

Replace <database_type> with one of: sqlite, mysql, mssql, postgres, csv, or clickhouse.

This command will create a database.yaml file in your current directory, which Data Neuron will use to connect to your database.

2. Generate Context​

Next, let's create a semantic layer for your data:

dnn --init

Follow the prompts to:

  1. Provide a name for your context (e.g., "product_analytics" or "customer_success")
  2. Select the tables you want to include in this context

This will create YAML files in the context/<contextname> directory, forming your semantic layer.

3. Start Chatting with Your Data​

Now you're ready to start querying your data using natural language:

dnn --chat <context_name>

For example:

dnn --chat product_analytics

You can now ask questions about your data in plain English. For example:

  • "How many active users do we have?"
  • "What were our top 5 selling products last month?"
  • "Show me the revenue trend for the past 6 months"

4. Saving Metrics to Dashboards​

While chatting, you can save interesting metrics to a dashboard:

  1. When you get a useful result, Data Neuron will ask if you want to save it.
  2. If you choose to save, you'll be prompted to name the metric and choose a dashboard.
  3. The metric will be saved in dashboards/<dashname>.yml

5. Generating Reports​

To generate a PDF report of your dashboard:

dnn --report

This will create a PDF report based on your saved metrics.

Using Data Neuron in Your Python Projects​

To integrate Data Neuron into your Python code:

from dataneuron import DataNeuron

# Initialize DataNeuron
dn = DataNeuron(db_config='database.yaml', context='your_context_name')
dn.initialize()

# Optional: Set client context for multi-tenant scenarios
dn.set_client_context("client_123")

# Ask a question
result = dn.query("How many users signed up last month?")

print(f"SQL Query: {result['sql']}")
print(f"Result: {result['result']}")
print(f"Explanation: {result['explanation']}")

Next Steps​

Now that you've got the basics, you can:

Happy data querying with Data Neuron!