Deep Data Agent

An intelligent data analysis assistant with dual-mode capabilities for code and SQL workflows.

Overview

Deep Data Agent provides two specialized AI copilots:

• Data Agent: Code-first assistant for Python data analysis, visualization, and file management
• SQL Agent: Natural language to SQL with automatic schema understanding and query validation

Key Features

• Dual-mode chat: Switch between Code and SQL modes based on your task
• Live streaming: Real-time responses with inline code, SQL, and visualizations
• RAG integration: Teach the agent about your data for context-aware answers
• Auto-visualization: Charts generated automatically from your data
• Multi-datasource: Connect to multiple databases simultaneously
• Session history: Conversations persist across sessions

Using Deep Data Agent

Code Mode

In Code mode, the Data Agent helps you:

• Write and execute Python code in a secure environment
• Analyze data files in your workspace
• Generate visualizations and downloadable artifacts
• Build data pipelines and transformations

Example prompts:

• “Load the sales.csv file and show me a summary”
• “Create a bar chart comparing revenue by region”
• “Clean this dataset and export it as Parquet”

SQL Mode

In SQL mode, the SQL Agent helps you:

• Query databases using natural language
• Explore database schemas automatically
• Generate and validate SQL queries
• Visualize query results with charts

Example prompts:

• “Show me the top 10 customers by order value”
• “What’s the monthly trend for product sales?”
• “Join orders and customers tables to find inactive users”

Connecting Your Data

Databases

Connect to your databases through the admin panel:

1. Click the Settings icon in Deep Data Agent
2. Select Datasources
3. Add your database connection details
4. Test the connection and save

Supported databases: PostgreSQL, MySQL, SQLite, and other SQL databases.

Documents (RAG)

Teach Deep Data Agent about your documentation:

1. Open the RAG section in settings
2. Upload documents or point to a directory
3. The system indexes content for semantic search
4. Context is automatically included in relevant queries

Supported formats: PDF, Markdown, text files, code files.

Tips for Best Results

• Be specific: “Show sales for Q4 2024” works better than “Show me some data”
• Iterate: Ask follow-up questions to refine results
• Use context: Reference previous results with “Using that data…”
• Request formats: Ask for “a table” or “a chart” explicitly

Capabilities

Documentation