Langchain Data Agent

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[ Natural Language → SQL Query Agent ]

A natural language to SQL (NL2SQL) platform built on LangGraph and Azure OpenAI. This multi-agent system automatically routes user questions to the appropriate database backend and generates optimized SQL queries and results.

Built on top of LangChain's SQLDatabase with extended support for Azure AD authentication, Cosmos DB, and built-in dialect validation.

Features

• Multi-Database Support: PostgreSQL, Azure SQL, Azure Synapse, Azure Cosmos DB, Databricks SQL, and Google BigQuery
• Intent Detection: Automatically routes queries to the correct data agent based on question context
• Multi-Turn Conversations: Follow-up questions with context awareness (e.g., "What's the average?" after a query)
• SQL Validation: Safe query execution with sqlglot-based validation across all dialects
• Data Visualization: Generate charts and graphs from query results using natural language (e.g., "show me a bar chart")
• Configurable Agents: YAML-based configuration for adding new data sources
• A2A Protocol: Agent-to-Agent interoperability for integration with other A2A-compliant systems

Architecture

Intent Detection Flow

Routes user questions to the appropriate data agent based on context.

Data Agent Flow

Generates, validates, and executes SQL queries with retry logic.

Documentation

• Database Setup
• Configuration
• Data Visualization
• A2A Protocol

Quick Start

Prerequisites

• Python 3.12+
• uv package manager
• Azure OpenAI deployment

Installation

git clone https://github.com/eosho/langchain_data_agent
cd langchain_data_agent
uv sync --all-extras
cp .env.example .env
# Edit .env with your values

CLI Usage

The CLI provides commands for querying data agents through natural language.

# Show available commands
data-agent --help

Commands:

Options:

Each config file contains multiple specialized data agents. The system automatically routes your question to the appropriate agent.

Example Questions by Config:

contoso_sales (Databricks)

1. How many orders were placed last month?
2. What is the total revenue by region?

contoso_products (Cosmos DB)

1. How many active products are there?
2. Find all products with low inventory
3. What is the most expensive product?

contoso_inventory (PostgreSQL)

1. Which products need to be reordered?
2. What is the total inventory by warehouse?
3. How many shipments are in transit?

# Single query (loads all configs by default)
data-agent query "How many shipments are in transit?"

# Interactive chat
data-agent chat

# Use specific config
data-agent query "How many shipments are in transit?" -c contoso
data-agent chat -c contoso

contoso_hr (Azure SQL)

1. How many employees are in each department?
2. What is the average salary by department?
3. Who are the top performers this year?

hotel_analytics (Synapse)

1. What is the total revenue by hotel?
2. What is the revenue breakdown by booking channel?
3. What is the average daily rate by room type?

data-agent query "What is the total revenue by hotel?" -c adventure_works
data-agent chat -c adventure_works

financial_transactions (BigQuery)

1. What are the total deposits by customer segment?
2. Show me all high-severity fraud alerts from the past week
3. Who are the top 5 customers by transaction volume?
4. Show me a bar chart of transactions by type

data-agent query "What are the total deposits by customer segment?" -c amex
data-agent chat -c amex

# List available configs
data-agent configs

# Validate configuration files
data-agent validate           # Validate all configs
data-agent validate contoso   # Validate specific config

Chainlit Web UI

The platform includes a Chainlit-based web interface for interactive data exploration.

# Start the Chainlit UI
chainlit run src/data_agent/ui/app.py

Available Profiles:

Environment Variables:

Ensure these are set in your .env file:

AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your-api-key
AZURE_OPENAI_DEPLOYMENT=gpt-4o

Programmatic Usage

import asyncio
from data_agent import DataAgentFlow

async def main():
    async with DataAgentFlow(
        config_path="data_agent/config/contoso.yaml",
        azure_endpoint="https://your-resource.openai.azure.com/",
        api_key="your-api-key",
        deployment_name="gpt-4o",
    ) as flow:
        result = await flow.query("Show me all warehouses")
        print(result["final_response"])

asyncio.run(main())

Supported Databases

The platform includes built-in configuration for these databases:

Note: Any SQLAlchemy-compatible database can be used via shared_db parameter or connection_string in config. The built-in types provide convenience configuration and AAD authentication support.

Development

# Format and lint
uv run pre-commit run --all-files

# Run tests
uv run pytest

License

MIT License - see LICENSE file for details.