LangChain Data Agent PoC

This is a small natural-language-to-SQL data agent inspired by the architecture of eosho/langchain_data_agent, but implemented from scratch for this cookbook.

The goal is a compact PoC, not a full data platform. It keeps the useful shape:

Rewrite follow-up questions into standalone questions.
Route the question to a specialized data domain.
Generate SQL for the selected schema.
Validate that the SQL is read-only and scoped to allowed tables.
Execute against a local sample dataset.
Summarize the result and suggest a simple chart.

It uses Nebius Token Factory through LangChain's OpenAI-compatible client, plus LangGraph for the pipeline and Streamlit for the UI.

What is included

Two data domains:
- sales: orders, products, customers, revenue, inventory
- support: support tickets, priorities, status, resolution time
Local CSV sample data loaded into an in-memory SQLite database.
A read-only SQL guard that blocks writes, multiple statements, comments, and tables outside the selected domain.
Streamlit UI with sample questions, generated SQL, result tables, and charts.
CLI for quick terminal testing.

Setup

cd simple_ai_agents/langchain_data_agent_poc
python -m venv .venv && source .venv/bin/activate
pip install -e .
cp env.example .env

Edit .env and add your Nebius key:

NEBIUS_API_KEY=your-nebius-token-factory-key
NEBIUS_MODEL=Qwen/Qwen3-30B-A3B

Run the Streamlit UI

streamlit run app.py

Run the CLI

python main.py

Sample questions

Sales:

What is revenue by region?
Which products generated the most revenue?
Show monthly revenue by channel.
Which products are below reorder level?

Support:

How many open support tickets are there by priority?
What is the average resolution time by ticket category?
Which customer segments have the most urgent tickets?
Show support tickets by status.

Project structure

langchain_data_agent_poc/
├── agent.py          # LangGraph pipeline and Nebius LLM calls
├── app.py            # Streamlit UI
├── config.py         # Domain definitions and sample prompts
├── dataset.py        # CSV to in-memory SQLite loader
├── main.py           # CLI entrypoint
├── sql_safety.py     # Read-only SQL validation
├── visualization.py  # Simple chart suggestion logic
├── data/
│   ├── customers.csv
│   ├── orders.csv
│   ├── products.csv
│   └── support_tickets.csv
├── env.example
└── pyproject.toml

Notes

This example deliberately avoids external database ingestion, cloud warehouse connectors, auth adapters, A2A protocol, and production config loaders. Those are valuable in a full platform, but they obscure the core data-agent loop for a cookbook PoC.

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