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Build Internal and Customer Facing Data Apps

Maintain semantic layer and chat with db accurately

pip install dataneuron

Choose Your Integration

Python SDK

from dataneuron import DataNeuron

dn = DataNeuron(db_config='database.yaml', context='your_context')
dn.initialize()

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

result = dn.query("How many users signed up last month?")
print(f"SQL: {result['sql']}")
print(f"Result: {result['result']}")
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API Endpoint

Server: dnn --server 

Request: POST /chat
{
"messages": [
{"role": "user", "content": "How many users signed up last month?"}
],
"context_name": "your_context",
"client_value": "client_123" // Optional: For multi-tenant scenarios
}

Response:
{
"sql": "SELECT COUNT(*) FROM users WHERE ...",
"result": {"count": 1234},
"explanation": "Last month, 1234 new users signed up."
}
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Chat using CLI

Semantic Layer

The framework to maintain your semantic layer

Key Features

Semantic Layer

Easily maintain and improve your data context using YAML files, ensuring accurate and relevant query responses.

Natural Language to SQL

Convert plain English questions into precise SQL queries effortlessly.

Multiple Databases

Support for SQLite, PostgreSQL, MySQL, MSSQL, CSV files (via DuckDB), and Clickhouse.

Multi-Tenant Support

Easily manage client-specific data access and queries within a single instance.

Flexible Integration

Use as a Python SDK, deploy as an API endpoint, or interact via CLI for versatile integration options.

LLM Integration

Powered by major Language Models like Claude (default), OpenAI, LLAMA, and more.

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Ready to transform how you interact with your data?

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