> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Timbr integrations

> Integrate with Timbr using LangChain Python.

> What is `Timbr`?

> * Timbr is a `semantic SQL knowledge graph platform` that specializes in connecting data through ontology-driven semantic layers.
> * Timbr allows you to represent and query data using business-friendly language, making it ideal for handling complex data relationships and business logic.
> * Timbr provides `natural language to SQL` capabilities, making it easy to interact with your data using plain English queries.
> * With Timbr, you can achieve high-performance `semantic data querying`, suitable for production-level analytics and business intelligence.

> Get started with Timbr by visiting [their website](https://docs.timbr.ai/doc/docs/getting-started/intro-timbr/).

## Installation and setup

* Install the Python SDK with `pip install langchain-timbr`

### Optional: Install with selected LLM provider

Choose one of: openai, anthropic, google, azure\_openai, snowflake, databricks, vertex\_ai (or 'all')

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install 'langchain-timbr[<your selected providers, separated by comma without spaces>]'
```

## Semantic SQL queries

Timbr provides a wrapper around its semantic layer that generates SQL statements based on natural language input and retrieves relevant information from your knowledge graph.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_timbr import create_timbr_sql_agent, ExecuteTimbrQueryChain, GenerateTimbrSqlChain
```

See a [usage example](/oss/python/integrations/graphs/timbr)

***

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