Timbr Gen-AI Examples

This repository provides a collection of examples and reference implementations for working with the Timbr LangChain SDK and related tools. The examples demonstrate how to use Timbr's ontology-driven semantic layer, natural language interfaces, and graph-based orchestration to build advanced data and knowledge applications.

Example Directories

1. LangChain

This folder contains end-to-end and component-level examples for using the Timbr LangChain SDK with natural language queries, SQL generation, and knowledge graph access. Key highlights:

• ./LangChain/streamlit_app.py: Interactive Streamlit web app for querying Timbr using natural language.
• ./LangChain/timbr_execute_query_chain.py: Example of using the Execute Query Chain for NL-to-SQL-to-results workflows.
• ./LangChain/timbr_generate_sql_chain.py: Example of generating Timbr SQL from natural language queries.
• ./LangChain/timbr_qa_pipeline.py: Example pipeline for question answering using Timbr and LangChain.
• ./LangChain/timbr_sql_agent.py: Example of creating and using a Timbr SQL Agent with LangChain.
• ./LangChain/README.md: Detailed instructions, requirements, and usage examples for each script.

2. GraphRAG

This folder demonstrates how to use Timbr with Graph-based Retrieval-Augmented Generation (GraphRAG) workflows. It includes:

• ./GraphRAG/app.py: Main application for running GraphRAG with Timbr.
• ./GraphRAG/snow_utils.py and ./GraphRAG/timbr_utils.py: Example of utility modules for working with Snowflake and Timbr.
• ./GraphRAG/data/: Example datasets and PDFs for use in the GraphRAG pipeline.
• ./GraphRAG/images/: Architecture and workflow diagrams.
• ./GraphRAG/setup/: SQL scripts and setup instructions for preparing your environment.
• ./GraphRAG/README.md: Overview, requirements, and step-by-step instructions for running GraphRAG examples.

3. LangGraph

This folder provides modular, node-based examples using LangGraph for building conversational and workflow graphs with Timbr. It features:

• ./LangGraph/timbr_execute_semantic_sql_node.py: Node for executing semantic SQL queries.
• ./LangGraph/timbr_generate_sql_node.py: Node for generating Timbr SQL from NL prompts.
• ./LangGraph/timbr_graph_pipeline.py: Example of a full LangGraph pipeline.
• ./LangGraph/timbr_identify_concept_node.py: Node for concept and schema identification.
• ./LangGraph/timbr_validate_semantic_sql.py: Node for validating and adjusting SQL.
• ./LangGraph/timbr-langgraph.png: Visual diagram of the LangGraph workflow.
• ./LangGraph/README.md: Usage, requirements, and code samples for each node and workflow.

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Getting Started

1. Review the README in each example subfolder for setup and usage instructions.
2. Install the required dependencies as listed in each example's README.
3. Follow the step-by-step guides to run the apps, pipelines, or workflows.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 📧 Email
• 📖 Timbr Documentation
• 📽️ Tutorials and videos
• 🌐 Timbr Website