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Explore the realms of graph databases with Neo4j, dive into Cypher queries, and integrate LLMs for dynamic data insights with Langchain. A personal journey to master graph data.

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MyGraphDBLearningLab

Welcome to MyGraphDBLearningLab, a personal exploration and learning journey into the world of graph databases, Cypher query language, and integrating Large Language Models (LLMs) for enhanced data insights. This repository serves as a hands-on guide to understand and apply Neo4j graph database operations, delve into the power of Cypher for querying, and leverage LLMs to transform and analyze graph-structured data.

Getting Started

Prerequisites

  • Python 3.8+
  • Neo4j Database
  • An API key for your preferred LLM provider (e.g., OpenAI for GPT-3)

What Will Be Covered

This repository is designed as a comprehensive learning journey into graph databases, Cypher query language, and the integration of Large Language Models (LLMs) for enhancing data insights. Here's what you'll explore:

  • Neo4j Basics: Start from scratch by setting up your Neo4j database, understanding the graph model, and performing basic Create, Read, Update, and Delete (CRUD) operations.

  • Cypher Queries: Dive deep into the Cypher query language, learning how to construct complex queries to manipulate and retrieve data efficiently from your Neo4j database.

  • LLM Integration: Learn how to leverage Large Language Models to interact with your graph data. This section covers how to use LLMs for generating Cypher queries, interpreting results, and even creating data narratives from your graph database.

  • Data to Graph Transformation: Discover techniques for transforming traditional data into graph-based structures. Understand the principles of graph thinking and how to apply this approach to enrich your data insights.

In addition to the interactive Jupyter notebooks, this repository includes standalone Python scripts (/scripts) that mirror the code presented in the notebooks. These scripts provide an alternative way to review and run the examples in a more traditional execution environment, facilitating a deeper understanding of the material.

Whether you're a beginner curious about graph databases and NLP or an experienced developer looking to integrate these technologies, this repository offers a structured path to mastering these exciting areas.

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