We're thrilled to announce a powerful integration between LangChain and Memgraph, bringing you an unparalleled natural language interface to your Memgraph database. Say goodbye to complex queries and welcome a seamless and intuitive way to interact with your data.

Memgraph QA chain tutorial

If you've ever wanted to effortlessly query your Memgraph database using natural language, this tutorial is for you. This step-by-step guide will walk you through the process, ensuring you have all the tools you need to get started.

Prerequisites

Before you dive in, make sure you have Docker and Python 3.x installed on your system.

Get started

Launch a Memgraph Instance: With a few simple commands, you can have your Memgraph instance up and running using Docker. Just follow our script to set it up.

Install dependencies: We've got you covered with the required packages. Use pip to install langchain, openai, neo4j, and gqlalchemy. Don't forget the --user flag to ensure smooth permissions.

Code playtime: Whether you prefer working within this notebook or want to use a separate Python file, the tutorial offers code snippets to guide you through the process.

What's inside

Explore the rich features and functionalities that LangChain and Memgraph offer together:

API reference: We provide an overview of the key components you'll be working with, such as ChatOpenAI, GraphCypherQAChain, and MemgraphGraph.

Populating the database: Learn how to populate your Memgraph database effortlessly using the Cypher query language. We guide you through the process of seeding data that serves as the foundation for your work.

Refresh graph schema: Familiarize yourself with refreshing the graph schema, a crucial step in setting up the Memgraph-LangChain graph for Cypher queries.

Querying the database: Discover how to interact with the OpenAI API and configure your API key. We'll show you how to utilize the GraphCypherQAChain to ask questions and receive informative responses.

Chain modifiers: Customize your chain's behavior with modifiers like return_direct, return_intermediate_steps, and top_k. Tailor the experience to your preferences.

Advanced querying: Delve into advanced querying techniques and uncover tips for refining your prompts to improve query accuracy.

Ready to take your data interaction to the next level? Join us in exploring the seamless synergy between LangChain and Memgraph. No more wrangling with queries – just natural language and meaningful insights. Simplify complexity, elevate your insights, and share your projects in our community.