Free Download Introduction to Neo4j with Python, LangChain & OpenAI
Published 1/2024
Created by Ali Binkowska
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 49 Lectures ( 1h 9m ) | Size: 572 MB
From CSV to Neo4j Database with Vector Index: Plug Your Data into LLM
What you'll learn:
Basics of Neo4j
Basics of Cypher language used to query Neo4j
How to create graph database using CSV files
How to create embeddings
How to create vector index based on embeddings
How to work with Neo4j with Python
How to plug Neo4j into ChatGPT using LangChain
Requirements:
Python Basics
Description:
Dive into the world of graph databases with 'Introduction to Neo4j with Python, LangChain & OpenAI'. This course guides you gently from the very basics of creating Neo4j database via a web browser. We will use AuraDB, cloud-based service by Neo4j that enables us to create one free instance of the database.On the way you will learn how to interact with the database using Cypher language.Next, we will use simple Python code for powerful data work. Initial code will be provided via repository.We'll also play with LangChain and OpenAI to make your data come alive. As we are gaining to use new Neo4j capability to create vector index, we will look at the data from two angles: - we will query database schema, using LLM as a translator of questions into cypher queries- we will query vector index using embeddings imported into the databaseWe will briefly also touch on the subject of creating embeddings for the data stored in the database.No heavy tech talk, just clear steps and support for your learning journey. As part of the training comes Neo4j Cheat Sheet and a list of cypher queries used in the project with detailed explanation.With that, knowledge from the course can be transferred to another project that uses Neo4j and Python.Join me to unlock the potential of your data with Neo4j!
Who this course is for:
Anyone with basic knowledge of Python who want to turn CSV into Neo4j database and plug it into an LLM
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