Complete Session On Knowledge Graph and GraphDb With Langchain

Introduction

Hello all, my name is Krishak, and welcome to my YouTube channel. Recently, I conducted a 3-hour live workshop to explain graph databases (graph DB) with Langchain. In this entire session, we discussed graph DB, knowledge graphs, and how to convert specific data into a knowledge graph and store it in some kind of graph DB, such as Neo4j. We also covered end-to-end RAG (retrieval augmented generation) projects with the help of graph DB and Langchain.

This particular session was a paid session, with a minimum token amount of 50 rupees because I conducted it on Zoom. Some kind people also donated more so everyone could attend the session. Seeing this kind generosity, going forward, I plan to make such sessions free for at least a thousand people, bearing some expenses myself. However, I will depend slightly on your donations to support these initiatives.

In this session, please check out the recorded content on graph DB and Langchain. It's an excellent session where I explain each and everything in great detail so you can start your journey. The main aim is to cover new, emerging topics in generative AI and explain them in live sessions where I can also interact with you.

Agenda

1. Understanding Knowledge Graphs

   • Knowledge graphs are semantic networks of real-world entities like events, situations, concepts, names, and terms.
   • They illustrate relationships between these entities.
   • Examples: "Rohit Sharma is the captain of the Indian cricket team" becomes Rohit Sharma -> captain -> Indian cricket team.

2. Neo4j Graph Database

   • Utilized for storing and managing graph data models.
   • Provides realtime insights and easy data retrieval with Cipher query language.
   • Compared to RDBMS:
     • Nodes vs. Tables
     • Relationships vs. Constraints (Primary Key, Foreign Key, etc.)
     • Easier querying (no nested queries or joins required).

3. Cipher Query Language

   • Declarative query language to represent graphs visually.
   • Examples include creating nodes, establishing relationships, and querying data.
   • It simplifies complex operations like nested queries and joins.

4. Graph DB with Langchain

   • Integration of Langchain with Neo4j.
   • Use of Langchain's experimental libraries for graph-to-text and text-to-graph transformations.
   • Application for RAG projects for easier and more efficient data retrieval.

Practical Implementation

1. Creating Nodes and Relationships

   • Examples of creating nodes such as actors and movies.
   • Using Cipher queries:

     CREATE (k:Person (name: "Krish", born: "1989"))
     CREATE (r:Person (name: "Robert Downey Jr.", born: "1970"))
     

2. Using Google Collab and Langchain for Integration

   • Setting up environment variables for Neo4j.
   • Using Grok API to access open-source LLM models.
   • Converting text data to documents and then to graph documents:

     from langchain.core.experimental.graphcore import LLmGraphTransformer
     llm_transformer = LLmGraphTransformer(llm=llm)
     graph_documents = llm_transformer.convert_to_graph_documents(documents)
     

3. Executing Cipher Queries with LLM

   • Automating the generation of Cipher queries using Langchain and LLM for simplified querying.
   • Example query:

     chain.invoke("Who was the director of the movie 'Golden Eye'")
     

keyword

• Knowledge Graph
• Graph DB
• Neo4j
• Cipher Query Language
• Langchain
• RAG Projects
• Generative AI
• Grok API

FAQ

Q1: What is a knowledge graph?
A knowledge graph is a semantic network of real-world entities like events, situations, concepts, names, and terms. It illustrates the relationships between these entities.

Q2: What advantages does Neo4j offer for graph databases?
Neo4j provides realtime insights, easy data retrieval using Cipher query language, and simplifies complex operations like nested queries and relationships.

Q3: How is Graph DB different from RDBMS?
Graph DB uses nodes and relationships to represent data, whereas RDBMS uses tables and constraints (like Primary Key, Foreign Key). Graph DBs are more versatile for complex queries and relationships.

Q4: How does Langchain integrate with Neo4j?
Langchain provides libraries to work with graphs, allowing for the conversion of text data into graph documents and automated query generation.

Q5: What is the purpose of using Grok API?
Grok API enables access to open-source LLM (Large Language Model) models, facilitating text-to-graph and graph-to-text transformations in Langchain.

Q6: Can we automate the generation of Cipher queries?
Yes, Langchain allows for the automation of Cipher query generation using integrated LLM models, making querying much easier.