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Creating Graph DB AI agents for applying GraphRAG

Instructor based workshop for creating AI agents for extracting context from Graph database for GraphRAG applications.

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4.7

Rating

10 Hrs

Duration

Advance

Level

10

Assignments

What you'll learn

What is Graph database and how to extract content from it
How to create an AI agent that can interact with knowledge DB using natural language
How to use this Agent to extract context for a Graph RAG application

Who should take this course

Ideal for anyone with python programming, LLM and Knowledge graph understanding
It is recommended to do the “LLM for beginners” training before joining this course
Also doing “Building Knowledge Graph from unstructured documents” workshop before this course will be beneficial

Skills you'll gain

AI Agents, Knowledge Graph, Neo4j DB, Graph RAG, Python, Streamlit, LLM
Knowledge Graph
Neo4j DB
Graph RAG
Python
Streamlit
LLM

Course contents

Limitation of Naive RAG
Benefits of GraphRAG
Application areas of GraphRAG

Components
How it works
Varieties of GraphRAG

Will be using a pre-built knowledge graph stored in Neo4j
Querying the GraphDB

How GraphRAG improves retrievals
Types of Retrievers

Create demo app
User Query processing and output retrieval
Evaluate the output performance metrics

Building Knowledge Graph from documents

Here we will built a pipeline to convert unstructured text data into a graph representation of knowledge base.

Building domain based Embedding model

In this course we will fine-tune a BERT models with medical data that can understand medical terminology and answer medical questions more accurately.

Building custom NER for medical domain

This workshop will focus on creating specialized NER model trained on medical texts to identify medical entities and extract relationship among them.