https://github.com/rathod-shubham/genai-4-agenticworkflows
Exploring graph-based LLMs, multi-agent systems, Neo4j, LangGraph, and Cypher query language. This repo is my sandbox for learning, experimenting, and building graph-powered intelligent agents.
https://github.com/rathod-shubham/genai-4-agenticworkflows
Last synced: 6 months ago
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Exploring graph-based LLMs, multi-agent systems, Neo4j, LangGraph, and Cypher query language. This repo is my sandbox for learning, experimenting, and building graph-powered intelligent agents.
- Host: GitHub
- URL: https://github.com/rathod-shubham/genai-4-agenticworkflows
- Owner: RATHOD-SHUBHAM
- Created: 2024-09-16T00:59:01.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-01-08T03:36:23.000Z (9 months ago)
- Last Synced: 2025-02-15T02:29:47.764Z (8 months ago)
- Language: Jupyter Notebook
- Size: 27 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Graph-Based LLM Exploration
Welcome to my personal repository where I dive into the fascinating world of graph-based language models (LLMs) and graph databases like Neo4j. This space serves as my playground for learning, experimenting, and exploring how multi-agent systems interact with graph structures.
## About This Repo
This repository houses notebooks and projects that revolve around the intersection of LLMs and graph databases. Specifically, I’m focusing on concepts like:
* Graph-Based LLMs: Exploring how LLMs can utilize graph structures to represent knowledge, query data, and reason across nodes and relationships.
* Multi-Agent Systems: Investigating how multi-agent setups operate within graph environments, where each agent can represent a distinct node or task, with the ability to communicate and exchange information across the graph.
* Graph Databases: Working with tools like Neo4j and using Cypher query language to manage and query the graph, simulating how agents interact with dynamic knowledge graphs.
* LangGraph: Exploring its capabilities in integrating LLMs with graph databases to enhance decision-making and state management in multi-agent systems.
## Key Concepts Explored
* Graph Agents: LLM-based agents that operate over graph structures, with memory and state evolving as they traverse and query the nodes.
* State Management: Keeping track of an agent's knowledge and how it's updated as new information is retrieved from the graph.
* Querying with Cypher: Writing and optimizing Cypher queries to navigate and extract data efficiently from Neo4j databases.