https://github.com/kanad13/knowledgegraphai

Build a knowledge graph in Neo4j, that is enriched with AI capabilities for data retrieval (using NLP).
https://github.com/kanad13/knowledgegraphai

ai ml mysql neo4j nlp

Last synced: 21 days ago
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Build a knowledge graph in Neo4j, that is enriched with AI capabilities for data retrieval (using NLP).

• Host: GitHub
• URL: https://github.com/kanad13/knowledgegraphai
• Owner: kanad13
• License: mit
• Created: 2024-05-27T14:03:52.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-16T17:59:13.000Z (7 months ago)
• Last Synced: 2024-11-03T08:42:01.142Z (2 months ago)
• Topics: ai, ml, mysql, neo4j, nlp
• Language: Shell
• Homepage:
• Size: 20.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        
# KnowledgeGraphAI

## project purpose and goals

- the code in this repository aims to showcase how

  - data from a legacy relational database can be transformed into a knowledge graph in Neo4j, and

  - enriched with AI capabilities for enhanced data retrieval and natural language processing

## tools involved

- **infrastructure**

  - `Docker`: for MySQL, Neo4J containers

  - `Google Cloud`: for porting local application to the cloud

- **Databases**

  - `MySQL`: legacy relational database

  - `Neo4j`: graph database for knowledge graph creation

- **Frontend**

  - `Streamlit`: for building the frontend application

- **AI**

  - `OpenAI/Gemini/Llama`: large language models for query processing

  - `LangChain`: for integrating LLMs with the knowledge graph

## sequence of steps

- this section lists the sequence of steps that will be taken as part of this project:

  - clone this repository locally and setup development environment

  - deploy MySQL and Neo4j containers using docker compose

  - load sample data into MySQL

  - move data from MySQL to Neo4J

  - build the knowledge graph inside Neo4J

  - develop frontend application with Streamlit

  - setup connection to LLM

  - implement query handling

  - port tool to cloud

## installation and setup

- detailed steps on setting up the dev environment and other topics are available inside the [setup folder](./setup/)

## references

- [Knowledge Graphs - A Practical Review of the Research Landscape](/references/Knowledge_Graphs-A_Practical_Review.pdf)

- [Building Semantic Knowledge Graphs](/references/Building_Semantic_Knowledge_Graphs.pdf)

- [Knowledge Graphs - Research Paper](/references/Knowledge_Graphs-Research_paper.pdf)

- O'Reilly - Building Knowledge Graphs Practitioner's Guide [listing here](https://www.oreilly.com/library/view/building-knowledge-graphs/9781098127091/) and [direct book here](https://go.neo4j.com/rs/710-RRC-335/images/Building-Knowledge-Graphs-Practitioner%27s-Guide-OReilly-book.pdf)

- [Neo4j Knowledge Graph Blog](https://neo4j.com/blog/what-is-knowledge-graph/)

- [Knowledge Graph RAG Application](https://neo4j.com/developer-blog/knowledge-graph-rag-application/)

- [Neo4j & LLM Fundamentals](https://graphacademy.neo4j.com/courses/llm-fundamentals/)

- [Introduction to Vector Indexes and Unstructured Data](https://graphacademy.neo4j.com/courses/llm-vectors-unstructured/)

- [Building Neo4j Applications with Python](https://graphacademy.neo4j.com/courses/app-python/)

- [LLM Graph Builder GitHub](https://github.com/neo4j-labs/llm-graph-builder)

- [Neo4j LLM Knowledge Graph Builder](https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/)

- [LangChain Documentation](https://python.langchain.com/v0.1/docs/use_cases/graph/constructing/)

- [Install Neo4J using Docker Compose](https://neo4j.com/docs/operations-manual/current/docker/introduction/)