Implementing Retrieval-Augmented Generation (RAG) with a Knowledge Graph (KG)

Constructing a KG with skincare products found on an e-commerce website

Product similarity search with vector embeddings

Tools: Python, OpenAI (free tier), LangChain, Neo4j

Set up

1. Create a Python virtual environment where the dependencies for this project will be installed.

cd server
python3 -m venv venv

2. Activate the environment and install all the packages available in the requirement.txt file.

source venv/bin/activate
pip install -r ./requirements.txt

3. If a .env file is not present in the server folder, create one to store the private OpenAI API key, which is required to use the LLMs.

OPENAI_API_KEY=XXXXXX
NEO4J_PW=XXX

4. Run the Python script to web scrape product information found online.

python3 web_scrape.py

5. The llm_kg.ipynb file explains:

• How to use LLM to extract new relations from product descriptions and construct Knowledge Graph
• Methods to query the graph database (based on embeddings, LLM-generated entities in prompt, Cypher)