RAG AI with Neo4j
In recent years, the fusion of graph databases and AI has opened new avenues for intelligent applications. One such innovative approach is Retrieval-Augmented Generation AI (RAG AI). RAG AI models combine the strengths of large language models (LLMs) with external knowledge bases, making them more effective for tasks requiring both reasoning and retrieval of domain-specific information.
In this blog post, we will explore how RAG AI works, why it's becoming increasingly popular, and how we can integrate it with Neo4j, a powerful graph database. We will also walk through practical examples to illustrate the process.
What is RAG AI?
RAG AI is a novel approach that enhances the abilities of traditional language models by integrating them with retrieval mechanisms. In essence, RAG AI combines two key components:
This approach helps to address the limitations of traditional language models by allowing them to reference specific, factual information from external sources, improving both accuracy and relevance.
Why Combine RAG AI with Neo4j?
Neo4j is a leading graph database that excels at managing complex relationships and interconnected data. By combining Neo4j with RAG AI, we can build AI systems that are capable of:
How RAG AI Works with Neo4j
The typical flow for using RAG AI with Neo4j can be summarized as follows:
Setting Up RAG AI with Neo4j: Step-by-Step Guide
Prerequisites
bashCopy codepip install neo4j transformers langchain openai
Step 1: Setting Up Neo4j Database
First, we need to set up a sample Neo4j database. Let's assume we're building a knowledge graph for a product recommendation system. Here's an example of how you can create nodes and relationships in Neo4j.
cypherCopy codeCREATE (p1:Product {name: 'Laptop', category: 'Electronics'})
CREATE (p2:Product {name: 'Headphones', category: 'Electronics'})
CREATE (p3:Product {name: 'Coffee Machine', category: 'Home Appliance'})
CREATE (c1:Category {name: 'Electronics'})
CREATE (c2:Category {name: 'Home Appliance'})
CREATE (p1)-[:BELONGS_TO]->(c1)
CREATE (p2)-[:BELONGS_TO]->(c1)
CREATE (p3)-[:BELONGS_TO]->(c2)
This will create a simple graph with products belonging to different categories.
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Step 2: Integrating Neo4j with RAG AI
Next, we'll integrate our Neo4j graph database with a retriever to extract information from the knowledge graph based on a user query.
pythonCopy codefrom neo4j import GraphDatabase
# Connect to Neo4j
uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "password"))
def retrieve_data(query):
with driver.session() as session:
result = session.run(query)
return [record["name"] for record in result]
query = "MATCH (p:Product)-[:BELONGS_TO]->(c:Category {name: 'Electronics'}) RETURN p.name as name"
products = retrieve_data(query)
print(products)
This code connects to the Neo4j database, runs a query to retrieve all products under the "Electronics" category, and prints the results.
Step 3: Using an LLM for Generation
Once we retrieve the relevant information from Neo4j, we can use a language model like GPT-3 to generate a response based on this data. Below is an example using OpenAI's GPT-3.
pythonCopy codeimport openai
openai.api_key = 'YOUR_OPENAI_API_KEY'
def generate_response(retrieved_data):
prompt = f"Based on the following products: {', '.join(retrieved_data)}, recommend a product for someone who loves technology."
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=100
)
return response.choices[0].text.strip()
recommendation = generate_response(products)
print(recommendation)
Here, we use the retrieved products from Neo4j to generate a product recommendation using GPT-3.
Step 4: Full RAG AI Pipeline
Finally, we can build the full pipeline by integrating retrieval and generation into a single function.
pythonCopy codedef rag_pipeline(user_query):
# Step 1: Retrieve relevant data from Neo4j
retrieval_query = "MATCH (p:Product)-[:BELONGS_TO]->(c:Category {name: 'Electronics'}) RETURN p.name as name"
retrieved_data = retrieve_data(retrieval_query)
# Step 2: Generate response using LLM
response = generate_response(retrieved_data)
return response
# Test the pipeline
user_query = "I need a recommendation for electronics products."
final_response = rag_pipeline(user_query)
print(final_response)
This simple pipeline retrieves data from the Neo4j knowledge graph and generates a personalized recommendation using GPT-3.
Use Cases of RAG AI with Neo4j
Conclusion
Retrieval-augmented generation AI represents a powerful way to enhance traditional language models by incorporating domain-specific knowledge from external sources. By combining RAG AI with Neo4j, developers can build intelligent systems that are not only able to understand natural language queries but also retrieve and generate responses based on structured data.
Whether you're building a recommendation engine, a Q&A system, or solving complex problems with graph-based relationships, RAG AI and Neo4j offer an innovative approach to bringing intelligence and accuracy to your AI applications.
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