💬 Advanced Real Time Graphrag Application With Langchain Neo4j And Gpt: You Need to Master GPT Expert!
Hey there! Ready to dive into Real Time Graphrag Application With Langchain Neo4j And Gpt? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to GraphRAG - Made Simple!
GraphRAG (Graph Retrieval-Augmented Generation) is an innovative approach that combines graph databases, natural language processing, and large language models to extract, organize, and query knowledge from unstructured data. This technology uses the power of graph structures to represent complex relationships between entities and uses cool language models to interpret and generate human-like responses.
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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Source Code for Introduction to GraphRAG - Made Simple!
Let me walk you through this step by step! Here’s how we can tackle this:
# Simulating a basic GraphRAG structure
class GraphRAG:
def __init__(self):
self.graph = {}
self.llm = None # Placeholder for language model
def add_node(self, node, attributes):
self.graph[node] = attributes
def add_edge(self, node1, node2, relationship):
if node1 not in self.graph:
self.graph[node1] = {}
if node2 not in self.graph:
self.graph[node2] = {}
self.graph[node1][node2] = relationship
def query(self, question):
# Simulate query processing
return f"Processed query: {question}"
# Usage example
graph_rag = GraphRAG()
graph_rag.add_node("Person", {"name": "John"})
graph_rag.add_node("City", {"name": "New York"})
graph_rag.add_edge("Person", "City", "lives_in")
result = graph_rag.query("Where does John live?")
print(result)
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✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Components of RAGraphX v1 - Made Simple!
RAGraphX v1 integrates several key technologies to create a powerful GraphRAG application. The main components are:
- LangChain: A framework for developing applications powered by language models.
- Neo4j: A graph database management system.
- OpenAI GPT: cool language models for natural language processing and generation.
- Streamlit: A framework for creating web applications with Python.
These components work together to process documents, extract knowledge, store it in a graph structure, and enable natural language querying.
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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Source Code for Components of RAGraphX v1 - Made Simple!
Here’s where it gets exciting! Here’s how we can tackle this:
# Simulating RAGraphX v1 components
from langchain import LLMChain
from neo4j import GraphDatabase
import openai
import streamlit as st
# LangChain setup
llm = LLMChain(prompt="Your prompt here")
# Neo4j setup
driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"))
# OpenAI GPT setup
openai.api_key = "your-api-key"
# Streamlit app
def main():
st.title("RAGraphX v1")
# File uploader
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
if uploaded_file is not None:
# Process the file (placeholder)
st.write("Processing file...")
# Query input
query = st.text_input("Enter your query")
if query:
# Process query (placeholder)
st.write(f"Processing query: {query}")
if __name__ == "__main__":
main()
🚀 Document Processing Workflow - Made Simple!
The document processing workflow in RAGraphX v1 involves several steps:
- PDF Upload: Users upload PDF documents through the Streamlit interface.
- Text Extraction: The application extracts text content from the PDFs.
- Entity Recognition: Using LangChain and GPT models, the system identifies key entities and relationships in the text.
- Graph Creation: Identified entities and relationships are stored in the Neo4j graph database.
This process transforms unstructured document data into a structured, queryable graph representation.
🚀 Source Code for Document Processing Workflow - Made Simple!
Let’s break this down together! Here’s how we can tackle this:
import PyPDF2
from langchain import OpenAI, LLMChain
from neo4j import GraphDatabase
def process_document(file):
# Extract text from PDF
pdf_reader = PyPDF2.PdfReader(file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
# Entity recognition (simplified)
llm = OpenAI(temperature=0)
entities = llm("Extract entities from: " + text[:1000]) # Using first 1000 chars as example
# Store in Neo4j (simplified)
driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
for entity in entities.split(','):
session.run("CREATE (e:Entity {name: $name})", name=entity.strip())
return "Document processed and entities stored in graph"
# Usage in Streamlit app
if uploaded_file is not None:
result = process_document(uploaded_file)
st.write(result)
🚀 Natural Language Query Processing - Made Simple!
RAGraphX v1 allows users to interact with the stored knowledge using natural language queries. The query processing involves:
- Query Input: Users enter their questions in natural language.
- Query Translation: The system uses GPT models to translate the natural language query into a Cypher query, which is the query language for Neo4j.
- Graph Querying: The generated Cypher query is executed on the Neo4j database.
- Response Generation: The retrieved information is processed and formatted into a human-readable response.
This way lets you intuitive interaction with complex graph data structures.
🚀 Source Code for Natural Language Query Processing - Made Simple!
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
from openai import OpenAI
from neo4j import GraphDatabase
def process_query(query):
# Translate query to Cypher
client = OpenAI(api_key="your-api-key")
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "Translate the following query to Cypher:"},
{"role": "user", "content": query}
]
)
cypher_query = response.choices[0].message.content
# Execute Cypher query
driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
result = session.run(cypher_query).data()
# Generate response (simplified)
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "Summarize this data:"},
{"role": "user", "content": str(result)}
]
)
return response.choices[0].message.content
# Usage in Streamlit app
query = st.text_input("Enter your query")
if query:
result = process_query(query)
st.write(result)
🚀 Real-Life Example: Scientific Literature Review - Made Simple!
Consider a researcher using RAGraphX v1 to analyze a large corpus of scientific papers. They upload multiple PDFs of research articles, and the system extracts key information such as authors, institutions, methodologies, and findings. This information is stored in the Neo4j graph, creating a network of interconnected research data.
The researcher can then query this knowledge graph with natural language questions like “What are the recent advancements in CRISPR gene editing?” The system would process this query, search the graph for relevant information, and provide a complete summary of recent CRISPR developments found in the uploaded papers.
🚀 Source Code for Scientific Literature Review Example - Made Simple!
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
# Simulating scientific literature review with RAGraphX v1
def process_scientific_papers(papers):
graph = {}
for paper in papers:
# Extract info (simplified)
info = extract_paper_info(paper)
# Add to graph
add_to_graph(graph, info)
return graph
def extract_paper_info(paper):
# Simulate extraction using LLM
llm_response = llm(f"Extract title, authors, and key findings from: {paper[:500]}")
# Parse LLM response (simplified)
return parse_llm_response(llm_response)
def add_to_graph(graph, info):
# Add nodes and edges to graph (simplified)
graph[info['title']] = {
'authors': info['authors'],
'findings': info['findings']
}
def query_graph(graph, query):
# Simulate natural language query processing
relevant_papers = [title for title, data in graph.items() if query.lower() in data['findings'].lower()]
return f"Relevant papers for '{query}': {', '.join(relevant_papers)}"
# Example usage
papers = ["Paper 1 content...", "Paper 2 content...", "Paper 3 content..."]
knowledge_graph = process_scientific_papers(papers)
result = query_graph(knowledge_graph, "CRISPR gene editing")
print(result)
🚀 Real-Life Example: Product Recommendation System - Made Simple!
Imagine an e-commerce platform implementing RAGraphX v1 to create a smart product recommendation system. The application processes product descriptions, customer reviews, and purchase histories, creating a complex graph of product relationships and customer preferences.
When a customer asks, “What hiking boots are best for rainy conditions?”, the system navigates the graph to find boots with high waterproof ratings, positive reviews for wet conditions, and frequently co-purchased with rain gear. It then generates a personalized recommendation based on the customer’s past purchases and preferences.
🚀 Source Code for Product Recommendation System Example - Made Simple!
Let me walk you through this step by step! Here’s how we can tackle this:
class ProductRecommendationSystem:
def __init__(self):
self.product_graph = {}
self.customer_preferences = {}
def add_product(self, product_id, attributes):
self.product_graph[product_id] = attributes
def add_customer_preference(self, customer_id, preferences):
self.customer_preferences[customer_id] = preferences
def recommend_products(self, query, customer_id):
# Simulate natural language processing
keywords = self.extract_keywords(query)
# Find matching products
matching_products = self.find_matching_products(keywords)
# Personalize recommendations
personalized_recommendations = self.personalize_recommendations(matching_products, customer_id)
return personalized_recommendations
def extract_keywords(self, query):
# Simplified keyword extraction
return [word.lower() for word in query.split() if len(word) > 3]
def find_matching_products(self, keywords):
matching_products = []
for product_id, attributes in self.product_graph.items():
if any(keyword in str(attributes).lower() for keyword in keywords):
matching_products.append(product_id)
return matching_products
def personalize_recommendations(self, products, customer_id):
if customer_id in self.customer_preferences:
preferences = self.customer_preferences[customer_id]
return sorted(products, key=lambda p: self.calculate_relevance(p, preferences), reverse=True)
return products
def calculate_relevance(self, product_id, preferences):
# Simplified relevance calculation
return sum(1 for pref in preferences if pref in str(self.product_graph[product_id]).lower())
# Example usage
recommender = ProductRecommendationSystem()
recommender.add_product("boot1", {"type": "hiking", "features": ["waterproof", "durable"]})
recommender.add_product("boot2", {"type": "hiking", "features": ["lightweight", "breathable"]})
recommender.add_customer_preference("customer1", ["waterproof", "hiking"])
recommendations = recommender.recommend_products("hiking boots for rainy conditions", "customer1")
print(f"Recommended products: {recommendations}")
🚀 Challenges and Future Developments - Made Simple!
While RAGraphX v1 represents a significant advancement in knowledge extraction and querying, several challenges and areas for future development remain:
- Scalability: Handling extremely large datasets and complex graphs smartly.
- Query Accuracy: Improving the accuracy of natural language to Cypher query translation.
- Knowledge Integration: Seamlessly integrating information from diverse sources and formats.
- Real-time Updates: Enabling dynamic updates to the knowledge graph as new information becomes available.
- Ethical Considerations: Addressing privacy concerns and ensuring responsible use of extracted knowledge.
Future versions of RAGraphX may incorporate cool graph algorithms, improved natural language understanding, and enhanced visualization capabilities to address these challenges.
🚀 Source Code for Challenges and Future Developments - Made Simple!
Let’s break this down together! Here’s how we can tackle this:
import time
from concurrent.futures import ThreadPoolExecutor
class AdvancedGraphRAG:
def __init__(self):
self.graph = {}
self.query_cache = {}
def add_node(self, node, attributes):
self.graph[node] = attributes
def query(self, natural_language_query):
# Check cache first
if natural_language_query in self.query_cache:
return self.query_cache[natural_language_query]
# Simulate complex query processing
time.sleep(2) # Simulating processing time
result = f"Processed: {natural_language_query}"
# Cache the result
self.query_cache[natural_language_query] = result
return result
def batch_process(self, queries):
with ThreadPoolExecutor(max_workers=5) as executor:
return list(executor.map(self.query, queries))
# Example usage
graph_rag = AdvancedGraphRAG()
graph_rag.add_node("Entity1", {"attribute": "value"})
# Single query
print(graph_rag.query("What is Entity1?"))
# Batch processing
batch_queries = ["Query1", "Query2", "Query3"]
results = graph_rag.batch_process(batch_queries)
print(f"Batch results: {results}")
🚀 Additional Resources - Made Simple!
For those interested in diving deeper into the concepts and technologies behind GraphRAG applications, the following resources are recommended:
- “Graph Neural Networks for Natural Language Processing” by Shikhar Vashishth et al. (2019) - Available on arXiv: https://arxiv.org/abs/1901.08746
- “Knowledge Graphs” by Aidan Hogan et al. (2020) - Available on arXiv: https://arxiv.org/abs/2003.02320
- “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” by Patrick Lewis et al. (2020) - Available on arXiv: https://arxiv.org/abs/2005.11401
These papers provide in-depth discussions on graph-based approaches in natural language processing, knowledge representation, and retrieval-augmented generation techniques.
🎊 Awesome Work!
You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.
What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.
Keep coding, keep learning, and keep being awesome! 🚀