In the last 2 blogs we saw how to install neo4j and load data into it. In this blog we are going to see how to build a simple graph query engine which answers our question by retrieving data from neo4j.
Step 1 : BUILD CYPHER QUERY
To build a cypher query we need to give schema information, property information to GPT along with our question. Using this metadata GPT will give us query.
I have structured the prompt to return 3 queries for every user input
- Regular expressions - This query will have regex patterns to match data in graphDB
- Levenshtein Similarity - This query will use levenshtein similarity with a threshold score of greater than 0.5 to match and fetch data from graph DB.
- Embedding based match - We have pushed embeddings into our database already, so this query will use embedding of user query to reorder the complete list using score from cosine similarity. Maybe this could be improved to return top 5 too.
class GraphQueryEngine:
def __init__(self):
self.client = OpenAI(api_key="")
self.url = "bolt://localhost:7687"
self.auth = ("neo4j", "neo4j@123")
def get_response(self, user_input):
"""Used to get cypher queries from user input"""
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are an expert in generating Cypher queries for a Neo4j database. Your task is to understand the input and generate only Cypher read queries. Do not return anything other than the Cypher queries, as the returned result will be executed directly in the database."},
{"role": "user",
"content": f"""
Schema Information:
NODES: Product_type - Contains the distinct types of products such as headphones/mobiles/laptops/washing machines, Product_details - Contains products within a product_type for example apple, samsung within mobiles, DELL within laptops
NODE PROPERTIES: In node Product_type there are name(name of the product type - String), embedding(embedding of the name), and in node Product_details there are name(name of the product - string), price(price of the product - integer), description(description of the product), product_release_date(when product was release on - date), available_stock(stock left - integer), review_rating(product review - float)
DIRECTION OF RELATIONSHIPS: Node Product_type is connected to node Product_details using relationship CONTAINS
Based on the schema, generate three read-only Cypher queries related to Product_type (e.g., chairs, headphones, fridge) or Product_details (e.g., name, description) or combination of both. Ensure that product category uses Product_type and product name/ price
Query 1: Use regular expressions (avoid 'contains') - Exclude the 'embedding' property from the result.
Query 2: Use `apoc.text.levenshteinSimilarity > 0.5` - Exclude the 'embedding' property from the result.
Query 3: Use `gds.similarity.cosine()` to reorder nodes based on similarity scores. The query must include a `%s` placeholder for embedding input but exclude the 'embedding' property in the result.
Generate targeted queries using relationships only when necessary. The embedding property should only be used in the logic and must not appear in the query results.
Strictly return only the Cypher queries with no embeddings. The returned result will be executed directly in the database.
{user_input}
"""},
],
)
response = completion.choices[0].message.content
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are an expert in parsing generating Cypher queries."},
{"role": "user",
"content": f"""Use this input - {response} and parse and return only the cypher queries from the input, ensure that in the cypher query if it returns embeddings then remove the embeddings alone from the query"""},
],
response_format=CypherQuery,
)
event = completion.choices[0].message.parsed
cypher_queries = event.cypher_queries
print("################################## CYPHER QUERIES ######################################")
for query in cypher_queries:
print(query)
return cypher_queries
STEP 2 - POPULATE EMBEDDINGS IN THE THIRD QUERY
- The 3rd query uses gds.similarity.cosine() so we are converting user query to embeddings and populating it in the 3rd query
def populate_embedding_in_query(self, user_input, cypher_queries):
"""Used to add embeddings of the user input in the 3rd query"""
model = "text-embedding-3-small"
user_input = user_input.replace("\n", " ")
embeddings = self.client.embeddings.create(input=[user_input], model=model).data[0].embedding
cypher_queries[2] = cypher_queries[2] % embeddings
return cypher_queries
STEP 3 - QUERY THE DB
- Query the DB using the prepared cypher queries
def execute_read_query(self, query):
"""Execute the cypher query"""
results = []
with GraphDatabase.driver(self.url, auth=self.auth) as driver:
with driver.session() as session:
try:
result = session.run(query)
# Collect the result from the read query
records = [record.data() for record in result]
if records:
results.append(records)
except Exception as error:
print(f"Error in executing query")
return results
def fetch_data(self, cypher_queries):
"""Return the fetched data from DB post formatting"""
results = None
for idx in range(len(cypher_queries)):
try:
results = self.execute_read_query(cypher_queries[idx])
if results:
if idx == len(cypher_queries) - 1:
results = results[0][:10]
break
except Exception:
pass
return results
STEP 4 - AUGMENTED GENERATION
- Using the fetched data hit GPT using augmented generation technique to generate response for user query with the help of augumented information
def get_final_response(self, user_input, fetched_data):
"""Augumented generation using data fetched from DB"""
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are a chatbot for an ecommerce website, you help users to identify their desired products"},
{"role": "user", "content": f"""User query - {user_input}
Use the below metadata to answer my query
{fetched_data}
"""},
],
)
response = completion.choices[0].message.content
return response
COMPLETE CODE
from openai import OpenAI
from pydantic import BaseModel
from typing import List
from neo4j import GraphDatabase
class CypherQuery(BaseModel):
cypher_queries: List[str]
class GraphQueryEngine:
def __init__(self):
self.client = OpenAI(api_key="")
self.url = "bolt://localhost:7687"
self.auth = ("neo4j", "neo4j@123")
def populate_embedding_in_query(self, user_input, cypher_queries):
"""Used to add embeddings of the user input in the 3rd query"""
model = "text-embedding-3-small"
user_input = user_input.replace("\n", " ")
embeddings = self.client.embeddings.create(input=[user_input], model=model).data[0].embedding
cypher_queries[2] = cypher_queries[2] % embeddings
return cypher_queries
def execute_read_query(self, query):
"""Execute the cypher query"""
results = []
with GraphDatabase.driver(self.url, auth=self.auth) as driver:
with driver.session() as session:
try:
result = session.run(query)
# Collect the result from the read query
records = [record.data() for record in result]
if records:
results.append(records)
except Exception as error:
print(f"Error in executing query")
return results
def get_response(self, user_input):
"""Used to get cypher queries from user input"""
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are an expert in generating Cypher queries for a Neo4j database. Your task is to understand the input and generate only Cypher read queries. Do not return anything other than the Cypher queries, as the returned result will be executed directly in the database."},
{"role": "user",
"content": f"""
Schema Information:
NODES: Product_type - Contains the distinct types of products such as headphones/mobiles/laptops/washing machines, Product_details - Contains products within a product_type for example apple, samsung within mobiles, DELL within laptops
NODE PROPERTIES: In node Product_type there are name(name of the product type - String), embedding(embedding of the name), and in node Product_details there are name(name of the product - string), price(price of the product - integer), description(description of the product), product_release_date(when product was release on - date), available_stock(stock left - integer), review_rating(product review - float)
DIRECTION OF RELATIONSHIPS: Node Product_type is connected to node Product_details using relationship CONTAINS
Based on the schema, generate three read-only Cypher queries related to Product_type (e.g., chairs, headphones, fridge) or Product_details (e.g., name, description) or combination of both. Ensure that product category uses Product_type and product name/ price
Query 1: Use regular expressions (avoid 'contains') - Exclude the 'embedding' property from the result.
Query 2: Use `apoc.text.levenshteinSimilarity > 0.5` - Exclude the 'embedding' property from the result.
Query 3: Use `gds.similarity.cosine()` to reorder nodes based on similarity scores. The query must include a `%s` placeholder for embedding input but exclude the 'embedding' property in the result.
Generate targeted queries using relationships only when necessary. The embedding property should only be used in the logic and must not appear in the query results.
Strictly return only the Cypher queries with no embeddings. The returned result will be executed directly in the database.
{user_input}
"""},
],
)
response = completion.choices[0].message.content
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are an expert in parsing generating Cypher queries."},
{"role": "user",
"content": f"""Use this input - {response} and parse and return only the cypher queries from the input, ensure that in the cypher query if it returns embeddings then remove the embeddings alone from the query"""},
],
response_format=CypherQuery,
)
event = completion.choices[0].message.parsed
cypher_queries = event.cypher_queries
print("################################## CYPHER QUERIES ######################################")
for query in cypher_queries:
print(query)
return cypher_queries
def get_final_response(self, user_input, fetched_data):
"""Augumented generation using data fetched from DB"""
completion = self.client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system",
"content": "You are a chatbot for an ecommerce website, you help users to identify their desired products"},
{"role": "user", "content": f"""User query - {user_input}
Use the below metadata to answer my query
{fetched_data}
"""},
],
)
response = completion.choices[0].message.content
return response
def fetch_data(self, cypher_queries):
"""Return the fetched data from DB post formatting"""
results = None
for idx in range(len(cypher_queries)):
try:
results = self.execute_read_query(cypher_queries[idx])
if results:
if idx == len(cypher_queries) - 1:
results = results[0][:10]
break
except Exception:
pass
return results
LET'S TRY IT
user_input = input("Enter your question : ")
query_engine = GraphQueryEngine()
cypher_queries = query_engine.get_response(user_input)
cypher_queries = query_engine.populate_embedding_in_query(user_input, cypher_queries)
fetched_data = query_engine.fetch_data(cypher_queries)
response = query_engine.get_final_response(user_input, fetched_data)
OUTPUT
In the next blog we'll build a simple FastAPI app to expose this setup as an API.
Hope this helps... !!!
LinkedIn - https://www.linkedin.com/in/praveenr2998/
Github - https://github.com/praveenr2998/Creating-Lightweight-RAG-Systems-With-Graphs/blob/main/fastapi_app/query_engine.py
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