在過去的 2 篇部落格中，我們了解如何安裝 neo4j 並將資料載入其中。在這篇部落格中，我們將了解如何建立一個簡單的圖形查詢引擎來回答我們的問題，但從 Neo4j 檢索資料。
要建立密碼查詢，我們需要向 GPT 提供架構資訊、屬性資訊以及我們的問題。使用此元資料 GPT 將為我們提供查詢。
我已經建立了提示，為每個使用者輸入回傳 3 個查詢
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
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 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
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
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
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)
在下一篇部落格中，我們將建立一個簡單的 FastAPI 應用程序，將此設定公開為 API。
希望有幫助...!!!
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
以上是建立一個簡單的圖查詢引擎的詳細內容。更多資訊請關注PHP中文網其他相關文章！
