Création d'un moteur de requête graphique simple

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)