Im vorherigen Blog haben wir gesehen, wie man neo4j lokal mit den beiden Plugins APOC und Graph Data Science Library – GDS installiert und einrichtet. In diesem Blog werde ich einen Spielzeugdatensatz (Produkte auf einer E-Commerce-Website) nehmen und diesen in Neo4j speichern.
Bevor Sie mit dem Laden der Daten beginnen, wenn Sie in Ihrem Anwendungsfall über große Datenmengen verfügen, stellen Sie sicher, dass neo4j ausreichend Speicher zugewiesen wird. Um das zu tun:
Grafiken haben zwei Hauptkomponenten: Knoten und Beziehungen. Erstellen wir zuerst die Knoten und etablieren später die Beziehungen.
Die Daten, die ich verwende, sind hier vorhanden – Daten
Verwenden Sie die hier vorhandene Datei „requirements.txt“, um eine virtuelle Python-Umgebung zu erstellen – „requirements.txt“
Lassen Sie uns verschiedene Funktionen zum Übertragen von Daten definieren.
Importieren notwendiger Bibliotheken
import pandas as pd from neo4j import GraphDatabase from openai import OpenAI
client = OpenAI(api_key="") product_data_df = pd.read_csv('../data/product_data.csv')
def get_embedding(text): """ Used to generate embeddings using OpenAI embeddings model :param text: str - text that needs to be converted to embeddings :return: embedding """ model = "text-embedding-3-small" text = text.replace("\n", " ") return client.embeddings.create(input=[text], model=model).data[0].embedding
def create_category(product_data_df): """ Used to generate queries for creating category nodes in neo4j :param product_data_df: pandas dataframe - data :return: query_list: list - list containing all create node queries for category """ cat_query = """CREATE (a:Category {name: '%s', embedding: %s})""" distinct_category = product_data_df['Category'].unique() query_list = [] for category in distinct_category: embedding = get_embedding(category) query_list.append(cat_query % (category, embedding)) return query_list
def create_product(product_data_df): """ Used to generate queries for creating product nodes in neo4j :param product_data_df: pandas dataframe - data :return: query_list: list - list containing all create node queries for product """ product_query = """CREATE (a:Product {name: '%s', description: '%s', price: %d, warranty_period: %d, available_stock: %d, review_rating: %f, product_release_date: date('%s'), embedding: %s})""" query_list = [] for idx, row in product_data_df.iterrows(): embedding = get_embedding(row['Product Name'] + " - " + row['Description']) query_list.append(product_query % (row['Product Name'], row['Description'], int(row['Price (INR)']), int(row['Warranty Period (Years)']), int(row['Stock']), float(row['Review Rating']), str(row['Product Release Date']), embedding)) return query_list
def execute_bulk_query(query_list): """ Executes queries is a list one by one :param query_list: list - list of cypher queries :return: None """ url = "bolt://localhost:7687" auth = ("neo4j", "neo4j@123") with GraphDatabase.driver(url, auth=auth) as driver: with driver.session() as session: for query in query_list: try: session.run(query) except Exception as error: print(f"Error in executing query - {query}, Error - {error}")
import pandas as pd from neo4j import GraphDatabase from openai import OpenAI client = OpenAI(api_key="") product_data_df = pd.read_csv('../data/product_data.csv') def preprocessing(df, columns_to_replace): """ Used to preprocess certain column in dataframe :param df: pandas dataframe - data :param columns_to_replace: list - column name list :return: df: pandas dataframe - processed data """ df[columns_to_replace] = df[columns_to_replace].apply(lambda col: col.str.replace("'s", "s")) df[columns_to_replace] = df[columns_to_replace].apply(lambda col: col.str.replace("'", "")) return df def get_embedding(text): """ Used to generate embeddings using OpenAI embeddings model :param text: str - text that needs to be converted to embeddings :return: embedding """ model = "text-embedding-3-small" text = text.replace("\n", " ") return client.embeddings.create(input=[text], model=model).data[0].embedding def create_category(product_data_df): """ Used to generate queries for creating category nodes in neo4j :param product_data_df: pandas dataframe - data :return: query_list: list - list containing all create node queries for category """ cat_query = """CREATE (a:Category {name: '%s', embedding: %s})""" distinct_category = product_data_df['Category'].unique() query_list = [] for category in distinct_category: embedding = get_embedding(category) query_list.append(cat_query % (category, embedding)) return query_list def create_product(product_data_df): """ Used to generate queries for creating product nodes in neo4j :param product_data_df: pandas dataframe - data :return: query_list: list - list containing all create node queries for product """ product_query = """CREATE (a:Product {name: '%s', description: '%s', price: %d, warranty_period: %d, available_stock: %d, review_rating: %f, product_release_date: date('%s'), embedding: %s})""" query_list = [] for idx, row in product_data_df.iterrows(): embedding = get_embedding(row['Product Name'] + " - " + row['Description']) query_list.append(product_query % (row['Product Name'], row['Description'], int(row['Price (INR)']), int(row['Warranty Period (Years)']), int(row['Stock']), float(row['Review Rating']), str(row['Product Release Date']), embedding)) return query_list def execute_bulk_query(query_list): """ Executes queries is a list one by one :param query_list: list - list of cypher queries :return: None """ url = "bolt://localhost:7687" auth = ("neo4j", "neo4j@123") with GraphDatabase.driver(url, auth=auth) as driver: with driver.session() as session: for query in query_list: try: session.run(query) except Exception as error: print(f"Error in executing query - {query}, Error - {error}") # PREPROCESSING product_data_df = preprocessing(product_data_df, ['Product Name', 'Description']) # CREATE CATEGORY query_list = create_category(product_data_df) execute_bulk_query(query_list) # CREATE PRODUCT query_list = create_product(product_data_df) execute_bulk_query(query_list)
from neo4j import GraphDatabase import pandas as pd product_data_df = pd.read_csv('../data/product_data.csv') def preprocessing(df, columns_to_replace): """ Used to preprocess certain column in dataframe :param df: pandas dataframe - data :param columns_to_replace: list - column name list :return: df: pandas dataframe - processed data """ df[columns_to_replace] = df[columns_to_replace].apply(lambda col: col.str.replace("'s", "s")) df[columns_to_replace] = df[columns_to_replace].apply(lambda col: col.str.replace("'", "")) return df def create_category_food_relationship_query(product_data_df): """ Used to create relationship between category and products :param product_data_df: dataframe - data :return: query_list: list - cypher queries """ query = """MATCH (c:Category {name: '%s'}), (p:Product {name: '%s'}) CREATE (c)-[:CATEGORY_CONTAINS_PRODUCT]->(p)""" query_list = [] for idx, row in product_data_df.iterrows(): query_list.append(query % (row['Category'], row['Product Name'])) return query_list def execute_bulk_query(query_list): """ Executes queries is a list one by one :param query_list: list - list of cypher queries :return: None """ url = "bolt://localhost:7687" auth = ("neo4j", "neo4j@123") with GraphDatabase.driver(url, auth=auth) as driver: with driver.session() as session: for query in query_list: try: session.run(query) except Exception as error: print(f"Error in executing query - {query}, Error - {error}") # PREPROCESSING product_data_df = preprocessing(product_data_df, ['Product Name', 'Description']) # CATEGORY - FOOD RELATIONSHIP query_list = create_category_food_relationship_query(product_data_df) execute_bulk_query(query_list)
Bewegen Sie den Mauszeiger über dasÖffnen-Symbol und klicken Sie aufneo4j-Browser, um die von uns erstellten Knoten anzuzeigen.
Und unsere Daten werden zusammen mit ihren Einbettungen in neo4j geladen.
In den kommenden Blogs werden wir sehen, wie man mit Python eine Graph-Abfrage-Engine erstellt und die abgerufenen Daten für die erweiterte Generierung verwendet.
Ich hoffe, das hilft... Wir sehen uns!!!
LinkedIn – https://www.linkedin.com/in/praveenr2998/
Github – https://github.com/praveenr2998/Creating-Lightweight-RAG-Systems-With-Graphs/tree/main/push_data_to_db
Das obige ist der detaillierte Inhalt vonLaden Sie Daten in Neo4j. Für weitere Informationen folgen Sie bitte anderen verwandten Artikeln auf der PHP chinesischen Website!
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