在上一篇部落格中，我們了解如何使用 2 個插件 APOC 和圖形資料科學庫 - GDS 在本地安裝和設定 neo4j。在這篇部落格中，我將取得一個玩具資料集（電子商務網站中的產品）並將其儲存在 Neo4j 中。
為 Neo4j 分配足夠的內存
在開始載入資料之前，如果您的用例中有大量數據，請確保為 Neo4j 分配了足夠的記憶體。為此：
- 在neo4j.conf 中搜尋heap，取消註釋第77、78 行，並將256m 更改為2048m，這樣可以確保Neo4j 中分配2048mb 用於資料儲存.
建立節點
import pandas as pd
from neo4j import GraphDatabase
from openai import OpenAI
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client = OpenAI(api_key="")
product_data_df = pd.read_csv('../data/product_data.csv')
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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
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- 根據我們的資料集，我們可以有兩個唯一的節點標籤，類別：產品類別，產品：產品名稱。讓我們建立類別標籤，neo4j 提供了一種稱為屬性的東西，您可以將它們想像為特定節點的元資料。這裡 name 和 embedding 是屬性。因此，我們將類別名稱及其相應的嵌入儲存在資料庫中。
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
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- 類似地，我們可以建立產品節點，這裡的屬性將是name, description, price, warranty_period, 🎜>可用庫存、評論評級、產品發布日期、嵌入
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
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現在讓我們建立另一個函數來執行上述兩個函數產生的查詢。適當更新您的使用者名稱和密碼。
-
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}")
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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)
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建立關係
我們將在 - 類別 和 產品 之間建立關係，關係的名稱為 CATEGORY_CONTAINS_PRODUCT
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)
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透過使用 MATCH 查詢來匹配已建立的節點，我們在它們之間建立關係。 -
可視化創建的節點
將滑鼠停留在
開啟 圖示上，然後按一下 neo4j 瀏覽器 以視覺化我們建立的節點。
我們的資料連同它們的嵌入一起載入到 Neo4j 中。
在接下來的部落格中，我們將看到如何使用 python 建立圖形查詢引擎並使用獲取的資料進行增強生成。
希望這有幫助...再見！ ！ ！
領英 - https://www.linkedin.com/in/praveenr2998/
Github - https://github.com/praveenr2998/Creating-Lightweight-RAG-Systems-With-Graphs/tree/main/push_data_to_db
以上是將資料載入到 Neo4j 中的詳細內容。更多資訊請關注PHP中文網其他相關文章！
