過去 2 つのブログでは、neo4j をインストールしてそこにデータをロードする方法を説明しました。このブログでは、質問に答える、neo4j からデータを取得する単純なグラフ クエリ エンジンを構築する方法を見ていきます。
ステップ 1 : CYPHER クエリを構築する
-
正規表現 - このクエリには、graphDB
内のデータと一致する正規表現パターンが含まれます。
-
レーベンシュタイン類似度 - このクエリは、0.5 を超えるしきい値スコアを持つレーベンシュタイン類似度を使用して、グラフ DB と照合してデータを取得します。
-
埋め込みベースの一致 - すでにデータベースに埋め込みをプッシュしているため、このクエリはユーザー クエリの埋め込みを使用して、コサイン類似度からのスコアを使用して完全なリストを並べ替えます。おそらくこれもトップ5に戻るように改善される可能性があります。
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
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ステップ 2 - 3 番目のクエリに埋め込みを設定する
- 3 番目のクエリは gds.similarity.cosine() を使用しているため、ユーザー クエリを埋め込みに変換し、3 番目のクエリに入力しています。
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
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ステップ 3 - DB にクエリを実行する
- 準備された暗号クエリを使用して DB にクエリを実行します
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
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ステップ 4 - 拡張生成
- 拡張生成技術を使用して取得したデータを GPT にヒットし、拡張情報を利用してユーザーのクエリに対する応答を生成します。
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
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完全なコード
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
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試してみましょう
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)
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出力
次のブログでは、この設定を API として公開するシンプルな FastAPI アプリを構築します。
これがお役に立てば幸いです...!!!
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
以上がシンプルなグラフクエリエンジンの構築の詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。
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