Dalam 2 blog terakhir kami melihat cara memasang neo4j dan memuatkan data ke dalamnya. Dalam blog ini, kami akan melihat cara membina enjin pertanyaan graf ringkas yang menjawab soalan kami tetapi mendapatkan semula data daripada neo4j.
Langkah 1 : BINA CYPHER QUERY
Untuk membina pertanyaan cypher, kami perlu memberikan maklumat skema, maklumat harta kepada GPT bersama-sama dengan soalan kami. Menggunakan metadata GPT ini akan memberi kami pertanyaan.
Saya telah menstrukturkan gesaan untuk mengembalikan 3 pertanyaan untuk setiap input pengguna
-
Ungkapan biasa - Pertanyaan ini akan mempunyai corak regex untuk memadankan data dalam graphDB
-
Persamaan Levenshtein - Pertanyaan ini akan menggunakan persamaan levenshtein dengan skor ambang lebih daripada 0.5 untuk memadankan dan mengambil data daripada graf DB.
-
Membenamkan padanan berasaskan - Kami telah pun menolak pembenaman ke dalam pangkalan data kami, jadi pertanyaan ini akan menggunakan pembenaman pertanyaan pengguna untuk menyusun semula senarai lengkap menggunakan skor daripada persamaan kosinus. Mungkin ini boleh diperbaiki untuk kembali 5 teratas juga.
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
Salin selepas log masuk
LANGKAH 2 - POPULASI PEMBEDASAN DALAM SOALAN KETIGA
- Pertanyaan ke-3 menggunakan gds.similarity.cosine() jadi kami menukar pertanyaan pengguna kepada pembenaman dan mengisinya dalam pertanyaan ke-3
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
Salin selepas log masuk
LANGKAH 3 - SOAL DB
- Soal DB menggunakan pertanyaan cypher yang disediakan
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
Salin selepas log masuk
LANGKAH 4 - PENJANAAN GENERASI
- Menggunakan data yang diambil memukul GPT menggunakan teknik penjanaan tambahan untuk menjana respons untuk pertanyaan pengguna dengan bantuan maklumat tambahan
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
Salin selepas log masuk
KOD LENGKAP
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
Salin selepas log masuk
JOM CUBA
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)
Salin selepas log masuk
OUTPUT
Dalam blog seterusnya kami akan membina apl FastAPI yang mudah untuk mendedahkan persediaan ini sebagai API.
Semoga ini membantu... !!!
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
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