from neo4j import GraphDatabase
uri = "bolt://neo4j_database:7687"
username = "neo4j"
password = "neo4j_admin"
driver = GraphDatabase.driver(uri, auth=(username, password))
def push_data(tx):
# Create historical figures as nodes
tx.run("""
CREATE (mahendra:Person {name: 'King Mahendra', role: 'King of Nepal', reign: '1955-1972'})
CREATE (rana_regime:Event {name: 'Rana Regime', description: 'Autocratic rule by the Rana family in Nepal', period: '1846-1951'})
CREATE (tribhuvan:Person {name: 'King Tribhuvan', role: 'King of Nepal', reign: '1911-1955'})
CREATE (prithvi:Person {name: 'King Prithvi Narayan Shah', role: 'Founder of Unified Nepal', reign: '1743-1775'})
CREATE (bp_koirala:Person {name: 'BP Koirala', role: 'First Democratically Elected Prime Minister of Nepal', term: '1959-1960'})
CREATE (military_coup:Event {name: '1960 Military Coup', description: 'King Mahendra’s coup that dissolved the democratic government', year: 1960})
""")
# Create relationships
tx.run("""
MATCH (mahendra:Person {name: 'King Mahendra'}),
(tribhuvan:Person {name: 'King Tribhuvan'}),
(rana_regime:Event {name: 'Rana Regime'}),
(prithvi:Person {name: 'King Prithvi Narayan Shah'}),
(bp_koirala:Person {name: 'BP Koirala'}),
(military_coup:Event {name: '1960 Military Coup'})
CREATE (tribhuvan)-[:ENDED]->(rana_regime)
CREATE (mahendra)-[:CONDUCTED]->(military_coup)
CREATE (bp_koirala)-[:OVERTHROWN_BY]->(military_coup)
CREATE (prithvi)-[:FOUNDED]->(:Entity {name: 'Unified Nepal'})
CREATE (mahendra)-[:SUCCEEDED]->(tribhuvan)
""")
try:
with driver.session() as session:
session.execute_write(push_data)
print("Data pushed successfully!")
print(uri)
except Exception as e:
print(f"An error occurred: {e}")
finally:
driver.close()
eRror：
Embedding model initialized successfully.
Error creating vector index: [Errno 111] Connection refused
langchain == 0.3.13
neo4j == 5.27.0
langchain_ollama == 0.2.2
Environment Variables:
NEO4J_URI=bolt://172.20.0.3:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=neo4j_admin
neo4j浏览器可在
http://localhost:7474和
bolt://172.20.0.3:7687.中访问。
但是，当我尝试将其与Langchain和Ollama一起使用时，我将面临这个问题。
这是我的Langchain代码，它不起作用：from langchain_ollama import OllamaEmbeddings
from langchain_community.vectorstores.neo4j_vector import Neo4jVector
import os
from dotenv import load_dotenv
from langchain.schema import Document
load_dotenv()
URI = os.getenv("NEO4J_URL")
username = os.getenv("NEO4J_USERNAME")
password = os.getenv("NEO4J_PASSWORD")
chunked_folder_path = os.getenv("CHUNK_FOLDER_PATH")
index = "vector"
keyword_index_name = "keyword"
print("neo4j url:" ,URI)
print("neo4j username:" ,username)
print("neo4j password:" ,password)
def read_documents(chunked_folder_path):
docs = []
try:
for filename in os.listdir(chunked_folder_path):
if filename.endswith(".txt"):
file_path = os.path.join(chunked_folder_path, filename)
with open(file_path, 'r', encoding='utf-8') as file:
content = file.read()
docs.append(Document(page_content=content, metadata={"filename": filename}))
else:
print(f"Skipped non-txt file: {filename}")
except Exception as e:
print(f"Error reading documents from folder: {e}")
return docs
def retrieval_from_graph(documents):
try:
embedding_model = OllamaEmbeddings(model="mxbai-embed-large")
print("Embedding model initialized successfully.")
except Exception as e:
print(f"Error initializing the embedding model: {e}")
return None
try:
vectorstore = Neo4jVector.from_existing_index(
embedding=embedding_model,
url=URI,
username=username,
password=password,
search_type="hybrid",
index_name = index,
keyword_index_name=keyword_index_name,
node_label=["Events", "Person"],
embedding_node_property="embedding",
)
print("Successfully connected to the existing Neo4j vector index.")
return vectorstore
except Exception as e:
print(f"Existing index not found, Creating a new one ......: {e}")
documents = read_documents(chunked_folder_path)
if not documents:
print("No documents were loaded. Cannot create index")
return None
try:
vectorstore = Neo4jVector.from_documents(
embedding=embedding_model,
documents=documents,
url=URI,
username=username,
password=password,
search_type="hybrid",
index_name = index,
keyword_index_name=keyword_index_name,
node_label=["Events", "Person"],
embedding_node_property="embedding",
)
print("New vector index created successfully")
return vectorstore
except Exception as creation_error:
print(f"Error creating vector index: {creation_error}")
def similarity_search(vectorstore, query):
try:
docs_with_score = vectorstore.similarity_search_with_score(query, k=2)
for doc, score in docs_with_score:
print(f"Document: {doc.page_content}\nScore: {score}")
except Exception as e:
print(f"Error during similarity search: {e}")
def query_similarity_search(query ):
documents = read_documents(chunked_folder_path)
if not documents:
print("No document found in the folder")
return
vectorstore = retrieval_from_graph(documents)
if not vectorstore:
print("Failed to create vector store")
return
result =similarity_search(vectorstore, query)
return result
if __name__ == "__main__":
query = "Who is King Birendra"
query_similarity_search(query)
这是
my docker-compose.yaml：
services:
web:
build:
context: .
dockerfile: Dockerfile
environment:
DATABASE_URL: "postgresql://postgres_user:postgres_password@db:5432/postgres_db"
OLLAMA_URL: "http://ollama:11434"
QDRANT_URL: "http://qdrant_database:6333"
NEO4J_URL: "bolt://neo4j_database:7687"
ports:
- "8000:8000"
depends_on:
- db
- ollama
- qdrant_database
- neo4j
volumes:
- .:/app
networks:
- my_network
command: ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
restart: always
db:
image: postgres:13
environment:
POSTGRES_USER: postgres_user
POSTGRES_PASSWORD: postgres_password
POSTGRES_DB: postgres_db
volumes:
- pgdata:/var/lib/postgresql/data
ports:
- "5432:5432"
networks:
- my_network
ollama:
image: ollama/ollama:latest
container_name: ollama
restart: unless-stopped
ports:
- "11434:11434"
volumes:
- ~/.ollama/models:/root/.ollama/models
deploy:
resources:
reservations:
devices:
- driver: nvidia
capabilities: [gpu]
count: all
networks:
- my_network
qdrant_database:
image: qdrant/qdrant:latest
container_name: qdrant_database
restart: unless-stopped
ports:
- "6333:6333"
environment:
QDRANT_CONFIG_PATH: /qdrant/config.yml
volumes:
- ./qdrant/config.yml:/qdrant/config.yml
- ./qdrant_data:/qdrant/storage/
- ./retrievers:/app/retrieval/
networks:
- my_network
neo4j:
image: neo4j:latest
container_name: neo4j_database
restart: unless-stopped
ports:
- "7474:7474"
- "7687:7687"
environment:
- NEO4J_AUTH=neo4j/neo4j_admin
- NEO4J_dbms_connector_bolt_listen__address=0.0.0.0:7687
- NEO4J_dbms_connector_bolt_advertised__address=:7687
- NEO4J_dbms_connector_http_advertised__address=:7474
- NEO4J_dbms_default__listen__address=0.0.0.0
volumes:
- ./neo4j/data:/data
- ./neo4j/logs:/logs
- ./neo4j/import:/neo4j/import
- ./neo4j/plugins:/plugins
networks:
- my_network
volumes:
pgdata:
driver: local
neo4j_data:
driver: local
neo4j_import:
driver: local
neo4j_plugins:
driver: local
neo4j_logs:
driver: local
networks:
my_network:
driver: bridge
当使用Langchain的Neo4JVector时，我何时会拒绝连接拒绝错误？在维护当前的Docker设置时，我该如何解决此问题？
为灵感来看：https：//github.com/docker/genai-stack/blob/main/main/docker-compose.yml问题可能是
NEO4J_URI=bolt://172.20.0.3:7687，尝试
NEO4J_URI=neo4j://neo4j:7687