app.polisplexity.tech/pxy_neo4j/neo4j_connector.py

112 lines
4.3 KiB
Python

import logging
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_openai import ChatOpenAI
from django.conf import settings
from langchain_community.graphs import Neo4jGraph
from langchain.schema import Document
from .models import Neo4jProfile
from datetime import datetime
from langchain.prompts import ChatPromptTemplate
from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
logger = logging.getLogger(__name__)
DEFAULT_PROFILE_NAME = "DefaultNeo4jProfile" # Define a default profile
class Neo4jDatabase:
"""
Handles connection to Neo4j using different profiles for various use cases.
"""
def __init__(self, profile_name=None):
try:
# Load the specified Neo4j profile, or fallback to the default
if profile_name:
self.profile = Neo4jProfile.objects.get(name=profile_name)
else:
logger.warning("No profile specified. Using default Neo4j profile.")
self.profile = Neo4jProfile.objects.get(name=DEFAULT_PROFILE_NAME)
# Connect to the assigned Neo4j instance
self.graph = Neo4jGraph(
url=self.profile.uri,
username=self.profile.username,
password=self.profile.password
)
# Load the correct AI model
self.llm = ChatOpenAI(
api_key=self.profile.openai_api_key,
model_name=self.profile.model_name,
temperature=0
)
# Add the custom prompt to enforce English
english_prompt = ChatPromptTemplate.from_template(
"Transform the following text into a graph structure. All nodes, relationships, and properties should be in English, regardless of the original language."
)
# Apply the custom English prompt here
self.graph_transformer = LLMGraphTransformer(
llm=self.llm,
prompt=english_prompt
)
logger.info(f"Neo4jDatabase initialized with profile: {self.profile.name}")
except Neo4jProfile.DoesNotExist:
logger.error(f"Neo4j profile '{profile_name}' not found.")
raise
except Exception as e:
logger.error(f"Failed to initialize Neo4jDatabase: {str(e)}")
raise
def store_interaction(self, user_id, bot_id, user_message, bot_response, platform):
"""
Stores a chatbot interaction as a structured graph in Neo4j.
Converts messages into `Document` objects to work with `LLMGraphTransformer`.
"""
try:
timestamp = datetime.utcnow().isoformat()
# Convert messages into `Document` objects
documents = [
Document(page_content=user_message, metadata={"role": "user", "user_id": user_id, "platform": platform, "created_at": timestamp}),
Document(page_content=bot_response, metadata={"role": "bot", "bot_id": bot_id, "platform": platform, "created_at": timestamp}),
]
# Convert text into structured graph documents
graph_docs = self.graph_transformer.convert_to_graph_documents(documents)
# Store the structured graph data into Neo4j
self.graph.add_graph_documents(graph_docs, include_source=True)
logger.info(f"Stored interaction in Neo4j (Profile: {self.profile.name})")
except Exception as e:
logger.error(f"Failed to store interaction in Neo4j: {str(e)}")
raise
def query_graph(self, user_query):
"""
Queries the graph using GraphCypherQAChain and returns a structured response.
"""
logger.info(f"Calling Neo4j {user_query}")
try:
# Use AI model to generate Cypher query
qa_chain = GraphCypherQAChain.from_llm(
llm=self.llm,
graph=self.graph,
verbose=True,
allow_dangerous_requests=True
)
result = qa_chain.invoke({"query": user_query})
logger.info(f"Resulting Neo4j {result}")
return result['result']
except Exception as e:
logger.error(f"Graph query failed: {e}")
return None