Update app.py

app.py

@@ -1,132 +1,141 @@
-import gradio as gr
-from langchain_community.chat_message_histories import Neo4jChatMessageHistory
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_core.prompts.chat import MessagesPlaceholder
-from langchain_community.vectorstores import Neo4jVector
-from langchain_neo4j import Neo4jGraph
-from langchain_google_genai import ChatGoogleGenerativeAI
-from uuid import uuid4
-import os
+# import gradio as gr
+# from langchain_community.chat_message_histories import Neo4jChatMessageHistory
+# from langchain_core.runnables.history import RunnableWithMessageHistory
+# from langchain.prompts import ChatPromptTemplate
+# from langchain.schema import StrOutputParser
+# from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_core.prompts.chat import MessagesPlaceholder
+# from langchain_community.vectorstores import Neo4jVector
+# from langchain_neo4j import Neo4jGraph
+# from langchain_google_genai import ChatGoogleGenerativeAI
+# from uuid import uuid4
+# import os
+# from dotenv import load_dotenv
+# import asyncio
+
+# # Load environment variables
+# load_dotenv()
+
+# # Initialize variables
+# SESSION_ID = str(uuid4())
+# print(f"Session ID: {SESSION_ID}")
+
+# # Neo4j graph setup
+# graph = Neo4jGraph(
+#     url="neo4j+s://6682e6ce.databases.neo4j.io",
+#     username="neo4j",
+#     password=os.getenv("NEO4J_PASSWORD")
+# )
+
+# # HuggingFace embeddings
+# embeddings = HuggingFaceEmbeddings(
+#     model_name="sentence-transformers/all-MiniLM-L6-v2",
+#     model_kwargs={'device': 'cpu'},
+#     encode_kwargs={'normalize_embeddings': False}
+# )
+
+# # Create Neo4j VectorStore
+# graph_store = Neo4jVector.from_existing_index(
+#     embeddings,
+#     graph=graph,
+#     index_name="vector",
+#     embedding_node_property="Embedding",
+#     text_node_property="text",
+#     retrieval_query="""
+# // get the document
+# MATCH (node)-[:PART_OF]->(d:Document)
+# WITH node, score, d
+
+# // get the entities and relationships for the document
+# MATCH (node)-[:HAS_ENTITY]->(e)
+# MATCH p = (e)-[r]-(e2)
+# WHERE (node)-[:HAS_ENTITY]->(e2)
+
+# // unwind the path, create a string of the entities and relationships
+# UNWIND relationships(p) as rels
+# WITH
+#     node,
+#     score,
+#     d,
+#     collect(apoc.text.join(
+#         [labels(startNode(rels))[0], startNode(rels).id, type(rels), labels(endNode(rels))[0], endNode(rels).id]
+#         ," ")) as kg
+# RETURN
+#     node.text as text, score,
+#     {
+#         document: d.id,
+#         entities: kg
+#     } AS metadata
+# """)
+# retriever = graph_store.as_retriever()
+
+# # Define Cypher Prompt
+# CYPHER_PROMPT = """
+# (
+#     "Use the given context to provide an in-depth and structured response."
+#     "Your answer should include:"
+#     "- A clear and concise introduction to the topic."
+#     "- Detailed explanation or relevant steps to address the query."
+#     "- Practical examples or applications where possible."
+#     "- A conclusion summarizing the main points."
+#     "Format your response in sections with appropriate headings for clarity."
+#     "Context: {context}"
+# )
+# """
+# prompt = ChatPromptTemplate.from_messages([
+#     ("system", CYPHER_PROMPT),
+#     MessagesPlaceholder(variable_name="chat_history"),
+#     ("human", "{question}")
+# ])
+
+# # Helper function to retrieve context
+# def get_retrieved_context(query: str) -> str:
+#     retrieved_documents = retriever.get_relevant_documents(query)
+#     context = "\n".join(doc.page_content for doc in retrieved_documents)
+#     return context
+
+# def get_memory(session_id):
+#     return Neo4jChatMessageHistory(session_id=session_id, graph=graph)
+
+# def ReturnResponse(query: str) -> str:
+#     llm =  ChatGoogleGenerativeAI(
+#         model='gemini-2.0-flash-exp',
+#         api_key=os.getenv("GOOGLE_AI_STUDIO_API_KEY")
+#     )
+#     chat_chain = prompt | llm | StrOutputParser()
+
+#     chat_with_message_history = RunnableWithMessageHistory(
+#         chat_chain,
+#         get_memory,
+#         input_messages_key="question",
+#         history_messages_key="chat_history",
+#     )
+
+#     context = get_retrieved_context(query)
+#     response = chat_with_message_history.invoke({
+#         "question": query,
+#         "context": context,
+#     }, config={
+#         "configurable": {"session_id": SESSION_ID}
+#     })
+
+#     return gr.Markdown(response)
+
+
+# iface = gr.Interface(
+#     fn=ReturnResponse,
+#     inputs=gr.Textbox(label="Enter your query:", placeholder="Type your question here..."),
+#     outputs=gr.Markdown(label="Chatbot Response"),
+#     title="GraphRAG with conversational Memory 🤖💬"
+# )
+
+# iface.launch()
 from dotenv import load_dotenv
-import asyncio
+import os
 
-# Load environment variables
+# Load the environment variables from .env file
 load_dotenv()
 
-# Initialize variables
-SESSION_ID = str(uuid4())
-print(f"Session ID: {SESSION_ID}")
-
-# Neo4j graph setup
-graph = Neo4jGraph(
-    url="neo4j+s://6682e6ce.databases.neo4j.io",
-    username="neo4j",
-    password=os.getenv("NEO4J_PASSWORD")
-)
-
-# HuggingFace embeddings
-embeddings = HuggingFaceEmbeddings(
-    model_name="sentence-transformers/all-MiniLM-L6-v2",
-    model_kwargs={'device': 'cpu'},
-    encode_kwargs={'normalize_embeddings': False}
-)
-
-# Create Neo4j VectorStore
-graph_store = Neo4jVector.from_existing_index(
-    embeddings,
-    graph=graph,
-    index_name="vector",
-    embedding_node_property="Embedding",
-    text_node_property="text",
-    retrieval_query="""
-// get the document
-MATCH (node)-[:PART_OF]->(d:Document)
-WITH node, score, d
-
-// get the entities and relationships for the document
-MATCH (node)-[:HAS_ENTITY]->(e)
-MATCH p = (e)-[r]-(e2)
-WHERE (node)-[:HAS_ENTITY]->(e2)
-
-// unwind the path, create a string of the entities and relationships
-UNWIND relationships(p) as rels
-WITH
-    node,
-    score,
-    d,
-    collect(apoc.text.join(
-        [labels(startNode(rels))[0], startNode(rels).id, type(rels), labels(endNode(rels))[0], endNode(rels).id]
-        ," ")) as kg
-RETURN
-    node.text as text, score,
-    {
-        document: d.id,
-        entities: kg
-    } AS metadata
-""")
-retriever = graph_store.as_retriever()
-
-# Define Cypher Prompt
-CYPHER_PROMPT = """
-(
-    "Use the given context to provide an in-depth and structured response."
-    "Your answer should include:"
-    "- A clear and concise introduction to the topic."
-    "- Detailed explanation or relevant steps to address the query."
-    "- Practical examples or applications where possible."
-    "- A conclusion summarizing the main points."
-    "Format your response in sections with appropriate headings for clarity."
-    "Context: {context}"
-)
-"""
-prompt = ChatPromptTemplate.from_messages([
-    ("system", CYPHER_PROMPT),
-    MessagesPlaceholder(variable_name="chat_history"),
-    ("human", "{question}")
-])
-
-# Helper function to retrieve context
-def get_retrieved_context(query: str) -> str:
-    retrieved_documents = retriever.get_relevant_documents(query)
-    context = "\n".join(doc.page_content for doc in retrieved_documents)
-    return context
-
-def get_memory(session_id):
-    return Neo4jChatMessageHistory(session_id=session_id, graph=graph)
-
-def ReturnResponse(query: str) -> str:
-    llm =  ChatGoogleGenerativeAI(
-        model='gemini-2.0-flash-exp',
-        api_key=os.getenv("GOOGLE_AI_STUDIO_API_KEY")
-    )
-    chat_chain = prompt | llm | StrOutputParser()
-
-    chat_with_message_history = RunnableWithMessageHistory(
-        chat_chain,
-        get_memory,
-        input_messages_key="question",
-        history_messages_key="chat_history",
-    )
-
-    context = get_retrieved_context(query)
-    response = chat_with_message_history.invoke({
-        "question": query,
-        "context": context,
-    }, config={
-        "configurable": {"session_id": SESSION_ID}
-    })
-
-    return gr.Markdown(response)
-
-
-iface = gr.Interface(
-    fn=ReturnResponse,
-    inputs=gr.Textbox(label="Enter your query:", placeholder="Type your question here..."),
-    outputs=gr.Markdown(label="Chatbot Response"),
-    title="GraphRAG with conversational Memory 🤖💬"
-)
-
-iface.launch()
+# Now you can access your secrets
+neo4j_password = os.getenv("NEO4J_PASSWORD")
+print(neo4j_password)