Upload app.py

app.py

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+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_community.graphs import Neo4jGraph
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.prompts.chat import MessagesPlaceholder
+from langchain.vectorstores import Neo4jVector
+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()