feat: send-demand node to generate mail, if information are missing

customer_support.py

@@ -3,6 +3,7 @@ from langchain_anthropic import ChatAnthropic
 from langgraph.graph import StateGraph, START, END
 
 from src.edges.check_agent_handling import route_agent_processing
+from src.edges.check_user_identification import verify_user
 from src.models import TicketState, create_ticket_state
 from src.nodes.create_ticket import create_ticket
 from src.nodes.extract_informations import extract_information
@@ -10,6 +11,7 @@ from src.nodes.forward_human_agent import forward_to_human_agent
 from src.nodes.generate_response import generate_response
 from src.nodes.identify_ticket_typ import identify_ticket_type
 from src.nodes.identify_user import identify_user
+from src.nodes.send_demand import send_demand
 from src.nodes.read_mail import read_email
 from src.nodes.solve_ticket_agent import solve_ticket_agent
 from src.nodes.separate_topics import separate_topics
@@ -25,6 +27,7 @@ ticket_graph = StateGraph(TicketState)
 ticket_graph.add_node("read_email", read_email)
 ticket_graph.add_node("extract_information", extract_information)
 ticket_graph.add_node("identify_user", identify_user)
+ticket_graph.add_node("send_demand", send_demand)
 ticket_graph.add_node("separate_topics", separate_topics)
 ticket_graph.add_node("identify_ticket_type", identify_ticket_type)
 ticket_graph.add_node("create_ticket", create_ticket)
@@ -35,7 +38,17 @@ ticket_graph.add_node("generate_response", generate_response)
 ticket_graph.add_edge(START, "read_email")
 ticket_graph.add_edge("read_email", "extract_information")
 ticket_graph.add_edge("extract_information", "identify_user")
-ticket_graph.add_edge("identify_user", "separate_topics")
+
+ticket_graph.add_conditional_edges(
+    "identify_user",
+    verify_user,
+    {
+        True: "separate_topics",
+        False: "send_demand"
+    }
+)
+
+ticket_graph.add_edge("send_demand", END)
 ticket_graph.add_edge("separate_topics", "identify_ticket_type")
 ticket_graph.add_edge("identify_ticket_type", "create_ticket")
 
@@ -56,44 +69,43 @@ compiled_graph = ticket_graph.compile()
 
 compiled_graph.get_graph().draw_mermaid_png(output_file_path="graph.png")
 
-def render_separated_topics(json_string):
-    try:
-        json_data = json.loads(json_string)  # JSON-String → Python-Dict
-    except json.JSONDecodeError:
-        return "<p style='color:red;'>Ungültiges JSON-Format!</p>"
-    
-    # Ensure there's a 'topics' key in the parsed data
-    if "topics" not in json_data:
-        return "<p style='color:red;'>Keine Themen im JSON gefunden!</p>"
-    
-    topics = json_data["topics"]
+def render_separated_topics(topics, user_information, user_id):
+    if user_id is None:
+        return ""
+
+    # Get user information
+    first_name = user_information['first_name']
+    last_name = user_information['last_name']
+    address = user_information['address']
+    email = user_information['email']
+    phone = user_information['phone']
     
     # HTML structure for the topics
     html = """
-    <div style="display: flex; flex-direction: column; gap: 10px; color: white; ">
+    <div style="display: flex; flex-direction: column; gap: 10px; color: white;">
     """
     
     # Loop through the topics and build HTML for each
     for topic in topics:
-        # Default to empty string if key doesn't exist
-        topic_num = str(topic.get('topic_num', ''))
-        user_name = str(topic.get('user_name', ''))
-        topic_title = str(topic.get('topic', ''))
-        topic_description = str(topic.get('topic_description', ''))
-        user_id = str(topic.get('user_id', ''))
-
+        # Get information for each topic
+        title = topic['title']
+        topic_num = topic['topic_num']
+        topic_description = topic['topic_description']
+        print( " WITH TYPE: ", type(topic))
+        print("TOPIC: " ,topic['title'])
+    
         # Build HTML for each topic
         html += f"""
         <div style="border: 1px solid #e4e4e7; border-radius: 8px; padding: 20px; background-color: #fafafa;">
-            <h2 style="margin-top: 0; color: #27272a;">Ticket Number: {topic_num}</h2>
+            <h2 style="margin-top: 0; color: #27272a;">Ticket {topic_num}: {title} </h2>
 
             <div style="margin-bottom: 15px; font-size: 14px;">
-                <strong>Name: </strong> {user_name} <br>
+                <strong>Name: </strong> {first_name} {last_name} <br>
                 <strong>User ID: </strong> {user_id} <br>
-            </div> 
+                <strong>Address: </strong> {address} <br>
+                <strong>Phone: </strong> {phone} <br>
+                <strong>Email: </strong> {email} <br>
 
-            <div style="margin-bottom: 15px;">
-                <strong style="font-size: 16px; color: #27272a;">Topic: </strong> <span style="font-size: 16px;">{topic_title}</span><br>
             </div>
 
             <div style="margin-top: 10px;">
@@ -122,7 +134,8 @@ def run_graph(sender, subject, body):
     state = create_ticket_state()
     state["email"] = email
     result = compiled_graph.invoke(state)
-    return render_separated_topics(result['extracted_topics']), result['response']
+    return render_separated_topics(result['extracted_topics'], result['extracted_information'], 
+    result['user_id']), result, result['response']
 
     #return result['extracted_topics'] result['response'], result['ticket_type'], result['user_id'], result['extracted_topics'], result['extracted_information'], result
 
@@ -174,18 +187,20 @@ if __name__ == '__main__':
         # Output components
         with gr.Row():
             with gr.Tab("Tickets"):
-                extracted_topics = gr.HTML(label="Separated Topics")
+                extracted_topics = gr.HTML(label="Separated Topics")          
             with gr.Tab("Response"):    
                 final_response_output = gr.Textbox(label="E-Mail Response",
                 lines=10,
                 max_lines=20,
                 interactive=True) 
+            with gr.Tab("Full Agent State"):
+                full_json_output = gr.JSON(label="Full Agent State")
         
         # Click submit button
         btn_1.click(
             fn=run_graph,
             inputs=[sender_input, subject_input, body_input],
-            outputs=[extracted_topics, final_response_output],
+            outputs=[extracted_topics,full_json_output, final_response_output],
         )
 
         # Click delete button
@@ -205,7 +220,7 @@ if __name__ == '__main__':
         gr.Examples(
             fn=run_graph,
             inputs=[sender_input, subject_input, body_input],
-            outputs=[extracted_topics, final_response_output],
+            outputs=[extracted_topics, full_json_output, final_response_output],
             examples = examples,
         )  
 

src/data/users.py

@@ -5,13 +5,15 @@ mock_user = {
         "address": "Friedrichstraße 123, 10117 Berlin",
         "email": "lena.meier@example.de",
         "phone": "+49 30 12345678",
+        "contract_number": "4821/9384721", 
     },
     "215493": {
         "first_name": "Markus",
-        "last_name": "Markus",
+        "last_name": "Keller",
         "address": "Prenzlauer Allee 45, 10405 Berlin",
         "email": "markus.keller@example.de",
         "phone": "+49 30 23456789",
+       "contract_number": "1740/2847563",
     },
     "309572": {
         "first_name": "Sophie",
@@ -19,6 +21,7 @@ mock_user = {
         "address": "Karl-Marx-Straße 3, 12043 Berlin",
         "email": "sophie.brunner@example.de",
         "phone": "+49 30 34567890",
+        "contract_number": "0100/12576240",
     },
     "407183": {
         "first_name": "Nico",
@@ -26,6 +29,7 @@ mock_user = {
         "address": "Kurfürstendamm 22, 10719 Berlin",
         "email": "nico.baumann@example.de",
         "phone": "+49 30 45678901",
+        "contract_number": "3652/7482956",
     },
     "512948": {
         "first_name": "Mira",
@@ -33,6 +37,7 @@ mock_user = {
         "address": "Alexanderplatz 18, 10178 Berlin",
         "email": "mira.schmid@example.de",
         "phone": "+49 30 56789012",
+        "contract_number": "8093/6271830",
     },
     "613405": {
         "first_name": "Daniel",
@@ -40,6 +45,7 @@ mock_user = {
         "address": "Frankfurter Allee 55, 10247 Berlin",
         "email": "daniel.fischer@example.de",
         "phone": "+49 30 67890123",
+        "contract_number": "2517/9038472",
     },
     "728490": {
         "first_name": "Sara",
@@ -54,6 +60,7 @@ mock_user = {
         "address": "Hauptstraße 23, 10827 Berlin",
         "email": "julian.vogel@example.de",
         "phone": "+49 30 89012345",
+        "contract_number": "0100/12576250",
     },
     "945128": {
         "first_name": "Laura",
@@ -61,6 +68,7 @@ mock_user = {
         "address": "Torstraße 6, 10119 Berlin",
         "email": "laura.maurer@example.de",
         "phone": "+49 30 90123456",
+        "contract_number": "0100/12576260",
     },
     "159348": {
         "first_name": "David",
@@ -68,6 +76,7 @@ mock_user = {
         "address": "Potsdamer Straße 20, 10785 Berlin",
         "email": "david.graf@example.de",
         "phone": "+49 30 91234567",
+        "contract_number": "0100/12576270",
     },
     "263759": {
         "first_name": "Anna",
@@ -75,6 +84,7 @@ mock_user = {
         "address": "Wilhelmstraße 45, 10963 Berlin",
         "email": "anna.weber@example.de",
         "phone": "+49 30 12349876",
+        "contract_number": "0100/12576280",
     },
     "375820": {
         "first_name": "Lukas",
@@ -82,6 +92,7 @@ mock_user = {
         "address": "Müllerstraße 85, 13349 Berlin",
         "email": "lukas.steiner@example.de",
         "phone": "+49 30 23498765",
+        "contract_number": "0100/12576290",
     },
     "481267": {
         "first_name": "Nina",
@@ -89,6 +100,7 @@ mock_user = {
         "address": "Oranienstraße 1, 10999 Berlin",
         "email": "nina.marti@example.de",
         "phone": "+49 30 34598765",
+        "contract_number": "0100/12576300",
     },
     "596481": {
         "first_name": "Tim",
@@ -96,5 +108,6 @@ mock_user = {
         "address": "Gneisenaustraße 20, 10961 Berlin",
         "email": "tim.arnold@example.de",
         "phone": "+49 30 45612378",
+        "contract_number": "0100/12576310",
     }
 }

src/edges/check_user_identification.py

@@ -0,0 +1,5 @@
+from src.models import TicketState
+
+def verify_user(state: TicketState) -> bool:
+    """Verify if the user is identified"""
+    return state.get('user_id') is not None

src/models.py

@@ -14,9 +14,11 @@ class ExtractedInformation(TypedDict):
 
 # TODO: ticket_type should be contained in extractedTopics => from each topic create a ticked 
 class ExtractedTopic(TypedDict):
+    title: str | None = None
     topic_num: int | None = None
     topic: str | None = None
-    user_id: str | None = None
+    topic_description: str | None = None
+    
 
 
 
@@ -58,9 +60,8 @@ def create_ticket_state() -> TicketState:
 
     extracted_topics: List[ExtractedTopic] = [
         ExtractedTopic(
+            title=None,
             topic_num=None,
-            user_name=None,
-            user_id=None,
             topic=None,
             topic_description=None
         )
@@ -71,5 +72,5 @@ def create_ticket_state() -> TicketState:
         user_id=None,
         extracted_topics=extracted_topics,
         ticket_type=None,
-        messages=[]
+        messages=[],
     )

src/nodes/generate_response.py

@@ -25,7 +25,7 @@ def generate_response(state: TicketState) -> TicketState:
     response = model.invoke(messages)
 
     # Simple logic to parse the response (in a real app, you'd want more robust parsing)
-    response_text = response.content.lower()
+    response_text = response.content
 
     print(f"Generated response:\n\n {response_text}")
 

src/nodes/identify_user.py

@@ -11,17 +11,33 @@ def identify_user(state: TicketState):
     address = (extracted_information.get("address") or "").lower()
     email = (extracted_information.get("email") or "").lower()
     phone = (extracted_information.get("phone") or "").strip()
+    contract_number = (extracted_information.get("contract_number") or "").strip()
 
-    # Check if the user exists in the mock database with fuzzy matching
-    for user_id, user_info in mock_user.items():
-        if (
-                (user_info.get("first_name") or "").lower() == first_name or
-                (user_info.get("last_name") or "").lower() == last_name or
-                (user_info.get("address") or "").lower() == address or
-                (user_info.get("email") or "").lower() == email or
-                (user_info.get("phone") or "").strip() == phone
-        ):
-            state["user_id"] = user_id
-            break  # Stop at the first match
+    
+    print("IS USER VERIFIED? ", first_name, last_name, address, contract_number)
+    # Check if we have enough information to even try
+    has_name_info = all([first_name, last_name])
+    has_contract_number = bool(contract_number)
+
+    identified_user_id = None
+
+    if has_name_info or has_contract_number:
+        for user_id, user_info in mock_user.items():
+            if (
+                (has_name_info and
+                 (user_info.get("first_name") or "").lower() == first_name and
+                 (user_info.get("last_name") or "").lower() == last_name)
+                or
+                (has_contract_number and
+                 (user_info.get("contract_number") or "").strip() == contract_number)
+            ):
+                identified_user_id = user_id
+                break  # Stop at the first match
+
+    if identified_user_id:
+        print("IDENTIFIED USER:", user_id)
+        state["user_id"] = identified_user_id
+
+    print("IS USER VERIFIED? ", state.get("user_id"))
 
     return state

src/nodes/send_demand.py

@@ -0,0 +1,48 @@
+import json
+import logging
+
+from langchain_core.messages import HumanMessage
+
+from src.foundation_models import model
+from src.models import TicketState
+
+def send_demand(state: TicketState) -> TicketState:
+    """Generate a demand to the user if information are missing, using a language model. """
+
+    email = state["email"]
+
+
+    # prompt for LLM
+    prompt = f"""
+    Du bist ein Kundenservice-Mitarbeiter in der Immobilienbranche. Analysiere die folgende E-Mail und verfasse eine freundliche und höfliche Antwort an den Absender.
+    Teile der Person mit, dass zur Identifikation und Bearbeitung ihres Anliegens noch wichtige Angaben fehlen – insbesondere die Vertragsnummer.
+    Bitte fordere diese Information klar, aber kundenorientiert an.
+    Die Antwort soll kurz, klar und professionell sein. Drücke aus, dass wir gerne weiterhelfen möchten und an einer schnellen Lösung interessiert sind.
+
+    ## E-Mail:
+    Betreff: {email['subject']}
+    Inhalt: {email['body']}
+
+    Bitte antworte nur mit dem Text der Antwort-E-Mail.
+    Achte dabei unbedingt auf korrekte Groß- und Kleinschreibung sowie auf eine grammatikalisch saubere Formulierung.
+    """
+    # Call the LLM
+    messages = [HumanMessage(content=prompt)]
+    response = model.invoke(messages)
+
+    # Simple logic to parse the response (in a real app, you'd want more robust parsing)
+    response_text = response.content
+    print(f"Generated demand:\n\n {response_text}")
+
+    state['response'] = response_text
+
+    # Update messages for tracking
+    new_messages = state.get("messages", []) + [
+        {"role": "user", "content": prompt},
+        {"role": "assistant", "content": response.content}
+    ]
+
+    state["messages"] = new_messages
+
+    # Return state updates
+    return state

src/nodes/separate_topics.py

@@ -12,28 +12,44 @@ def separate_topics(state: TicketState):
     email = state["email"]
     first_name = state["extracted_information"]["first_name"]
     last_name = state["extracted_information"]["last_name"]
+    address = state["extracted_information"]["address"]
+    mail = state["extracted_information"]["email"]
+    phone = state["extracted_information"]["phone"]
     user_id = state["user_id"]
 
     # promp for the LLM 
+    """
+     "user_name": {first_name} {last_name},
+        "address": {address},
+        "email": {mail}
+        "phone": {phone}
+        "user_id": {user_id},
+    """
     prompt = f"""
-    As a real estate customer support agent, analyze this email and extract different topics. 
-    Return the different topics as a list.
+    Du bist ein Kundenservice-Mitarbeiter in der Immobilienbranche. Analysiere die folgende E-Mail und extrahiere daraus unterschiedliche Themen (Topics).
+    Gib die erkannten Themen als Liste von JSON-Objekten zurück.
 
-    ## Each JSON object should have the following structure:
+    ## Jedes JSON-Objekt soll folgende Struktur haben:
     {{
-        "topic_num": int,
-        "user_name": "{first_name} {last_name}",
-        "user_id": "{user_id}",
-        "topic": str,
-        "topic_description": str
+        "title": str,                    # Kurzer, prägnanter Titel für das Thema
+        "topic_num": int,               # Laufende Nummer des Themas innerhalb der E-Mail
+        "topic": str,                   # Kurzbeschreibung des Themas
+        "topic_description": str        # Ausführliche Beschreibung mit allen relevanten Informationen
     }}
 
-    ## Email: 
-    From: {email['sender']}
-    Subject: {email['subject']}
-    Body: {email['body']}
-    
-    Make sure that the topics are well separated and the description holds all relevant information. Response only with a JSON!
+    ## E-Mail:
+    Von: {email['sender']}
+    Betreff: {email['subject']}
+    Inhalt: {email['body']}
+
+    Stelle sicher, dass die Themen klar voneinander getrennt sind, sodass zusammengehörige Anliegen in einem gemeinsamen Ticket erscheinen. 
+    Es sollen keine separaten Topics für Themen erstellt werden, die zusammengefasst werden können – außer sie betreffen klar unterschiedliche Verantwortungsbereiche. 
+    Die Beschreibung muss alle relevanten Informationen enthalten. 
+    Aus jeder E-Mail muss mindestens ein Topic erstellt werden. 
+    Falls kein eindeutiges Thema erkennbar ist, soll die Original-E-Mail mit einem passenden Titel als Topic übernommen werden.
+
+    Antworte ausschließlich in einer Sprache (Deutsch oder Englisch – abhängig vom E-Mail-Inhalt) und achte auf korrekte Rechtschreibung sowie Gross- und Kleinschreibung.
+    Gib ausschließlich eine Liste von JSON-Objekten als Antwort zurück.
     """
 
     print(state)
@@ -42,7 +58,7 @@ def separate_topics(state: TicketState):
     messages = [HumanMessage(content=prompt)]
     response = model.invoke(messages)
 
-    response_text = response.content.lower()
+    response_text = response.content
 
     # Parse the LLM's response 
     try:
@@ -51,7 +67,7 @@ def separate_topics(state: TicketState):
         logging.error(f"Failed to parse the response: {response_text}")
         return {}
 
-    state['extracted_topics'] = response_text
+    state['extracted_topics'] = extracted_topics
 
     # Update messages for tracking
     new_messages = state.get("messages", []) + [