Upload app.py and requirements.txt

app.py

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+import os
+import gradio as gr
+
+# Import necessary LlamaIndex components
+from llama_index.indices.managed.llama_cloud import (
+    LlamaCloudIndex,
+    LlamaCloudCompositeRetriever,
+)
+from llama_index.core import Settings
+from llama_index.llms.anthropic import Anthropic
+from llama_cloud.types import CompositeRetrievalMode
+from llama_index.core.memory import ChatMemoryBuffer
+from llama_index.core.chat_engine import CondensePlusContextChatEngine
+from llama_index.core.chat_engine.types import (
+    AgentChatResponse,
+)  # Import for type hinting agent response
+from llama_index.core.schema import (
+    NodeWithScore,
+)  # Import for type hinting source_nodes
+
+# Phoenix/OpenInference imports
+from phoenix.otel import register
+from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
+
+# --- Configuration ---
+# Replace with your actual LlamaCloud Project Name
+LLAMA_CLOUD_PROJECT_NAME = "CustomerSupportProject"
+
+# Configure Anthropic LLM
+# Ensure ANTHROPIC_API_KEY is set in your environment variables
+Settings.llm = Anthropic(model="claude-sonnet-4-0", temperature=0)
+print(f"[INFO] Configured LLM: {Settings.llm.model}")
+
+# Configure LlamaTrace (Arize Phoenix)
+PHOENIX_PROJECT_NAME = os.environ.get("PHOENIX_PROJECT_NAME")
+PHOENIX_API_KEY = os.environ.get("PHOENIX_API_KEY")
+
+if PHOENIX_PROJECT_NAME and PHOENIX_API_KEY:
+    os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
+    tracer_provider = register(
+        project_name=PHOENIX_PROJECT_NAME,
+        endpoint="https://app.phoenix.arize.com/v1/traces",
+        auto_instrument=True,
+    )
+    LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)
+    print("[INFO] LlamaIndex tracing configured for LlamaTrace (Arize Phoenix).")
+else:
+    print(
+        "[INFO] PHOENIX_PROJECT_NAME or PHOENIX_API_KEY not set. LlamaTrace (Arize Phoenix) not configured."
+    )
+
+# --- Assume LlamaCloud Indices are pre-created ---
+# In a real scenario, you would have uploaded your documents to these indices
+# via LlamaCloud UI or API. Here, we connect to existing indices.
+print("[INFO] Connecting to LlamaCloud Indices...")
+
+try:
+    product_manuals_index = LlamaCloudIndex(
+        name="ProductManuals",
+        project_name=LLAMA_CLOUD_PROJECT_NAME,
+    )
+    faq_general_info_index = LlamaCloudIndex(
+        name="FAQGeneralInfo",
+        project_name=LLAMA_CLOUD_PROJECT_NAME,
+    )
+    billing_policy_index = LlamaCloudIndex(
+        name="BillingPolicy",
+        project_name=LLAMA_CLOUD_PROJECT_NAME,
+    )
+    company_intro_slides_index = LlamaCloudIndex(
+        name="CompanyIntroductionSlides",
+        project_name=LLAMA_CLOUD_PROJECT_NAME,
+    )
+    print("[INFO] Successfully connected to LlamaCloud Indices.")
+
+except Exception as e:
+    print(
+        f"[ERROR] Error connecting to LlamaCloud Indices. Please ensure they exist and API key is correct: {e}"
+    )
+    print(
+        "[INFO] Exiting. Please create your indices on LlamaCloud and set environment variables."
+    )
+    exit()  # Exit if indices cannot be connected, as the rest of the code depends on them
+
+# --- Create LlamaCloudCompositeRetriever for Agentic Routing ---
+print("[INFO] Creating LlamaCloudCompositeRetriever...")
+composite_retriever = LlamaCloudCompositeRetriever(
+    name="Customer Support Retriever",
+    project_name=LLAMA_CLOUD_PROJECT_NAME,
+    create_if_not_exists=True,
+    mode=CompositeRetrievalMode.ROUTING,  # Enable intelligent routing
+    rerank_top_n=2,  # Rerank and return top 2 results from all queried indices
+)
+
+# Add indices to the composite retriever with descriptive descriptions
+# These descriptions are crucial for the agent's routing decisions.
+print("[INFO] Adding sub-indices to the composite retriever with descriptions...")
+composite_retriever.add_index(
+    product_manuals_index,
+    description="Information source for detailed product features, technical specifications, troubleshooting steps, and usage guides for various products.",
+)
+composite_retriever.add_index(
+    faq_general_info_index,
+    description="Contains common questions and answers, general company policies, public announcements, and basic information about services.",
+)
+composite_retriever.add_index(
+    billing_policy_index,
+    description="Provides information related to pricing, subscriptions, invoices, payment methods, and refund policies.",
+)
+composite_retriever.add_index(
+    company_intro_slides_index,
+    description="Contains presentations that provide an overview of the company, its mission, leadership, and key information for new employees, partners, or investors.",
+)
+print("[INFO] Sub-indices added.")
+
+# --- Create CondensePlusContextChatEngine ---
+memory = ChatMemoryBuffer.from_defaults(token_limit=4096)
+chat_engine = CondensePlusContextChatEngine.from_defaults(
+    retriever=composite_retriever,
+    memory=memory,
+    system_prompt=(
+        """
+You are a Smart Customer Support Triage Agent.
+Always be polite and friendly.
+Provide accurate answers from product manuals, FAQs, and billing policies by intelligently routing queries to the most relevant knowledge base.
+Provide accurate, precise, and useful information directly.
+Never refer to or mention your information sources (e.g., "the manual says", "from the document").
+State facts authoritatively.
+When asked about file-specific details like the author, creation date, or last modification date, retrieve this information from the document's metadata if available in the provided context.
+        """
+    ),
+    verbose=True,
+)
+print("[INFO] ChatEngine initialized.")
+
+# --- Gradio Chat UI ---
+def initial_submit(message: str, history: list):
+    """
+    Handles the immediate UI update after user submits a message.
+    Adds user message to history and shows a loading state for retriever info.
+    """
+    # Append user message in the 'messages' format
+    history.append({"role": "user", "content": message})
+    # Return updated history, clear input box, show loading for retriever info, and the original message
+    # outputs=[chatbot, msg, retriever_output, user_message_state]
+    return history, "", "Retrieving relevant information...", message
+
+def get_agent_response_and_retriever_info(message_from_state: str, history: list):
+    """
+    Processes the LLM response and extracts retriever information.
+    This function is called AFTER initial_submit, so history already contains user's message.
+    """
+    retriever_output_text = "Error: Could not retrieve information."
+
+    try:
+        # Call the chat engine to get the response
+        response: AgentChatResponse = chat_engine.chat(message_from_state)
+
+        # AgentChatResponse.response holds the actual LLM generated text: `response=str(response)`
+        # Append the assistant's response in the 'messages' format
+        history.append({"role": "assistant", "content": response.response})
+
+        # Prepare the retriever information for the new textbox
+        check_retriever_text = []
+
+        # Safely attempt to get condensed_question
+        condensed_question = "Condensed question not explicitly exposed by chat engine."
+        # `chat` method returns `sources=[context_source]` within `AgentChatResponse`
+        if hasattr(response, "sources") and response.sources is not None:
+            context_source = response.sources[0]
+            if (
+                hasattr(context_source, "raw_input")
+                and context_source.raw_input is not None
+                and "message" in context_source.raw_input
+            ):
+                condensed_question = context_source.raw_input["message"]
+
+        check_retriever_text.append(f"Condensed question: {condensed_question}")
+        check_retriever_text.append("==============================")
+
+        # Safely get source_nodes. Ensure it's iterable.
+        nodes: list[NodeWithScore] = (
+            response.source_nodes
+            if hasattr(response, "source_nodes") and response.source_nodes is not None
+            else []
+        )
+
+        if nodes:
+            for i, node in enumerate(nodes):
+                # Safely access node metadata and attributes
+                metadata = (
+                    node.metadata
+                    if hasattr(node, "metadata") and node.metadata is not None
+                    else {}
+                )
+                score = (
+                    node.score
+                    if hasattr(node, "score") and node.score is not None
+                    else "N/A"
+                )
+
+                file_name = metadata.get("file_name", "N/A")
+                page_info = ""
+                # Add page number for .pptx files
+                if file_name.lower().endswith(".pptx"):
+                    page_label = metadata.get("page_label")
+                    if page_label:
+                        page_info = f" p.{page_label}"
+
+                node_block = f"""\
+[Node {i + 1}]
+Index: {metadata.get("retriever_pipeline_name", "N/A")}
+File: {file_name}{page_info}
+Score: {score}
+=============================="""
+                check_retriever_text.append(node_block)
+        else:
+            check_retriever_text.append("No source nodes found for this query.")
+
+        retriever_output_text = "\n".join(check_retriever_text)
+
+        # Return updated history and the retriever text
+        return history, retriever_output_text
+
+    except Exception as e:
+        # Log the full error for debugging
+        import traceback
+
+        print(f"Error in get_agent_response_and_retriever_info: {e}")
+        traceback.print_exc()
+
+        # Append a generic error message from the assistant
+        history.append(
+            {
+                "role": "assistant",
+                "content": "I'm sorry, I encountered an error while processing your request. Please try again.",
+            }
+        )
+
+        # Only return the detailed error in the retriever info box
+        retriever_output_text = f"Error generating retriever info: {e}"
+        return history, retriever_output_text
+
+# Markdown text for the application and chatbot welcoming message
+description_text = """
+Hello! I'm your Smart Customer Support Triage Agent. I can answer questions about our product manuals, FAQs, and billing policies. Ask me anything!
+
+Explore the documents in `./data` directory for sample knowledge base 📑
+"""
+
+# Markdown text for `./data` folder structure
+knowledge_base_md = """
+### 📁 Sample Knowledge Base ([click to explore!](https://huggingface.co/spaces/Agents-MCP-Hackathon/cs-agent/tree/main/data))
+```
+./data/
+├── ProductManuals/
+│   ├── product_manuals_metadata.csv
+│   ├── product_manuals.pdf
+│   ├── task_automation_setup.pdf
+│   └── collaboration_tools_overview.pdf
+├── FAQGeneralInfo/
+│   ├── faqs_general_metadata.csv
+│   ├── faqs_general.pdf
+│   ├── remote_work_best_practices_faq.pdf
+│   └── sustainability_initiatives_info.pdf
+├── BillingPolicy/
+│   ├── billing_policies_metadata.csv
+│   ├── billing_policies.pdf
+│   ├── multi_user_discount_guide.pdf
+│   ├── late_payment_policy.pdf
+│   └── late_payment_policy_v2.pdf
+└── CompanyIntroductionSlides/
+    ├── company_introduction_slides_metadata.csv
+    └── TechSolve_Introduction.pptx
+```
+"""
+
+# Create a Gradio `Blocks` layout to structure the application
+print("[INFO] Launching Gradio interface...")
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    # Custom CSS
+    demo.css = """
+    .monospace-font textarea {
+        font-family: monospace; /* monospace font for better readability of structured text */
+    }
+    .center-title { /* centering the title */
+        text-align: center;
+    }
+    """
+
+    # `State` component to hold the user's message between chained function calls
+    user_message_state = gr.State(value="")
+
+    # Retriever info begin message
+    retriever_info_begin_msg = "Retriever information will appear here after each query."
+
+    # Center-aligned title
+    gr.Markdown("# 💬 Smart Customer Support Triage Agent", elem_classes="center-title")
+    gr.Markdown(description_text)
+
+    with gr.Row():
+        with gr.Column(scale=2):  # Main chat area
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=500,
+                show_copy_button=True,
+                resizable=True,
+                avatar_images=(None, "./logo.png"),
+                type="messages",
+                value=[
+                    {"role": "assistant", "content": description_text}
+                ],  # Welcoming message
+            )
+            msg = gr.Textbox(  # User input
+                placeholder="Type your message here...", lines=1, container=False
+            )
+
+            with gr.Row():
+                send_button = gr.Button("Send")
+                clear_button = gr.ClearButton([msg, chatbot])
+
+        with gr.Column(scale=1):  # Retriever info area
+            retriever_output = gr.Textbox(
+                label="Agentic Retrieval & Smart Routing",
+                interactive=False,
+                lines=28,
+                show_copy_button=True,
+                autoscroll=False,
+                elem_classes="monospace-font",
+                value=retriever_info_begin_msg,
+            )
+
+    # New row for Examples and Sample Knowledge Base Tree Diagram
+    with gr.Row():
+        with gr.Column(scale=1):  # Examples column (left)
+            gr.Markdown("### 🗣️ Example Questions")
+            # Store the Examples component in a variable to access its `load_input_event`
+            examples_component = gr.Examples(
+                examples_per_page=8,
+                examples=[
+                    ["Help! No response from the app, I can't do anything. What should I do? Who can I contact?"],
+                    ["I got an $200 invoice outstanding for 45 days. How much is the late charge?"],
+                    ["Who is the author of the product manual and when is the last modified date?"],
+                    ["Who are the founders of this company? What are their backgrounds?"],
+                    ["Is your company environmentally friendly?"],
+                    ["What are the procedures to set up task automation?"],
+                    ["If I sign up for an annual 'Pro' subscription today and receive the 10% discount, but then decide to cancel after 20 days because the software isn't working for me, what exact amount would I be refunded, considering the 14-day refund policy for annual plans?"],
+                    ["I have a question specifically about the 'Sustainable Software Design' aspect mentioned in your sustainability initiatives, which email address should I use for support, [email protected] or [email protected], and what kind of technical detail can I expect in a response?"],                  
+                    ["How can your software help team collaboration?"],
+                    ["What is your latest late payment policy?"],
+                    ["If I enable auto-pay to avoid late payments, but my payment method on file expires, will TechSolve send me a notification before the payment fails and potentially incurs late fees?"], 
+                    ["In shared workspaces, when multiple users are co-editing a document, how does the system handle concurrent edits to the exact same line of text by different users, and what mechanism is in place to prevent data loss or conflicts?"],
+                    ["The refund policy states that annual subscriptions can be refunded within 14 days. Does this '14 days' refer to 14 calendar days or 14 business days from the purchase date?"],
+                    ["Your multi-user discount guide states that discounts apply to accounts with 5+ users. If my team currently has 4 users and I add a 5th user mid-billing cycle, will the 10% discount be applied immediately to all 5 users, or only at the next billing cycle, and how would the prorated amount for the new user be calculated?"], 
+                ],
+                inputs=[msg],  # This tells examples to populate the 'msg' textbox
+            )
+        with gr.Column(scale=1):  # Knowledge Base column (right)
+            gr.Markdown(knowledge_base_md)
+
+    # Define the interaction for sending messages (via Enter key in textbox)
+    submit_event = msg.submit(  # Step 1: Immediate UI update (updated history, clear input box, show loading for retriever info, and the original message)
+        fn=initial_submit,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg, retriever_output, user_message_state],
+        queue=False,
+    ).then(  # Step 2: Call LLM and update agent response and detailed retriever info
+        fn=get_agent_response_and_retriever_info,
+        inputs=[user_message_state, chatbot],
+        outputs=[chatbot, retriever_output],
+        queue=True,  # Allow queuing for potentially long LLM calls
+    )
+
+    # Define the interaction for send button click
+    send_button.click(
+        fn=initial_submit,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg, retriever_output, user_message_state],
+        queue=False,
+    ).then(
+        fn=get_agent_response_and_retriever_info,
+        inputs=[user_message_state, chatbot],
+        outputs=[chatbot, retriever_output],
+        queue=True,
+    )
+
+    # Define the interaction for example questions click
+    examples_component.load_input_event.then(
+        fn=initial_submit,
+        inputs=[msg, chatbot],  # 'msg' will have been populated by the example
+        outputs=[chatbot, msg, retriever_output, user_message_state],
+        queue=False,
+    ).then(
+        fn=get_agent_response_and_retriever_info,
+        inputs=[user_message_state, chatbot],
+        outputs=[chatbot, retriever_output],
+        queue=True,
+    )
+
+    # Define the interaction for clearing all outputs
+    clear_button.click(
+        fn=lambda: (
+            [],
+            "",
+            retriever_info_begin_msg,
+            "",
+        ),
+        inputs=[],
+        outputs=[chatbot, msg, retriever_output, user_message_state],
+        queue=False,
+    )
+
+    # DeepLinkButton for sharing current conversation
+    gr.DeepLinkButton()
+
+    # Privacy notice and additional info at the bottom
+    gr.Markdown(
+        """
+        _\*By using this chat, you agree that conversations may be recorded for improvement and evaluation. DO NOT disclose any privacy information in the conversation._
+
+        _\*This space is dedicated to the [Gradio Agents & MCP Hackathon 2025](https://huggingface.co/Agents-MCP-Hackathon) submission. Future updates will be available in my personal space: [karenwky/cs-agent](https://huggingface.co/spaces/karenwky/cs-agent)._
+        """
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(show_error=True)

requirements.txt

@@ -0,0 +1,6 @@
+llama-index
+llama-index-indices-managed-llama-cloud
+llama-index-llms-anthropic
+llama-index-callbacks-arize-phoenix
+arize-phoenix
+gradio