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+# Use an official Python runtime as the base image
+FROM python:3.11-slim
+
+# Set working directory in the container
+WORKDIR /app
+
+# Copy project files
+COPY app.py utils.py agents.py tasks.py requirements.txt ./
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    libffi-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port 7860 for Gradio
+EXPOSE 7860
+
+# Set environment variables
+ENV CREWAI_TELEMETRY_ENABLED=false
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860
+
+# Command to run the Gradio app
+CMD ["python", "app.py"]

agents.py

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+# agents.py
+
+from utils import gemini_llm
+from crewai import Agent
+
+# Agents
+finance_knowledge_agent = Agent(
+    role="Finance Knowledge Expert",
+    goal="Provide accurate, concise, and structured answers to general finance-related questions using provided documents and web data.",
+    backstory="An expert with deep knowledge of financial concepts, trained on documents including Basics.pdf, Statementanalysis.pdf, and Financialterms.pdf.",
+    llm=gemini_llm,
+    verbose=True,
+    allow_delegation=False
+)
+
+market_news_agent = Agent(
+    role="Market News Analyst",
+    goal="Fetch, summarize, and analyze recent financial news and market trends to provide actionable insights.",
+    backstory="A financial journalist with expertise in identifying key market trends and summarizing news for actionable insights.",
+    llm=gemini_llm,
+    verbose=True,
+    allow_delegation=False
+)
+
+stock_analysis_agent = Agent(
+    role="Stock Analysis Expert",
+    goal="Provide detailed and actionable analysis of specific stocks, including performance trends and basic technical insights.",
+    backstory="A seasoned stock market analyst with expertise in fundamental analysis and basic trend interpretation based on real-time data.",
+    llm=gemini_llm,
+    verbose=True,
+    allow_delegation=False
+)
+
+response_refiner_agent = Agent(
+    role="Response Refiner and Reporter",
+    goal="Simplify, verify, and format responses from other agents into a concise, professional report for the user.",
+    backstory="A meticulous editor with a background in finance, specializing in simplifying complex information and presenting it in a clear, professional report format.",
+    llm=gemini_llm,
+    verbose=True,
+    allow_delegation=False
+)

app.py

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+ # interface.py
+
+import gradio as gr
+from crewai import Crew, Process
+from agents import finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent
+from tasks import get_finance_knowledge_task, get_market_news_task, get_stock_analysis_task, get_response_refiner_task
+from utils import determine_question_type, search_qdrant
+
+# Initialize Crew 
+finance_crew = Crew(
+    agents=[finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent],
+    tasks=[],
+    process=Process.sequential,
+    verbose=1 
+)
+
+def get_response(query):
+    """Get chatbot response."""
+    finance_crew.tasks = []  # Reset tasks for each query
+
+    try:
+        question_type, processed_query = determine_question_type(query)
+
+        # Determine RAG usage
+        rag_note = "RAG_SUFFICIENT"  # Default for finance_knowledge
+        if question_type == "finance_knowledge":
+            contexts = search_qdrant(query, top_k=2)
+            if contexts and len(contexts) > 0:
+                shortened_contexts = []
+                for ctx in contexts:
+                    text = ctx["text"]
+                    if len(text) > 300: 
+                        text = text[:297] + "..."
+                    shortened_contexts.append({
+                        "source": ctx["source"],
+                        "text": text
+                    })
+                context_text = "\n\n".join([f"Source: {ctx['source']}\nContent: {ctx['text']}" for ctx in shortened_contexts])
+                is_context_useful = len(context_text) > 30 and any(keyword in context_text.lower() for keyword in query.lower().split())
+                rag_note = "RAG_SUFFICIENT" if is_context_useful else "RAG_NOT_USED"
+            else:
+                rag_note = "RAG_NOT_USED"
+            initial_task = get_finance_knowledge_task(query)
+        elif question_type == "market_news":
+            rag_note = "NO_RAG_NEEDED"
+            initial_task = get_market_news_task(query)
+        elif question_type == "stock_analysis":
+            rag_note = "NO_RAG_NEEDED"
+            initial_task = get_stock_analysis_task(processed_query)
+        else:
+            initial_task = get_finance_knowledge_task(query)
+
+        finance_crew.tasks.append(initial_task)
+        initial_response = finance_crew.kickoff()
+
+        refiner_task = get_response_refiner_task(query, initial_response, question_type, rag_note=rag_note)
+        finance_crew.tasks = [refiner_task]
+        final_report = finance_crew.kickoff()
+
+        return final_report
+    except Exception as e:
+        return f"Error: {e}\nPlease try again."
+
+# CSS
+custom_css = """
+    :root {
+        --primary-color: #00416a;
+        --secondary-color: #047fb7;
+        --title-color: #FFD700;
+        --text-color: #ffffff;
+        --bg-dark: #1a1a1a;
+        --bg-medium: #2a2a2a;
+        --bg-light: #353535;
+        --input-bg: #ffffff;
+        --input-text: #333333;
+        --button-green: #4CAF50;
+    }
+    body, html {
+        background-color: #1a1a1a !important;
+        margin: 0;
+        padding: 0;
+        width: 100%;
+        height: 100%;
+        overflow-x: hidden;
+    }
+    .gradio-container {
+        background: linear-gradient(135deg, #1a1a1a 0%, #00416a 100%) !important;
+        font-family: 'Montserrat', 'Arial', sans-serif !important;
+        max-width: 100% !important;
+        width: 100% !important;
+        margin: 0 !important;
+        padding: 25px !important;
+        min-height: 100vh !important;
+    }
+    .gradio-container .input-box,
+    .gradio-container .output-box,
+    .gr-form,
+    .gr-box,
+    .gr-padded,
+    .gr-panel,
+    .gr-input,
+    .gr-input-label,
+    textarea,
+    .gr-textarea,
+    .gr-textbox {
+        background-color: #ffffff !important;
+    }
+    textarea,
+    .gr-textarea textarea,
+    .gr-textbox textarea,
+    .gr-textbox input {
+        background-color: #ffffff !important;
+        color: #333333 !important;
+    }
+    .gr-button.submit-button {
+        background-color: #4CAF50 !important;
+        color: #ffffff !important;
+        border: none !important;
+        padding: 10px 20px !important;
+        border-radius: 8px !important;
+        font-weight: bold !important;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2) !important;
+        text-align: center !important;
+        width: 150px !important;
+        margin: 10px auto !important;
+        display: block !important;
+    }
+    button,
+    .gr-button span,
+    .submit-button span {
+        color: #ffffff !important;
+        background-color: transparent !important;
+    }
+    .examples-title,
+    .gr-examples .gr-interface-title,
+    footer div,
+    footer span {
+        color: #ffffff !important;
+        background-color: transparent !important;
+        font-weight: bold !important;
+    }
+    .gr-box {
+        border-radius: 8px !important;
+        padding: 12px !important;
+        background-color: #ffffff !important;
+        border: 1px solid #047fb7 !important;
+    }
+    label, .gr-block.gr-box label {
+        color: #ffffff !important;
+        font-weight: bold !important;
+        background-color: transparent !important;
+    }
+    .gr-examples .gr-sample-btn {
+        background-color: #e0e0e0 !important;
+        border: 1px solid #047fb7 !important;
+        color: #333333 !important;
+        border-radius: 8px !important;
+    }
+    h1 {
+        color: #FFD700 !important;
+        text-align: center !important;
+        text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3) !important;
+        font-weight: bold !important;
+    }
+    h3 {
+        color: #ffffff !important;
+        text-align: center !important;
+    }
+    .finance-icons {
+        text-align: center !important;
+        color: #ffffff !important;
+    }
+    .ticker-tape {
+        background-color: #353535 !important;
+        overflow: hidden !important;
+        white-space: nowrap !important;
+        padding: 8px 0 !important;
+        margin: 15px 0 !important;
+        border-radius: 5px !important;
+    }
+    .ticker-content {
+        display: inline-block !important;
+        animation: ticker 30s linear infinite !important;
+        color: #ffffff !important;
+    }
+    @keyframes ticker {
+        0% { transform: translateX(100%); }
+        100% { transform: translateX(-100%); }
+    }
+    .stock-symbol {
+        margin: 0 15px !important;
+        display: inline-block !important;
+        color: #ffffff !important;
+        font-weight: bold !important;
+    }
+    .up { color: #4CAF50 !important; }
+    .down { color: #FF5252 !important; }
+    .examples {
+        background-color: #ffffff !important;
+        border-radius: 8px !important;
+        padding: 10px !important;
+    }
+    .footer-container, .footer, .gr-footnote {
+        display: none !important;
+    }
+    .gradio-container .prose,
+    .gradio-container .prose p {
+        background-color: transparent !important;
+    }
+"""
+
+# HTML components for design
+ticker_html = """
+<div class="ticker-tape">
+    <div class="ticker-content">
+        <span class="stock-symbol">AAPL <span class="up">+1.2%</span></span>
+        <span class="stock-symbol">MSFT <span class="up">+0.8%</span></span>
+        <span class="stock-symbol">GOOGL <span class="down">-0.4%</span></span>
+        <span class="stock-symbol">AMZN <span class="up">+2.1%</span></span>
+        <span class="stock-symbol">TSLA <span class="down">-1.7%</span></span>
+        <span class="stock-symbol">JPM <span class="up">+0.5%</span></span>
+        <span class="stock-symbol">BAC <span class="down">-0.2%</span></span>
+        <span class="stock-symbol">WMT <span class="up">+0.3%</span></span>
+    </div>
+</div>
+"""
+
+finance_icons = """
+<div class="finance-icons">
+    <span style="color: #4CAF50;">📊 📈 💹 💰 📉 🏦 📃 </span>
+</div>
+"""
+
+# Gradio interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Base(), analytics_enabled=False) as interface:
+    gr.HTML("<h1>Professional Finance Assistant</h1>")
+    gr.HTML("<h3>Your AI-powered financial advisor and market analyst</h3>")
+    
+    gr.HTML(finance_icons)
+    gr.HTML(ticker_html)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_text = gr.Textbox(
+                lines=4,
+                placeholder="Ask me anything about finance (e.g., 'What is dividend investing?', 'Analyze Tesla stock', 'Latest market news on tech sector')",
+                label="Your Financial Query",
+                show_label=True,
+                interactive=True
+            )
+            submit_btn = gr.Button("Submit", elem_classes=["submit-button"])
+    
+    output_text = gr.Textbox(label="Financial Analysis", lines=10)
+    
+    example_queries = [
+        ["What is the difference between bull and bear markets?"],
+        ["Analyze AAPL stock performance"],
+        ["Latest news about cryptocurrency market"],
+        ["Explain P/E ratio and its importance"]
+    ]
+    gr.Examples(example_queries, inputs=input_text, examples_per_page=5, label="Examples")
+    
+    # Event handlers
+    submit_btn.click(fn=get_response, inputs=input_text, outputs=output_text)
+
+# Launch the interface
+interface.launch(share=False, inbrowser=True)

requirements.txt

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+# Core dependencies
+python-dotenv  # For loading environment variables from .env file
+pypdf  # For PDF parsing
+numpy  # For numerical operations
+
+# Embeddings
+sentence-transformers  # For generating embeddings using HuggingFace models
+
+# Qdrant integration
+qdrant-client  # Qdrant vector database client
+langchain-qdrant  # LangChain integration for Qdrant
+
+# LangChain and related
+langchain  # Core LangChain library
+langchain-community  # Community-contributed LangChain modules
+langchain_huggingface  # LangChain integration for HuggingFace embeddings
+
+# CrewAI for agent-based workflows
+crewai  # Framework for multi-agent workflows
+
+# Web and data handling
+requests  # For making HTTP requests (e.g., Serper API, Alpha Vantage API)
+pandas  # For data manipulation and analysis
+
+# Jupyter (if running notebook locally)
+jupyter  # For running Jupyter notebooks (e.g., setup_qdrant.ipynb)
+
+# Web interface
+gradio  # For creating web interfaces
+
+# Production server
+gunicorn  # For running the app in production

tasks.py

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+# tasks.py
+
+from utils import search_qdrant, search_news, get_stock_data
+from crewai import Task
+from agents import finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent
+
+def get_finance_knowledge_task(query):
+    """Task for answering general finance knowledge questions."""
+    contexts = search_qdrant(query, top_k=3)
+    context_text = "\n\n".join([f"Source: {ctx['source']}\nContent: {ctx['text']}" for ctx in contexts])
+    is_context_useful = len(context_text) > 50 and any(query.lower() in ctx["text"].lower() for ctx in contexts)
+
+    web_results = search_news(query, max_results=3)
+    web_text = "\n\n".join([f"Title: {item['title']}\nSummary: {item['snippet']}" for item in web_results]) if web_results else "No additional info from the web."
+
+    if is_context_useful:
+        prompt = f"""
+        User query: '{query}'
+
+        You are a Finance Knowledge Expert. Use the following RAG data and web search results to provide a concise, accurate response:
+
+        RAG Data:
+        {context_text}
+
+        Web Search Results:
+        {web_text}
+
+        ### Instructions:
+        - Provide a clear definition or explanation related to the query.
+        - Include a practical example or implication if relevant.
+        - Cite your sources (e.g., "According to Basics.pdf" or "Based on web search").
+        - Keep the response concise, under 200 words.
+        """
+    else:
+        prompt = f"""
+        User query: '{query}'
+
+        The RAG data from Qdrant was insufficient:
+        {context_text}
+
+        Web search results:
+        {web_text}
+
+        ### Instructions:
+        - Rely on your own knowledge and web results to provide a concise, accurate response.
+        - Note that RAG data was insufficient.
+        - Include a practical example or implication if relevant.
+        - Cite your sources (e.g., "Based on web search").
+        - Keep the response concise, under 200 words.
+        """
+    return Task(
+        description=prompt,
+        agent=finance_knowledge_agent,
+        expected_output="A concise explanation of the financial concept, with an example and cited sources, under 200 words."
+    )
+
+def get_market_news_task(query):
+    """Task for summarizing and analyzing market news."""
+    news = search_news(query, max_results=3)
+    news_text = "\n\n".join([f"Title: {item['title']}\nSummary: {item['snippet']}" for item in news]) if news else "No recent news found."
+
+    prompt = f"""
+    User query: '{query}'
+
+    You are a Market News Analyst. Analyze the following news data and provide a summary with actionable insights:
+
+    News Data:
+    {news_text}
+
+    ### Instructions:
+    - Summarize the key points from the news in 3-4 sentences.
+    - Highlight any trends or events that could impact the market.
+    - Provide one actionable insight or recommendation for investors.
+    - Cite the news sources (e.g., "According to [title]").
+    - Keep the response concise, under 200 words.
+    """
+    return Task(
+        description=prompt,
+        agent=market_news_agent,
+        expected_output="A concise summary of market news, highlighting trends, with an actionable insight, under 200 words."
+    )
+
+def get_stock_analysis_task(symbol):
+    """Task for analyzing a specific stock with basic technical insights."""
+    stock_data = get_stock_data(symbol)
+    if "error" in stock_data:
+        prompt = f"""
+        User query: 'Analyze {symbol}'
+
+        You are a Stock Analysis Expert. There was an error fetching data for the stock:
+
+        Error: {stock_data['error']}
+
+        ### Instructions:
+        - Provide a general overview of the stock based on your knowledge.
+        - Suggest a potential reason for the error.
+        - Recommend an action for the user.
+        - Keep the response concise, under 200 words.
+        """
+    else:
+        data_text = f"Price: {stock_data['price']}\nChange: {stock_data['change']} ({stock_data['change_percent']})"
+        prompt = f"""
+        User query: 'Analyze {symbol}'
+
+        You are a Stock Analysis Expert. Analyze the following stock data with basic technical insights:
+
+        Stock Data:
+        {data_text}
+
+        ### Instructions:
+        - Interpret the stock's performance and identify any price trend (e.g., upward/downward movement).
+        - Identify potential factors influencing the stock (e.g., market trends, sector performance).
+        - Provide an investment recommendation (e.g., "Hold", "Buy", "Sell") with a brief rationale.
+        - Keep the response concise, under 200 words.
+        """
+    return Task(
+        description=prompt,
+        agent=stock_analysis_agent,
+        expected_output="A concise analysis of the stock's performance with an investment recommendation, under 150 words."
+    )
+
+def get_response_refiner_task(query, initial_response, question_type, rag_note="NO_RAG_NEEDED"):
+    """Task for refining and reporting the response."""
+    
+    # Create a special note for RAG information
+    rag_message = ""
+    if rag_note == "RAG_NOT_USED":
+        rag_message = "Note: No relevant information found in RAG system, web search results were used."
+    elif rag_note == "RAG_LIMITED":
+        # Don't show any special message when RAG and web search are used together
+        rag_message = ""
+    elif rag_note == "RAG_SUFFICIENT":
+        # Don't show any special message when RAG is sufficient
+        rag_message = ""
+    
+    # Task for refining finance response
+    prompt = f"""
+    User query: '{query}'
+    Initial Response: '{initial_response}'
+    Question Type: '{question_type}'
+    
+    {rag_message}
+    
+    You are a Response Refiner and Reporter. Refine the initial response and present it in a professional report format.
+
+    ### Instructions:
+    - Simplify the language for a general audience.
+    - Verify accuracy and logical alignment with the query; add a note if issues are found.
+    - Format as a report:
+      **Financial Report for Query: '{query}'**
+      - **Summary**: [Simplified summary in 3-4 sentences]
+      - **Key Insight**: [One key takeaway or recommendation]
+      - **Source/Note**: [Cite source or add note]
+    - Keep under 200 words and do not tell word count.
+    """
+    return Task(
+        description=prompt,
+        agent=response_refiner_agent,
+        expected_output="A simplified and professionally formatted report, under 200 words."
+    )

utils.py

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+# utils.py
+
+import os
+from dotenv import load_dotenv
+from langchain_qdrant import QdrantVectorStore
+from langchain.embeddings import HuggingFaceEmbeddings
+from crewai import Agent, Task, Crew, Process, LLM
+import requests
+from requests.exceptions import ConnectionError, Timeout, HTTPError
+from functools import lru_cache
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Settings
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
+QDRANT_URL = os.getenv("QDRANT_URL")
+COLLECTION_NAME = "finance-chatbot"
+MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
+ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY")
+SERPER_API_KEY = os.getenv("SERPER_API_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+# Initialize embeddings
+embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+
+# Connect to the existing Qdrant collection
+qdrant = QdrantVectorStore.from_existing_collection(
+    embedding=embeddings,
+    url=QDRANT_URL,
+    api_key=QDRANT_API_KEY,
+    collection_name=COLLECTION_NAME
+)
+
+# Initialize Mistral LLM
+mistral_llm = LLM(model="mistral/mistral-large-latest", api_key=MISTRAL_API_KEY, temperature=0.7)
+
+# Initialize Gemini LLM
+gemini_llm = LLM( model="gemini/gemini-2.0-flash", gemini_llm=GEMINI_API_KEY, temperature=0.7)
+
+# Functions
+@lru_cache(maxsize=100)
+def search_qdrant(query, top_k=3):
+    """Search Qdrant for relevant documents."""
+    try:
+        retriever = qdrant.as_retriever(search_type="similarity", search_kwargs={"k": top_k})
+        results = retriever.invoke(query)
+        return [{"text": doc.page_content, "source": doc.metadata.get("source", "Unknown")} for doc in results]
+    except Exception:
+        return []
+
+def search_news(query, max_results=5):
+    """Search for recent financial news using Serper API."""
+    try:
+        url = "https://google.serper.dev/search"
+        headers = {
+            "X-API-KEY": SERPER_API_KEY,
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "q": f"{query} finance news",
+            "num": max_results
+        }
+        response = requests.post(url, json=payload, headers=headers, timeout=10)
+        response.raise_for_status()
+        data = response.json()
+
+        results = data.get("organic", [])
+        if not results:
+            return [{"title": "No recent news available", "url": "", "snippet": "Could not fetch news. Please try again later."}]
+
+        formatted_results = [
+            {
+                "title": item.get("title", ""),
+                "url": item.get("link", ""),
+                "snippet": item.get("snippet", "")
+            }
+            for item in results[:max_results]
+        ]
+        return formatted_results
+
+    except ConnectionError:
+        return [{"title": "Connection Error", "url": "", "snippet": "Failed to connect to the news API. Please check your internet connection."}]
+    except Timeout:
+        return [{"title": "Timeout Error", "url": "", "snippet": "News API request timed out. Please try again later."}]
+    except HTTPError as e:
+        if response.status_code == 429:
+            return [{"title": "Rate Limit Exceeded", "url": "", "snippet": "Too many requests to the news API. Please try again later."}]
+        return [{"title": "HTTP Error", "url": "", "snippet": f"Failed to fetch news due to HTTP error: {e}"}]
+    except Exception:
+        return [{"title": "Error", "url": "", "snippet": "An unexpected error occurred while fetching news. Please try again later."}]
+
+def get_stock_data(symbol):
+    """Fetch stock data using Alpha Vantage API."""
+    try:
+        url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={ALPHA_VANTAGE_API_KEY}"
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        data = response.json().get("Global Quote", {})
+        if not data:
+            return {"symbol": symbol, "error": "No data found for this symbol."}
+        return {
+            "symbol": symbol,
+            "price": data.get("05. price", "N/A"),
+            "change": data.get("09. change", "N/A"),
+            "change_percent": data.get("10. change percent", "N/A")
+        }
+    except ConnectionError:
+        return {"symbol": symbol, "error": "Failed to connect to the stock API. Please check your internet connection."}
+    except Timeout:
+        return {"symbol": symbol, "error": "Stock API request timed out. Please try again later."}
+    except HTTPError as e:
+        if response.status_code == 429:
+            return {"symbol": symbol, "error": "Too many requests to the stock API. Please try again later."}
+        return {"symbol": symbol, "error": f"Failed to fetch stock data due to HTTP error: {e}"}
+    except Exception:
+        return {"symbol": symbol, "error": "An unexpected error occurred while fetching stock data. Please try again later."}
+
+@lru_cache(maxsize=100)
+def determine_question_type(query):
+    """Determine the type of user query using Mistral LLM via CrewAI's task mechanism."""
+    prompt = f"""
+    Analyze the following user query and determine its category:
+    - finance_knowledge: General questions about financial terms, concepts, or strategies
+    - market_news: Questions about current market news, trends, or events
+    - stock_analysis: Questions about specific stock analysis (e.g., mentioning a stock ticker like AAPL)
+
+    Query: "{query}"
+
+    Provide your response in this format:
+    Category: <category>
+    Extra Data: <additional info, such as the stock ticker for stock_analysis, or the query itself>
+    """
+
+    classifier_agent = Agent(
+        role="Query Classifier",
+        goal="Classify user queries into appropriate categories.",
+        backstory="An expert in natural language understanding, capable of analyzing queries and categorizing them accurately.",
+        llm=mistral_llm,
+        verbose=True,
+        allow_delegation=False
+    )
+
+    classifier_task = Task(
+        description=prompt,
+        agent=classifier_agent,
+        expected_output="A classification of the query in the format: Category: <category>\nExtra Data: <additional info>"
+    )
+
+    temp_crew = Crew(
+        agents=[classifier_agent],
+        tasks=[classifier_task],
+        process=Process.sequential,
+        verbose=False
+    )
+    
+    try:
+        response = temp_crew.kickoff()
+        response_text = response.raw if hasattr(response, 'raw') else str(response)
+        lines = response_text.strip().split("\n")
+        if len(lines) < 2:
+            raise ValueError("Invalid response format from LLM")
+        category_line = lines[0].replace("Category: ", "").strip()
+        extra_data_line = lines[1].replace("Extra Data: ", "").strip()
+        if category_line not in ["finance_knowledge", "market_news", "stock_analysis"]:
+            raise ValueError(f"Invalid category: {category_line}")
+        return category_line, extra_data_line
+    except Exception:
+        return "finance_knowledge", query