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SalesBuddy

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rchrdgwr commited on Oct 17

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0e9ef68

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1 Parent(s): 918fcc8

first revision - all pieces hooked up

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.chainlit/config.toml +23 -19
README.md +5 -7
app_main.py +100 -0
cache/text-embedding-3-small07d93d4d-d9c5-5018-937d-ac0e3b6ea1d7 +1 -0
cache/text-embedding-3-small0cf00a1f-5235-5f18-ba0b-28185b0d64f2 +1 -0
cache/text-embedding-3-small1177d18d-4b5a-56d3-8b08-dc86a06c62c3 +1 -0
cache/text-embedding-3-small126b4793-0087-5113-8835-fb5e47cc3f7a +1 -0
cache/text-embedding-3-small1a376229-0c15-5a9a-8f17-7d088e779d17 +1 -0
cache/text-embedding-3-small25e00269-dcef-5da0-8ffa-384e0d946d6c +1 -0
cache/text-embedding-3-small312de67a-e95c-5406-8bf4-2aa68c02a520 +1 -0
cache/text-embedding-3-small36ffaf33-d485-5a4f-b083-758eb47c0905 +1 -0
cache/text-embedding-3-small3efc162e-e62f-5160-a14d-e69f6acc074a +1 -0
cache/text-embedding-3-small5be2520a-7be5-5742-8d63-7d8718d9caa0 +1 -0
cache/text-embedding-3-small63133d17-e391-593c-a45f-0a3fef1193f5 +1 -0
cache/text-embedding-3-small70facfcb-5acf-5ca1-8fa3-7dd127a2cbac +1 -0
cache/text-embedding-3-small7207fc36-c323-54a0-a009-2ffe69479452 +1 -0
cache/text-embedding-3-small77934f95-4140-5ded-bd96-04dfdcd7d1ac +1 -0
cache/text-embedding-3-small783d53d8-52ce-5fc9-b624-ae065995abe4 +1 -0
cache/text-embedding-3-small83cc408d-be6c-513c-85b9-25da32f314c6 +1 -0
cache/text-embedding-3-small88bb9443-f42b-5cda-b784-d50b5db098fb +1 -0
cache/text-embedding-3-small8a92c220-717f-52f7-9280-9ad8515f1be0 +1 -0
cache/text-embedding-3-small8b18ee60-ac62-5ef1-9864-7c844d3ffb9e +1 -0
cache/text-embedding-3-small916092b0-ff39-5d26-8841-043471b41090 +1 -0
cache/text-embedding-3-smalla2832f4d-778f-56d1-9dc0-1c9cdd03cf65 +1 -0
cache/text-embedding-3-smalla993d80a-bc52-53f4-8b4c-d1274ed316c3 +1 -0
cache/text-embedding-3-smallb39466d6-0c8d-5aa8-b277-871b510757be +1 -0
cache/text-embedding-3-smallb4e8ceb0-43c9-5139-ae70-4bb7893aa5e2 +1 -0
cache/text-embedding-3-smalld8606e2a-f1e3-519e-a3fb-dfa06bee2780 +1 -0
cache/text-embedding-3-smalld9347aa7-bdf6-5051-af1d-b6dc336ddf3c +1 -0
cache/text-embedding-3-smalle8920016-2984-557d-afce-7235b7f9fa89 +1 -0
chainlit.md +4 -10
classes.py +152 -0
data/AIE4_SalesBuddy_QandA.csv +93 -0
data/BetterTech Lending Analytics Solution.pdf +0 -0
data/BetterTechLendingAnalyticsSolution.txt +39 -0
data/Opportunity_Information.csv +3 -0
public/custom_styles.css +13 -0
reports/HSBC Quarterly Report 2024-10-16 00:39:38.pdf +0 -0
reports/HSBC Quarterly Report 2024-10-16 09:36:43.pdf +0 -0
reports/HSBC Quarterly Report 2024-10-16.md +29 -0
reports/HSBC Quarterly Report 2024-10-16.pdf +0 -0
requirements.txt +29 -6
utils_actions.py +10 -0
utils_callbacks.py +85 -0
utils_chain_parameters.py +78 -0
utils_control_messages.py +26 -0
utils_customer_research.py +337 -0
utils_data.py +173 -0
utils_evaluate.py +80 -0
utils_objections.py +192 -0

.chainlit/config.toml CHANGED Viewed

@@ -29,13 +29,20 @@ multi_modal = true
 # Allows user to use speech to text
 [features.speech_to_text]
-    enabled = false
     # See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
-    # language = "en-US"
 [UI]
 # Name of the app and chatbot.
-name = "Chatbot"
 # Show the readme while the conversation is empty.
 show_readme_as_default = true
@@ -58,27 +65,24 @@ hide_cot = false
 # Specify a CSS file that can be used to customize the user interface.
 # The CSS file can be served from the public directory or via an external link.
 # custom_css = "/public/test.css"
 # Override default MUI light theme. (Check theme.ts)
 [UI.theme.light]
-    #background = "#FAFAFA"
-    #paper = "#FFFFFF"
     [UI.theme.light.primary]
-        #main = "#F80061"
-        #dark = "#980039"
-        #light = "#FFE7EB"
-# Override default MUI dark theme. (Check theme.ts)
 [UI.theme.dark]
-    #background = "#FAFAFA"
-    #paper = "#FFFFFF"
     [UI.theme.dark.primary]
-        #main = "#F80061"
-        #dark = "#980039"
-        #light = "#FFE7EB"
 [meta]
 generated_by = "0.7.700"

 # Allows user to use speech to text
 [features.speech_to_text]
+    enabled = true
     # See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
+    language = "en-US"
+[features.audio]
+    min_decibels = -45
+    initial_silence_timeout = 3000
+    silence_timeout = 1500
+    max_duration = 15000
+    chunk_duration = 1000
+    sample_rate = 44100
 [UI]
 # Name of the app and chatbot.
+name = "SalesBuddy for BetterTech"
 # Show the readme while the conversation is empty.
 show_readme_as_default = true
 # Specify a CSS file that can be used to customize the user interface.
 # The CSS file can be served from the public directory or via an external link.
 # custom_css = "/public/test.css"
+custom_css = "/public/custom_styles.css"
 # Override default MUI light theme. (Check theme.ts)
 [UI.theme.light]
+    background = "#E0F7FA"  # Light Cyan for a refreshing background
+    paper = "#FFFFFF"       # Keep the paper white for contrast
     [UI.theme.light.primary]
+        main = "#0288D1"     # A vibrant blue as the primary color
+        dark = "#01579B"     # A deeper blue for darker elements
+        light = "#B3E5FC"    # A light blue for accents and highlights
 [UI.theme.dark]
+    background = "#1E3A5F"  # A deep, rich blue for the background
+    paper = "#2C3E50"       # Slightly lighter for paper elements
     [UI.theme.dark.primary]
+        main = "#0288D1"     # Same vibrant blue for consistency
+        dark = "#01579B"     # A rich dark blue
+        light = "#4FC3F7"    # A lighter blue for accents
 [meta]
 generated_by = "0.7.700"

README.md CHANGED Viewed

@@ -1,12 +1,10 @@
 ---
-title: SalesBuddy
-emoji: 🌖
 colorFrom: pink
-colorTo: indigo
 sdk: docker
 pinned: false
 license: mit
-short_description: AIE4 Final Project
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: SalesBuddy for BetterTech
+emoji: 👁
 colorFrom: pink
+colorTo: blue
 sdk: docker
 pinned: false
 license: mit
+short_description: AIE4 Project - SalesBuddy for BetterTech
+---

app_main.py ADDED Viewed

	@@ -0,0 +1,100 @@

+import chainlit as cl
+import dotenv
+import os
+import pandas as pd
+import whisper
+from datetime import datetime
+from langchain_core.globals import set_llm_cache
+from langchain_core.caches import InMemoryCache
+from openai import OpenAI
+from classes import SessionState
+from utils_callbacks import callback_run_scenario, callback_start_scenario, callback_evaluate_performance, callback_display_queries_responses
+from utils_customer_research import get_latest_news
+from utils_pose_objections import pose_objections
+from utils_prep import prep_research, prep_opportunities, prep_start, prep_opportunity_analysis
+from utils_simulation import do_simulation
+dotenv.load_dotenv()
+openai_api_key = os.getenv("OPENAI_API_KEY")
+tavily_api_key = os.getenv("TAVILY_API_KEY")
+llm_model = "gpt-4o-mini"
+set_llm_cache(InMemoryCache())
+client = OpenAI(api_key=openai_api_key)
+whisper_model = whisper.load_model("base")
+#############################################
+# Action callbacks
+#############################################
+@cl.action_callback("HSBC: Lending - Loan Origination System (Qualification)")
+async def on_action_anayze_opportunity(action):
+    await prep_opportunity_analysis()
+@cl.action_callback("Get Latest News on this Customer")
+async def on_action_get_latest_news(action):
+    await get_latest_news(action.value)
+@cl.action_callback("Enter Meeting Simulation")
+async def on_action_run_scenario(action):
+    await callback_run_scenario(action)
+@cl.action_callback("Start Simulation")
+async def on_action_start_scenario(action):
+    print("on_action_start_scenario()")
+    await callback_start_scenario()
+@cl.action_callback("Evaluate Performance")
+async def on_action_evaluate_performance(action):
+    await callback_evaluate_performance()
+@cl.action_callback("Display Queries and Responses")
+async def on_action_display_queries_responses(action):
+    await callback_display_queries_responses()
+#############################################
+### On Chat Start (Session Start) Section ###
+#############################################
+@cl.on_chat_start
+async def on_chat_start():
+    session_state = SessionState()
+    cl.user_session.set("session_state", session_state)
+    session_state.llm_model = llm_model
+    print(session_state)
+    cl.user_session.set("messages", [])
+    if client is None:
+        await cl.Message(content="Error: OpenAI client not initialized. Please check your API key.").send()
+    if whisper_model is None:
+        await cl.Message(content="Error: Whisper model not loaded. Please check your installation.").send()
+    await prep_start(session_state)
+    await prep_opportunities(session_state)
+    # await prep_opportunity_analysis(session_state)
+    # await prep_research(session_state)
+    # session_state.session_stage = "research"
+    # Ask for the first PDF file (Potential Customer Business Domain)
+    # await process_pdf_files()
+@cl.on_message
+async def main(message):
+    content = message.content.strip()
+    session_state = cl.user_session.get("session_state", None)
+    if session_state is None:
+        await cl.Message(content="Error: Session state not initialized. Please check your installation.").send()
+        return
+    if session_state.do_objections:
+        await pose_objections(session_state)
+    else:
+        await do_simulation(client, session_state, message)

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chainlit.md CHANGED Viewed

@@ -1,14 +1,8 @@
-# Welcome to Chainlit! 🚀🤖
-Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
-## Useful Links 🔗
-- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
-- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
-We can't wait to see what you create with Chainlit! Happy coding! 💻😊
-## Welcome screen
-To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

+# Sales Buddy
+An AI-powered sales assistant developed for the AI Engineering Bootcamp Cohort 4.
+AI Makerspace
+Developers: Aaron Moss, Nitin Gupta, Richard Gower

classes.py ADDED Viewed

	@@ -0,0 +1,152 @@

+class SessionState:
+    session_stage = ""
+    do_evaluation = False
+    do_opportunity_analysis = False
+    do_customer_research = False
+    do_objections = False
+    add_objections_to_analysis = True
+    do_ragas_evaluation = False
+    customer_research_report_md = "HSBC Quarterly Report 2024-10-16.md"
+    customer_research_report_pdf = "HSBC Quarterly Report 2024-10-16.pdf"
+    bettetech_value_proposition_pdf = "BetterTech Lending Analytics Solution.pdf"
+    do_voice = True
+    status = "active"
+    scenario = None
+    qa_mode = "single"
+    questions = []
+    start_time = None
+    end_time = None
+    duration_minutes = None
+    attitude = "Happy"
+    mood_score = 5
+    num_questions = 1
+    current_question_index = 0
+    previous_answer = None
+    question = ""
+    ground_truth = ""
+    rep_answer = ""
+    responses = []
+    queries = []
+    llm_responses = []
+    command = ""
+    scores = []
+    llm_next_steps = ""
+    def __init__(self):
+        self.session_stage = "research"
+        self.do_evaluation = False
+        self.do_opportunity_analysis = False
+        self.do_customer_research = True
+        self.do_objections = False
+        self.add_objections_to_analysis = True
+        self.do_ragas_evaluation = False
+        self.customer_research_report_md = "HSBC Quarterly Report 2024-10-16.md"
+        self.customer_research_report_pdf = "HSBC Quarterly Report 2024-10-16.pdf"
+        self.bettetech_value_proposition_pdf = "BetterTech Lending Analytics Solution.pdf"
+        self.do_voice = True
+        self.status = "active"
+        self.scenario = None
+        self.qa_mode = "single"
+        self.questions = []
+        self.start_time = None
+        self.end_time = None
+        self.duration_minutes = None
+        self.attitude = "Happy"
+        self.mood_score = 5
+        self.num_questions = 1
+        self.current_question_index = 0
+        self.previous_answer = None
+        self.question = ""
+        self.ground_truth = ""
+        self.rep_answer = ""
+        self.responses = []
+        self.queries = []
+        self.llm_responses = []
+        self.command = ""
+        self.scores = []
+        self.llm_next_steps = ""
+        self.company = None
+        self.customer = None
+        self.opportunity = None
+        self.scenario = None
+    class Company:
+        def __init__(self, name, description, product, product_summary, product_description):
+            self.name = name
+            self.description = description
+            self.product = product
+            self.product_summary = product_summary
+            self.product_description = product_description
+    class Customer:
+        def __init__(self, name, contact_name, contact_role):
+            self.name = name
+            self.contact_name = contact_name
+            self.contact_role = contact_role
+            self.background = None
+    class Opportunity:
+        def __init__(self, id, name, stage, description, value, close_date, activity, next_steps):
+            self.id = id
+            self.name = name
+            self.stage = stage
+            self.description = description
+            self.value = value
+            self.close_date = close_date
+            self.activity = activity
+            self.next_steps = next_steps
+    def __init__(self):
+        self.company = None
+        self.customer = None
+        self.opportunity = None
+        self.scenario = None
+    def add_company_info(self, name, description, product, product_summary, product_description):
+        self.company = self.Company(name, description, product, product_summary, product_description)
+    def add_customer_info(self, name, contact_name, contact_role):
+        self.customer = self.Customer(name, contact_name, contact_role)
+    def add_opportunity_info(self, id, name, stage, description, value, close_date, activity, next_steps):
+        self.opportunity = self.Opportunity(id, name, stage, description, value, close_date, activity, next_steps)
+    def add_scenario_info(self, scenario_data):
+        self.scenario = scenario_data
+        self.add_opportunity_info(
+            id=scenario_data['Opportunity ID'],
+            name=scenario_data['Opportunity Name'],
+            stage=scenario_data['Opportunity Stage'],
+            description=scenario_data['Opportunity Description'],
+            value=scenario_data['Opportunity Value'],
+            close_date=scenario_data['Close Date'],
+            activity=scenario_data['Activity'],
+            next_steps=scenario_data['Next Steps']
+        )
+        self.add_customer_info(
+            name=scenario_data['Customer Name'],
+            contact_name=scenario_data['Customer Contact'],
+            contact_role=scenario_data['Customer Contact Role']
+        )
+    def get_opening(self):
+        output_lines = [
+            "**Simulation Scenario**",
+            f"**Customer:** {self.customer.name if self.customer else 'Unknown'}",
+            f"**Opportunity:** {self.opportunity.name if self.opportunity else 'Unknown'}",
+            f"**Value:** {self.opportunity.value if self.opportunity and hasattr(self.opportunity, 'value') else 'Unknown'}",
+            f"**Stage:** {self.opportunity.stage if self.opportunity else 'N/A'}",
+            f"**Target Close Date:** {self.opportunity.close_date if self.opportunity and hasattr(self.opportunity, 'close_date') else 'Unknown'}",
+            f"**Opportunity Description:** {self.opportunity.description if self.opportunity else 'Unknown'}",
+            f"**Meeting with:** {self.customer.contact_name} ({self.customer.contact_role})",
+            f"**Activity:** {self.opportunity.activity if self.opportunity and hasattr(self.opportunity, 'activity') else 'Unknown'}",
+            f"**Current Meeting:** {self.opportunity.next_steps if self.opportunity and hasattr(self.opportunity, 'next_steps') else 'Unknown'}"
+        ]
+        output = "\n".join(output_lines)
+        return output
+    def __str__(self):
+        company_info = f"{self.company.name} - " if self.company else ""
+        customer_info = f"for {self.customer.name}" if self.customer else "No Customer"
+        opportunity_info = f"{self.opportunity.name} ({self.opportunity.stage})" if self.opportunity else "No opportunity set"
+        return f"{company_info}SessionState: {customer_info} {opportunity_info}".strip()

data/AIE4_SalesBuddy_QandA.csv ADDED Viewed

	@@ -0,0 +1,93 @@

+Stage,Customer Question (Provided by Claude),Salesperson Response (Provided by Claude),Ground Truth (Provided by Nitin). Marked as Salesperson 2 by Claude,Rating of Salesperson response (per Claude),Rating of Groundtruth (per Claude),Comments (per Claude)
+Discovery,What exactly does your software do?,"""Our software is a comprehensive financial data analytics platform. It aggregates data from multiple sources, provides real-time insights, and helps financial institutions make data-driven decisions more efficiently.""","Our software is a comprehensive financial data analytics platform. It aggregates data from multiple sources, provides real-time insights, and helps financial institutions make data-driven decisions.
+Our customer list includes 15 of the Fortune 100 companies. PNC bank is one of our customers and they aggregated data from their different loan systems to reduce charge-offs by 17%, and costs by 30%.
+Can you share a little about your data challenges in your organization?",7,9,Salesperson 2 provides more specific details and examples
+Discovery,How is your solution different from what we're currently using?,"""I'd love to learn more about your current system. Our solution stands out with its AI-powered predictive analytics and ability to integrate seamlessly with a wide range of financial systems. Could you tell me about your current pain points?""","The main reasons cited by our customers for selecting our software are the AI-powered predictive analytics and ability to integrate seamlessly with a wide range of financial systems. A large credit union customer said that our AI capabilities were far superior than what they would have developed internally.
+It will be super helpful for me to learn about your current system and your pain points to explore if there is a fit",8,9,Salesperson 2 offers concrete examples and asks about pain points
+Discovery,Can your software handle the volume of transactions we process daily?,"Absolutely. Our platform is designed to handle high-volume, high-frequency data. We currently serve clients processing millions of transactions daily. May I ask what volume you're dealing with so I can provide more specific information?""","Absolutely. Our platform is designed to handle high-volume, high-frequency data. PNC bank processes 3.5 million deposit and payment transactions every hour with our systems. May I ask what volume you're dealing with so I can provide more specific information?""",7,9,Salesperson 2 gives a specific example with numbers
+Discovery,Does your solution comply with financial regulations like GDPR and CCPA?,"""Yes, regulatory compliance is a top priority for us. Our solution is fully compliant with GDPR, CCPA, and other major financial regulations. We also regularly update our system to adapt to new regulations. Are there any specific compliance concerns you have?""","Regulatory compliance is a top priority for us. Our solution is fully compliant with GDPR, CCPA, and other major financial regulations. We also regularly update our system to adapt to new regulations. We produce the detailed compliance findings in the SOC2 report that I am happy to share with you.
+Can you share the compliance requirements for your firm?",8,9,Salesperson 2 mentions SOC2 report and asks about specific requirements
+Discovery,How does your software integrate with legacy systems?,"""We have extensive experience integrating with legacy systems common in the financial sector. Our platform uses APIs and custom connectors to ensure smooth data flow. Could you share what systems you're currently using? This will help me explain how we'd approach integration in your specific case.""",We offer many integration options for our software. Most customers feed data into our software from their internal system via APIs. Others build customer connectors to ensure smooth data flows. Can you share what systems you're currently using? This will help me explain how we'd approach integration in your specific case.,8,7,Salesperson 1 provides more detailed integration options
+Discovery,What kind of support do you offer for implementation and ongoing use?,"""We provide comprehensive support throughout the journey. This includes a dedicated implementation team, 24/7 technical support, regular training sessions, and a customer success manager. How hands-on would you like the support to be?""","For implementation, our customers use a combination of our professional services team and resources from our certified SI partners.
+For ongoing support, we offer a couple of support options, ranging from developer support to enterprise support. How do you typically implement and support software within your organization?",7,9,Salesperson 2 gives more specific implementation and support options
+Discovery,Can your solution help us with risk management and fraud detection?,"""Definitely. Our AI algorithms are particularly strong in risk assessment and fraud detection. They can identify unusual patterns and potential risks in real-time. What are your current challenges in risk management and fraud detection?""","Some of our customers have used the aggregation and analytics capabilities of our solution to ascertain supplier and vendor risk in their organization. A large retail customer aggregated all their supplier data sources including shipping, land transportation etc to review risk to the shipping timelines to determine appropriate inventory levels.
+Can you tell me more about the risk and fraud use cases you are thinking about so we can determine if our solution is a fit?",7,9,Salesperson 2 provides a specific example and asks about use cases
+Discovery,How scalable is your solution?,"""Our solution is highly scalable, designed to grow with your business. We use cloud infrastructure that can easily expand to handle increased data volume and user base. What kind of growth are you anticipating in the next few years?""","One of the reasons customers choose our solution is because it can be scaled up or down depending on their business needs. During the pandemic, some of our large online retail customers had to scale overnight as the ecommerce volumes went up 10X. They depended on our platform for their analytics needs.
+As you think about scalability, can you give me some idea of the data volumes within your org?",8,9,Salesperson 2 gives a concrete example of scalability during the pandemic
+Discovery,Do you have experience working with companies of our size?,"""Yes, we work with several large financial institutions. While I can't name them due to confidentiality, I can share that we serve three of the top 10 banks in the country. Would you be interested in case studies or speaking with references in your industry?""","Our customers range from large Fortune 100 enterprises to young startups innovating on a business idea.
+Can you tell me more about some of the intended use cases and things you are struggling with regarding your analytics needs?",7,8,Salesperson 2 asks about specific use cases and challenges
+Discovery,How does your pricing model work?,"""Our pricing is based on the modules you choose and the volume of data processed. We aim to align our pricing with the value we provide. To give you an accurate quote, could you share more about your specific needs and usage expectations?""","We have a consumption based pricing model, based on the modules you choose and the volume of data processed. We will be providing you a dashboard so you can get a daily view of the costs incurred. You can set limits at an org level or a user level so you can keep a tab of the costs.",7,9,Salesperson 2 explains the consumption-based model and mentions a dashboard
+Qualification,What's the total cost of ownership for your solution?,"""The total cost includes the software license, implementation, training, and ongoing support. For a company of your size, we estimate the first-year cost to be between $500,000 to $750,000, with annual renewals at about 20% of that. Would you like a detailed breakdown?""","Our customers have to consider annual running costs (consumption), one time implementation fee, training costs, and support costs as they come on to our platform. Customers have cited productivity gains of 25% and analytics costs going down by 30% within 18 months post implementation.  ",9,8,Salesperson 1 provides specific numbers while 2 focuses on ROI
+Qualification,How long does the implementation usually take?,"""For a company of your size and complexity, implementation typically takes 3-6 months. This includes system integration, data migration, and user training. Do you have any specific timeline constraints we should be aware of?""","Implementation ranges from 8-16 weeks. Our platform is modular but the key things that impact the implementation timeframe are the number of data sources we have to connect to, and the number of downstream systems we need to integrate with.
+Do you have any specific timeline constraints we should be aware of?""",8,9,Salesperson 2 gives more detail on factors affecting implementation time
+Qualification,What kind of ROI can we expect?,"""Based on our experience with similar clients, most see a full ROI within 12-18 months. This comes from improved efficiency, better risk management, and data-driven decision making. One client reduced operational costs by 22% in the first year. What specific metrics are most important to you?""","Customers have cited productivity gains of 25% and analytics costs going down by 30% within 18 months post implementation.
+Over the long run, our customers have also been to consolidate the number of vendor systems, reducing their technical debt, seeing ROI of 75% within 3 years.",8,9,Salesperson 2 provides more detailed ROI figures and timeframes
+Qualification,Who will be our main point of contact during and after implementation?,"""You'll have a dedicated project manager during implementation and a customer success manager post-launch. Both will be deeply familiar with the financial services sector. How hands-on do you prefer your points of contact to be?""","Our Customer Success and Professional services teams works with customers during the entire implementation phase. During the implementation phase, the project lead from the Professional Services team will be the main point of contact, and then they will hand over to the customer success manager who will be the POC post implementation to drive training and adoption.",8,9,Salesperson 2 provides more detailed information about the process
+Qualification,What happens if we need to scale up or down?,"""Our contracts are flexible. You can easily add users or increase data processing capacity as you grow. If you need to scale down, we can adjust at your annual renewal. What kind of fluctuations do you anticipate in your usage?""","We run on a highly secure and scalable infrastructure backend which allows you to scale up or down, on-demand, depending on your business needs. During the pandemic, some of our large online retail customers had to scale overnight as the ecommerce volumes went up 10X and they needs stronger real time analytics to run their business.
+They were not only able to scale up, but also scale down as the business volume decreased as they dealt with inflationary pressures last year. ",8,9,Salesperson 2 gives a specific example of scaling during the pandemic
+Qualification,How often do you release updates and how disruptive are they?,"""We release major updates quarterly and minor ones monthly. Updates are scheduled during off-hours and typically take less than an hour. We always notify clients in advance and provide detailed release notes. How does this align with your change management processes?""","We have 4 major releases a year, and 8 minor releases a year. The releases are all made into the core platform and available to all customers. Updates are scheduled during off-hours and typically take less than an hour. We always notify clients in advance and provide detailed release notes.
+How does this align with your change management processes?""",9,9,Both provide similar comprehensive information
+Qualification,What kind of training do you provide for our staff?,"""We offer a mix of online and on-site training. This includes admin training, end-user workshops, and ongoing webinars. We also provide a comprehensive knowledge base. How many users would need training, and do you have any specific training preferences?""","We offer a mix of online and in-person trainings on our platform and solution. Prior to launch, we will conduct a half day training delivered in a hybrid environment with our team at your office so we can walk you through different functionality and how to use the platform. Post launch, there are different trainings available on-demand. We can also provide customized training at any team for your team.
+What is the size and makeup of your team that will need to be trained on the solution?",8,9,Salesperson 2 offers more detailed training options and asks about team size
+Qualification,Can we customize the solution to our specific needs?,"""Absolutely. While our core product is robust, we understand that every financial institution has unique needs. We offer customization services to tailor the solution to your specific workflows and requirements. What kind of customizations do you think you'd need?""","Abolutely. Our platform is highly configurable and can be tailored to meet different customer needs and use cases. To give you a couple of examples, we have retail customers running their supply chain risk analysis on our platform and we have financial services customers running loan loss forecasting analysis on their credit card portfolios.
+Can you tell me more about your needs and the use case you have in mind?",8,9,Salesperson 2 provides specific examples of customization
+Qualification,What security measures does your solution have in place?,"""Security is paramount for us. We use bank-grade encryption, multi-factor authentication, and regular third-party security audits. We're also SOC 2 Type II compliant. Are there any specific security protocols or certifications you require?""","Security is job zero for us. Our customers put confidential data into our platform and we need to make sure we have the right protections in place to safeguard their data. Security measures include masking of data to remove any PII, encryption of data both at rest and transit, and deleting data copies on a periodic basis. We also partner with leading security companies like Palo Alto and Crowdstrike to use their tooling to monitor our securty posture. I can share with you our security whitepaper and also share the SOC Type II report that outlines our security certifications.
+Are there any specific security protocols or certifications you require?",8,10,Salesperson 2 provides extensive details on security measures and offers documentation
+Qualification,"If we decide to proceed, what are the next steps?,","""Great question! The next steps would be a technical deep dive with your IT team, followed by a customized demo for your key stakeholders. Then we'd move to contract discussions and implementation planning. Does this align with your decision-making process?""","We should first identify the 1-2 use cases you want to tackle in phase 1 of the project. Following that, we will sit down with your team to understand your technical landscape and the integration requirements. We can then discuss timelines, and provide you a pricing and implementation proposal.
+Does this sound reasonable to you? ",8,9,Salesperson 2 outlines a more specific action plan
+Technical Assessment,Can you walk us through how your system handles data encryption?,"""Certainly. We use AES-256 encryption for data at rest and TLS 1.3 for data in transit. All encryption keys are managed through a robust key management system with regular rotation. Would you like me to go into more technical detail on any specific aspect of our encryption process?""","Security is job zero for us. Our customers put confidential data into our platform and we need to make sure we have the right protections in place to safeguard their data. Security measures include masking of data to remove any PII, encryption of data both at rest and transit, and deleting data copies on a periodic basis. We use AES-256 encryption for data at rest and TLS 1.3 for data in transit. All encryption keys are managed through a robust key management system with regular rotation. I can share with you our security whitepaper and also share the SOC1 report that outlines our security certifications.
+Can you talk about your policies and compliance requirements regarding data encryption?",9,10,Salesperson 2 provides more comprehensive information on encryption and security
+Technical Assessment,How does your solution integrate with our core banking system?,"""We have pre-built connectors for most major core banking systems. Our integration process typically involves setting up secure API connections, defining data mapping rules, and establishing real-time or batch data synchronization based on your needs. Which core banking system are you using? I can provide more specific integration details.""","We have pre-built connectors for most major core banking systems. Our integration process typically involves setting up secure API connections, defining data mapping rules, and establishing real-time or batch data synchronization based on your needs. We use data masking so no PII gets included in the data that gets sent to us. We will use encryption at rest and in transit when dealing with any data transfers. Our customers have integrated our analytics engine with core banking providers like FIS, Jack Henry, and Temenos.
+Which core banking provider do you use?",8,9,Salesperson 2 mentions specific core banking providers and data protection measures
+Technical Assessment,What's your system's average response time for complex queries?,"""Our system is optimized for performance. For complex queries involving large datasets, the average response time is under 2 seconds. This is achieved through advanced indexing, in-memory processing, and distributed computing architecture. What kind of response times are you currently experiencing with your system?""","We have designed our platform to be highly scalable and process thousands of queries per minute. One of our banking customers runs their entire credit decisioning process on our platform, getting the entire loan approval process in under 3 minutes.
+What kind of response time are you looking for?",9,8,Salesperson 1 provides more specific response time information
+Technical Assessment,How do you handle data backups and disaster recovery?,"""We maintain real-time replicas across multiple geographically distributed data centers. Full backups are performed daily, with incremental backups every hour. Our disaster recovery plan ensures a Recovery Time Objective (RTO) of 4 hours and a Recovery Point Objective (RPO) of 15 minutes. How do these metrics compare to your current system and requirements?""","""We maintain real-time replicas across multiple geographically distributed data centers. Full backups are performed daily, with incremental backups every hour. Our disaster recovery plan ensures SLAs for Recovery Time Objective (RTO) and a Recovery Point Objective (RPO).
+What recovery metrics are you looking to meet? ",9,8,Salesperson 1 provides more specific RTO and RPO metrics
+Technical Assessment,Can your system handle real-time fraud detection?,"""Yes, our system excels at real-time fraud detection. We use machine learning models that analyze transaction patterns in real-time. The system can flag suspicious activities instantly and even automate certain preventive actions. What specific fraud scenarios are you most concerned about?""","Fraud detection is a common use case for our software. Our platform can process both structured and unstructured data to make real time fraud detection better, and reduce the chances of false positives. We also use both predictive and generative AI for better pattern matching and detect fraud across our network of customers. One of our large banking customers migrated to our platform from a large legacy provider and they saw 40% reduction in false positives and 20% reduction in overall fraud cases. ",8,10,Salesperson 2 provides a specific customer example with impressive results
+Technical Assessment,How flexible is your reporting module?,"""Our reporting module is highly flexible. It includes a drag-and-drop report builder, a library of pre-built financial reports, and the ability to create custom reports using SQL. Reports can be scheduled, exported in various formats, and even embedded in other applications. What are your most critical reporting needs?""","Our customers run a variety of reports on our platform. The reporting module is highly configurable allowing you to select data from different table structures. You can configure report, table, and field level permissions to ensure only the authorized users in your organization get access to the right data. Reports can be scheduled, exported in various formats, and even embedded in other applications. What are your most critical reporting needs?",9,9,Both provide comprehensive information on reporting capabilities
+Technical Assessment,What APIs do you offer for extending the system's functionality?,"""We provide a comprehensive set of RESTful APIs that cover all major functionalities of our system. This includes APIs for data ingestion, analytics, reporting, and administrative functions. We also offer SDKs for popular programming languages. Are there specific integrations or extensions you're considering?""","We have existing integrations with many 3rd party systems include core banking, payment, risk, and transaction processing systems to ensure we can exchange data feeds from upstream or downstream systems. In addition, we provide a comprehensive set of RESTful APIs that cover all major functionalities of our system. This includes APIs for data ingestion, analytics, reporting, and administrative functions. We also offer SDKs for popular programming languages.
+Can you share some details of your integration requirements?",8,9,Salesperson 2 mentions existing integrations in addition to APIs
+Technical Assessment,How does your system handle data governance and lineage?,"""Data governance is built into our system's core. We provide full data lineage tracking, allowing you to trace any piece of data back to its source. Our governance module includes features for data classification, access control, and audit trails. How complex are your data governance requirements?""","We comply with the data governance and sovereignty requirements of different states and countries. In the insurance sector, each state has different data governance requriements and our system can be configured to ensure those requirements are always adhered to. Similarly, in the case of a country specific sovereign requirements, we will ensure that the data stays within that country only.
+For lineage, we provide an audit trail so you know each step when your data was enhanced or modified, offering you complete transparency. ",8,9,Salesperson 2 provides more specific information on governance and compliance
+Technical Assessment,Can you explain your approach to ensuring system scalability?,"""Our system is built on a microservices architecture deployed on cloud infrastructure. This allows us to scale individual components independently based on demand. We use auto-scaling groups to handle traffic spikes and have successfully scaled to handle over 10 million transactions per hour for some clients. What kind of growth do you anticipate in your transaction volume?""","Our system is built on a microservices architecture deployed on cloud infrastructure. This allows us to scale individual components independently based on demand. We offer deployment to multiple cloud providers including AWS, Azure, and GCP giving you choice and freedom of deployment options.
+We use auto-scaling groups to handle traffic spikes and have successfully scaled to handle over 10 million transactions per hour for some clients.
+What kind of growth do you anticipate in your transaction volume? ",9,9,Both provide comprehensive information on scalability
+Technical Assessment,How do you ensure the accuracy of your AI/ML models in financial predictions?,"Technical Assessment,,""We take a rigorous approach to model accuracy. Our models are trained on vast amounts of anonymized financial data and are continuously refined. We use techniques like cross-validation, backtesting, and A/B testing to ensure accuracy. Models are monitored in real-time and retrained regularly. We also provide transparency reports on model performance. What specific predictive capabilities are you most interested in?""","We take a rigorous approach to model accuracy. Our models are trained on vast amounts of anonymized financial data and are continuously refined. We use techniques like cross-validation, backtesting, and A/B testing to ensure accuracy. Models are monitored in real-time and retrained regularly. We also provide transparency reports on model performance.
+For unstructured data, we use a number of techniques including RAG and fine-tuning to minimize hallucinations in our predictions. Post model runs, we run different evaluations to calculate model drift, precision, and relevancy.
+As we are running across hundreds of customers, we have been able to achieve very high levels of accuracy in our AI/ML models",9,10,Salesperson 2 provides more detailed information on AI/ML model accuracy and techniques
+Proposal,Can you provide a detailed breakdown of the costs?,"""Certainly. For your organization, we're proposing our Enterprise plan, which includes all modules. The first-year cost breaks down as follows: Software license: $400,000, Implementation and integration: $150,000, Training: $50,000, First-year support: $100,000. This totals $700,000 for the first year, with subsequent annual costs of $500,000 for license renewal and ongoing support. Would you like me to walk you through each component in more detail?""","Our customers have to consider annual running costs (consumption), one time implementation fee, training costs, and support costs as they come on to our platform. Actual costs depend on the size of the datasets and number of use cases we are getting deployed. If you would like, we can do a business and technical assessment so I can provide you a cost proposal. Would you want that?",9,8,Salesperson 1 provides specific numbers while 2 offers to do an assessment
+Proposal,What specific ROI can we expect in the first year?,",""Based on our analysis of your current processes and the efficiencies our system will bring, we project a first-year ROI of 120%. This comes from a 15% reduction in operational costs, 20% improvement in fraud detection, and 10% increase in customer retention due to improved service quality. I'd be happy to provide a detailed ROI report with all our assumptions clearly stated. Would that be helpful?""","Customers have cited productivity gains of 25% and analytics costs going down by 30% within 18 months post implementation.
+Over the long run, our customers have also been to consolidate the number of vendor systems, reducing their technical debt, seeing ROI of 75% within 3 years.",9,8,Salesperson 1 provides more detailed first-year ROI projections
+Proposal,How quickly can we get the system up and running?,"""Given the scope of the project, we estimate a 4-month implementation timeline. This includes 1 month for initial setup and data migration, 2 months for integration and customization, and 1 month for testing and training. We can start within two weeks of contract signing. Does this timeline align with your expectations?""","We have seen customers get up and running in as little as 8 weeks. Our platform is modular but the key things that impact the implementation timeframe are the number of data sources we have to connect to, and the number of downstream systems we need to integrate with.
+We would need to do a technical assessment to provide you a good estimate. Do you have a technical lead identified for this project? If so, can you make an introduction?",9,8,Salesperson 1 provides a more detailed timeline
+Proposal,What guarantees do you offer if the system doesn't perform as promised?,"""We're confident in our solution, but we also understand the need for assurances. We offer a performance guarantee in our Service Level Agreement. If we don't meet the agreed-upon performance metrics, you're entitled to service credits. Additionally, we offer a 90-day warranty period post-implementation. If you're not satisfied during this period, we'll work to resolve issues at no additional cost. Would you like to discuss specific performance metrics for your guarantee?""","We offer Service Level Agreements (SLAs) for uptime and response time. We will work with you to fine tune the data models or the underlying infrastructure if performance is not performing for any reason. Our goal is to have a long term relationship, and we want to make sure you are a happy and satisfied customer. ",9,8,Salesperson 1 offers more specific guarantees and warranty information
+Proposal,Can you provide customer references in our industry?,"""Absolutely. We have several clients in the financial services sector who have agreed to serve as references. I can arrange calls with two of our clients: a large national bank and a regional credit union. They can speak to their experiences with implementation, day-to-day use, and the ROI they've seen. Would you like me to set up these reference calls?""","Absolutely. We have a number of customers across the financial services industry running mission critical workloads on our platform.
+PNC bank has aggregated data from their different loan systems to reduce charge-offs by 17%, and costs by 30%.  They also process 3.5 million deposit and payment transactions every hour with our systems.
+One other large banking customers is running their fraud detection process on us and saw 40% reduction in false positives and 20% reduction in overall fraud cases.
+What use cases did you have in mind so I can share more relevant case studies?",8,9,Salesperson 2 provides specific customer examples and results
+Proposal,What kind of ongoing support and maintenance is included?,"""Our proposal includes our Premium Support package. This provides 24/7 technical support with a 1-hour response time for critical issues, quarterly system health checks, and a dedicated customer success manager. It also covers all software updates and patches. Is there any specific area of support you'd like more information about?""","For ongoing support, we offer a couple of support options, ranging from developer support to enterprise support. Depending on your needs, we can price the support option and include them into the proposal.
+We maintain and update the platform on a regular basis. We have 4 major releases a year, and 8 minor releases a year. The releases are all made into the core platform and available to all customers.",8,9,Salesperson 2 provides specific customer examples and results
+Proposal,How do you ensure business continuity during the transition to your system?,"""We understand the critical nature of your operations. Our implementation plan includes a phased approach with parallel running of systems to ensure no disruption. We also provide a rollback plan at each stage. Your dedicated project manager will work closely with your team to minimize any potential business impact. Would you like to review our detailed transition plan?""","We take full back-up of your data before you start the transition process. Our implementation plan includes a phased approach with parallel running of systems to ensure no disruption. Your dedicated project manager will work closely with your team to minimize any potential business impact.
+Would you like to review our detailed transition plan?""",8,9,Salesperson 2 provides more options for ongoing support
+Proposal,What training options are available for our team?,"""We offer a comprehensive training program tailored to different user roles. This includes on-site workshops for administrators and power users, web-based training for end-users, and a full e-learning platform with on-demand courses. We also provide train-the-trainer sessions if you prefer to manage ongoing training internally. How would you like to approach training for your team?""","We offer a mix of online and in-person trainings on our platform and solution. Prior to launch, we will conduct a half day training delivered in a hybrid environment with our team at your office so we can walk you through different functionality and how to use the platform. Post launch, there are different trainings available on-demand. We can also provide customized training at any team for your team.
+What is the size and makeup of your team that will need to be trained on the solution?",9,8,Salesperson 1 provides more detailed information on the transition process
+Proposal,Can you outline the key milestones in the implementation process?,"""Certainly. The key milestones in our 4-month implementation plan are: Month 1 - Project kickoff and initial setup, Month 2 - Data migration and basic integration, Month 3 - Custom feature development and advanced integration, Month 4 - User acceptance testing and staff training. We have checkpoints and sign-offs at each milestone. Would you like a more detailed project plan?""","There are 5 major checkpoints for us during the implemenation process. First is to ensure that we have done all the integrations with the data feeds and the internal systems. Second is to run a Proof of Concept for a small use case to ensure we have got the models and analytics running correctly. Third is to scale the use case to the larger organization. Fourth is User acceptance testing and staff training. And fiith and final is going into Production with the launch.
+Would you like a more detailed project plan?",9,9,Both provide comprehensive information on training options
+Proposal,What makes your solution the best fit for our company compared to other options?,"""Based on our discussions, I believe our solution is the best fit for three main reasons: 1) Our deep expertise in the financial sector, which means faster implementation and industry-specific features, 2) Our advanced AI capabilities, which align with your goals for improved risk management and fraud detection, and 3) Our scalable architecture, which can support your projected growth. Moreover, our commitment to ongoing innovation ensures you'll always have cutting-edge capabilities. How do these align with your decision criteria?""","Our customers cite the advanced analytics, precision of the AI models, and the configrability of the platform as the key reasons to select us. PNC bank has aggregated data from their different loan systems to reduce charge-offs by 17%, and costs by 30%.  They also process 3.5 million deposit and payment transactions every hour with our systems. They went live with our system in 16 weeks. Some of the other reasons include the underlying scalability of the platform and enterprise grade security. ",9,9,Both provide strong arguments for their solution's fit
+Negotiation,Your pricing seems high compared to some other solutions we've seen. Is there any flexibility?,"""I appreciate your concern about the pricing. While our initial quote reflects the comprehensive nature of our solution, we do have some flexibility. We could look at adjusting the feature set or exploring a phased implementation to reduce the initial investment. Alternatively, we could discuss a longer contract term in exchange for a discount. What approach would you prefer to explore?""",We think we have a best in class solution and have a number of customers today running varied use cases on our platform. We offer a very transparent pricing model which allows us to scale across many customers. Some of the possibilities are discounts against commitments for long term or volume commitments. ,9,8,Salesperson 1 offers more specific options for flexibility
+Negotiation,Can we spread out the payments over a longer period to reduce the upfront cost?,"""Absolutely, we can discuss alternative payment structures. One option is to spread the implementation cost over the first year, combined with quarterly payments for the license fee. This would reduce your initial outlay. We could also explore a pay-as-you-go model for certain modules. How would you like to structure the payments to best fit your budgeting process?""","Potentially. I will need to understand this more before I can make any commitments. As you can understand, we are a young company and have cash flow considerations as well. Can you help me understand how you want to structure the payments?",9,7,Salesperson 1 offers more specific payment structure options
+Negotiation,What kind of discount can you offer for a three-year commitment?,"""For a three-year commitment, we can offer a 15% discount on the annual license fee. This would bring your annual cost down from $500,000 to $425,000 after the first year. Additionally, we could lock in this price for the full three years, protecting you from any potential price increases. Would you like me to draw up a comparison of the one-year versus three-year terms?""","Absolutely, we do offer price reductions to customers who are willing to do a longer term commitment to us. There is a 15% discount if you were to make a three-year commitment.
+Would you like me to draw up a comparison of the one-year versus three-year terms?",9,8,Salesperson 1 provides more specific discount information
+Negotiation,Can we start with a smaller implementation and scale up over time?,"""That's definitely an option. We could begin with core modules like data aggregation and basic analytics, then add advanced features like AI-driven predictions in subsequent phases. This approach would reduce your initial investment to about $400,000 for the first year. We can then add modules as your needs grow and as you realize ROI from the initial implementation. How does this aligned approach sound to you?""","Absolutely. Many of our customers start with 1-2 use cases and then scale up over time. What we do suggest however that you pick a use case that is a material and challenging use case for you so you are able to get a good idea of the results.
+Do you have such a use case already identified?",9,8,Salesperson 1 provides more specific information on a phased approach
+Negotiation,Is there any room for negotiation on the support costs?,"""I understand your concern about the support costs. While our support is comprehensive, we could look at tailoring it more specifically to your needs. For instance, we could reduce the cost by adjusting the support hours or response times. Alternatively, if you have a strong internal IT team, we could explore a co-support model. What aspects of the support package are most valuable to you?""","I understand your concern about the support costs. While our support is comprehensive, we could look at tailoring it more specifically to your needs. For instance, we could reduce the cost by adjusting the support hours or response times. Alternatively, if you have a strong internal IT team, we could explore a co-support model. One other option available to you is a discount if you were to commit to a longer term contract.
+What aspects of the support package are most valuable to you?",9,9,Both offer similar levels of flexibility on support costs
+Negotiation,Can you include additional training or consulting hours in the package without increasing the price?,"""I appreciate you asking about this. While our proposed package already includes substantial training, I understand the value of additional support. I can offer an additional 20 hours of consulting time at no extra cost. This could be used for advanced training, workflow optimization, or custom report development. Would this added value address your concerns?""","I appreciate you asking about this. Our goal is to ensure you gain adoption and value from the platform within your organzation. What we have done with other customers is offered additional training linked to adoption goals within their organization.
+Is that something you are willing to consider?",9,8,Salesperson 1 offers a specific number of additional hours
+Negotiation,What guarantees can you offer on the implementation timeline?,"""We're confident in our 4-month implementation estimate, but I understand the importance of timely delivery. We can add a clause to our contract that provides a service credit if we exceed the agreed-upon timeline. For instance, we could offer a 5% credit for each week of delay, up to a maximum of 20%. Would this type of guarantee give you the assurance you're looking for?""","Our implementation resources are available on a T&M basis. There are many dependencies on the implementation timeline including availability of your resources and integration with your internal systems. While we have done many such integrations offer, it is hard for us to offer guarantees in this area since we do not control availability of resources to guide these decisions. ",9,7,Salesperson 1 offers specific guarantees while 2 explains limitations
+Negotiation,"If we commit to becoming a reference customer, can you offer any additional incentives?","""We greatly value reference customers and would be delighted to have you as one. In exchange for agreeing to be a reference (including potential case studies and peer recommendations), we can offer an additional 5% discount on your annual license fee. We would also prioritize your feature requests in our product roadmap. How does this sound to you?""",I would be happy to discuss that. We have offered some incentives to customers who agreed to becoming lighthouse customers for us and agreed to produce case studies and participate in joint speaking sessions. Is that something you are willing to consider?,9,8,Salesperson 1 offers more specific incentives for being a reference customer
+Negotiation,Can we include a performance-based pricing component?,"""That's an interesting proposal. We're open to exploring a performance-based pricing model. We could structure it so that a portion of your fee is tied to agreed-upon KPIs","We have a consumption based pricing model, based on the modules you choose and the volume of data processed. We will be providing you a dashboard so you can get a daily view of the costs incurred.
+On the performance, we will work with you to fine tune the data models or the underlying infrastructure if performance is not performing for any reason. Our goal is to have a long term relationship, and we want to make sure you are a happy and satisfied customer. ",8,7,Salesperson 1 is more open to performance-based pricing
+Negotiation,"The incumbent provider just offered us a 25% discount so we stay on their platform, and now your pricing is much higher than theirs. Can you match their pricing so we can reconsider you?","I understand the appeal of a 25% discount from your current provider. While we may not be able to directly match that price, I'm confident our solution offers superior value that more than justifies the cost difference. Our advanced features like AI-driven analytics and real-time fraud detection often lead to cost savings and revenue increases that far exceed the price gap.
+Would you be open to a detailed ROI analysis comparing our solution to your discounted current system? We could also explore performance-based pricing to align our fees with the value we deliver. Our goal is to be a long-term partner in driving your business success, not just a low-cost provider.","Looks like we are already delivering value for you! I am definitely willing to go back to my executives to see if there are any reductions possible but I wanted you to consider a few things. 1. our customers choose us because of the the advanced analytics, precision of the AI models, and the configrability of the platform. Those were some of the things you had cited were missing from the incumbent provider and reasons for the migration. 2. we offer very transparent pricing which has allowed us to scale quickly across many customers. Offering bait and switch pricing techniques erode customer trust and slow us down in terms of pricing.
+What I have seen work in terms of a discount is when are customers are willing to do a longer term contract or agree to be a reference customer. Are you willing to consider those?",8,10,Salesperson 2 provides a more strategic and value-focused response

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	@@ -0,0 +1,39 @@

+Real-time analysis with BetterTech Lending Analytics
+While most technologies on the market are able to fulfill regulatory risk management requirements, they often fall short of meeting the team’s actual needs. What works when Lending Analytics calculations are run weekly or monthly is simply not enough on a day-to-day basis. Maintaining regulations is no longer enough
+There are three functions that an efficient Lending Analytics solution must be able to fulfill: report, explain and optimize.
+Reporting is what every solution made for Lending Analytics can do: calculating risk figures and generating reports at the right level of aggregation that are ready to be sent to internal and external auditors. With the data volumes involved with Lending Analytics, however, reporting alone is simply not enough. Explaining is the most critical need for business users. Regulators ask for detailed explanations of what is included in the report, request that banks test alternative stress scenarios, and demonstrate the accuracy of their models — particularly with the Internal Models Approach. With a solution that only reports, business teams end up scrambling to answer those requests as best as they can, falling back on other tools such as Excel or PowerBI, which are simply not designed to analyze data at this scale. Optimizing is where growth and profitability reside.
+Lending Analytics framework creates many decision points that are crucial to improving performance:
+• How best to organize or reorganize loan portfolios?
+• What would be the impact of changing positions or making certain loans?
+• How can we improve our models?
+BetterTech Lending Analytics handles this level of analysis deftly and can quickly answer these questions. By relying on inadequate tools to perform analysis, organizations often see reduced productivity and accuracy. Inaccurate reporting negatively impacts relationships with regulators, potentially leading to fines, leading to fines. Furthermore, companies that do not have the analytical capabilities to optimize their risk management and, by extension their capital charge, risk falling behind other, better equipped organizations. How can you get ahead if you cannot efficiently simulate different scenarios to choose the best outcome, while your competitors do it dozens of times a day? A robust analytics solutions that is affordable and easy to implement
+BetterTech’s Lending Analytics represents an attractive offering for any bank looking to drastically improve their analytics capabilities within a short timeframe, while keeping their costs under control. BetterTech Lending Analytics can be used to build or replace your risk analysis and reporting system top to bottom, or simply complement your existing reporting capabilities with much improved analytics. Its capabilities and flexibility adapt to your architecture and always deliver the same outcome: putting into the hands of business users and executive managers the tools they need to maximize productivity and profitability.
+BetterTech Lending Analytics is in production today at many banks of all sizes around the world, including HSBC, Erste Bank, CIBC and Mizuho. It has successfully passed the ISDA Lending Analytics SA Regulatory Benchmarking Unit Test. It has even been selected by one of the most important global regulators to serve as the benchmark against which to test other banks’ architectures.
+Available as SaaS or on-premise, BetterTech Lending Analytics can run on any Cloud platform or in-house hardware and be fully deployed within 4 to 6 weeks.
+Runs anywhere BetterTech Lending Analytics can be deployed under several configurations that all bring the same benefits. Reach out to our team of experts to find the setup that will work best for your organization.
+On-premise: BetterTech Lending Analytics can be deployed on a large variety of hardware that are able to provide the requisite capabilities. We have partnerships in place with most global providers to ensure compatibility. In the Cloud: We have extensively tested BetterTech Lending Analytics on all the main cloud platforms (Azure, AWS, Google Cloud...). In fact, it has been deployed in production on those platforms.
+As SaaS: We can provide BetterTech Lending Analytics under a managed services contract. This configuration offers the fastest time-to-production while giving you complete control over your investment. The future of market risk analytics Organizations who want to outsmart the competition in their trading strategies must take the long-term view for their market risk architecture. In a sense, Lending Analytics has created a new, level playing field; simply meeting its requirements only puts you in the middle of the pack. The cloud has reached maturity for the financial industry, and makes the best technologies now not just available, but also scalable, affordable and quick to deploy. It is for this new world that BetterTech Lending Analytics was designed, not just to walk along with the rest, but to run ahead. To get ahead, the time is ripe to expand capabilities and onboard new technology. Don’t get left behind.
+About BetterTech
+Founded by industry experts, BetterTech understands the data analytics challenges faced by financial institutions across lending desks, risk, and compliance. BetterTech pioneered the use of high-performance analytics in finance, helping the banks and lenders make better decisions, explain results with confidence, and simulate the impact of their decisions. BetterTech’s mission is to deliver train-of-thought analysis on terabytes of data in the most cost-effective way so clients can explain their results with confidence and model the scenarios that will optimize their business. BetterTech specializes in risk data analytics for one of the fastest moving and most regulated industries with a presence in the world’s leading financial marketplaces - London, New York, Singapore, Sydney, Hong Kong, Paris and Frankfurt.

data/Opportunity_Information.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+Opportunity ID,Customer Name,Opportunity Name,Opportunity Stage,Opportunity Description,Opportunity Value,Close Date,Customer Contact,Customer Contact Role,Activity,Next Steps
+XFR0001,HSBC,Lending - Loan Origination System,Qualification,Developing analytic capabilities for the loan origination system,"$250,000",12/30/2024,John Smith,"VP, Information Technology",Had first meeting with John on 9/16. Identified that they are unhappy with the analytic capability of their current lending solution. Next step is to meet with them to understand their business drivers and pain points better. Will also use the next meeting to explain our company's value proposition.,Meet with John on 10/12 to qualify the opportunity better and share our value proposition
+XFR0002,Citi,Competitive Analysis SaaS,Negotiation,Develop SaaS for analysis of Citi's competitors in foreign markets,"$100,000",11/15/2024,Peter Branson,"CEO",Contract finally ironed out by both parties' lawyers. Have verbal agreement that this will be moving forwards.,Finalize contract with Peter and lawyers on October 25th

public/custom_styles.css ADDED Viewed

	@@ -0,0 +1,13 @@

+.message {
+    background-color: #E3F2FD !important;  /* Light Blue background */
+    color: #1A237E !important;  /* Dark Indigo text */
+}
+.MuiToolbar-root {
+    background-color: #b7dcf1 !important;  /* Medium Blue background */
+}
+.css-h5tjrl {
+    background-color: #1fa11f !important;  /* Light Green background */
+    color: #d3e6d3 !important;  /* Dark Green text */
+}

reports/HSBC Quarterly Report 2024-10-16 00:39:38.pdf ADDED Viewed

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reports/HSBC Quarterly Report 2024-10-16 09:36:43.pdf ADDED Viewed

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reports/HSBC Quarterly Report 2024-10-16.md ADDED Viewed

	@@ -0,0 +1,29 @@

+### HSBC Quarterly Report - Q3 2024
+#### Overview
+HSBC Holdings plc, one of the world's largest banking and financial services organizations, has been navigating a transformative period marked by strategic shifts, leadership changes, and a focus on digital innovation. As of October 2024, the bank is actively pursuing cost-cutting measures, expanding its wealth management services, and launching new ventures in embedded finance.
+#### Key Developments
+1. **Cost-Cutting Initiatives**: HSBC is reportedly considering a cost-cutting plan aimed at saving up to $300 million, primarily targeting senior management positions. This move is part of a broader strategy to enhance operational efficiency and respond to changing market conditions ([Reuters](https://www.reuters.com/business/finance/hsbc-mulls-cost-cutting-plan-save-up-300-mln-ft-reports-2024-10-10/)).
+2. **Leadership Changes**: The bank has seen significant leadership transitions, with Noel Quinn announcing his retirement as Group Chief Executive after nearly five years. Georges Elhedery has been appointed as his successor, tasked with leading HSBC through its next phase of development ([HSBC News](https://www.hsbc.com/news-and-views/news)).
+3. **Expansion of Wealth Management**: HSBC aims to double its assets under management in the UK wealth business to £100 billion ($131 billion) over the next five years. This initiative is part of a strategy to target the 'mass affluent' market, which is increasingly becoming a focus for major banks ([Financial Express](https://today.thefinancialexpress.com.bd/trade-market/hsbc-looks-to-double-uk-wealth-business-as-banks-target-mass-affluent-1724516994)).
+4. **Launch of Embedded Finance Venture**: In October 2024, HSBC launched a joint venture called SemFi with Tradeshift, aimed at providing embedded finance solutions to business clients. This venture will initially operate in the UK and is designed to integrate HSBC's financial services into e-commerce platforms, enhancing accessibility for SMEs ([Finextra](https://www.finextra.com/newsarticle/44827/hsbc-launches-embedded-finance-venture)).
+5. **Market Outlook and Economic Implications**: As the US elections approach, HSBC's market insights suggest that while election outcomes may introduce volatility, historical trends indicate that US equity markets tend to perform positively in the six months following elections, regardless of the political party in power ([HSBC UK](https://www.hsbc.co.uk/wealth/insights/market-outlook/special-coverage/2024-10-14/)).
+6. **Write-Offs and Strategic Divestments**: HSBC has also been restructuring its portfolio, including writing off its remaining $5.8 million stake in UK fintech Monese and considering the sale of its South African business to focus on more lucrative markets ([FinTech Futures](https://www.fintechfutures.com/2024/10/hsbc-reportedly-writes-off-remaining-5-8m-stake-in-uk-fintech-monese/), [FinTech Futures](https://www.fintechfutures.com/2024/08/hsbc-reportedly-considering-sale-of-its-south-african-business/)).
+#### Conclusion
+HSBC is positioning itself for future growth through strategic cost management, leadership renewal, and a focus on digital transformation. The bank's initiatives in wealth management and embedded finance reflect its commitment to adapting to the evolving financial landscape while maintaining a strong operational foundation. As it navigates these changes, HSBC remains focused on delivering value to its shareholders and clients alike.
+### Citations
+- [HSBC mulls cost-cutting plan of up to $300 mln, targeting top ranks, FT ...](https://www.reuters.com/business/finance/hsbc-mulls-cost-cutting-plan-save-up-300-mln-ft-reports-2024-10-10/): "HSBC mulls cost-cutting plan of up to $300 mln, targeting top ranks."
+- [News: Latest Articles and Media Releases | HSBC Holdings plc](https://www.hsbc.com/news-and-views/news): "Georges Elhedery becomes our new Group Chief Executive."
+- [HSBC looks to double UK wealth business as banks target 'mass affluent' - Finance News: Latest Financial News, Finance News today in Bangladesh](https://today.thefinancialexpress.com.bd/trade-market/hsbc-looks-to-double-uk-wealth-business-as-banks-target-mass-affluent-1724516994): "HSBC aims to double the assets under management in its British wealth business to 100 billion pounds ($131 billion) in the next five years."
+- [HSBC launches embedded finance venture - Finextra](https://www.finextra.com/newsarticle/44827/hsbc-launches-embedded-finance-venture): "HSBC launches embedded finance venture."
+- [Special Coverage | Market and Economic News | HSBC UK](https://www.hsbc.co.uk/wealth/insights/market-outlook/special-coverage/2024-10-14/): "For the overall market direction, the election result may matter less than is sometimes assumed."
+- [HSBC reportedly writes off remaining $5.8m stake in UK fintech Monese - FinTech Futures](https://www.fintechfutures.com/2024/10/hsbc-reportedly-writes-off-remaining-5-8m-stake-in-uk-fintech-monese/): "HSBC reportedly writes off remaining $5.8m stake in UK fintech Monese."
+- [HSBC reportedly considering sale of its South African business - FinTech Futures](https://www.fintechfutures.com/2024/08/hsbc-reportedly-considering-sale-of-its-south-african-business/): "HSBC reportedly considering sale of its South African business."

reports/HSBC Quarterly Report 2024-10-16.pdf ADDED Viewed

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requirements.txt CHANGED Viewed

@@ -1,9 +1,9 @@
 ## midterm depends
 chainlit==0.7.700
-langchain==0.2.16
-langchain-community==0.2.17
-langchain-core==0.2.41
-langchain-openai==0.1.25
 langchain-qdrant
 jsonpointer==3.0.0
 jiter==0.5.0
@@ -16,6 +16,7 @@ PyMuPDFb==1.24.10
 marshmallow==3.22.0
 multidict==6.1.0
 idna==3.10
 httpx==0.24.1
 opentelemetry-api==1.27.0
 opentelemetry-exporter-otlp==1.27.0
@@ -27,6 +28,28 @@ opentelemetry-proto==1.27.0
 opentelemetry-sdk==1.27.0
 opentelemetry-semantic-conventions==0.48b0
 sniffio==1.3.1
-git+https://github.com/explodinggradients/ragas
 nltk
-python-dotenv

 ## midterm depends
 chainlit==0.7.700
+#langchain==0.2.16
+#langchain-community==0.2.17
+#langchain-core==0.2.41
+#langchain-openai==0.1.25
 langchain-qdrant
 jsonpointer==3.0.0
 jiter==0.5.0
 marshmallow==3.22.0
 multidict==6.1.0
 idna==3.10
+#httpx==0.24.1
 httpx==0.24.1
 opentelemetry-api==1.27.0
 opentelemetry-exporter-otlp==1.27.0
 opentelemetry-sdk==1.27.0
 opentelemetry-semantic-conventions==0.48b0
 sniffio==1.3.1
+#git+https://github.com/explodinggradients/ragas
 nltk
+python-dotenv
+# Richards dependencies
+openai>=1.26.0
+langchain==0.3.0
+langchain-core==0.3.1
+langchain-community==0.3.0
+langchain-openai==0.2.0
+pyttsx3==2.90
+comtypes==1.2.0
+pandas==2.1.4
+tabulate==0.9.0
+datasets==2.14.5
+ragas==0.1.0
+nltk==3.9.1
+rouge-score==0.1.2
+sentence-transformers==2.3.0
+pydantic==2.7.4
+#openai-whisper==20240607
+# Nitins dependencies
+fpdf==1.7.2
+tavily-python==0.5.0
+langgraph==0.2.34

utils_actions.py ADDED Viewed

	@@ -0,0 +1,10 @@

+import chainlit as cl
+async def offer_actions():
+    actions = [
+        cl.Action(name="Get Latest News on this Customer", value="HSBC", description="Get Latest News"),
+        cl.Action(name="Enter Meeting Simulation", value="enter-meeting-simulation", description="Enter Meeting Simulation"),
+        cl.Action(name="Review Another Opportunity", value="review-another-opportunity", description="Review Another Opportunity"),
+    ]
+    await cl.Message(content="Select an action", actions=actions).send()

utils_callbacks.py ADDED Viewed

	@@ -0,0 +1,85 @@

+import asyncio
+import chainlit as cl
+from datetime import datetime
+from utils_data import  get_company_data, get_opportunities, get_questions, get_customer_background
+from utils_output import display_evaluation_results, display_llm_responses
+from utils_objections import create_objections
+# async def callback_show_scenarios():
+#     scenarios = get_opportunities()
+#     cl.user_session.set("scenarios", scenarios)
+#     scenarios = cl.user_session.get("scenarios", None)
+#     if scenarios is None:
+#         await cl.Message(content="No scenarios found.").send()
+#         return
+#     scenario_actions = []
+#     for idx, row in scenarios.iterrows():
+#         if row['Opportunity Description'] != "":
+#             scenario_action = cl.Action(
+#                 name="Scenario",
+#                 value=f"{idx}",  # Send the row index as value
+#                 description=f"{row['Customer Name']}: {row['Opportunity Name']} ({row['Opportunity Stage']}) "
+#                             f"Value: {row['Opportunity Value']}. Meeting with {row['Customer Contact']} "
+#                             f"({row['Customer Contact Role']})"
+#             )
+#             scenario_actions.append(scenario_action)
+#     await cl.Message(content="Select a scenario (hover for details):", actions=scenario_actions).send()
+async def callback_run_scenario(action):
+    await cl.Message(content="*Preparing simulation - please wait ...*").send()
+    index = 0
+    opportunities = cl.user_session.get("opportunities", None)
+    if opportunities is None:
+        await cl.Message(content="No scenarios found.").send()
+        return
+    await cl.Message(content="*Gathering opportunity information ...*").send()
+    await asyncio.sleep(1)
+    await cl.Message(content="*Customizing questions for this opportunity ...*").send()
+    await asyncio.sleep(1)
+    selected_opportunity = opportunities.iloc[index]
+    session_state = cl.user_session.get("session_state", None)
+    session_state.add_scenario_info(selected_opportunity)
+    get_customer_background(session_state, selected_opportunity['Customer Name'])
+    print("creating objections")
+    session_state.objections = await create_objections(session_state)
+    for obj in session_state.objections:
+        print(obj)
+    print("questions created")
+    session_state.questions = get_questions(session_state.opportunity.stage, session_state.num_questions)
+    for q in session_state.questions:
+        print(q)
+    opening_message = session_state.get_opening()
+    await cl.Message(content=opening_message).send()
+    start_actions = [
+        cl.Action(name="Start Simulation", value="start_simulation", description="Start Simulation"),
+    ]
+    await cl.Message(content="Click to start simulation", actions=start_actions).send()
+async def callback_start_scenario():
+    print("callback_start_scenario()")
+    session_state = cl.user_session.get("session_state", None)
+    start_time = datetime.now()
+    print("setting start time")
+    session_state.start_time = start_time
+    output = f"{session_state.customer.contact_name} joins the zoom call"
+    print(output)
+    await cl.Message(content=output).send()
+async def callback_evaluate_performance():
+    session_state = cl.user_session.get("session_state", None)
+    if session_state is None:
+        await cl.Message(content="No session found.").send()
+        return
+    await display_evaluation_results(cl, session_state)
+async def callback_display_queries_responses():
+    session_state = cl.user_session.get("session_state", None)
+    if session_state is None:
+        await cl.Message(content="No session found.").send()
+        return
+    await display_llm_responses(cl, session_state)

utils_chain_parameters.py ADDED Viewed

	@@ -0,0 +1,78 @@

+def prepare_chain_parameters(session_state, message, history):
+    message = message.content
+    previous_question = ""
+    rep_answer = ""
+    next_question = ""
+    ground_truth = ""
+    command = ""
+    if session_state.current_question_index == 0:
+        previous_question = ""
+        rep_answer = ""
+        ground_truth = ""
+        next_question = session_state.questions[session_state.current_question_index]["question"]
+        command = "You should greet the rep"
+    elif session_state.current_question_index >= len(session_state.questions):
+        next_question = ""
+        previous_question = session_state.questions[session_state.current_question_index - 1]["question"]
+        rep_answer = session_state.previous_answer
+        ground_truth = session_state.questions[session_state.current_question_index - 1]["ground_truth"]
+        command = """Thank the customer, offer a comment on the answer and overall performance.
+            Conclude the conversation with a summary and give a farewell.
+            If the answers were good, give a positive farewell and offer a follow up meeting.
+            If the answers were poor, give a poor farewell.
+            You can add additional comments as needed.
+        """
+    else:
+        previous_question = session_state.questions[session_state.current_question_index - 1]["question"]
+        rep_answer = session_state.previous_answer
+        next_question = session_state.questions[session_state.current_question_index]["question"]
+        ground_truth = session_state.questions[session_state.current_question_index]["ground_truth"]
+        command = "You should respond to the answer based on how well the rep answered the previous question."
+    session_state.ground_truth = ground_truth
+    session_state.question = previous_question
+    session_state.rep_answer = rep_answer
+    print("--------------------------------")
+    print(f"Message: {message}")
+    print("Sending the following:")
+    print(f"Command: {command}")
+    print(f"Previous question: {previous_question}")
+    print(f"Rep answer: {rep_answer}")
+    print(f"Next question: {next_question}")
+    rep_company_details = f"""
+        Name: {session_state.company.name}
+        Description: {session_state.company.description}
+        Product: {session_state.company.product}
+        Product Summary: {session_state.company.product_summary}
+        Product Description: {session_state.company.product_description}
+    """
+    company_details = f"""
+        Name: {session_state.customer.name}
+        Description: {session_state.customer.background}
+    """
+    scenario = f"""
+        Opportunity Name: {session_state.opportunity.name}
+        Opportunity Description: {session_state.opportunity.description}
+        Opportunity Stage: {session_state.opportunity.stage}
+        Opportunity Value: {session_state.opportunity.value}
+        Opportunity Close Date: {session_state.opportunity.close_date}
+    """
+    parm = {"conversation_mode": session_state.qa_mode,
+            "message": message,
+            "name": session_state.customer.contact_name,
+            "company": company_details,
+            "role": session_state.customer.contact_role,
+            "sales_rep": "Tony Snell",
+            "rep_company": rep_company_details,
+            "attitude": session_state.attitude,
+            "mood_score": session_state.mood_score,
+            "scenario": scenario,
+            "stage": session_state.opportunity.stage,
+            "previous_question": previous_question,
+            "next_question": next_question,
+            "rep_answer": rep_answer,
+            "conversation_history": history,
+            "command": command,
+            }
+    return parm

utils_control_messages.py ADDED Viewed

	@@ -0,0 +1,26 @@

+async def handle_control_message(cl, command):
+    command_parts = command.split()
+    main_command = command_parts[0].lower()
+    if main_command == 'start':
+        await cl.Message(content="Starting new session...").send()
+    elif main_command == 'stop':
+        session_state = cl.user_session.get("session_state")
+        end_time = datetime.now()
+        duration = end_time - session_state.start_time
+        duration_minutes = round(duration.total_seconds() / 60)
+        session_state.end_time = end_time
+        session_state.duration_minutes = duration_minutes
+        await cl.Message(content=f"Ending current session after {session_state.duration_minutes} minutes").send()
+    elif main_command == 'pause':
+        await cl.Message(content="Ending current session...").send()
+    elif main_command == 'time':
+        session_state = cl.user_session.get("session_state")
+        duration = session_state.get_session_duration()
+        await cl.Message(content=f"Current session duration: {duration}").send()
+    else:
+        await cl.Message(content=f"Unknown command: {main_command}").send()

utils_customer_research.py ADDED Viewed

	@@ -0,0 +1,337 @@

+import asyncio
+import chainlit as cl
+import json
+import operator
+import os
+from typing import TypedDict, List, Annotated, Literal, Dict, Union, Optional
+from datetime import datetime
+from langchain_core.tools import tool
+from langchain_core.messages import AnyMessage, AIMessage, SystemMessage, HumanMessage, ToolMessage
+from langchain_core.pydantic_v1 import BaseModel, Field, conlist
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, START, END, add_messages
+from tavily import AsyncTavilyClient, TavilyClient
+from utils_actions import offer_actions
+from utils_pdf import generate_pdf_from_md
+async def get_latest_news(customer):
+    session_state = cl.user_session.get("session_state", None)
+    await cl.Message(content=f"*Researching Latest News on {customer}*").send()
+    if session_state.do_customer_research:
+        print("Searching for news items ...")
+        workflow = StateGraph(ResearchState)
+        # Add nodes
+        workflow.add_node("research", research_model)
+        workflow.add_node("tools", tool_node)
+        workflow.add_node("curate", select_and_process)
+        workflow.add_node("write", write_report)
+        workflow.add_node("publish", generete_pdf)
+        # Set the entrypoint as route_query
+        workflow.set_entry_point("research")
+        # Determine which node is called next
+        workflow.add_conditional_edges(
+            "research",
+            # Next, we pass in the function that will determine which node is called next.
+            should_continue,
+        )
+        # Add a normal edge from `tools` to `research`.
+        # This means that after `tools` is called, `research` node is called next in  order to determine if we should keep  or move to the 'curate' step
+        workflow.add_edge("tools", "research")
+        workflow.add_edge("curate","write")
+        workflow.add_edge("write", "publish")  # Option in the future, to add another step and filter the documents retrieved using rerhank before writing the report
+        workflow.add_edge("publish", END)  # Option in the future, to add another step and filter the documents retrieved using rerhank before writing the report
+        app = workflow.compile()
+        company = "HSBC"
+        company_keywords = "banking, financial services, investment, wealth management, digital banking"
+        # (Optional) exclude_keywords: Use this field when you need to differentiate the company from others with the same name in a different industry
+        # or when you want to exclude specific types of documents or information. Leave it as an empty string ("") if not needed.
+        exclude_keywords = "insurance"
+        # You may uncomment your_additional_guidelines and HumanMessage and update the content with some guidelines of your own
+        # your_additional_guidelines=f"Note that the {company} is ... / focus on ...."
+        messages = [
+            SystemMessage(content="You are an expert researcher ready to begin the information gathering process.")
+            # ,HumanMessage(content=your_additional_guidelines)
+        ]
+        async for s in app.astream({"company": company, "company_keywords": company_keywords, "exclude_keywords": exclude_keywords, "messages":messages}, stream_mode="values"):
+            message = s["messages"][-1]
+            if isinstance(message, tuple):
+                print(message)
+            else:
+                message.pretty_print()
+    else:
+        await cl.Message(content=f"*Searching for news items ...*").send()
+        await asyncio.sleep(2)
+        await cl.Message(content=f"*Curating 8 documents ...*").send()
+        await asyncio.sleep(3)
+        await cl.Message(content=f"*Research complete. Generating report*").send()
+        await asyncio.sleep(1)
+        markdown_file_path = f'reports/{session_state.customer_research_report_md}'
+        await cl.Message(content=read_markdown_file(markdown_file_path)).send()
+    await offer_actions()
+def read_markdown_file(file_path):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            content = file.read()
+        return content
+    except FileNotFoundError:
+        return f"Error: The file {file_path} was not found."
+    except Exception as e:
+        return f"An error occurred while reading the file: {str(e)}"
+# Define the research state
+class ResearchState(TypedDict):
+    company: str
+    company_keywords: str
+    exclude_keywords: str
+    report: str
+    # Declare a dictionary where:
+    # - The outer dictionary has string keys.
+    # - The inner dictionary can have keys of different types (e.g., str, int).
+    # - The inner dictionary values can be of different types (e.g., str, float).
+    documents: Dict[str, Dict[Union[str, int], Union[str, float]]]
+    RAG_docs: Dict[str, Dict[Union[str, int], Union[str, float]]]
+    messages: Annotated[list[AnyMessage], add_messages]
+# Define the structure for the model's response, which includes citations.
+class Citation(BaseModel):
+    source_id: str = Field(
+        ...,
+        description="The url of a SPECIFIC source which justifies the answer.",
+    )
+    quote: str = Field(
+        ...,
+        description="The VERBATIM quote from the specified source that justifies the answer.",
+    )
+class QuotedAnswer(BaseModel):
+    """Answer the user question based only on the given sources, and cite the sources used."""
+    answer: str = Field(
+        ...,
+        description="The answer to the user question, which is based only on the given sources. Include any relevant sources in the answer as markdown hyperlinks. For example: 'This is a sample text ([url website](url))'"
+    )
+    citations: List[Citation] = Field(
+        ..., description="Citations from the given sources that justify the answer."
+    )
+# Add Tavily's arguments to enhance the web search tool's capabilities
+class TavilyQuery(BaseModel):
+    query: str = Field(description="web search query")
+    topic: str = Field(description="type of search, should be 'general' or 'news'. Choose 'news' ONLY when the company you searching is publicly traded and is likely to be featured on popular news")
+    days: int = Field(description="number of days back to run 'news' search")
+    # raw_content: bool = Field(description="include raw content from found sources, use it ONLY if you need more information besides the summary content provided")
+    domains: Optional[List[str]] = Field(default=None, description="list of domains to include in the research. Useful when trying to gather information from trusted and relevant domains")
+# Define the args_schema for the tavily_search tool using a multi-query approach, enabling more precise queries for Tavily.
+class TavilySearchInput(BaseModel):
+    sub_queries: List[TavilyQuery] = Field(description="set of sub-queries that can be answered in isolation")
+class TavilyExtractInput(BaseModel):
+    urls: List[str] = Field(description="list of a single or several URLs for extracting raw content to gather additional information")
+@tool("tavily_search", args_schema=TavilySearchInput, return_direct=True)
+async def tavily_search(sub_queries: List[TavilyQuery]):
+    """Perform searches for each sub-query using the Tavily search tool concurrently."""
+    # Define a coroutine function to perform a single search with error handling
+    async def perform_search(itm):
+        try:
+            # Add date to the query as we need the most recent results
+            query_with_date = f"{itm.query} {datetime.now().strftime('%m-%Y')}"
+            # Attempt to perform the search, hardcoding days to 90 (days will be used only when topic is news)
+            response = await tavily_client.search(query=query_with_date, topic=itm.topic, days=itm.days, max_results=1)
+            return response['results']
+        except Exception as e:
+            # Handle any exceptions, log them, and return an empty list
+            print(f"Error occurred during search for query '{itm.query}': {str(e)}")
+            return []
+    # Run all the search tasks in parallel
+    search_tasks = [perform_search(itm) for itm in sub_queries]
+    search_responses = await asyncio.gather(*search_tasks)
+    # Combine the results from all the responses
+    search_results = []
+    for response in search_responses:
+        search_results.extend(response)
+    await cl.Message(content=f"Searching for news items ...").send()
+    return search_results
+# Code for adding Tavily Extract as a tool (found it more useful to use Tavily Extract in a separate node)
+# @tool("tavily_extract", args_schema=TavilyExtractInput, return_direct=True)
+# async def tavily_extract(urls: TavilyExtractInput):
+#     """Extract raw content from urls to gather additional information."""
+#     try:
+#         response = await tavily_client.extract(urls=urls)
+#         return response['results']
+#     except Exception as e:
+#         # Handle any exceptions, log them, and return an empty list
+#         print(f"Error occurred during extract: {str(e)}")
+#         return []
+tools = [tavily_search]
+tools_by_name = {tool.name: tool for tool in tools}
+tavily_client = AsyncTavilyClient()
+# Define an async custom research tool node to store Tavily's search results for improved processing and later on filtering
+async def tool_node(state: ResearchState):
+    docs = state.get('documents',{})
+    docs_str = ""
+    msgs = []
+    for tool_call in state["messages"][-1].tool_calls:
+        tool = tools_by_name[tool_call["name"]]
+        new_docs = await tool.ainvoke(tool_call["args"])
+        for doc in new_docs:
+            # Make sure that this document was not retrieved before
+            if not docs or doc['url'] not in docs:
+                docs[doc['url']] = doc
+                docs_str += json.dumps(doc)
+            # For Tavily Extract tool, checking if raw_content was retrieved a document
+            # if doc.get('raw_content', None) and doc['url'] in docs:
+            #     docs[doc['url']]['raw_content'] = doc['raw_content'] # add raw content retrieved by extract
+            #     docs_str += json.dumps(doc)
+        msgs.append(ToolMessage(content=f"Found the following new documents/information: {docs_str}", tool_call_id=tool_call["id"]))
+    return {"messages": msgs, "documents": docs}
+# Invoke a model with research tools to gather data about the company
+def research_model(state: ResearchState):
+    prompt = f"""Today's date is {datetime.now().strftime('%d/%m/%Y')}.\n
+You are an expert researcher tasked with gathering information for a quarterly report on recent developments in portfolio companies.\n
+Your current objective is to gather documents about any significant events that occurred in the past quarter for the following company: {state['company']}.\n
+The user has provided the following company keywords: {state['company_keywords']} to help you find documents relevant to the correct company.\n
+**Instructions:**\n
+- Use the 'tavily_search' tool to search for relevant documents
+- Focus on gathering documents by making appropriate tool calls
+- If you believe you have gathered enough information, state 'I have gathered enough information and am ready to proceed.'
+"""
+    messages = state['messages'] + [SystemMessage(content=prompt)]
+    model = ChatOpenAI(model="gpt-4o-mini",temperature=0)
+    response = model.bind_tools(tools).invoke(messages)
+    return {"messages": [response]}
+# Define the function that decides whether to continue research using tools or proceed to writing the report
+def should_continue(state: ResearchState) -> Literal["tools", "curate"]:
+    messages = state['messages']
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user with citations)
+    return "curate"
+async def select_and_process(state: ResearchState):
+    prompt = f"""You are an expert researcher specializing in analyzing portfolio companies.\n
+Your current task is to review a list of documents and select the most relevant URLs related to recent developments for the following company: {state['company']}.\n
+Be aware that some documents may refer to other companies with similar or identical names, potentially leading to conflicting information.\n
+Your objective is to choose the documents that pertain to the correct company and provide the most consistent and synchronized information, using the following keywords provided by the user to help identify the correct company as a guide:{state['company_keywords']}.\n"""
+    # Optionally include exclusion keywords if provided by the user
+    if state['exclude_keywords'] != "":
+        prompt += f"""Additionally, if any form of the following exclusion words are present in the documents, do not include them and filter out those documents: {state['exclude_keywords']}.\n"""
+    # Append the list of gathered documents to the prompt
+    prompt += f"""\nHere is the list of documents gathered for your review:\n{state['documents']}\n\n"""
+    # Use the model to filter documents and obtain relevant URLs structured as TavilyExtractInput
+    messages = [SystemMessage(content=prompt)]
+    model = ChatOpenAI(model="gpt-4o-mini",temperature=0)
+    relevant_urls = model.with_structured_output(TavilyExtractInput).invoke(messages)
+    # Create a dictionary of relevant documents based on the URLs returned by the model
+    RAG_docs = {url: state['documents'][url] for url in relevant_urls.urls if url in state['documents']}
+    try:
+        # Extract raw content from the selected URLs using the Tavily client
+        response = await tavily_client.extract(urls=relevant_urls.urls)
+        # Save the raw content into the RAG_docs dictionary for each URL
+        msg += "Extracted raw content for:\n"
+        for itm in response['results']:
+            url = itm['url']
+            msg += f"{url}\n"
+            raw_content = itm['raw_content']
+            RAG_docs[url]['raw_content'] = raw_content
+    except Exception as e:
+        print(f"Error occurred during Tavily Extract request")
+    msg += f"ֿֿ\n\nState of RAG documents that will be used for the report:\n\n{RAG_docs}"
+    return {"messages": [AIMessage(content=msg)],"RAG_docs": RAG_docs}
+# Define the function to write the report based on the retrieved documents.
+async def write_report(state: ResearchState):
+    # Create the prompt
+    prompt = f"""Today's date is {datetime.now().strftime('%d/%m/%Y')}\n.
+You are an expert researcher, writing a quarterly report about recent events in portfolio companies.\n
+Your task is to write an in-depth, well-written, and detailed report on the following company: {state['company']}. in markdown syntax\n
+Here are all the documents you should base your answer on:\n{state['RAG_docs']}\n"""
+    # messages = [state['messages'][-1]] + [SystemMessage(content=prompt)]
+    # Create a system message with the constructed prompt (no need to include entire chat history)
+    messages = [SystemMessage(content=prompt)]
+    model = ChatOpenAI(model="gpt-4o-mini",temperature=0)
+    response = model.with_structured_output(QuotedAnswer).invoke(messages)
+    full_report = response.answer
+    msg = "Curating Documents ...\n"
+    await cl.Message(content=f"*Curating {len(response.citations)} documents ...*").send()
+    # Add Citations Section to the report
+    full_report += "\n\n### Citations\n"
+    for citation in response.citations:
+        doc = state['RAG_docs'].get(citation.source_id)
+        full_report += f"- [{doc.get('title',citation.source_id)}]({citation.source_id}): \"{citation.quote}\"\n"
+    # We return a list, because this will get added to the existing list
+    return {"messages": [AIMessage(content=f"Generated Report:\n{full_report}")], "report": full_report}
+async def generete_pdf(state: ResearchState):
+    await cl.Message(content=f"*Research complete. Generating report*").send()
+    directory = "reports"
+    file_name = f"{state['company']} Quarterly Report {datetime.now().strftime('%Y-%m-%d')}"
+    # Check if the directory exists
+    if not os.path.exists(directory):
+        # Create the directory
+        os.makedirs(directory)
+    markdown_file_path = f'{directory}/{file_name}.md'
+    pdf_file_path = f'{directory}/{file_name}.pdf'
+    session_state = cl.user_session.get("session_state", None)
+    session_state.customer_research_report_md = f"{file_name}.md"
+    session_state.customer_research_report_pdf = f"{file_name}.pdf"
+    for file_path in [markdown_file_path, pdf_file_path]:
+        if os.path.exists(file_path):
+            os.remove(file_path)
+            print(f"Existing file deleted: {file_path}")
+    with open(markdown_file_path, 'w', encoding='utf-8') as md_file:
+        md_file.write(state['report'])
+    await cl.Message(content=state['report']).send()
+    msg = generate_pdf_from_md(state['report'], filename=pdf_file_path)
+    return {"messages": [AIMessage(content=msg)]}

utils_data.py ADDED Viewed

	@@ -0,0 +1,173 @@

+import pandas as pd
+import os
+def get_questions(stage=None, num_questions=0):
+    q_and_a_filename = "AIE4_SalesBuddy_QandA.csv"
+    data = read_csv_from_data_folder(q_and_a_filename)
+    if data is None:
+        print(f"Error: Failed to read {q_and_a_filename}")
+        return []
+    questions = []
+    for index, row in data.iterrows():
+        if stage is not None and row['Stage'] != stage:
+            continue
+        question = {
+            'stage': row['Stage'],
+            'question': row['Customer Question (Provided by Claude)'],
+            'ground_truth': row['Ground Truth (Provided by Nitin). Marked as Salesperson 2 by Claude'],
+        }
+        if num_questions == 0 or num_questions > len(questions):
+            print(f"Adding question: {question['question']}")
+            questions.append(question)
+    print(f"Returned {len(questions)} questions for stage {stage}")
+    return questions
+def get_customer_background(scenario, customer_name="HSBC" ):
+    background_filename = f"{customer_name}_background.txt"
+    data = read_txt_from_data_folder(background_filename)
+    scenario.customer.background = data
+    print(f"Read background for {customer_name} - {background_filename}")
+def get_company_data(scenario):
+    company = {
+        "name": "BetterTech",
+        "description": """
+            Founded by industry experts, BetterTech understands the data analytics challenges faced by financial
+            institutions across lending desks, risk, and compliance.
+            BetterTech pioneered the use of high-performance analytics in finance, helping the banks and lenders
+            make better decisions, explain results with confidence, and simulate the impact of their decisions.
+            BetterTech’s mission is to deliver train-of-thought analysis on terabytes of data in the most
+            cost-effective way so clients can explain their results with confidence and model the scenarios
+            that will optimize their business. BetterTech specializes in risk data analytics for one of the
+            fastest moving and most regulated industries with a presence in the world’s leading
+            financial marketplaces - London, New York, Singapore, Sydney, Hong Kong, Paris and Frankfurt.
+        """,
+        "product": "BetterTech Lending Analytics Solution",
+        "product_summary": """
+            A comprehensive solution for real-time analysis in lending, surpassing basic regulatory compliance
+            by enabling reporting, explaining, and optimizing. Deployed as SaaS, on-premise, or in the cloud, it
+            empowers organizations to improve performance, enhance decision-making, and stay competitive by
+            simulating various scenarios efficiently and accurately.
+        """,
+        "product_description": """
+            Real-time analysis with BetterTech Lending Analytics
+            While most technologies on the market are able to fulfill regulatory risk management requirements,
+            they often fall short of meeting the team’s actual needs. What works when Lending Analytics calculations
+            are run weekly or monthly is simply not enough on a day-to-day basis. Maintaining regulations is no
+            longer enough
+            There are three functions that an efficient Lending Analytics solution must be able to fulfill:
+            report, explain and optimize.
+            Reporting is what every solution made for Lending Analytics can do: calculating risk figures and
+            generating reports at the right level of aggregation that are ready to be sent to internal and
+            external auditors. With the data volumes involved with Lending Analytics, however, reporting alone
+            is simply not enough. Explaining is the most critical need for business users. Regulators ask for
+            detailed explanations of what is included in the report, request that banks test alternative stress
+            scenarios, and demonstrate the accuracy of their models — particularly with the Internal Models Approach.
+            With a solution that only reports, business teams end up scrambling to answer those requests as best
+            as they can, falling back on other tools such as Excel or PowerBI, which are simply not designed to
+            analyze data at this scale. Optimizing is where growth and profitability reside.
+            Lending Analytics framework creates many decision points that are crucial to improving performance:
+            • How best to organize or reorganize loan portfolios?
+            • What would be the impact of changing positions or making certain loans?
+            • How can we improve our models?
+            BetterTech Lending Analytics handles this level of analysis deftly and can quickly answer these questions.
+            By relying on inadequate tools to perform analysis, organizations often see reduced productivity and
+            accuracy. Inaccurate reporting negatively impacts relationships with regulators, potentially leading
+            to fines, leading to fines. Furthermore, companies that do not have the analytical capabilities to
+            optimize their risk management and, by extension their capital charge, risk falling behind other,
+            better equipped organizations. How can you get ahead if you cannot efficiently simulate different
+            scenarios to choose the best outcome, while your competitors do it dozens of times a day?
+            A robust analytics solutions that is affordable and easy to implement
+            BetterTech Lending Analytics is in production today at many banks of all sizes around the world,
+            including HSBC, Erste Bank, CIBC and Mizuho. It has successfully passed the ISDA Lending Analytics
+            SA Regulatory Benchmarking Unit Test. It has even been selected by one of the most important global
+            regulators to serve as the benchmark against which to test other banks’ architectures.
+            Available as SaaS or on-premise, BetterTech Lending Analytics can run on any Cloud platform or
+            in-house hardware and be fully deployed within 4 to 6 weeks.
+            Runs anywhere BetterTech Lending Analytics can be deployed under several configurations that all
+            bring the same benefits. Reach out to our team of experts to find the setup that will work best for
+            your organization.
+            On-premise: BetterTech Lending Analytics can be deployed on a large variety of hardware that are
+            able to provide the requisite capabilities. We have partnerships in place with most global providers
+            to ensure compatibility. In the Cloud: We have extensively tested BetterTech Lending Analytics on
+            all the main cloud platforms (Azure, AWS, Google Cloud...). In fact, it has been deployed in production
+            on those platforms.
+            As SaaS: We can provide BetterTech Lending Analytics under a managed services contract. This
+            configuration offers the fastest time-to-production while giving you complete control over your
+            investment. The future of market risk analytics Organizations who want to outsmart the competition
+        """,
+    }
+    scenario.add_company_info(
+        name=company['name'],
+        description=company['description'],
+        product=company['product'],
+        product_summary=company['product_summary'],
+        product_description=company['product_description']
+    )
+def get_q_and_a():
+    q_and_a_filename = "AIE4_SalesBuddy_QandA.csv"
+    data = read_csv_from_data_folder(q_and_a_filename)
+    return data
+def get_opportunities():
+    opportunities_filename = "Opportunity_Information.csv"
+    data = read_csv_from_data_folder(opportunities_filename)
+    return data
+def read_csv_from_data_folder(filename, handle_nan='drop'):
+    data_folder = "./data/"
+    file_path = os.path.join(data_folder, filename)
+    try:
+        df = pd.read_csv(file_path)
+        print(f"Successfully read {filename}")
+        # Handle NaN values
+        if handle_nan == 'drop':
+            df = df.dropna()
+        elif handle_nan == 'fill_na':
+            df = df.fillna('N/A')
+        elif handle_nan == 'fill_empty':
+            df = df.fillna('')
+        else:
+            print(f"Warning: Unknown NaN handling method '{handle_nan}'. NaN values were not processed.")
+        # Reset index if rows were dropped
+        df = df.reset_index(drop=True)
+        return df
+    except FileNotFoundError:
+        print(f"Error: File '{filename}' not found in the data folder.")
+        return None
+    except pd.errors.EmptyDataError:
+        print(f"Error: File '{filename}' is empty.")
+        return None
+    except Exception as e:
+        print(f"An error occurred while reading '{filename}': {str(e)}")
+        return None
+def read_txt_from_data_folder(filename):
+    data_folder = "./data/"
+    file_path = os.path.join(data_folder, filename)
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            data = file.read()
+        print(f"Successfully read {filename}")
+        return data
+    except FileNotFoundError:
+        print(f"Error: File '{filename}' not found in the data folder.")
+        return None
+    except Exception as e:
+        print(f"An error occurred while reading '{filename}': {str(e)}")
+        return None

utils_evaluate.py ADDED Viewed

	@@ -0,0 +1,80 @@

+import pandas as pd
+from datasets import Dataset
+from nltk.translate.bleu_score import sentence_bleu
+from ragas import evaluate
+from ragas.metrics import (
+    answer_relevancy,
+    answer_correctness,
+)
+from rouge_score import rouge_scorer
+from sentence_transformers import SentenceTransformer, util
+def evaluate_answers(session):
+    ragas_results = evaluate_with_ragas(session)
+    session.ragas_results = ragas_results
+    scores = []
+    for response in session.responses:
+        bleu_score = calculate_bleu_score(response.get("response", ""), response.get("ground_truth", ""))
+        rouge_score = calculate_rouge_score(response.get("response", ""), response.get("ground_truth", ""))
+        semantic_similarity_score = calculate_semantic_similarity(response.get("response", ""), response.get("ground_truth", ""))
+        all_scores = {
+            "bleu_score": bleu_score,
+            "rouge_score": rouge_score,
+            "semantic_similarity_score": semantic_similarity_score
+        }
+        scores.append(all_scores)
+    session.scores = scores
+    return scores
+def evaluate_with_ragas(session):
+    questions = []
+    answers = []
+    ground_truths = []
+    contexts = []
+    for i, response in enumerate(session.responses, 1):
+        questions.append(response.get("question", ""))
+        answers.append(response.get("response", ""))
+        ground_truths.append(response.get("ground_truth", ""))
+        contexts.append([session.company.product_description])
+    evaluation_dataset = Dataset.from_dict({
+        "question" : questions,
+        "answer" : answers,
+        "contexts" : contexts,
+        "ground_truth" : ground_truths
+    })
+    print(evaluation_dataset)
+    metrics = [
+        # faithfulness,
+        answer_relevancy,
+        # context_recall,
+        # context_precision,
+        answer_correctness,
+    ]
+    results = evaluate(evaluation_dataset, metrics)
+    print(results)
+    return results
+def calculate_bleu_score(answer, ground_truth):
+    bleu_score = sentence_bleu([ground_truth.split()], answer.split())
+    print(f"BLEU score: {bleu_score}")
+    return bleu_score
+def calculate_rouge_score(answer, ground_truth):
+    scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)
+    rouge_scores = scorer.score(ground_truth, answer)
+    print(f"ROUGE score: {rouge_scores}")
+    return rouge_scores
+def calculate_semantic_similarity(answer, ground_truth):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    answer_embedding = model.encode(answer)
+    ground_truth_embedding = model.encode(ground_truth)
+    similarity_score = util.cos_sim(answer_embedding, ground_truth_embedding)
+    print(f"Semantic Similarity: {similarity_score.item()}")
+    return similarity_score.item()

utils_objections.py ADDED Viewed

	@@ -0,0 +1,192 @@

+import chainlit as cl
+import tempfile
+import uuid
+from chainlit.types import AskFileResponse
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_openai import ChatOpenAI
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.storage import LocalFileStore
+from operator import itemgetter
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+async def create_objections(session_state):
+    customer_document_file = session_state.customer_research_report_pdf
+    customer_file_path = "reports/" + customer_document_file
+    bettertech_document_file = session_state.bettetech_value_proposition_pdf
+    bettertech_file_path = "data/" + bettertech_document_file
+    objections = await process_files(customer_file_path, bettertech_file_path)
+    return objections
+def process_value_prop_pdf(file_path) -> str:
+    """
+    Process the value proposition PDF file and return its content as a string.
+    Args:
+        file (AskFileResponse): The uploaded PDF file.
+    Returns:
+        str: The extracted content from the PDF.
+    """
+    # Create a temporary file to store the uploaded content
+    # with tempfile.NamedTemporaryFile(mode="wb", delete=False) as temp_file:
+    #     temp_file.write(file.content)
+    #     temp_file_path = temp_file.name
+    # Load the PDF using PyMuPDFLoader
+    loader = PyMuPDFLoader(file_path)
+    documents = loader.load()
+    # Combine the content of all pages into a single string
+    value_prop_text = "\n".join(doc.page_content for doc in documents)
+    # Return the text extracted from the PDF
+    return value_prop_text
+def process_text_file(file_path: str):
+    # import tempfile
+    text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n","\n"],chunk_size=200, chunk_overlap=20)
+    # with tempfile.NamedTemporaryFile(mode="w", delete=False) as temp_file:
+    #     with open(temp_file.name, "wb") as f:
+    #         f.write(file.content)
+    loader = PyMuPDFLoader(file_path)
+    documents = loader.load()
+    docs = text_splitter.split_documents(documents)
+    for i, doc in enumerate(docs):
+        doc.metadata["source"] = f"source_{i}"
+    return docs
+# Function to generate a response to the user's objection
+def generate_response_to_objection(user_response, num):
+    from langchain_openai import ChatOpenAI
+    from ragas.llms.base import LangchainLLMWrapper
+    openai_model = LangchainLLMWrapper(ChatOpenAI(model_name="gpt-4o"))
+    scorer = SatisfyRate(llm=openai_model)
+    satify_0_1 = scorer.single_turn_ascore(user_response['objection'][num], user_response['response'][num])
+    # Implement your logic to generate a response based on the user's input
+    return f"Response to your objection: {user_response['objection'][num],user_response['response'][num],  satify_0_1}"
+async def process_files(customer_document, bettertech_document):
+    objections = []
+    core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    collection_name = f"pdf_to_parse_{uuid.uuid4()}"
+    qdrant_client = QdrantClient(":memory:")
+    qdrant_client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+    )
+    # msg = cl.Message(
+    #     content=f"Processing Customer Business Domain...", disable_human_feedback=True
+    # )
+    # await msg.send()
+    # Process the customer
+    texts = process_text_file(customer_document)
+    print(f"Processing {len(texts)} text chunks from Customer Research Report")
+    # Notify the user that the second file is being processed
+    # msg = cl.Message(
+    #     content=f"Processing BetterTech Value Proposition...", disable_human_feedback=True
+    # )
+    # await msg.send()
+    # Process the second file
+    value_prop_content = process_value_prop_pdf(bettertech_document)
+    print(f"Processing {len(value_prop_content)} text chunks from BetterTech Value Proposition")
+    # Create a Local File Store for caching
+    store = LocalFileStore("./cache/")
+    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings, store, namespace=core_embeddings.model
+    )
+    # QDrant Vector Store Setup
+    vectorstore = QdrantVectorStore(
+        client=qdrant_client,
+        collection_name=collection_name,
+        embedding=cached_embedder
+    )
+    vectorstore.add_documents(texts)
+    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 5})
+    chat_openai = ChatOpenAI() #model='gpt-4o')
+    # RAG Chain for generating objections
+    objection_prompt_template = """\
+    Internally, review the value proposition information of sales rep's company then review your Context.
+    Internally, find areas where the sales' product/service could help add value and where it fails to fit.
+    Internally, review this final list and think step-by-step on what likely objections to buying product/service.
+    Using these thoughts, generate 5 Context-based sales objections.
+    The output is numbered objections only.
+    For example:
+    '1. Our current pricing structure is already optimized and we do not see the immediate need for AI assistance in pricing complex structural options in Foreign Exchange.'
+    '2. We have a dedicated team handling customer experience and efficiency, and we do not see how integrating AI for pricing options would significantly improve these aspects.',
+    '3. While we acknowledge the importance of technology and innovation in banking, we are currently focusing on other areas for digital transformation and do not prioritize the use of AI in pricing at this time.'
+    '4. Our customer base might not be ready for a shift towards AI-driven pricing models, and introducing such a change could potentially create confusion and resistance among our clients.',
+    '5. We are cautious about the potential risks and uncertainties associated with relying heavily on AI for pricing, especially in the volatile Foreign Exchange market where human expertise and judgment are highly valued.'
+    The output is NOT intro phrases or ** text **:
+    Context: {context}
+    Value Proposition: {{value_prop_content}}
+    Sales Opportunity: {{sales_opportunity}}
+    """
+    # Create a chain for generating objections with the retrieved context
+    objection_chain = (
+        {"context": itemgetter("question") | retriever}
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | ChatPromptTemplate.from_messages([
+            ("system", "You a potential customer interested in the offering from this sales rep. Please use context business name and your name found in sales_opportunity."),
+            ("human", objection_prompt_template)
+        ])
+        | chat_openai
+    )
+    # Ask the user for the sales opportunity
+    # sales_opportunity = await cl.AskUserMessage(
+    #     content="Please describe the sales opportunity you want to discuss.", timeout=300
+    # ).send()
+    sales_opportunity = "Developing analytic capabilities for the loan origination system"
+    #print(sales_opportunity['content'])
+    # Retrieve the documents based on the query (here we're simulating with the sales opportunity)
+    retrieved_docs = retriever.get_relevant_documents(value_prop_content)
+    # Extract the content of the retrieved documents (chunks)
+    context_chunks = [doc.page_content for doc in retrieved_docs]
+    # Combine the retrieved context chunks into a single string
+    context = "\n\n".join(context_chunks)
+    # Log and display the retrieved chunks to Chainlit
+    # await cl.Message(content=f"Retrieved context chunks:\n{context}", author="retriever").send()
+    #print (sales_opportunity["content"])
+    # Generate objections using the chain, with the context included
+    #print ({"question": "Generate sales objections from {{value_prop_content}}", "sales_opportunity": sales_opportunity["content"], "context": context})
+    objection_response = objection_chain.invoke({"question": "Generate 3 sales objections", "sales_opportunity": sales_opportunity, "context": context})
+    objections.extend(objection_response.content.split('\n'))  # Assuming each objection is on a new line
+    # Remove empty strings or strings with only spaces
+    cleaned_objections = [objection for objection in objections if objection.strip()]
+    # Output the cleaned list
+    # print(cleaned_objections)
+    # Store the objection chain in user session
+    cl.user_session.set("objection_chain", objection_chain)
+    cl.user_session.set("objections", objections)
+    return cleaned_objections
+    #await cl.Message(content="We are ready to enter Sales simulation. Ok? ").send()