aaron repo

.chainlit/config.toml

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+[project]
+# Whether to enable telemetry (default: true). No personal data is collected.
+enable_telemetry = true
+
+# List of environment variables to be provided by each user to use the app.
+user_env = []
+
+# Duration (in seconds) during which the session is saved when the connection is lost
+session_timeout = 3600
+
+# Enable third parties caching (e.g LangChain cache)
+cache = false
+
+# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
+# follow_symlink = false
+
+[features]
+# Show the prompt playground
+prompt_playground = true
+
+# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
+unsafe_allow_html = false
+
+# Process and display mathematical expressions. This can clash with "$" characters in messages.
+latex = false
+
+# Authorize users to upload files with messages
+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
+
+# Description of the app and chatbot. This is used for HTML tags.
+# description = ""
+
+# Large size content are by default collapsed for a cleaner ui
+default_collapse_content = true
+
+# The default value for the expand messages settings.
+default_expand_messages = false
+
+# Hide the chain of thought details from the user in the UI.
+hide_cot = false
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+# 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"

Dockerfile

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+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

aimakerspace/openai_utils/chatmodel.py

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+from openai import OpenAI, AsyncOpenAI
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+
+class ChatOpenAI:
+    def __init__(self, model_name: str = "gpt-4o-mini"):
+        self.model_name = model_name
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if self.openai_api_key is None:
+            raise ValueError("OPENAI_API_KEY is not set")
+
+    def run(self, messages, text_only: bool = True, **kwargs):
+        if not isinstance(messages, list):
+            raise ValueError("messages must be a list")
+
+        client = OpenAI()
+        response = client.chat.completions.create(
+            model=self.model_name, messages=messages, **kwargs
+        )
+
+        if text_only:
+            return response.choices[0].message.content
+
+        return response
+    
+    async def astream(self, messages, **kwargs):
+        if not isinstance(messages, list):
+            raise ValueError("messages must be a list")
+        
+        client = AsyncOpenAI()
+
+        stream = await client.chat.completions.create(
+            model=self.model_name,
+            messages=messages,
+            stream=True,
+            **kwargs
+        )
+
+        async for chunk in stream:
+            content = chunk.choices[0].delta.content
+            if content is not None:
+                yield content

aimakerspace/openai_utils/embedding.py

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+from dotenv import load_dotenv
+from openai import AsyncOpenAI, OpenAI
+import openai
+from typing import List
+import os
+import asyncio
+
+
+class EmbeddingModel:
+    def __init__(self, embeddings_model_name: str = "text-embedding-3-small"):
+        load_dotenv()
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        self.async_client = AsyncOpenAI()
+        self.client = OpenAI()
+
+        if self.openai_api_key is None:
+            raise ValueError(
+                "OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key."
+            )
+        openai.api_key = self.openai_api_key
+        self.embeddings_model_name = embeddings_model_name
+
+    async def async_get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
+        embedding_response = await self.async_client.embeddings.create(
+            input=list_of_text, model=self.embeddings_model_name
+        )
+
+        return [embeddings.embedding for embeddings in embedding_response.data]
+
+    async def async_get_embedding(self, text: str) -> List[float]:
+        embedding = await self.async_client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+    def get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:
+        embedding_response = self.client.embeddings.create(
+            input=list_of_text, model=self.embeddings_model_name
+        )
+
+        return [embeddings.embedding for embeddings in embedding_response.data]
+
+    def get_embedding(self, text: str) -> List[float]:
+        embedding = self.client.embeddings.create(
+            input=text, model=self.embeddings_model_name
+        )
+
+        return embedding.data[0].embedding
+
+
+if __name__ == "__main__":
+    embedding_model = EmbeddingModel()
+    print(asyncio.run(embedding_model.async_get_embedding("Hello, world!")))
+    print(
+        asyncio.run(
+            embedding_model.async_get_embeddings(["Hello, world!", "Goodbye, world!"])
+        )
+    )

aimakerspace/openai_utils/prompts.py

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+import re
+
+
+class BasePrompt:
+    def __init__(self, prompt):
+        """
+        Initializes the BasePrompt object with a prompt template.
+
+        :param prompt: A string that can contain placeholders within curly braces
+        """
+        self.prompt = prompt
+        self._pattern = re.compile(r"\{([^}]+)\}")
+
+    def format_prompt(self, **kwargs):
+        """
+        Formats the prompt string using the keyword arguments provided.
+
+        :param kwargs: The values to substitute into the prompt string
+        :return: The formatted prompt string
+        """
+        matches = self._pattern.findall(self.prompt)
+        return self.prompt.format(**{match: kwargs.get(match, "") for match in matches})
+
+    def get_input_variables(self):
+        """
+        Gets the list of input variable names from the prompt string.
+
+        :return: List of input variable names
+        """
+        return self._pattern.findall(self.prompt)
+
+
+class RolePrompt(BasePrompt):
+    def __init__(self, prompt, role: str):
+        """
+        Initializes the RolePrompt object with a prompt template and a role.
+
+        :param prompt: A string that can contain placeholders within curly braces
+        :param role: The role for the message ('system', 'user', or 'assistant')
+        """
+        super().__init__(prompt)
+        self.role = role
+
+    def create_message(self, format=True, **kwargs):
+        """
+        Creates a message dictionary with a role and a formatted message.
+
+        :param kwargs: The values to substitute into the prompt string
+        :return: Dictionary containing the role and the formatted message
+        """
+        if format:
+            return {"role": self.role, "content": self.format_prompt(**kwargs)}
+        
+        return {"role": self.role, "content": self.prompt}
+
+
+class SystemRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "system")
+
+
+class UserRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "user")
+
+
+class AssistantRolePrompt(RolePrompt):
+    def __init__(self, prompt: str):
+        super().__init__(prompt, "assistant")
+
+
+if __name__ == "__main__":
+    prompt = BasePrompt("Hello {name}, you are {age} years old")
+    print(prompt.format_prompt(name="John", age=30))
+
+    prompt = SystemRolePrompt("Hello {name}, you are {age} years old")
+    print(prompt.create_message(name="John", age=30))
+    print(prompt.get_input_variables())

aimakerspace/text_utils.py

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+import os
+from typing import List
+
+
+class TextFileLoader:
+    def __init__(self, path: str, encoding: str = "utf-8"):
+        self.documents = []
+        self.path = path
+        self.encoding = encoding
+
+    def load(self):
+        if os.path.isdir(self.path):
+            self.load_directory()
+        elif os.path.isfile(self.path) and self.path.endswith(".txt"):
+            self.load_file()
+        else:
+            raise ValueError(
+                "Provided path is neither a valid directory nor a .txt file."
+            )
+
+    def load_file(self):
+        with open(self.path, "r", encoding=self.encoding) as f:
+            self.documents.append(f.read())
+
+    def load_directory(self):
+        for root, _, files in os.walk(self.path):
+            for file in files:
+                if file.endswith(".txt"):
+                    with open(
+                        os.path.join(root, file), "r", encoding=self.encoding
+                    ) as f:
+                        self.documents.append(f.read())
+
+    def load_documents(self):
+        self.load()
+        return self.documents
+
+
+class CharacterTextSplitter:
+    def __init__(
+        self,
+        chunk_size: int = 1000,
+        chunk_overlap: int = 200,
+    ):
+        assert (
+            chunk_size > chunk_overlap
+        ), "Chunk size must be greater than chunk overlap"
+
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+
+    def split(self, text: str) -> List[str]:
+        chunks = []
+        for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
+            chunks.append(text[i : i + self.chunk_size])
+        return chunks
+
+    def split_texts(self, texts: List[str]) -> List[str]:
+        chunks = []
+        for text in texts:
+            chunks.extend(self.split(text))
+        return chunks
+
+
+if __name__ == "__main__":
+    loader = TextFileLoader("data/KingLear.txt")
+    loader.load()
+    splitter = CharacterTextSplitter()
+    chunks = splitter.split_texts(loader.documents)
+    print(len(chunks))
+    print(chunks[0])
+    print("--------")
+    print(chunks[1])
+    print("--------")
+    print(chunks[-2])
+    print("--------")
+    print(chunks[-1])

aimakerspace/vectordatabase.py

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+import numpy as np
+from collections import defaultdict
+from typing import List, Tuple, Callable
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+import asyncio
+
+
+def cosine_similarity(vector_a: np.array, vector_b: np.array) -> float:
+    """Computes the cosine similarity between two vectors."""
+    dot_product = np.dot(vector_a, vector_b)
+    norm_a = np.linalg.norm(vector_a)
+    norm_b = np.linalg.norm(vector_b)
+    return dot_product / (norm_a * norm_b)
+
+
+class VectorDatabase:
+    def __init__(self, embedding_model: EmbeddingModel = None):
+        self.vectors = defaultdict(np.array)
+        self.embedding_model = embedding_model or EmbeddingModel()
+
+    def insert(self, key: str, vector: np.array) -> None:
+        self.vectors[key] = vector
+
+    def search(
+        self,
+        query_vector: np.array,
+        k: int,
+        distance_measure: Callable = cosine_similarity,
+    ) -> List[Tuple[str, float]]:
+        scores = [
+            (key, distance_measure(query_vector, vector))
+            for key, vector in self.vectors.items()
+        ]
+        return sorted(scores, key=lambda x: x[1], reverse=True)[:k]
+
+    def search_by_text(
+        self,
+        query_text: str,
+        k: int,
+        distance_measure: Callable = cosine_similarity,
+        return_as_text: bool = False,
+    ) -> List[Tuple[str, float]]:
+        query_vector = self.embedding_model.get_embedding(query_text)
+        results = self.search(query_vector, k, distance_measure)
+        return [result[0] for result in results] if return_as_text else results
+
+    def retrieve_from_key(self, key: str) -> np.array:
+        return self.vectors.get(key, None)
+
+    async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase":
+        embeddings = await self.embedding_model.async_get_embeddings(list_of_text)
+        for text, embedding in zip(list_of_text, embeddings):
+            self.insert(text, np.array(embedding))
+        return self
+
+
+if __name__ == "__main__":
+    list_of_text = [
+        "I like to eat broccoli and bananas.",
+        "I ate a banana and spinach smoothie for breakfast.",
+        "Chinchillas and kittens are cute.",
+        "My sister adopted a kitten yesterday.",
+        "Look at this cute hamster munching on a piece of broccoli.",
+    ]
+
+    vector_db = VectorDatabase()
+    vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text))
+    k = 2
+
+    searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k)
+    print(f"Closest {k} vector(s):", searched_vector)
+
+    retrieved_vector = vector_db.retrieve_from_key(
+        "I like to eat broccoli and bananas."
+    )
+    print("Retrieved vector:", retrieved_vector)
+
+    relevant_texts = vector_db.search_by_text(
+        "I think fruit is awesome!", k=k, return_as_text=True
+    )
+    print(f"Closest {k} text(s):", relevant_texts)

app_am.py

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+import os
+from typing import List, Dict
+from chainlit.types import AskFileResponse
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain.storage import LocalFileStore
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.globals import set_llm_cache
+from langchain_openai import ChatOpenAI
+from langchain_core.caches import InMemoryCache
+from operator import itemgetter
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain.schema.runnable.config import RunnableConfig
+from langsmith.evaluation import LangChainStringEvaluator, evaluate
+from datetime import datetime
+from objection_eval import generate_response_to_objection
+import pandas as pd 
+import uuid
+import chainlit as cl
+import dotenv
+import tempfile
+
+## Aaron to improve
+## add customer-based value prop to load: 
+## compare and contract sales rep value prop & business domain
+## use that list to generate objections that is close to sales opportunity 
+
+# Load environment variables from .env file
+dotenv.load_dotenv()
+
+# Access the OpenAI API key
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+set_llm_cache(InMemoryCache())
+
+def process_value_prop_pdf(file: AskFileResponse) -> 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(temp_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
+
+text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n","\n"],chunk_size=200, chunk_overlap=20)
+
+# QDrant Client Setup
+collection_name = f"pdf_to_parse_{uuid.uuid4()}"
+client = QdrantClient(":memory:")
+client.create_collection(
+    collection_name=collection_name,
+    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+)
+
+# Embedding Model
+core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Internal object to store objections
+objections = []
+
+def process_text_file(file: AskFileResponse):
+    import tempfile
+    with tempfile.NamedTemporaryFile(mode="w", delete=False) as temp_file:
+        with open(temp_file.name, "wb") as f:
+            f.write(file.content)
+
+    Loader = PyMuPDFLoader
+    loader = Loader(temp_file.name)
+    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}" # Placeholder response
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    # Ask for the first PDF file (Potential Customer Business Domain)
+    files = None
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload a PDF for Potential Customer Business Domain to begin!",
+            accept=["application/pdf"],
+            max_size_mb=2,
+            timeout=180,
+        ).send()
+
+    first_file = files[0]
+
+    # Notify the user that the file is being processed
+    msg = cl.Message(
+        content=f"Processing `{first_file.name}`...", disable_human_feedback=True
+    )
+    await msg.send()
+
+    # Process the first file
+    texts = process_text_file(first_file)
+    print(f"Processing {len(texts)} text chunks from the first file")
+
+    # Ask for the second PDF file (Value Proposition)
+    files = None
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload our customer-specific value proposition PDF.",
+            accept=["application/pdf"],
+            max_size_mb=2,
+            timeout=180,
+        ).send()
+
+    second_file = files[0]
+
+    # Notify the user that the second file is being processed
+    msg = cl.Message(
+        content=f"Processing `{second_file.name}`...", disable_human_feedback=True
+    )
+    await msg.send()
+
+    # Process the second file
+    value_prop_content = process_value_prop_pdf(second_file)
+    print(f"Processing {len(value_prop_content)} text chunks from the second file")
+    #print(value_prop_content)
+    
+    
+    
+    # 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=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()
+
+    #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["content"], "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)
+
+    await cl.Message(content="We are ready to enter Sales 'Sparring'. Ok? ").send()
+
+@cl.on_message
+async def main(message):
+    """
+    This function will be called every time a message is received from a session.
+
+    We will use the LCEL RAG chain to generate a response to the user query.
+
+    The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
+    """
+    await cl.AskUserMessage(
+        content="Are you ready?", timeout=300
+    ).send()
+
+    objection_chain = cl.user_session.get("objection_chain")
+
+    #msg = cl.Message(content="")
+
+    # Retrieve the list of objections
+    objections = cl.user_session.get("objections")
+    #print (objections[0])
+    objection_responses = {}
+
+    # Iterate through each objection
+    for i, objection in enumerate(objections):
+        # Return the objection in the form of a question
+        await cl.Message(content=f"Objection: {objection}").send()
+
+        # Capture user input
+        user_response = await cl.AskUserMessage(
+            content="How would you respond to this objection?", timeout=600
+        ).send()
+
+        objection_responses[objection] = user_response['content']
+
+        # Process the user's response (you can implement your logic here)
+        #new_objection_response = generate_response_to_objection(user_response.content)
+
+        # Send the response back to the user
+        #await cl.Message(content=f"Response to objection {i + 1}: {new_objection_response}").send()
+    print (objection_responses)
+    data = []
+    for objection, response in objection_responses.items():
+        data.append({
+            "timestamp": datetime.now(),  # Capture the current timestamp
+            "objection": objection,
+            "response": response
+        })
+
+    # Create a DataFrame
+    user_response = pd.DataFrame(data)
+    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+
+    #response = await generate_response_to_objection(user_response, 0)
+    #user_response.to_csv(f'data/user_response_{timestamp}.csv', index=False)
+
+

cache/text-embedding-3-small00144338-d6e8-57af-9ace-37899a76e852

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