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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building a Customer Support Service Chatbot for ShopEase"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HX-habWAfTEv"
},
"source": [
"## Intallations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5MzQYvspK-QQ",
"outputId": "ab44ec14-4d1d-4e76-bd28-57049f877334"
},
"outputs": [],
"source": [
"# Necessary installations\n",
"!pip install -q langchain-openai chromadb gradio langchain python-dotenv"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating External Knowledge Base"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Document loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"file_path=\"Demo_Data_ShopEase.txt\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Create a TextLoader instance and pass file_path\n",
"loader = TextLoader(file_path)\n",
"# Load the document\n",
"document = loader.load()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8VAHT6XmfzX0"
},
"source": [
"## Step 2: Split document block into chunks"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Import the RecursiveCharacterTextSplitter class\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"# By default splits on characters [\"\\n\\n\", \"\\n\", \" \", \"\"]\n",
"\n",
"# Initialize a text splitter with specified parameters\n",
"text_splitter = RecursiveCharacterTextSplitter(\n",
" chunk_size=2000,\n",
" chunk_overlap=800, # To maintain continuity\n",
" length_function=len, # Define how the length of chunks is calculated\n",
") "
]
},
{
"cell_type": "code",
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"metadata": {
"id": "BRViMDlpMMWl"
},
"outputs": [],
"source": [
"# Split the loaded documents into chunks using the configured text splitter\n",
"chunks = text_splitter.split_documents(document)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Embed and store chunks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3iKgj7gLf3CK"
},
"source": [
"### Using Text embedding model"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import AzureOpenAIEmbeddings\n",
"from dotenv import load_dotenv\n",
"import os\n",
"# Load environment variables from the .env file\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Initialize AzureOpenAIEmbeddings instance\n",
"embeddings_model= AzureOpenAIEmbeddings(\n",
" api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
" azure_deployment=os.getenv(\"EMBEDDING_MODEL\"),\n",
" azure_endpoint=os.getenv(\"BASE_URL\"),\n",
" openai_api_version=\"2023-05-15\" \n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GeV91vkff-ZN"
},
"source": [
"### Storing in Vector store(Chroma)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Create a Chroma vector store from the previously split documents and using the specified embeddings\n",
"vectorstore = Chroma.from_documents(\n",
" embedding=embeddings_model # Embeddings instance for encoding\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eQXyw6qvgidj"
},
"source": [
"#### Making retriever object"
]
},
{
"cell_type": "code",
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"metadata": {
"id": "jwgJMs8r51hA"
},
"outputs": [],
"source": [
"# Create a retriever object from the 'db' with a search configuration where it retrieves up to 4 relevant splits/documents.\n",
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 4})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Our knowledge base is ready!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w0nPxfRXgUUg"
},
"source": [
"# Retrieval and Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Generation - Using OpenAI model for Question Answering"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import AzureChatOpenAI"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\haroon trader\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\langchain_core\\_api\\deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.azure_openai.AzureChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import AzureChatOpenAI`.\n",
" warn_deprecated(\n"
]
}
],
"source": [
"# Initialize AzureChatOpenAI instance\n",
"llm = AzureChatOpenAI(\n",
" api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
" azure_endpoint=os.getenv(\"BASE_URL\"),\n",
" deployment_name=os.getenv(\"CHAT_MODEL\"),\n",
" openai_api_version=\"2023-05-15\",\n",
" temperature=0.2 #Set temperature parameter for adding creativity in chat responses\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZtLV-K9qgpCG"
},
"source": [
"### Crafting prompt "
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"prompt =\"\"\"\n",
"You are a dedicated customer support representative for ShopEase, a leading online retailer.\\\n",
"Your primary goal is to assist customers with any inquiries they may have about ShopEase's products, services, policies, or any other related concerns.\n",
"\n",
"Ensure that every answer adheres to each of the following rules always:\n",
"1: Answer is always generated using the sources listed below.\n",
"2: Your responses should be complete, concise and helpful.\n",
"3. Do not list the sources in the answer.\n",
"\n",
"----------------\n",
"\n",
"Sources:\n",
"{context}\n",
"----------------\n",
"\n",
"Question: \n",
"{query}\n",
"----------------\n",
"\n",
"Helpful Answer:\n",
" \"\"\""
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Define a PromptTemplate \n",
"prompt_template = PromptTemplate(\n",
" input_variables=[\"context\",\"query\"],\n",
" template=prompt)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Using LangChain expression_language\n",
"chain = prompt_template|llm"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o0hl4iJcg3XJ"
},
"source": [
"# Understanding the flow through sample question"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"query=\"What is the vision of the company?\""
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Retrieve 4 relevant chunks \n",
"relevant_docs = retriever.get_relevant_documents(query)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Count: 0\n",
"Content: These core values serve as the guiding principles that shape our culture, drive our actions, and define our identity as a company committed to serving our customers, employees, and communities with integrity, innovation, and sustainability.\n",
"\n",
"**Vision**\n",
"\n",
"At ShopEase, our vision is clear: we aspire to become the preferred destination for online shopping, renowned for our exceptional customer service, diverse product selection, and unwavering commitment to sustainability. We are dedicated to continually expanding our offerings and reaching new markets, all while upholding our core values and cultivating long-term relationships with our customers.\n",
"\n",
"\n",
" - Innovation is key to staying ahead in the ever-evolving retail landscape, and at ShopEase, we embrace a culture of continuous innovation.\n",
" - We encourage creativity, experimentation, and out-of-the-box thinking to drive new ideas, products, and solutions that address emerging customer needs and trends.\n",
" - Our dedicated research and development teams are constantly exploring new technologies, trends, and market insights to innovate and improve our offerings, keeping us at the forefront of the industry.\n",
"\n",
"4. Teamwork:\n",
" - At ShopEase, we recognize that no individual can achieve success alone. We believe in the power of collaboration and teamwork to achieve our common goals and deliver exceptional results.\n",
" - We foster a supportive and inclusive work environment where employees are encouraged to share ideas, collaborate across teams, and leverage each other's strengths.\n",
" - Our emphasis on teamwork extends beyond internal collaboration to partnerships with suppliers, vendors, and stakeholders, enabling us to deliver value to our customers and drive mutual success.\n",
"\n",
"5. Sustainability:\n",
" - Sustainability is a core value that guides our business decisions and practices at ShopEase. We recognize our responsibility to minimize our environmental impact and promote sustainability in all aspects of our operations.\n",
" - We are committed to reducing our carbon footprint, conserving natural resources, and implementing eco-friendly practices throughout our supply chain, logistics, and facilities.\n",
" - Our sustainability initiatives include sourcing from environmentally responsible suppliers, reducing waste and packaging, optimizing energy efficiency, and supporting community and environmental causes.\n",
"\n",
"These core values serve as the guiding principles that shape our culture, drive our actions, and define our identity as a company committed to serving our customers, employees, and communities with integrity, innovation, and sustainability.\n",
"\n",
"**Vision**\n",
"\n",
"\n",
"Count: 2\n",
"Content: In pursuit of our vision, we aim to:\n",
"\t1. Provide Exceptional Customer Service: We prioritize customer satisfaction above all else, striving to exceed expectations at every touchpoint. Our dedicated team is committed to delivering personalized support and assistance to ensure a seamless shopping experience for every customer.\n",
"\t2. Offer Diverse Product Selection: We understand that every shopper is unique, which is why we curate a diverse range of products across various categories to cater to different tastes, preferences, and needs. From the latest gadgets to trendy fashion items and home essentials, ShopEase offers something for everyone.\n",
"\t3. Champion Sustainability: We recognize our responsibility to minimize our environmental impact and promote sustainable practices throughout our operations. From eco-friendly packaging to sourcing products from environmentally responsible suppliers, we are committed to reducing our carbon footprint and preserving the planet for future generations.\n",
"\t4. Expand Offerings and Reach New Markets: As a dynamic and forward-thinking company, we are constantly seeking opportunities to expand our product offerings and reach new markets. Whether through strategic partnerships, innovative marketing initiatives, or geographic expansion, we are committed to growing our brand and serving customers around the world.\n",
"\t5. Uphold Core Values and Foster Long-Term Relationships: Our success is built on a foundation of integrity, innovation, teamwork, and sustainability. We remain steadfast in our commitment to these core values, which guide our decision-making and interactions with customers, partners, and stakeholders. By prioritizing honesty, transparency, and trust, we aim to cultivate long-term relationships built on mutual respect and shared values.\n",
"\n",
"\n",
"Count: 3\n",
"Content: As we work towards achieving our vision, we invite you to join us on this journey of innovation, sustainability, and customer-centricity. Together, we can shape the future of online shopping and create lasting value for our customers and communities.\n",
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"\n",
"Emma Thompson,\n",
"Chief Vision Officer (CVO),\n",
"ShopEase\n",
"\n",
"\n",
"**Products Available at ShopEase**\n",
"\n",
"Explore ShopEase's extensive selection of products across various categories:\n",
"\n",
"1. Electronics:\n",
"\t- TVs: Immerse yourself in stunning visuals with our wide range of televisions, including LED, OLED, and QLED models in various sizes and resolutions. Choose from top brands such as Samsung, LG, Sony, and more.\n",
"\t- Smartphones: Stay connected on the go with the latest smartphones featuring advanced cameras, powerful processors, and innovative features. Discover options from Apple, Samsung, Google, OnePlus, and others.\n",
"\t- Laptops: Whether you need a sleek ultrabook for work or a gaming powerhouse for entertainment, we have laptops to suit every need and budget. Explore models from Dell, HP, Lenovo, Asus, and more.\n",
"\t- Cameras: Capture life's moments in stunning detail with our selection of digital cameras, DSLRs, mirrorless cameras, and accessories from Canon, Nikon, Sony, and Fujifilm.\n",
"\n",
"2. Fashion:\n",
"\t- Clothing: Elevate your wardrobe with stylish apparel for men, women, and children. From casual basics to formal attire, we offer a diverse range of clothing options from popular brands and designers.\n",
"\t- Shoes: Step out in style with our collection of footwear, including sneakers, boots, sandals, heels, and more. Find the perfect pair to complement any outfit and occasion.\n",
" \t- Accessories: Complete your look with our selection of fashion accessories, including handbags, wallets, belts, scarves, hats, and jewelry. Add the perfect finishing touch to any ensemble.\n",
"\n",
"\n"
]
}
],
"source": [
"for count,doc in enumerate(relevant_docs):\n",
" print(\"Count: \",count)\n",
" print(\"Content: \", doc.page_content)\n",
" print(\"\\n\")"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"# Add query and chunks to the prompt and generate answer\n",
"result=chain.invoke({\"query\":query,\"context\":relevant_docs})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We did it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making GUI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Building Gradio Application"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gradio is an open-source Python package that allows you to quickly build a demo or web application for your machine learning model."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Making a function for retrieval of documents and generating responses\n",
"def my_func(question):\n",
" # Retrieve relevant documents\n",
" relevant_docs = retriever.get_relevant_documents(question)\n",
" # Generate answer using retrieved documents\n",
" result=chain.invoke({\"query\":question,\"context\":relevant_docs})\n",
" response=result.content\n",
" return response\n",
"\n",
"# Define a function to respond to user input\n",
"def respond(user_message, chat_history):\n",
" bot_message = my_func(user_message)\n",
" chat_history.append((user_message, bot_message))\n",
" return \"\", chat_history"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making Interface using Gradio\n",
"with gr.Blocks() as demo:\n",
" \n",
" # Display a markdown message as the interface header\n",
" gr.Markdown(\n",
" \"\"\"# Welcome to ShopEase!\n",
" \"\"\")\n",
"\n",
" # Initialize a chatbot component\n",
" chatbot = gr.Chatbot(\n",
" label=\"ShopEaseBot\",\n",
" bubble_full_width=False,\n",
" avatar_images=((\"user.png\"), (\"bot.png\")),\n",
" height=500,\n",
" )\n",
"\n",
" # Create a textbox for user input\n",
" msg = gr.Textbox(\n",
" scale=4,\n",
" show_label=False,\n",
" placeholder=\"Enter text and press enter\",\n",
" container=False,\n",
" )\n",
" \n",
" with gr.Row():\n",
" with gr.Column():\n",
" # Create a submit button\n",
" submit_btn= gr.Button(\"Submit\")\n",
" with gr.Column():\n",
" # Create a clear button for clearing messages\n",
" clear = gr.ClearButton([msg, chatbot])\n",
"\n",
" # Connect the respond function to the submit button's click event\n",
" msg.submit(respond, [msg, chatbot], [msg, chatbot])\n",
" submit_btn.click(respond, [msg, chatbot], [msg, chatbot]) \n",
"\n",
"# Launch the Gradio interface\n",
"demo.launch()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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