├── README.md └── EcommerceChatBot.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # EcommerceRecommendationChatbot_LLMs 2 | Ecommerce Recommendation Chatbot with Retrieval-Augmented Generation using Llamaindex and Mistral-7B 3 | 4 | In the rapidly evolving world of e-commerce, providing personalized and efficient customer experiences has become a key priority for businesses. One innovative solution that has gained prominence is the implementation of chatbots powered by Large Language Models (LLMs). These advanced models, such as GPT-3, have revolutionized the way businesses interact with their customers, offering a seamless and highly tailored shopping experience. In this tutorial, we will explore the intricacies of developing an e-commerce recommendation chatbot using LLMs, understanding the benefits, challenges, and best practices for creating an intelligent and effective conversational agent. 5 | 6 | -------------------------------------------------------------------------------- /EcommerceChatBot.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "colab": { 8 | "base_uri": "https://localhost:8080/" 9 | }, 10 | "id": "nSN0kDIVBE5E", 11 | "outputId": "9446f098-b256-4294-b138-6b0a167e06e7" 12 | }, 13 | "outputs": [ 14 | { 15 | "name": "stdout", 16 | "output_type": "stream", 17 | "text": [ 18 | "Collecting sentence_transformers\n", 19 | " Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n", 20 | "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m 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/usr/local/lib/python3.10/dist-packages (from pandas->llama-index) (2023.3.post1)\n", 172 | "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->llama-index) (1.2.0)\n", 173 | "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json->llama-index) (23.2)\n", 174 | "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama-index) (1.16.0)\n", 175 | "Installing collected packages: typing-extensions, mypy-extensions, marshmallow, h11, deprecated, beautifulsoup4, typing-inspect, tiktoken, httpcore, httpx, dataclasses-json, openai, llama-index\n", 176 | " Attempting uninstall: typing-extensions\n", 177 | " Found existing installation: typing_extensions 4.5.0\n", 178 | " Uninstalling typing_extensions-4.5.0:\n", 179 | " Successfully uninstalled typing_extensions-4.5.0\n", 180 | " Attempting uninstall: beautifulsoup4\n", 181 | " Found existing installation: beautifulsoup4 4.11.2\n", 182 | " Uninstalling beautifulsoup4-4.11.2:\n", 183 | " Successfully uninstalled beautifulsoup4-4.11.2\n", 184 | "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", 185 | "llmx 0.0.15a0 requires cohere, which is not installed.\n", 186 | "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", 187 | "\u001b[0mSuccessfully installed beautifulsoup4-4.12.2 dataclasses-json-0.6.3 deprecated-1.2.14 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 llama-index-0.9.32 marshmallow-3.20.2 mypy-extensions-1.0.0 openai-1.8.0 tiktoken-0.5.2 typing-extensions-4.9.0 typing-inspect-0.9.0\n" 188 | ] 189 | } 190 | ], 191 | "source": [ 192 | "!pip install numpy\n", 193 | "!pip install pandas\n", 194 | "!pip install gdown\n", 195 | "!pip install llama-index" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": null, 201 | "metadata": { 202 | "id": "urTSBI508VJP" 203 | }, 204 | "outputs": [], 205 | "source": [ 206 | "import pandas as pd\n", 207 | "\n", 208 | "MAX_TEXT_LENGTH=1024 # Maximum num of text characters to use\n", 209 | "\n", 210 | "def auto_truncate(val):\n", 211 | "\n", 212 | " \"\"\"Truncate the given text.\"\"\"\n", 213 | "\n", 214 | " return val[:MAX_TEXT_LENGTH]\n", 215 | "\n", 216 | "# Load Product data and truncate long text fields\n", 217 | "\n", 218 | "all_prods_df = pd.read_csv(\"product_data.csv\", converters={\n", 219 | "\n", 220 | " 'bullet_point': auto_truncate,\n", 221 | "\n", 222 | " 'item_keywords': auto_truncate,\n", 223 | "\n", 224 | " 'item_name': auto_truncate,\n", 225 | "\n", 226 | " 'material': auto_truncate\n", 227 | "\n", 228 | "})" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": null, 234 | "metadata": { 235 | "id": "w0f2DRdC-p42" 236 | }, 237 | "outputs": [], 238 | "source": [ 239 | "all_prods_df['item_keywords'].replace('', None, inplace=True)\n", 240 | "all_prods_df.dropna(subset=['item_keywords'], inplace=True)\n", 241 | "all_prods_df.reset_index(drop=True, inplace=True)" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": { 248 | "id": "t-wepLTECp7r" 249 | }, 250 | "outputs": [], 251 | "source": [ 252 | "all_prods_df['material'].replace('', None, inplace=True)\n", 253 | "all_prods_df.dropna(subset=['material'], inplace=True)\n", 254 | "all_prods_df.reset_index(drop=True, inplace=True)" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": null, 260 | "metadata": { 261 | "colab": { 262 | "base_uri": "https://localhost:8080/" 263 | }, 264 | "id": "M3Gqguxf-vqC", 265 | "outputId": "9d632682-eece-4d45-cca5-7a05527b8571" 266 | }, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "{'item_id': 'B07T2JY31Y',\n", 272 | " 'marketplace': 'Amazon',\n", 273 | " 'country': 'IN',\n", 274 | " 'main_image_id': '71vX7qIEAIL',\n", 275 | " 'domain_name': 'amazon.in',\n", 276 | " 'bullet_point': '3D Printed Hard Back Case Mobile Cover for Sony Xperia Z1 L39H Easy to put & take off with perfect cutouts for volume buttons, audio & charging ports. Stylish design and appearance, express your unique personality. Extreme precision design allows easy access to all buttons and ports while featuring raised bezel to life screen and camera off flat surface. Slim Hard Back Cover No Warranty',\n", 277 | " 'item_keywords': 'mobile cover back cover mobile case phone case mobile panel phone panel LG mobile case LG phone cover LG back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Sony Xperia mobile case Sony Xperia phone cover Sony Xperia back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Sony Xperia mobile case Sony Xperia phone cover Sony Xperia back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Sony Xperia mobile case Sony Xperia phone cover Sony Xperia back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Sony Xperia mobile case Sony Xperia phone cover Sony Xperia back case hard case 3D printed mobile cover mobile cover back cover mobile case phone case mobile panel phone panel Sony Xperia mobile case Sony Xperia phone cover Sony Xperia back case hard case 3D printed mobile c',\n", 278 | " 'material': 'Wood',\n", 279 | " 'brand': 'Amazon Brand - Solimo',\n", 280 | " 'color': 'others',\n", 281 | " 'item_name': 'Amazon Brand - Solimo Designer Leaf on Wood 3D Printed Hard Back Case Mobile Cover for Sony Xperia Z1 L39H',\n", 282 | " 'model_name': 'Sony Xperia Z1 L39H',\n", 283 | " 'model_number': 'gz8056-SL40528',\n", 284 | " 'product_type': 'CELLULAR_PHONE_CASE'}" 285 | ] 286 | }, 287 | "execution_count": 4, 288 | "metadata": {}, 289 | "output_type": "execute_result" 290 | } 291 | ], 292 | "source": [ 293 | "# Num products to use (subset)\n", 294 | "NUMBER_PRODUCTS = 2500\n", 295 | "\n", 296 | "# Get the first 2500 products\n", 297 | "product_metadata = (\n", 298 | " all_prods_df\n", 299 | " .head(NUMBER_PRODUCTS)\n", 300 | " .to_dict(orient='index')\n", 301 | ")\n", 302 | "\n", 303 | "# Check one of the products\n", 304 | "product_metadata[0]" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": null, 310 | "metadata": { 311 | "id": "qj-JkRLC-zAD" 312 | }, 313 | "outputs": [], 314 | "source": [ 315 | "from llama_index.embeddings import HuggingFaceEmbedding\n", 316 | "\n", 317 | "# data that will be embedded and converted to vectors\n", 318 | "texts = [\n", 319 | " v['item_name'] for k, v in product_metadata.items()\n", 320 | "]\n", 321 | "\n", 322 | "# product metadata that we'll store along our vectors\n", 323 | "metadatas = list(product_metadata.values())\n", 324 | "\n", 325 | "# name of the Redis search index to create\n", 326 | "index_name = \"products\"" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": null, 332 | "metadata": { 333 | "id": "NcCdi7NZ-coZ" 334 | }, 335 | "outputs": [], 336 | "source": [ 337 | "# setup prompts - specific to StableLM\n", 338 | "from llama_index.prompts import PromptTemplate\n", 339 | "\n", 340 | "query_wrapper_prompt = PromptTemplate(\n", 341 | " \"Below is an instruction that describes a task. \"\n", 342 | " \"Write a response that appropriately completes the request.\\n\\n\"\n", 343 | " \"### Instruction:\\n{query_str}\\n\\n### Response:\"\n", 344 | ")" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": null, 350 | "metadata": { 351 | "colab": { 352 | "base_uri": "https://localhost:8080/" 353 | }, 354 | "id": "51AJlt7p-k9B", 355 | "outputId": "3dbe4a68-d690-4a7e-f650-c8899e489654" 356 | }, 357 | "outputs": [ 358 | { 359 | "name": "stdout", 360 | "output_type": "stream", 361 | "text": [ 362 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 363 | "\u001b[?25h" 364 | ] 365 | } 366 | ], 367 | "source": [ 368 | "!pip -qqq install bitsandbytes accelerate" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": null, 374 | "metadata": { 375 | "colab": { 376 | "base_uri": 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"055748f37878407f8ada1e561dadd8c0", 493 | "7653deecae914306bcaac32effabca4f", 494 | "12674e062c83439aa3dbc5e08b05f079", 495 | "85f0ac886fda426d82c5ed1972bf41d6", 496 | "159c0a7497e44223adde51341e75cfa5", 497 | "c944e6f81bf34f10a98d01403ae20000", 498 | "3d3cc6d4b77746fbb31098b4e750d646", 499 | "5e51570847a644adb3d93a482f0d612c", 500 | "03b910820ec7445fb68418ce039b7ab4", 501 | "6e7916031c9d4c29ab3a5283c46e9475", 502 | "76865abd79104339b23839a674b8f2e2", 503 | "72d4352003df4a51a2aada693135e82d", 504 | "92d9a772d207408886d94fd6de94e741", 505 | "98b4cb0af11b4a99bf97aab069edb63f", 506 | "84f6b9038cbd49cb9c0fac7e1cf4959c", 507 | "5000a8f102a7412f97023fc19c6a46f5", 508 | "38d94cbd3053459bb5effe62fa10cdf9", 509 | "1cd88cb6475440a4a598e010fa0f40dd", 510 | "4032257679404d7295007193d0fb4389", 511 | "e626bd232ea84144938d9592d4e3a9bf", 512 | "6de9eda5d0af4934813c82711f49128a", 513 | "17c8181c22914a3cab85a59d3d1049ff", 514 | "5ec77358cbf34876ad97f0c1389005e7", 515 | "d3a7fc578baa4e9699fdcb7cbd170925", 516 | "04ccf654829645e8836b74a156cce26a", 517 | "1fbd012fec064740900d8d23d20dabc1", 518 | "c1cc92ead63b450194131696cf2c9260", 519 | "036a7ce8d92a42b98ad202b78e340b0a", 520 | "15bef7f2b4c4488abb190e9869a1cd18", 521 | "105e580044844891894c9df883cfb8fe" 522 | ] 523 | }, 524 | "id": "Qw_u8OuF9v7f", 525 | "outputId": "69a3f4c8-eb99-443c-c9cf-c0b9e2c811cd" 526 | }, 527 | "outputs": [ 528 | { 529 | "name": "stderr", 530 | "output_type": "stream", 531 | "text": [ 532 | "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", 533 | "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", 534 | "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", 535 | "You will be able to reuse this secret in all of your notebooks.\n", 536 | "Please note that authentication is recommended but still optional to access public models or datasets.\n", 537 | " warnings.warn(\n" 538 | ] 539 | }, 540 | { 541 | "data": { 542 | "application/vnd.jupyter.widget-view+json": { 543 | "model_id": "63ac58a915374d9cad4af4b25348ae2e", 544 | "version_major": 2, 545 | "version_minor": 0 546 | }, 547 | "text/plain": [ 548 | "config.json: 0%| | 0.00/608 [00:00, line 7)", 758 | "output_type": "error", 759 | "traceback": [ 760 | "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m documents = loader.load_data(text = texts, product_metadata)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" 761 | ] 762 | } 763 | ], 764 | "source": [ 765 | "from llama_index import download_loader\n", 766 | "from llama_index import VectorStoreIndex\n", 767 | "\n", 768 | "\n", 769 | "JsonDataReader = download_loader(\"JsonDataReader\")\n", 770 | "loader = JsonDataReader()\n", 771 | "documents = loader.load_data(product_metadata)\n", 772 | "\n", 773 | "index = VectorStoreIndex.from_documents(\n", 774 | " documents, service_context=service_context\n", 775 | ")" 776 | ] 777 | }, 778 | { 779 | "cell_type": "code", 780 | "execution_count": null, 781 | "metadata": { 782 | "colab": { 783 | "base_uri": "https://localhost:8080/" 784 | }, 785 | "id": "JMY94kqFy0N_", 786 | "outputId": "f65dd0a1-acae-4fd6-f018-641091b98d18" 787 | }, 788 | "outputs": [ 789 | { 790 | "name": "stdout", 791 | "output_type": "stream", 792 | "text": [ 793 | "<|assistant|>\n", 794 | "Here is a sample response to a customer inquiry about gold-plated earrings:\n", 795 | "\n", 796 | "Customer: Hi there! I'm interested in purchasing some gold-plated earrings. Can you provide me with some information about the specific design and material?\n", 797 | "\n", 798 | "Assistant: Absolutely! Here are some details about our gold-plated earrings:\n", 799 | "\n", 800 | "- Design: Our gold-plated earrings feature a delicate and elegant design that is perfect for any occasion. The earrings are made from high-quality gold plating, which gives them a shiny and luxurious finish.\n", 801 | "\n", 802 | "- Material: Our gold-plated earrings are made from 14k gold, which means they are both durable and lightweight. The gold plating is carefully applied to the earrings to ensure that they remain shiny and glossy over time.\n", 803 | "\n", 804 | "- Color: Our gold-plated earrings come in a variety of colors, including yellow, rose, and white gold. The color of the gold plating may vary slightly from piece to piece, but the overall effect is always beautiful and elegant.\n", 805 | "\n", 806 | "- Size" 807 | ] 808 | } 809 | ], 810 | "source": [ 811 | "from llama_index.memory import ChatMemoryBuffer\n", 812 | "\n", 813 | "memory = ChatMemoryBuffer.from_defaults(token_limit=3900)\n", 814 | "\n", 815 | "chat_engine = index.as_chat_engine(\n", 816 | " chat_mode=\"condense_plus_context\",\n", 817 | " memory=memory,\n", 818 | " context_prompt=(\n", 819 | " \"You are a friendly, conversational retail shopping assistant. Use the following context including product names, descriptions, and keywords to show the shopper whats available, help find what they want, and answer any questions\"\n", 820 | " \"It's ok if you don't know the answer.\"\n", 821 | " \"Here are the relevant documents for the context:\\n\"\n", 822 | " \"{context_str}\"\n", 823 | " \"\\nInstruction: Use the previous chat history, or the context above, to interact and help the user.\"\n", 824 | " ),\n", 825 | " verbose=False,\n", 826 | ")\n", 827 | "\n", 828 | "response = chat_engine.stream_chat(\"gold-plated earrings\")\n", 829 | "for token in response.response_gen:\n", 830 | " print(token, end=\"\")" 831 | ] 832 | } 833 | ], 834 | "metadata": { 835 | "accelerator": "GPU", 836 | "colab": { 837 | "gpuType": "T4", 838 | "provenance": [] 839 | }, 840 | "kernelspec": { 841 | "display_name": "Python 3", 842 | "name": "python3" 843 | }, 844 | "language_info": { 845 | "name": "python" 846 | }, 847 | "widgets": { 848 | 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