L4-QnA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LangChain: Q&A over Documents\n",
    "\n",
    "An example might be a tool that would allow you to query a product catalog for items of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting docarray\n",
      "  Using cached docarray-0.40.0-py3-none-any.whl.metadata (36 kB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from docarray) (1.26.3)\n",
      "Collecting orjson>=3.8.2 (from docarray)\n",
      "  Using cached orjson-3.9.12-cp311-none-win_amd64.whl.metadata (50 kB)\n",
      "Requirement already satisfied: pydantic>=1.10.8 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from docarray) (2.6.0)\n",
      "Collecting rich>=13.1.0 (from docarray)\n",
      "  Using cached rich-13.7.0-py3-none-any.whl.metadata (18 kB)\n",
      "Collecting types-requests>=2.28.11.6 (from docarray)\n",
      "  Using cached types_requests-2.31.0.20240125-py3-none-any.whl.metadata (1.8 kB)\n",
      "Requirement already satisfied: typing-inspect>=0.8.0 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from docarray) (0.9.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from pydantic>=1.10.8->docarray) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.16.1 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from pydantic>=1.10.8->docarray) (2.16.1)\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from pydantic>=1.10.8->docarray) (4.9.0)\n",
      "Collecting markdown-it-py>=2.2.0 (from rich>=13.1.0->docarray)\n",
      "  Using cached markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from rich>=13.1.0->docarray) (2.17.2)\n",
      "Requirement already satisfied: urllib3>=2 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from types-requests>=2.28.11.6->docarray) (2.2.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in c:\\users\\dell\\.conda\\envs\\chatbot\\lib\\site-packages (from typing-inspect>=0.8.0->docarray) (1.0.0)\n",
      "Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich>=13.1.0->docarray)\n",
      "  Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
      "Using cached docarray-0.40.0-py3-none-any.whl (270 kB)\n",
      "Using cached orjson-3.9.12-cp311-none-win_amd64.whl (134 kB)\n",
      "Using cached rich-13.7.0-py3-none-any.whl (240 kB)\n",
      "Using cached types_requests-2.31.0.20240125-py3-none-any.whl (14 kB)\n",
      "Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
      "Installing collected packages: types-requests, orjson, mdurl, markdown-it-py, rich, docarray\n",
      "Successfully installed docarray-0.40.0 markdown-it-py-3.0.0 mdurl-0.1.2 orjson-3.9.12 rich-13.7.0 types-requests-2.31.0.20240125\n"
     ]
    }
   ],
   "source": [
    "# ! pip install langchain\n",
    "! pip install docarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "KEY  = os.environ['JINA_EMBEDDING_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# account for deprecation of LLM model\n",
    "import datetime\n",
    "# Get the current date\n",
    "current_date = datetime.datetime.now().date()\n",
    "\n",
    "# Define the date after which the model should be set to \"gpt-3.5-turbo\"\n",
    "target_date = datetime.date(2024, 6, 12)\n",
    "\n",
    "# Set the model variable based on the current date\n",
    "if current_date > target_date:\n",
    "    llm_model = \"gpt-3.5-turbo\"\n",
    "else:\n",
    "    llm_model = \"gpt-3.5-turbo-0301\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "from langchain.chat_models.openai import ChatOpenAI\n",
    "from langchain.document_loaders.csv_loader import CSVLoader\n",
    "from langchain.vectorstores.docarray import DocArrayInMemorySearch\n",
    "from IPython.display import display, Markdown\n",
    "from langchain.llms.openai import OpenAI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'OutdoorClothingCatalog_1000.csv'\n",
    "loader = CSVLoader(file_path=file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.indexes import VectorstoreIndexCreator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = VectorstoreIndexCreator(\n",
    "    \n",
    "    vectorstore_cls=DocArrayInMemorySearch\n",
    ").from_loaders([loader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "query =\"Please list all your shirts with sun protection \\\n",
    "in a table in markdown and summarize each one.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValidationError",
     "evalue": "2 validation errors for DocArrayDoc\ntext\n  Field required [type=missing, input_value={'embedding': [0.00335164... -0.021174797216912216]}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.6/v/missing\nmetadata\n  Field required [type=missing, input_value={'embedding': [0.00335164... -0.021174797216912216]}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.6/v/missing",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValidationError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[46], line 8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# llm_replacement_model = OpenAI(temperature=0, \u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#                                    llm_model = \"gpt-3.5-turbo\"\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#                                model='gpt-3.5-turbo-instruct'\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m#                                )\u001b[39;00m\n\u001b[0;32m      6\u001b[0m llm_replacement_model \u001b[38;5;241m=\u001b[39m ChatOpenAI(model_name\u001b[38;5;241m=\u001b[39mllm_model, temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m----> 8\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mllm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mllm_replacement_model\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\indexes\\vectorstore.py:46\u001b[0m, in \u001b[0;36mVectorStoreIndexWrapper.query\u001b[1;34m(self, question, llm, retriever_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m     42\u001b[0m retriever_kwargs \u001b[38;5;241m=\u001b[39m retriever_kwargs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[0;32m     43\u001b[0m chain \u001b[38;5;241m=\u001b[39m RetrievalQA\u001b[38;5;241m.\u001b[39mfrom_chain_type(\n\u001b[0;32m     44\u001b[0m     llm, retriever\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvectorstore\u001b[38;5;241m.\u001b[39mas_retriever(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mretriever_kwargs), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m     45\u001b[0m )\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_core\\_api\\deprecation.py:145\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    143\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    144\u001b[0m     emit_warning()\n\u001b[1;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\base.py:538\u001b[0m, in \u001b[0;36mChain.run\u001b[1;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[0;32m    536\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    537\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 538\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m[\n\u001b[0;32m    539\u001b[0m         _output_key\n\u001b[0;32m    540\u001b[0m     ]\n\u001b[0;32m    542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[0;32m    543\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs, callbacks\u001b[38;5;241m=\u001b[39mcallbacks, tags\u001b[38;5;241m=\u001b[39mtags, metadata\u001b[38;5;241m=\u001b[39mmetadata)[\n\u001b[0;32m    544\u001b[0m         _output_key\n\u001b[0;32m    545\u001b[0m     ]\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_core\\_api\\deprecation.py:145\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    143\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    144\u001b[0m     emit_warning()\n\u001b[1;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\base.py:363\u001b[0m, in \u001b[0;36mChain.__call__\u001b[1;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[0;32m    331\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Execute the chain.\u001b[39;00m\n\u001b[0;32m    332\u001b[0m \n\u001b[0;32m    333\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    354\u001b[0m \u001b[38;5;124;03m        `Chain.output_keys`.\u001b[39;00m\n\u001b[0;32m    355\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    356\u001b[0m config \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    357\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks,\n\u001b[0;32m    358\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m: tags,\n\u001b[0;32m    359\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m: metadata,\n\u001b[0;32m    360\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: run_name,\n\u001b[0;32m    361\u001b[0m }\n\u001b[1;32m--> 363\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mRunnableConfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43minclude_run_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_run_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    368\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\base.py:162\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[1;32m--> 162\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    163\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[0;32m    164\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[0;32m    165\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[0;32m    166\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\base.py:156\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[0;32m    150\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    151\u001b[0m     inputs,\n\u001b[0;32m    152\u001b[0m     name\u001b[38;5;241m=\u001b[39mrun_name,\n\u001b[0;32m    153\u001b[0m )\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 156\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    157\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    158\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[0;32m    159\u001b[0m     )\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    161\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\retrieval_qa\\base.py:141\u001b[0m, in \u001b[0;36mBaseRetrievalQA._call\u001b[1;34m(self, inputs, run_manager)\u001b[0m\n\u001b[0;32m    137\u001b[0m accepts_run_manager \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    138\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_docs)\u001b[38;5;241m.\u001b[39mparameters\n\u001b[0;32m    139\u001b[0m )\n\u001b[0;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accepts_run_manager:\n\u001b[1;32m--> 141\u001b[0m     docs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_docs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_run_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    143\u001b[0m     docs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_docs(question)  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain\\chains\\retrieval_qa\\base.py:221\u001b[0m, in \u001b[0;36mRetrievalQA._get_docs\u001b[1;34m(self, question, run_manager)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_docs\u001b[39m(\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    216\u001b[0m     question: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m    218\u001b[0m     run_manager: CallbackManagerForChainRun,\n\u001b[0;32m    219\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Document]:\n\u001b[0;32m    220\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get docs.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 221\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretriever\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_relevant_documents\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    222\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    223\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_core\\retrievers.py:224\u001b[0m, in \u001b[0;36mBaseRetriever.get_relevant_documents\u001b[1;34m(self, query, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    223\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_retriever_error(e)\n\u001b[1;32m--> 224\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    225\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    226\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_retriever_end(\n\u001b[0;32m    227\u001b[0m         result,\n\u001b[0;32m    228\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_core\\retrievers.py:217\u001b[0m, in \u001b[0;36mBaseRetriever.get_relevant_documents\u001b[1;34m(self, query, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[0;32m    215\u001b[0m _kwargs \u001b[38;5;241m=\u001b[39m kwargs \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expects_other_args \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m    216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_arg_supported:\n\u001b[1;32m--> 217\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_relevant_documents\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    218\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_kwargs\u001b[49m\n\u001b[0;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    221\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_relevant_documents(query, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_core\\vectorstores.py:654\u001b[0m, in \u001b[0;36mVectorStoreRetriever._get_relevant_documents\u001b[1;34m(self, query, run_manager)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_relevant_documents\u001b[39m(\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;28mself\u001b[39m, query: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39m, run_manager: CallbackManagerForRetrieverRun\n\u001b[0;32m    652\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Document]:\n\u001b[0;32m    653\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimilarity\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 654\u001b[0m         docs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvectorstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimilarity_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    655\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimilarity_score_threshold\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    656\u001b[0m         docs_and_similarities \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    657\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvectorstore\u001b[38;5;241m.\u001b[39msimilarity_search_with_relevance_scores(\n\u001b[0;32m    658\u001b[0m                 query, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_kwargs\n\u001b[0;32m    659\u001b[0m             )\n\u001b[0;32m    660\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_community\\vectorstores\\docarray\\base.py:127\u001b[0m, in \u001b[0;36mDocArrayIndex.similarity_search\u001b[1;34m(self, query, k, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msimilarity_search\u001b[39m(\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;28mself\u001b[39m, query: \u001b[38;5;28mstr\u001b[39m, k: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any\n\u001b[0;32m    117\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Document]:\n\u001b[0;32m    118\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return docs most similar to query.\u001b[39;00m\n\u001b[0;32m    119\u001b[0m \n\u001b[0;32m    120\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;124;03m        List of Documents most similar to the query.\u001b[39;00m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 127\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimilarity_search_with_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [doc \u001b[38;5;28;01mfor\u001b[39;00m doc, _ \u001b[38;5;129;01min\u001b[39;00m results]\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\langchain_community\\vectorstores\\docarray\\base.py:106\u001b[0m, in \u001b[0;36mDocArrayIndex.similarity_search_with_score\u001b[1;34m(self, query, k, **kwargs)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return docs most similar to query.\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \n\u001b[0;32m     96\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;124;03m    Lower score represents more similarity.\u001b[39;00m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    105\u001b[0m query_embedding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding\u001b[38;5;241m.\u001b[39membed_query(query)\n\u001b[1;32m--> 106\u001b[0m query_doc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdoc_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[43membedding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_embedding\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m    107\u001b[0m docs, scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdoc_index\u001b[38;5;241m.\u001b[39mfind(query_doc, search_field\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m\"\u001b[39m, limit\u001b[38;5;241m=\u001b[39mk)\n\u001b[0;32m    109\u001b[0m result \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    110\u001b[0m     (Document(page_content\u001b[38;5;241m=\u001b[39mdoc\u001b[38;5;241m.\u001b[39mtext, metadata\u001b[38;5;241m=\u001b[39mdoc\u001b[38;5;241m.\u001b[39mmetadata), score)\n\u001b[0;32m    111\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m doc, score \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(docs, scores)\n\u001b[0;32m    112\u001b[0m ]\n",
      "File \u001b[1;32mc:\\Users\\DELL\\.conda\\envs\\chatbot\\Lib\\site-packages\\pydantic\\main.py:171\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[1;34m(self, **data)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[0;32m    170\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValidationError\u001b[0m: 2 validation errors for DocArrayDoc\ntext\n  Field required [type=missing, input_value={'embedding': [0.00335164... -0.021174797216912216]}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.6/v/missing\nmetadata\n  Field required [type=missing, input_value={'embedding': [0.00335164... -0.021174797216912216]}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.6/v/missing"
     ]
    }
   ],
   "source": [
    "# llm_replacement_model = OpenAI(temperature=0, \n",
    "#                                    llm_model = \"gpt-3.5-turbo\"\n",
    "\n",
    "#                                model='gpt-3.5-turbo-instruct'\n",
    "#                                )\n",
    "llm_replacement_model = ChatOpenAI(model_name=llm_model, temperature=0)\n",
    "\n",
    "response = index.query(question=query, llm = llm_replacement_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step by Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import CSVLoader\n",
    "loader = CSVLoader(file_path=file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from langchain.embeddings import OpenAIEmbeddings\n",
    "# embeddings = OpenAIEmbeddings()\n",
    "from langchain.embeddings import JinaEmbeddings\n",
    "embeddings = JinaEmbeddings(jina_api_key = KEY, model_name=\"jina-embeddings-v2-base-en\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed = embeddings.embed_query(\"Hi my name is Hamza Qureshi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768\n",
      "[-0.602528, -0.47703412, 0.1974959, -0.08134877, -0.14021301]\n"
     ]
    }
   ],
   "source": [
    "print(len(embed))\n",
    "print(embed[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = DocArrayInMemorySearch.from_documents(\n",
    "    docs, \n",
    "    embeddings\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"Please suggest a shirt with sunblocking\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = db.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chatbot",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}