langchain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import LlamaCpp\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.callbacks.manager import CallbackManager\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.agents.agent_types import AgentType\n",
    "\n",
    "from langchain_experimental.agents.agent_toolkits import create_csv_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's work this out in a step by step way to be sure we have the right answer.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks support token-wise streaming\n",
    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
     ]
    }
   ],
   "source": [
    "# Make sure the model path is correct for your system!\n",
    "llm = LlamaCpp(\n",
    "    model_path=\"llama-2-7b-chat.Q5_K_M.gguf\",\n",
    "    temperature=0.00001,\n",
    "    max_tokens=2000,\n",
    "    n_ctx=10000,\n",
    "    top_p=1,\n",
    "    callback_manager=callback_manager,\n",
    "    verbose=True,  # Verbose is required to pass to the callback manager\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u000b\n",
      "Step 1: Identify the main dish you want to make with these ingredients. Based on the ingredients provided, it seems like you want to make a hearty and flavorful chicken dish. How about a Chicken and Artichoke Casserole?\n",
      "Step 2: Gather all the necessary ingredients. Here are the ingredients we have so far:\n",
      "* Potato\n",
      "* Chicken\n",
      "* Onion\n",
      "* Tomato\n",
      "* Cashew\n",
      "* Butter\n",
      "* Thyme\n",
      "* Honey\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-7cddccfa3a41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mquestion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Generate a recipe with  the following ingredients:- Potato, Chicken,onion , tomato , cashew, butter, thyme , honey and artichoke \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mllm_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquestion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[\n\u001b[0m\u001b[1;32m    506\u001b[0m                 \u001b[0m_output_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m             ]\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    308\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    311\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         final_outputs: Dict[str, Any] = self.prep_outputs(\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m             outputs = (\n\u001b[0;32m--> 304\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallbackManagerForChainRun\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     ) -> Dict[str, str]:\n\u001b[0;32m--> 108\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseLanguageModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             return self.llm.generate_prompt(\n\u001b[0m\u001b[1;32m    121\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/base.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    506\u001b[0m         \u001b[0mprompt_strings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_strings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m     async def agenerate_prompt(\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/base.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    654\u001b[0m                 )\n\u001b[1;32m    655\u001b[0m             ]\n\u001b[0;32m--> 656\u001b[0;31m             output = self._generate_helper(\n\u001b[0m\u001b[1;32m    657\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m             )\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/base.py\u001b[0m in \u001b[0;36m_generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m                 \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m         \u001b[0mflattened_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflattened_output\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_managers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflattened_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/base.py\u001b[0m in \u001b[0;36m_generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m             output = (\n\u001b[0;32m--> 531\u001b[0;31m                 self._generate(\n\u001b[0m\u001b[1;32m    532\u001b[0m                     \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m                     \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/base.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m             text = (\n\u001b[0;32m-> 1053\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1054\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/llamacpp.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, prompt, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;31m# and return the combined strings from the first choices's text:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m             \u001b[0mcombined_text_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m             for chunk in self._stream(\n\u001b[0m\u001b[1;32m    292\u001b[0m                 \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/langchain/llms/llamacpp.py\u001b[0m in \u001b[0;36m_stream\u001b[0;34m(self, prompt, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m             \u001b[0mlogprobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"choices\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"logprobs\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m             chunk = GenerationChunk(\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36m_create_completion\u001b[0;34m(self, prompt, suffix, max_tokens, temperature, top_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar)\u001b[0m\n\u001b[1;32m   1366\u001b[0m         \u001b[0mfinish_reason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"length\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m         \u001b[0mmultibyte_fix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1368\u001b[0;31m         for token in self.generate(\n\u001b[0m\u001b[1;32m   1369\u001b[0m             \u001b[0mprompt_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m             \u001b[0mtop_k\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop_k\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, tokens, top_k, top_p, temp, repeat_penalty, reset, frequency_penalty, presence_penalty, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, logits_processor, stopping_criteria, grammar)\u001b[0m\n\u001b[1;32m   1180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1181\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1182\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1183\u001b[0m             token = self.sample(\n\u001b[1;32m   1184\u001b[0m                 \u001b[0mtop_k\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop_k\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, tokens)\u001b[0m\n\u001b[1;32m   1011\u001b[0m                 \u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits_all\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m             )\n\u001b[0;32m-> 1013\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0;31m# Save tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_tokens\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_tokens\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn_tokens\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/llama_cpp/llama.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mctx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m         return_code = llama_cpp.llama_decode(\n\u001b[0m\u001b[1;32m    463\u001b[0m             \u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m             \u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/llama_cpp/llama_cpp.py\u001b[0m in \u001b[0;36mllama_decode\u001b[0;34m(ctx, batch)\u001b[0m\n\u001b[1;32m   1134\u001b[0m \u001b[0;31m#           struct llama_batch   batch);\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mllama_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mllama_context_p\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mllama_batch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1136\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllama_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "question = \"Generate a recipe with  the following ingredients:- Potato, Chicken,onion , tomato , cashew, butter, thyme , honey and artichoke \"\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm(\"Give me a poem on Ferrari\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm(\"Generate a recipe with  the following ingredients:- Potato, Chicken,onion \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders.csv_loader import CSVLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = CSVLoader(file_path=\"total1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import cuda\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "\n",
    "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
    "\n",
    "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
    "\n",
    "embed_model = HuggingFaceEmbeddings(\n",
    "    model_name=embed_model_id,\n",
    "    model_kwargs={'device': device},\n",
    "    encode_kwargs={'device': device, 'batch_size': 32}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma \n",
    "\n",
    "vector_store = Chroma.from_documents(data , embedding = embed_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from langchain.chains import RetrievalQA\n",
    "\n",
    "rag_pipeline = RetrievalQA.from_chain_type(\n",
    "    llm=llm, chain_type='refine',\n",
    "    retriever=vector_store.as_retriever())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0010"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please let me know if you need any further clarification or details."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Llama.generate: prefix-match hit\n",
      "Llama.generate: prefix-match hit\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'query': 'Generate a recipe considering I have the following ingredients :- Chicken, Onion , carrot , celery , honey , ham and flour',\n",
       " 'result': ''}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag_pipeline(\"Generate a recipe considering I have the following ingredients :- Chicken, Onion , carrot , celery , honey , ham and flour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=\": 21683\\nRecipe Title: 30-Minute Spicy Pork and Sweet Potato Stew\\nIngredients: ['1 cup canned unsalted diced tomatoes', '1 medium white onion, quartered', '2 cloves garlic', '1/2 chipotle in adobo sauce, plus 1 tablespoon of the sauce', '2 tablespoons vegetable oil', '1 large pork tenderloin, trimmed and cut into 1-inch chunks (about 1 1/2 pounds)', 'Kosher salt and freshly ground black pepper', '1 tablespoon chili powder', 'One 8-ounce sweet potato, peeled and cut into 1/2-inch chunks', '2 cups low-sodium chicken broth', '1 tablespoon pickled jalapenos', '1 cup 2 percent Greek yogurt', '1/4 medium head red cabbage', '1 medium Haas avocado', '1 lime', '1/2 cup loosely packed fresh cilantro', 'Eight 6-inch corn tortillas, warmed']\\nSteps: {'Step 1': 'Puree the tomatoes, onions, garlic, chipotle and adobo sauce in a food processor until completely smooth.', 'Step 2': 'Heat the oil in a large Dutch oven or pot over medium-high heat until nearly smoking. Add the pork in a single layer, season with 1 teaspoon salt and 1/2 teaspoon pepper and cook, undisturbed, until beginning to brown, about 2 minutes. Turn the pieces and cook until brown, about 1 minute more. Dust the meat with the chili powder, and add the tomato puree, sweet potatoes, chicken broth, 1/4 teaspoon salt and a few grinds of pepper. Bring to a boil, reduce the heat to medium, cover and gently simmer until the meat is cooked through and the potatoes are tender, about 15 minutes.', 'Step 3': 'Meanwhile, prepare the toppings: Chop the jalapenos, and whisk together with the yogurt in a small bowl. Shred the cabbage, cut the avocado into chunks and cut the lime into wedges.', 'Step 4': 'Ladle the stew into large, deep bowls. Top with the yogurt mixture, garnish with the cabbage, avocado, lime and cilantro and serve with the tortillas.'}\", metadata={'row': 21683, 'source': 'total1.csv'}),\n",
       " Document(page_content=\": 20466\\nRecipe Title: Eula Mae's Chicken and Ham Jambalaya\\nIngredients: ['Makes 6 to 8 servings', '1 fryer chicken (about 3 pounds), boned, skinned, and cut into 1-inch cubes, or 1 1/2 pounds skinless, boneless breasts and thighs, cut into 1-inch cubes', '1 teaspoon salt', '1/8 teaspoon freshly ground black pepper', '1/8 teaspoon cayenne', '2 tablespoons vegetable oil', '1/2 pound cooked ham, cut into 1/2-inch cubes', '2 cups chopped yellow onions', '1 cup seeded and chopped green bell peppers', '1 cup chopped celery', '4 garlic cloves, peeled', '3 cups chicken broth', 'One 16-ounce can whole tomatoes, chopped, liquid reserved', '1/2 cup chopped green onions (green part only)', '2 tablespoons chopped fresh parsley leaves', '2 pounds medium-size shrimp, peeled and deveined', '1 teaspoon Tabasco brand pepper sauce', '2 cups long-grain white rice, rinsed and drained']\\nSteps: ['Step 1', 'Sprinkle the chicken with the salt, black pepper, and cayenne. Heat the oil in a large, heavy pot or Dutch oven over medium heat. Add the chicken and cook, stirring, until browned on all sides, 8 to 10 minutes. Transfer the chicken to a large mixing bowl.', 'Step 2', 'Add the ham to the pot and cook, stirring, until lightly browned, about 5 minutes. Add the ham to the chicken in the bowl.', 'Step 3', 'Add the onions, bell peppers, celery, and garlic to the pot and cook, scraping the bottom of the pot to loosen any browned bits. Return the chicken and ham to the pot, reduce the heat to low, cover, and cook for 25 minutes, stirring occasionally.', 'Step 4', 'Add the chicken broth and reserved tomato juice, cover, and simmer for 45 minutes.', 'Step 5', 'Mash the cooked garlic against the side of the pot and blend into the mixture. Add the tomatoes, green onions, parsley, shrimp, and Tabasco and adjust the seasonings to taste. Add the rice, cover the pot, and bring to a boil. Reduce the heat to medium-low and simmer, covered, stirring occasionally, until the rice is tender and fluffy and the liquid is absorbed, about 25 minutes. Serve warm.']\", metadata={'row': 20466, 'source': 'total1.csv'}),\n",
       " Document(page_content=\": 35379\\nRecipe Title: Vegetarian Skillet Chili Topped with Cornbread\\nIngredients: ['2 tablespoons vegetable oil', '1 pound (about 4 cups) store-bought diced butternut squash, cut into 1/2-inch cubes', 'Kosher salt and freshly ground black pepper', '3 cloves garlic, thinly sliced', '1 medium onion, finely chopped', '4 tablespoons chili powder, or to taste\\\\xa0', '2 teaspoons ground cumin', '3 cups low-sodium vegetable stock', 'Two 14.5-ounce cans diced tomatoes with chiles, drained', 'One 14.5-ounce can kidney beans, drained and rinsed', '1 tablespoons sugar', '5 ounces (about 6 cups lightly packed) baby spinach', '3/4 cup fine cornmeal', '3/4 cup all-purpose flour', '3/4 teaspoon baking soda', '3 tablespoons sugar', 'Kosher salt', '1/2 cup whole milk', '1/4 cup sour cream, plus more for serving', '1 large egg', '3 tablespoons unsalted butter, melted', '3/4 cup shredded Cheddar (about 3 ounces)', 'Pickled jalapenos, for serving']\\nSteps: {'Step 1': 'For the chili: Heat the oil in a 12-inch cast-iron skillet over medium-high heat. Add the squash and a pinch of salt and black pepper and cook, stirring occasionally, until the squash is tender and browned in spots, about 8 minutes. Remove the squash from the pan and set aside.', 'Step 2': 'Reduce the heat to medium. Add the garlic and onion and cook, stirring often, until the onion is soft, about 8 minutes. Add the chili powder (use less if you like milder chili) and cumin and cook until fragrant, about 1 minute. Add the vegetable stock, tomatoes, beans, sugar and cooked squash and bring to a simmer. Cook until all of the vegetables are tender and the sauce has thickened, about 30 minutes. (If the liquid reduces to less than three-quarters of the way up the sides of the squash, stir in a little water.) Stir in the baby spinach by the handful until all is incorporated and wilted.', 'Step 3': 'For the cornbread topping: Meanwhile, position a rack in the center of the oven and preheat to 400 degrees F. Whisk the cornmeal, flour, baking soda, sugar and 1 teaspoon salt together in a medium bowl. Whisk the milk, sour cream and egg together in another bowl. Add the wet ingredients to the dry ingredients, whisking until well combined. Stir in the melted butter and Cheddar. Drop spoonfuls of the batter on top of the chili, leaving space in between. (The batter will not completely cover the chili.)', 'Step 4': 'Bake until the cornbread is golden brown and springs back when touched and the chili is hot and bubbly, about 18 minutes. Serve with more sour cream and pickled jalapenos.'}\", metadata={'row': 35379, 'source': 'total1.csv'}),\n",
       " Document(page_content=\": 2651\\nRecipe Title: Grilled Vegetables with Lemon, Thyme and Mustard Basting Sauce\\nIngredients: ['4 Side-dish servings', 'Basting Sauce', '1/2 cup (1 stick) butter, diced', '1/3 cup chopped shallots', '1/4 cup olive oil', '3 tablespoons Dijon mustard', '2 tablespoons fresh lemon juice', '2 tablespoons chopped fresh thyme', '1 tablespoon grated lemon peel', 'Vegetables', '2 large ears fresh corn, husked, each cut crosswise into 4 pieces', '1 small eggplant (about 1 pound), cut crosswise into 1/2-inch-thick rounds', '1 large red onion, cut into 3/4-inch-thick wedges', '1 large red bell pepper, seeded, cut lengthwise into 6 strips', '1 large yellow or green bell pepper, seeded, cut lengthwise into 6 strips', '1 large zucchini, trimmed, quartered lengthwise', '8 asparagus spears, trimmed', '1 large carrot, peeled, cut on deep diagonal into 1/4-inch-thick slices', 'Grilling And Serving', 'Fresh herb sprigs', 'Lemon wedges']\\nSteps: ['Step 1', 'Combine all ingredients in heavy medium saucepan. Whisk over medium heat until butter melts and sauce is well blended. Season to taste with salt and pepper. (Can be made 3 hours ahead. Cool. Whisk over low heat to rewarm before using.)', 'Step 2', 'Arrange corn pieces, eggplant rounds and onion wedges in single layer on large baking sheet. arrange bell pepper strips, zucchini spears, asparagus spears and carrot slices in single layer on another large baking sheet. Transfer 1/2 cup sauce to small saucepan and reserve for dipping. Brush both sides of vegetables lightly with some of remaining basting sauce.', 'Step 3', 'Prepare barbecue (medium-high heat). Grill Corn, eggplant and onion until tender and lightly charred, brushing occasionally with basting sauce and turning with tongs, about 6 minutes. Transfer vegetables to small platter as vegetables finish grilling. Tent with foil to keep warm.', 'Step 4', 'Grill bell peppers, zucchini, asparagus and carrot until tender and lightly charred, brushing occasionally with basting sauce and turning with tongs, about 6 minutes. Transfer vegetables to same platter, arranging alongside other vegetables. Season all vegetables with salt and pepper. Garnish with fresh herb sprigs and lemon.', 'Step 5', 'Place pan with reserved 1/2 cup sauce over low heat or at edge of barbecue and whisk sauce until warmed through. Transfer to small bowl. Serve vegetables, passing warm dipping sauce separately.']\", metadata={'row': 2651, 'source': 'total1.csv'})]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"Generate a recipe considering I have the following ingredients :- Chicken, Onion , carrot , celery , honey , ham and flour\"\n",
    "docs = vector_store.similarity_search(question)\n",
    "#result = llm_chain(docs)\n",
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'Document' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-311ad6debf93>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'Document' has no len()"
     ]
    }
   ],
   "source": [
    "len(docs[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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 },
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