Upload 3 files

email_generator.ipynb

@@ -0,0 +1,559 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0eccd20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_groq import ChatGroq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c16ff50e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AuthenticationError",
+     "evalue": "Error code: 401 - {'error': {'message': 'Invalid API Key', 'type': 'invalid_request_error', 'code': 'invalid_api_key'}}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAuthenticationError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m llm \u001b[38;5;241m=\u001b[39m ChatGroq(\n\u001b[0;32m      2\u001b[0m     temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, \n\u001b[0;32m      3\u001b[0m     groq_api_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgsk_nPtJPIAE7Nh3VqfWIpzBWGdyb3FYkVwX3MOTaxY6F1SG9n2rodmy\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      4\u001b[0m     model_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mllama-3.1-70b-versatile\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      5\u001b[0m )\n\u001b[1;32m----> 6\u001b[0m response \u001b[38;5;241m=\u001b[39m llm\u001b[38;5;241m.\u001b[39minvoke(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe first person to land on moon was ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(response\u001b[38;5;241m.\u001b[39mcontent)\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:277\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[1;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[0;32m    267\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    268\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    272\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    273\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[0;32m    274\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m    275\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[0;32m    276\u001b[0m         ChatGeneration,\n\u001b[1;32m--> 277\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m    278\u001b[0m             [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_input(\u001b[38;5;28minput\u001b[39m)],\n\u001b[0;32m    279\u001b[0m             stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[0;32m    280\u001b[0m             callbacks\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    281\u001b[0m             tags\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    282\u001b[0m             metadata\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    283\u001b[0m             run_name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    284\u001b[0m             run_id\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m    285\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    286\u001b[0m         )\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    287\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:777\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    769\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m    770\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    771\u001b[0m     prompts: List[PromptValue],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    774\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    775\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m    776\u001b[0m     prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 777\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate(prompt_messages, stop\u001b[38;5;241m=\u001b[39mstop, callbacks\u001b[38;5;241m=\u001b[39mcallbacks, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:634\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[0;32m    633\u001b[0m             run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[1;32m--> 634\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    635\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    636\u001b[0m     LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output)  \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[0;32m    637\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[0;32m    638\u001b[0m ]\n\u001b[0;32m    639\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:624\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m    621\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[0;32m    622\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    623\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m--> 624\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_with_cache(\n\u001b[0;32m    625\u001b[0m                 m,\n\u001b[0;32m    626\u001b[0m                 stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[0;32m    627\u001b[0m                 run_manager\u001b[38;5;241m=\u001b[39mrun_managers[i] \u001b[38;5;28;01mif\u001b[39;00m run_managers \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    628\u001b[0m                 \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    629\u001b[0m             )\n\u001b[0;32m    630\u001b[0m         )\n\u001b[0;32m    631\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    632\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:846\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    844\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    845\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 846\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(\n\u001b[0;32m    847\u001b[0m             messages, stop\u001b[38;5;241m=\u001b[39mstop, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    848\u001b[0m         )\n\u001b[0;32m    849\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    850\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\langchain_groq\\chat_models.py:472\u001b[0m, in \u001b[0;36mChatGroq._generate\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    467\u001b[0m message_dicts, params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_message_dicts(messages, stop)\n\u001b[0;32m    468\u001b[0m params \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    469\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams,\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    471\u001b[0m }\n\u001b[1;32m--> 472\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mcreate(messages\u001b[38;5;241m=\u001b[39mmessage_dicts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m    473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_chat_result(response)\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\groq\\resources\\chat\\completions.py:298\u001b[0m, in \u001b[0;36mCompletions.create\u001b[1;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    159\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    186\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[0;32m    187\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m    188\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;124;03m    Creates a model response for the given chat conversation.\u001b[39;00m\n\u001b[0;32m    190\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    296\u001b[0m \u001b[38;5;124;03m      timeout: Override the client-level default timeout for this request, in seconds\u001b[39;00m\n\u001b[0;32m    297\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 298\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post(\n\u001b[0;32m    299\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/openai/v1/chat/completions\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    300\u001b[0m         body\u001b[38;5;241m=\u001b[39mmaybe_transform(\n\u001b[0;32m    301\u001b[0m             {\n\u001b[0;32m    302\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: messages,\n\u001b[0;32m    303\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: model,\n\u001b[0;32m    304\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrequency_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: frequency_penalty,\n\u001b[0;32m    305\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction_call\u001b[39m\u001b[38;5;124m\"\u001b[39m: function_call,\n\u001b[0;32m    306\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctions\u001b[39m\u001b[38;5;124m\"\u001b[39m: functions,\n\u001b[0;32m    307\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogit_bias\u001b[39m\u001b[38;5;124m\"\u001b[39m: logit_bias,\n\u001b[0;32m    308\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogprobs\u001b[39m\u001b[38;5;124m\"\u001b[39m: logprobs,\n\u001b[0;32m    309\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_tokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: max_tokens,\n\u001b[0;32m    310\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m: n,\n\u001b[0;32m    311\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparallel_tool_calls\u001b[39m\u001b[38;5;124m\"\u001b[39m: parallel_tool_calls,\n\u001b[0;32m    312\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpresence_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: presence_penalty,\n\u001b[0;32m    313\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse_format\u001b[39m\u001b[38;5;124m\"\u001b[39m: response_format,\n\u001b[0;32m    314\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed,\n\u001b[0;32m    315\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m: stop,\n\u001b[0;32m    316\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream,\n\u001b[0;32m    317\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemperature\u001b[39m\u001b[38;5;124m\"\u001b[39m: temperature,\n\u001b[0;32m    318\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtool_choice\u001b[39m\u001b[38;5;124m\"\u001b[39m: tool_choice,\n\u001b[0;32m    319\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtools\u001b[39m\u001b[38;5;124m\"\u001b[39m: tools,\n\u001b[0;32m    320\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_logprobs\u001b[39m\u001b[38;5;124m\"\u001b[39m: top_logprobs,\n\u001b[0;32m    321\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_p\u001b[39m\u001b[38;5;124m\"\u001b[39m: top_p,\n\u001b[0;32m    322\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m: user,\n\u001b[0;32m    323\u001b[0m             },\n\u001b[0;32m    324\u001b[0m             completion_create_params\u001b[38;5;241m.\u001b[39mCompletionCreateParams,\n\u001b[0;32m    325\u001b[0m         ),\n\u001b[0;32m    326\u001b[0m         options\u001b[38;5;241m=\u001b[39mmake_request_options(\n\u001b[0;32m    327\u001b[0m             extra_headers\u001b[38;5;241m=\u001b[39mextra_headers, extra_query\u001b[38;5;241m=\u001b[39mextra_query, extra_body\u001b[38;5;241m=\u001b[39mextra_body, timeout\u001b[38;5;241m=\u001b[39mtimeout\n\u001b[0;32m    328\u001b[0m         ),\n\u001b[0;32m    329\u001b[0m         cast_to\u001b[38;5;241m=\u001b[39mChatCompletion,\n\u001b[0;32m    330\u001b[0m         stream\u001b[38;5;241m=\u001b[39mstream \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    331\u001b[0m         stream_cls\u001b[38;5;241m=\u001b[39mStream[ChatCompletionChunk],\n\u001b[0;32m    332\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\groq\\_base_client.py:1263\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[1;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[0;32m   1249\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[0;32m   1250\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1251\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1258\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1259\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m   1260\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[0;32m   1261\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m   1262\u001b[0m     )\n\u001b[1;32m-> 1263\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequest(cast_to, opts, stream\u001b[38;5;241m=\u001b[39mstream, stream_cls\u001b[38;5;241m=\u001b[39mstream_cls))\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\groq\\_base_client.py:955\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m    952\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    953\u001b[0m     retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m--> 955\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request(\n\u001b[0;32m    956\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m    957\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m    958\u001b[0m     stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m    959\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[0;32m    960\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[0;32m    961\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\haris\\anaconda3\\Lib\\site-packages\\groq\\_base_client.py:1058\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[0;32m   1055\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m   1057\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1058\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1060\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[0;32m   1061\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m   1062\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1066\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[0;32m   1067\u001b[0m )\n",
+      "\u001b[1;31mAuthenticationError\u001b[0m: Error code: 401 - {'error': {'message': 'Invalid API Key', 'type': 'invalid_request_error', 'code': 'invalid_api_key'}}"
+     ]
+    }
+   ],
+   "source": [
+    "llm = ChatGroq(\n",
+    "    temperature=0, \n",
+    "    groq_api_key='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', \n",
+    "    model_name=\"llama-3.1-70b-versatile\"\n",
+    ")\n",
+    "response = llm.invoke(\"The first person to land on moon was ...\")\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "90d33612",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apply for Software Engineer II, AI/ML Platforms\n",
+      "\n",
+      "Search JobsSkip navigationSearch JobsNIKE, INC. JOBSContract JobsJoin The Talent CommunityLife @ NikeOverviewBenefitsBrandsOverviewJordanConverseTeamsOverviewAdministrative SupportAdvanced InnovationAir Manufacturing InnovationAviationCommunicationsCustomer ServiceDesignDigitalFacilitiesFinance & AccountingGovernment & Public AffairsHuman ResourcesInsights & AnalyticsLegalManufacturing & EngineeringMarketingMerchandisingPlanningPrivacyProcurementProduct Creation, Development & ManagementRetail CorporateRetail StoresSalesSocial & Community ImpactSports MarketingStrategic PlanningSupply Chain, Distribution & LogisticsSustainabilityTechnologyLocationsOverviewNike WHQNike New York HQEHQ: Hilversum, The NetherlandsELC: Laakdal, BelgiumGreater China HQDiversity, Equity & InclusionOverviewMilitary InclusionDisability InclusionIndigenous InclusionInternshipsTechnologySoftware Engineer II, AI/ML PlatformsBeaverton, OregonBecome a Part of the NIKE, Inc. TeamNIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At NIKE, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.NIKE is a technology company. From our flagship website and five-star mobile apps to developing products, managing big data and providing leading edge engineering and systems support, our teams at NIKE Global Technology exist to revolutionize the future at the confluence of tech and sport. We invest and develop advances in technology and employ the most creative people in the world, and then give them the support to constantly innovate, iterate and serve consumers more directly and personally.  Our teams are innovative, diverse, multidisciplinary and collaborative, taking technology into the future and bringing the world with it.WHO WE ARE LOOKING FORWe are looking for a Software Engineer II to design and implement core components of our data science services and platform and scale it to serve Nike globally. The focus of this engineer will be at the intersection of computer systems and machine learning. You will also work collaboratively with the data science and analytics teams to ship scalable, performant and reliable solutions.WHAT YOU WILL WORK ONAs a Software Engineer in AI/ML Platforms, you will be part of a fast-paced engineering team developing services and platform that drive AI/ML at Nike. AI/ML is used in Nike to accelerate its digital transformation and real-time data-based decision-making capabilities. The focus is to leverage AI/ML technology to improve the user experience by using personalization, recommendation, smart search, digital demand sensing and scheduling techniques. These enhancements will be used to power consumer experiences on nike.com, Nike App, Nike SNKRS App as well as product innovation, supply chain, and logistics.This Software Engineer will develop and/or implement services and paved-path solutions to enable data scientists and ML engineers at Nike. These services will enable and accelerate model training, model serving and model management. Additionally, they will evaluate new AI/ML platform services and capabilities for possible long-term approaches, and work with the user community to anticipate services and platform needs required to support algorithm development, model training, model management, data engineering and model serving. Lastly, they will advance and improve development practices with the engineering team through participation in architecture, technical design and code reviewsWHO YOU WILL WORK WITHYou'll be working with Data Scientists, ML Engineers and Product teams to identify and deliver platform capabilities to accelerate and streamline the delivery and maintenance of ML use cases. Additionally, you’ll work with teams integrating ML capabilities into Nike experiences and business applications. This position reports to the Director of AI/ML Tech Platforms.WHAT YOU BRINGBachelor's degree in Computer Science, Engineering, Information Systems, or similar field or relevant professional experience, education, and training.Strong Computer Science fundamentals2+ years professional software development experienceProgramming experience with at least one modern language such as Java, Python, GolangExperience with Infrastructure as Code / DevOps practices and technologies like TerraformExperience with Cloud services (e.g. AWS Cloud EC2, S3, DynamoDB, Azure Cloud, etc.)Experience developing and using microservicesExcellent written and oral communication skills on both technical and non-technical topicsNIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world.NIKE, Inc. is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.How We HireAt NIKE, Inc. we promise to provide a premium, inclusive, compelling and authentic candidate experience. Delivering on this promise means we allow you to be at your best — and to do that, you need to understand how the hiring process works. Transparency is key.\r\n",
+      "\r\n",
+      "* This overview explains our hiring process for corporate roles. Note there may be different hiring steps involved for non-corporate roles.Start nowBenefitsWhether it’s transportation or financial health, we continually invest in our employees to help them achieve greatness — inside and outside of work. All who work here should be able to realize their full potential.Employee Assistance ProgramEmployee Stock Purchase Plan (ESPP)HolidaysMedical PlanPaid Time Off (PTO)Product DiscountsSabbaticalsLearn moreGIFT CARDSPROMOTIONSFIND A STORESIGN UP FOR EMAILBECOME A MEMBERNIKE JOURNALSEND US FEEDBACKGET HELPGET HELPOrder StatusShipping and DeliveryReturnsPayment OptionsGift Cards BalanceContact UsABOUT NIKEABOUT NIKENewsCareersInvestorsPurposeSustainabilityUnited States© 2024 Nike, Inc. All Rights ReservedGuidesNike AdaptNike Air MaxNike FlyleatherNike ReactSpace HippieNike AirNike FlyEaseNike Free Nike VaporflyNike Air Force 1 Nike FlyknitNike JoyrideNike ZoomXTerms of SaleTerms of UseNike Privacy PolicyYour Privacy ChoicesCA Supply Chain Act\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "\n",
+    "loader = WebBaseLoader(\"https://jobs.nike.com/job/R-33460\")\n",
+    "page_data = loader.load().pop().page_content\n",
+    "print(page_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "85c89a57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "prompt_extract = PromptTemplate.from_template(\n",
+    "        \"\"\"\n",
+    "        ### SCRAPED TEXT FROM WEBSITE:\n",
+    "        {page_data}\n",
+    "        ### INSTRUCTION:\n",
+    "        The scraped text is from the career's page of a website.\n",
+    "        Your job is to extract the job postings and return them in JSON format containing the \n",
+    "        following keys: `role`, `experience`, `skills` and `description`.\n",
+    "        Only return the valid JSON.\n",
+    "        ### VALID JSON (NO PREAMBLE):    \n",
+    "        \"\"\"\n",
+    ")\n",
+    "\n",
+    "chain_extract = prompt_extract | llm \n",
+    "res = chain_extract.invoke(input={'page_data':page_data})\n",
+    "type(res.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5415fd54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'Software Engineer II, AI/ML Platforms',\n",
+       " 'experience': '2+ years professional software development experience',\n",
+       " 'skills': ['Strong Computer Science fundamentals',\n",
+       "  'Programming experience with at least one modern language such as Java, Python, Golang',\n",
+       "  'Experience with Infrastructure as Code / DevOps practices and technologies like Terraform',\n",
+       "  'Experience with Cloud services (e.g. AWS Cloud EC2, S3, DynamoDB, Azure Cloud, etc.)',\n",
+       "  'Experience developing and using microservices'],\n",
+       " 'description': 'We are looking for a Software Engineer II to design and implement core components of our data science services and platform and scale it to serve Nike globally. The focus of this engineer will be at the intersection of computer systems and machine learning. You will also work collaboratively with the data science and analytics teams to ship scalable, performant and reliable solutions.'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "json_parser = JsonOutputParser()\n",
+    "json_res = json_parser.parse(res.content)\n",
+    "json_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "39961ed6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(json_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1e8a0f74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Techstack</th>\n",
+       "      <th>Links</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>React, Node.js, MongoDB</td>\n",
+       "      <td>https://example.com/react-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Angular,.NET, SQL Server</td>\n",
+       "      <td>https://example.com/angular-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vue.js, Ruby on Rails, PostgreSQL</td>\n",
+       "      <td>https://example.com/vue-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Python, Django, MySQL</td>\n",
+       "      <td>https://example.com/python-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Java, Spring Boot, Oracle</td>\n",
+       "      <td>https://example.com/java-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Flutter, Firebase, GraphQL</td>\n",
+       "      <td>https://example.com/flutter-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>WordPress, PHP, MySQL</td>\n",
+       "      <td>https://example.com/wordpress-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Magento, PHP, MySQL</td>\n",
+       "      <td>https://example.com/magento-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>React Native, Node.js, MongoDB</td>\n",
+       "      <td>https://example.com/react-native-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>iOS, Swift, Core Data</td>\n",
+       "      <td>https://example.com/ios-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Android, Java, Room Persistence</td>\n",
+       "      <td>https://example.com/android-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Kotlin, Android, Firebase</td>\n",
+       "      <td>https://example.com/kotlin-android-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Android TV, Kotlin, Android NDK</td>\n",
+       "      <td>https://example.com/android-tv-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>iOS, Swift, ARKit</td>\n",
+       "      <td>https://example.com/ios-ar-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cross-platform, Xamarin, Azure</td>\n",
+       "      <td>https://example.com/xamarin-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Backend, Kotlin, Spring Boot</td>\n",
+       "      <td>https://example.com/kotlin-backend-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Frontend, TypeScript, Angular</td>\n",
+       "      <td>https://example.com/typescript-frontend-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Full-stack, JavaScript, Express.js</td>\n",
+       "      <td>https://example.com/full-stack-js-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Machine Learning, Python, TensorFlow</td>\n",
+       "      <td>https://example.com/ml-python-portfolio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>DevOps, Jenkins, Docker</td>\n",
+       "      <td>https://example.com/devops-portfolio</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Techstack  \\\n",
+       "0                React, Node.js, MongoDB   \n",
+       "1               Angular,.NET, SQL Server   \n",
+       "2      Vue.js, Ruby on Rails, PostgreSQL   \n",
+       "3                  Python, Django, MySQL   \n",
+       "4              Java, Spring Boot, Oracle   \n",
+       "5             Flutter, Firebase, GraphQL   \n",
+       "6                  WordPress, PHP, MySQL   \n",
+       "7                    Magento, PHP, MySQL   \n",
+       "8         React Native, Node.js, MongoDB   \n",
+       "9                  iOS, Swift, Core Data   \n",
+       "10       Android, Java, Room Persistence   \n",
+       "11             Kotlin, Android, Firebase   \n",
+       "12       Android TV, Kotlin, Android NDK   \n",
+       "13                     iOS, Swift, ARKit   \n",
+       "14        Cross-platform, Xamarin, Azure   \n",
+       "15          Backend, Kotlin, Spring Boot   \n",
+       "16         Frontend, TypeScript, Angular   \n",
+       "17    Full-stack, JavaScript, Express.js   \n",
+       "18  Machine Learning, Python, TensorFlow   \n",
+       "19               DevOps, Jenkins, Docker   \n",
+       "\n",
+       "                                                Links  \n",
+       "0                 https://example.com/react-portfolio  \n",
+       "1               https://example.com/angular-portfolio  \n",
+       "2                   https://example.com/vue-portfolio  \n",
+       "3                https://example.com/python-portfolio  \n",
+       "4                  https://example.com/java-portfolio  \n",
+       "5               https://example.com/flutter-portfolio  \n",
+       "6             https://example.com/wordpress-portfolio  \n",
+       "7               https://example.com/magento-portfolio  \n",
+       "8          https://example.com/react-native-portfolio  \n",
+       "9                   https://example.com/ios-portfolio  \n",
+       "10              https://example.com/android-portfolio  \n",
+       "11       https://example.com/kotlin-android-portfolio  \n",
+       "12           https://example.com/android-tv-portfolio  \n",
+       "13               https://example.com/ios-ar-portfolio  \n",
+       "14              https://example.com/xamarin-portfolio  \n",
+       "15       https://example.com/kotlin-backend-portfolio  \n",
+       "16  https://example.com/typescript-frontend-portfolio  \n",
+       "17        https://example.com/full-stack-js-portfolio  \n",
+       "18            https://example.com/ml-python-portfolio  \n",
+       "19               https://example.com/devops-portfolio  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"my_portfolio.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f7e888d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "import chromadb\n",
+    "\n",
+    "client = chromadb.PersistentClient('vectorstore')\n",
+    "collection = client.get_or_create_collection(name=\"portfolio\")\n",
+    "\n",
+    "if not collection.count():\n",
+    "    for _, row in df.iterrows():\n",
+    "        collection.add(documents=row[\"Techstack\"],\n",
+    "                       metadatas={\"links\": row[\"Links\"]},\n",
+    "                       ids=[str(uuid.uuid4())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "39ad2fa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[{'links': 'https://example.com/ml-python-portfolio'},\n",
+       "  {'links': 'https://example.com/magento-portfolio'}],\n",
+       " [{'links': 'https://example.com/ml-python-portfolio'},\n",
+       "  {'links': 'https://example.com/python-portfolio'}],\n",
+       " [{'links': 'https://example.com/devops-portfolio'},\n",
+       "  {'links': 'https://example.com/xamarin-portfolio'}],\n",
+       " [{'links': 'https://example.com/xamarin-portfolio'},\n",
+       "  {'links': 'https://example.com/wordpress-portfolio'}],\n",
+       " [{'links': 'https://example.com/magento-portfolio'},\n",
+       "  {'links': 'https://example.com/wordpress-portfolio'}]]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "links = collection.query(query_texts=job['skills'], n_results=2).get('metadatas', [])\n",
+    "links"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8bd36844",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'Software Engineer II, AI/ML Platforms',\n",
+       " 'experience': '2+ years professional software development experience',\n",
+       " 'skills': ['Strong Computer Science fundamentals',\n",
+       "  'Programming experience with at least one modern language such as Java, Python, Golang',\n",
+       "  'Experience with Infrastructure as Code / DevOps practices and technologies like Terraform',\n",
+       "  'Experience with Cloud services (e.g. AWS Cloud EC2, S3, DynamoDB, Azure Cloud, etc.)',\n",
+       "  'Experience developing and using microservices'],\n",
+       " 'description': 'We are looking for a Software Engineer II to design and implement core components of our data science services and platform and scale it to serve Nike globally. The focus of this engineer will be at the intersection of computer systems and machine learning. You will also work collaboratively with the data science and analytics teams to ship scalable, performant and reliable solutions.'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1ccfd720",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Strong Computer Science fundamentals',\n",
+       " 'Programming experience with at least one modern language such as Java, Python, Golang',\n",
+       " 'Experience with Infrastructure as Code / DevOps practices and technologies like Terraform',\n",
+       " 'Experience with Cloud services (e.g. AWS Cloud EC2, S3, DynamoDB, Azure Cloud, etc.)',\n",
+       " 'Experience developing and using microservices']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job = json_res\n",
+    "job['skills']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "64a97dd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject: Expert AI/ML Solutions for Nike's Data Science Services and Platform\n",
+      "\n",
+      "Dear Hiring Manager,\n",
+      "\n",
+      "I came across the job description for a Software Engineer II, AI/ML Platforms at Nike, and I'm excited to introduce AtliQ, a leading AI & Software Consulting company that can help you design and implement core components of your data science services and platform.\n",
+      "\n",
+      "With our expertise in AI, ML, and software development, we can help you scale your platform to serve Nike globally. Our team of experts has a strong foundation in computer science and experience with modern programming languages such as Java, Python, and Golang. We're well-versed in Infrastructure as Code / DevOps practices and technologies like Terraform, and have hands-on experience with Cloud services like AWS and Azure.\n",
+      "\n",
+      "Our portfolio showcases our capabilities in developing and deploying scalable, performant, and reliable solutions. I'd like to highlight a few relevant examples:\n",
+      "\n",
+      "* Our Machine Learning with Python portfolio (https://example.com/ml-python-portfolio) demonstrates our expertise in building predictive models and deploying them on cloud platforms.\n",
+      "* Our DevOps portfolio (https://example.com/devops-portfolio) showcases our ability to automate infrastructure provisioning and deployment using Terraform and other tools.\n",
+      "\n",
+      "At AtliQ, we've empowered numerous enterprises with tailored solutions, fostering scalability, process optimization, cost reduction, and heightened overall efficiency. We're confident that our expertise can help Nike achieve its goals in the AI/ML space.\n",
+      "\n",
+      "I'd love to schedule a call to discuss how AtliQ can support Nike's data science services and platform. Please let me know if you're available for a quick call this week.\n",
+      "\n",
+      "Best regards,\n",
+      "\n",
+      "Mohan\n",
+      "Business Development Executive\n",
+      "AtliQ\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt_email = PromptTemplate.from_template(\n",
+    "        \"\"\"\n",
+    "        ### JOB DESCRIPTION:\n",
+    "        {job_description}\n",
+    "        \n",
+    "        ### INSTRUCTION:\n",
+    "        You are Mohan, a business development executive at AtliQ. AtliQ is an AI & Software Consulting company dedicated to facilitating\n",
+    "        the seamless integration of business processes through automated tools. \n",
+    "        Over our experience, we have empowered numerous enterprises with tailored solutions, fostering scalability, \n",
+    "        process optimization, cost reduction, and heightened overall efficiency. \n",
+    "        Your job is to write a cold email to the client regarding the job mentioned above describing the capability of AtliQ \n",
+    "        in fulfilling their needs.\n",
+    "        Also add the most relevant ones from the following links to showcase Atliq's portfolio: {link_list}\n",
+    "        Remember you are Mohan, BDE at AtliQ. \n",
+    "        Do not provide a preamble.\n",
+    "        ### EMAIL (NO PREAMBLE):\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        )\n",
+    "\n",
+    "chain_email = prompt_email | llm\n",
+    "res = chain_email.invoke({\"job_description\": str(job), \"link_list\": links})\n",
+    "print(res.content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

my_portfolio.csv

@@ -0,0 +1,21 @@
+"Techstack","Links"
+"React, Node.js, MongoDB","https://example.com/react-portfolio"
+"Angular,.NET, SQL Server","https://example.com/angular-portfolio"
+"Vue.js, Ruby on Rails, PostgreSQL","https://example.com/vue-portfolio"
+"Python, Django, MySQL","https://example.com/python-portfolio"
+"Java, Spring Boot, Oracle","https://example.com/java-portfolio"
+"Flutter, Firebase, GraphQL","https://example.com/flutter-portfolio"
+"WordPress, PHP, MySQL","https://example.com/wordpress-portfolio"
+"Magento, PHP, MySQL","https://example.com/magento-portfolio"
+"React Native, Node.js, MongoDB","https://example.com/react-native-portfolio"
+"iOS, Swift, Core Data","https://example.com/ios-portfolio"
+"Android, Java, Room Persistence","https://example.com/android-portfolio"
+"Kotlin, Android, Firebase","https://example.com/kotlin-android-portfolio"
+"Android TV, Kotlin, Android NDK","https://example.com/android-tv-portfolio"
+"iOS, Swift, ARKit","https://example.com/ios-ar-portfolio"
+"Cross-platform, Xamarin, Azure","https://example.com/xamarin-portfolio"
+"Backend, Kotlin, Spring Boot","https://example.com/kotlin-backend-portfolio"
+"Frontend, TypeScript, Angular","https://example.com/typescript-frontend-portfolio"
+"Full-stack, JavaScript, Express.js","https://example.com/full-stack-js-portfolio"
+"Machine Learning, Python, TensorFlow","https://example.com/ml-python-portfolio"
+"DevOps, Jenkins, Docker","https://example.com/devops-portfolio"

requirements.txt

@@ -0,0 +1,9 @@
+langchain==0.2.14
+langchain-community==0.2.12
+langchain-groq===0.1.9
+unstructured==0.14.6
+selenium==4.21.0
+chromadb==0.5.0
+streamlit==1.35.0
+pandas==2.0.2
+python-dotenv==1.0.0