next_mode_pdf_readear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "149c6ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "352922b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "901d232c",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI(api_key="Your api key")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b41073c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pdf_path1 =(r\"D:\\Development\\LoanAi\\lendiq\\uploads\\Manickam\\Lilias Active - Balance Sheet.pdf\")\n",
    "pdf_path2= (r\"D:\\muthukumar\\new\\uploads\\muthuKumar - Copy (2)\\ArticleofIncorporation.pdf\")\n",
    "pdf_path3=(r\"D:\\muthukumar\\new\\uploads\\muthuKumar - Copy (2)\\Chase-Bank-Statement-LiliasDec2023.pdf\")\n",
    "pdf_path4=(r\"D:\\muthukumar\\new\\uploads\\muthuKumar - Copy (2)\\Lilias Active - CashFlow.pdf\")\n",
    "pdf_path5=(r\"D:\\muthukumar\\new\\uploads\\muthuKumar - Copy (2)\\Lilias Active - IncomeStatement.pdf\")\n",
    "pdf_path6=(r\"D:\\muthukumar\\new\\uploads\\muthuKumar - Copy (2)\\LiliasActive - Business Plan.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "eef69250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Only. Created with Aspose.Cells for Python via Java.Copyright 2003 - 2024 Aspose Pty Ltd.\n",
      "Balance Sheet\n",
      "Construction Company\n",
      "Balance Sheet\n",
      "5/1/2015\n",
      "Current Period Prior Period Increase (Decrease)\n",
      "05/01/15 to 05/01/16 05/01/14 to 05/01/15 05/01/15 to 05/01/16\n",
      "ASSETS\n",
      "Current Assets:\n",
      "Cash $ 21,506.00 $ 20,000.00 $ 1,506.00\n",
      "Petty Cash 200.00 200.00 -\n",
      "Accounts Receivables 5,013.00 5,000.00 13.00\n",
      "Inventory 20,887.00 21,000.00 (113.00)\n",
      "Prepaid Expenses 1,098.00 1,100.00 (2.00)\n",
      "Employee Advances 100.00 - 100.00\n",
      "Temporary Investments - - -\n",
      "Total Current Assets 48,804.00 47,300.00 1,504.00\n",
      "Fixed Assets:\n",
      "Land 150,000.00 150,000.00 -\n",
      "Buildings 60,235.00 60,235.00 -\n",
      "Furniture and Equipment 28,777.00 30,777.00 (2,000.00)\n",
      "Computer Equipment 5,195.00 5,195.00 -\n",
      "Vehicles 32,513.00 32,513.00 -\n",
      "Less: Accumulated Depreciation (43,183.00) (45,201.00) 2,018.00\n",
      "Total Fixed Assets 233,537.00 233,519.00 18.00\n",
      "Other Assets:\n",
      "Trademarks 4,171.00 4,171.00 -\n",
      "Patents - - -\n",
      "Security Deposits 1,500.00 1,500.00 -\n",
      "Other Assets 583.00 600.00 (17.00)\n",
      "Total Other Assets 6,254.00 6,271.00 (17.00)\n",
      "-\n",
      "TOTAL ASSETS $ 288,595.00 $ 287,090.00 $ 1,505.00\n",
      "LIABILITIES\n",
      "Current Liabilities:\n",
      "Accounts Payable $ 18,237.00 $ 18,000.00 $ 237.00\n",
      "Business Credit Cards 7,523.00 6,000.00 1,523.00\n",
      "Sales Tax Payable 1,055.00 1,000.00 55.00\n",
      "Payroll Liabilities 2,382.00 2,500.00 (118.00)\n",
      "Other Liabilities 941.00 1,000.00 (59.00)\n",
      "Current Portion of Long-Term Debt 12,111.00 12,111.00 -\n",
      "Total Current Liabilities 42,249.00 40,611.00 1,638.00\n",
      "Long-Term Liabilities:\n",
      "Notes Payable 50,195.00 48,301.00 1,894.00\n",
      "Mortgage Payable 148,222.00 146,231.00 1,991.00\n",
      "Less: Current portion of Long-term debt (1,200.00) (1,200.00) -\n",
      "Total Long-Term Liabilities 197,217.00 193,332.00 3,885.00\n",
      "EQUITY\n",
      "Capital Stock/Partner's Equity 20,000.00 20,000.00 -\n",
      "Opening Retained Earnings 25,530.00 29,129.00 (3,599.00)\n",
      "Dividends Paid/Owner's Draw (5,000.00) (5,000.00) -\n",
      "Net Income (Loss) 8,599.00 9,018.00 (419.00)\n",
      "Total Equity 49,129.00 53,147.00 (4,018.00)\n",
      "TOTAL LIABILITIES & EQUITY $ 288,595.00 $ 287,090.00 $ 1,505.00\n",
      "Balance Sheet Check - -\n",
      "Secretary of State\n",
      "Business Programs Division\n",
      "Business Entities\n",
      "1500 11th Street, Sacramento, CA 95814\n",
      "P.O. Box 944260, Sacramento, CA 94244-2600\n",
      "Submission Cover Sheet\n",
      "For faster service, file online at bizfileOnline.sos.ca.gov.\n",
      "Instructions:\n",
      "• Complete and include this form with your paper submission. This information only will be\n",
      "used to communicate in writing about the submission, if needed. This form will be\n",
      "treated as correspondence and will not be made part of the filed document.\n",
      "• Make all checks or money orders payable to the Secretary of State.\n",
      "• In person submissions (excluding Statements of Information): $15 handling fee; do not include\n",
      "a $15 handling fee when submitting documents by mail.\n",
      "• Standard processing time for submissions to this office is approximately 5 business days from\n",
      "receipt. All submissions are reviewed in the date order of receipt with online submissions\n",
      "given priority. For updated processing time information, visit\n",
      "www.sos.ca.gov/business/be/processing-dates.\n",
      "Optional Copy and Certification Fees:\n",
      "• If applicable, include optional certification fees with your submission.\n",
      "• For applicable certification fee information, refer to the instructions of the specific form you are\n",
      "submitting.\n",
      "Contact Person: (Please type or print legibly)\n",
      "Osman M\n",
      "First Name: Last Name:\n",
      "Phone (optional):\n",
      "Entity Information: (Please type or print legibly)\n",
      "LILIAS ACTIVE LLC\n",
      "Name:\n",
      "32-2124444\n",
      "Entity Number (if applicable):\n",
      "935 South Morgan Street , Dallas , TX, 75204\n",
      "Address:\n",
      "Comments\n",
      "Submission Cover Sheet (REV 03/2022)\n",
      "LLC-1\n",
      "Secretary of State\n",
      "Articles of Organization\n",
      "Limited Liability Company (LLC)\n",
      "Filing Fee - $70.00\n",
      "Certified Copy Fee (Optional) - $5.00\n",
      "Note: LLC s may h ave to pa y mini mum $ 800 tax to the California Fra nchise Tax\n",
      "Board each y ear. F or m ore info rm atio n, go to http s:// www.ftb .ca.gov/.\n",
      "This Space For Office Use Only\n",
      "1. Limited Liability Company Name (Must contain an LLC identifier such as LLC or L.L.C. “LLC” will be added, if not included.)\n",
      "LILIAS ACTIVE LLC\n",
      "2. Business Addresses\n",
      "a. Initial Street Address of Principal Of fice - Do not enter a P.O. Box City (no abbreviations) State Zip Code\n",
      "935 South Morgan Street Dallas TX 75204\n",
      "b. Initial Mailing Address of LLC, if different than item 2a City (no abbreviations) State Zip Code\n",
      "3. Service of Process (Must provide either Individual OR Corporation.)\n",
      "INDIVIDUAL – Complete Items 3a and 3b only. Must include agent’s full name and California street address.\n",
      "a.California Agent's First Name (if agent is not a corporation) Middle Name Last Name Suffix\n",
      "b.Street Address (if agent is not a corporation) - Do not enter a P.O. Box City (no abbreviations) State Zip Code\n",
      "CA\n",
      "CORPORATION – Complete Item 3c. Only include the name of the registered agent Corporation.\n",
      "c.California Registered Corporate Agent’s Name (if agent is a corporation) – Do not complete Item 3a or 3b\n",
      "4. Management (Select only one box)\n",
      "The LLC will be managed by:\n",
      "4 One Manager More than One Manager All LLC Member(s)\n",
      "5. Purpose Statement (Do not alter Purpose Statement)\n",
      "The purpose of the limited liability company is to engage in any lawful act or activity for which a limited liability company\n",
      "may be organized under the California Revised Uniform Limited Liability Company Act.\n",
      "6. By signing, I affirm under penalty of perjury that the information herein is true and correct and that I am authorized by\n",
      "California law to sign.\n",
      "Additional signatures set forth on attached pages, if any, are incorporated herein by reference and made part of this Form LLC-1. (All attachments\n",
      "should be 8 ½ x 11, one-sided, legible and clearly marked as an attachment to this Form LLC-1.)\n",
      "Osman\n",
      "_____________________________________________________________ __________________________________________________________\n",
      "Organizer sign here Print your name here\n",
      "LLC-1 (REV 11/2023) 2023 California Secr etary of State\n",
      "bizfileOnline.sos. ca.gov\n",
      "Dec01, 2023 through Dec 31,2023\n",
      "JPMorgan Chase Bank, N.A. Account Number: 000000593037148\n",
      "PO Box 659754\n",
      "San Antonio, TX 78265-9754\n",
      "CUSTOMER SERVICE INFORMATION\n",
      "as\n",
      "Service Center: 1-800-242-7338\n",
      "Hearing Impaired: 1-800-242-7383\n",
      "Para Espanol: 1-888-622-4273\n",
      "International Calls: 1-713-262-1679\n",
      "LILIAS ACTIVE LLX\n",
      "935 South Morgan Street, 4th Floor\n",
      "DALLAS TX, 75204\n",
      "CHECKING SUMMARY Chase Business Select Checking\n",
      "INSTANCES AMOUNT\n",
      "Beginning Balance $15,050.80\n",
      "Deposits and Additions 2 130.00\n",
      "Electronic Withdrawals 2 -356.04\n",
      "Ending Balance 4 $14,824.76\n",
      "Your monthly service fee was waived because you maintained an average checking balance of $7,500.00 or a minimum checking balance of\n",
      "$5,000.00 or more during the statement period.\n",
      "DEPOSIT AND ADDITIONS\n",
      "DATE DESCRIPTION AMOUNT\n",
      "09/15 ATM Check Deposit $125.00\n",
      "09/15 ATM Cash Deposit 5.00\n",
      "Total Deposits and Additions $130.00\n",
      "DEPOSIT AND ADDITIONS\n",
      "DATE DESCRIPTION AMOUNT\n",
      "09/30 Qwest 8002441111 Telephone 2063864199818 CCID ID : 9Qc0273801 $249.96\n",
      "09/30 Qwest 8002441111 Telephone 2063654528111 CCID ID : 9Qc0273801 106.08\n",
      "Total Electronic Withdrawals $356.04\n",
      "DAILY ENDING BALANCE\n",
      "DATE AMOUNT\n",
      "09/15 $15,180.80\n",
      "09/30 14,824.76\n",
      "Evaluation Only. Created with Aspose.Cells for Python via Java.Copyright 2003 - 2024 Aspose Pty Ltd.\n",
      "Statement of Cash Flows\n",
      "Lilias Active\n",
      "Statement of Cash Flows\n",
      "12/31/2023\n",
      "Current Period Prior Period\n",
      "09/30/23 to 12/31/23 07/01/23 to 09/30/23\n",
      "BEGINNING CASH ON HAND 24,110.00 31,885.00\n",
      "ADD: CASH RECEIPTS\n",
      "Cash Sales 12,000.00 12,600.00\n",
      "Collections from Customer Credit Accounts 35,000.00 36,750.00\n",
      "Loan or Other Cash Injection - -\n",
      "Interest Income 250.00 250.00\n",
      "Income Tax Refund - -\n",
      "Misc. Cash Receipts 50.00 50.00\n",
      "TOTAL CASH RECEIPTS 47,300.00 49,650.00\n",
      "LESS: CASH PAYMENTS\n",
      "COST OF GOODS SOLD:\n",
      "Direct Product/Service Costs 13,000.00 13,650.00\n",
      "Salaries-Direct 1,900.00 1,995.00\n",
      "Payroll Taxes and Benefits-Direct 225.00 236.25\n",
      "Supplies 400.00 420.00\n",
      "Other Costs 50.00 52.50\n",
      "Sub-Total Cost of Goods Sold 15,575.00 16,353.75\n",
      "OPERATING EXPENSES:\n",
      "Advertising and Promotion 300.00 315.00\n",
      "Automobile/Transportation 50.00 52.50\n",
      "Bank Service Charges 25.00 26.25\n",
      "Business Licenses and Permits 30.00 31.50\n",
      "Charitable Contributions 50.00 52.50\n",
      "Computer and Internet 200.00 210.00\n",
      "Continuing Education 125.00 131.25\n",
      "Dues and Subscriptions 25.00 26.25\n",
      "Insurance 600.00 630.00\n",
      "Meals and Entertainment 200.00 210.00\n",
      "Merchant Account Fees 600.00 630.00\n",
      "Miscellaneous Expense 25.00 26.25\n",
      "Office Supplies 50.00 52.50\n",
      "Payroll Processing 200.00 210.00\n",
      "Postage and Delivery 20.00 21.00\n",
      "Printing and Reproduction 50.00 52.50\n",
      "Professional Services - Legal, Accounting 250.00 262.50\n",
      "Occupancy 1,600.00 1,680.00\n",
      "Rental Payments 525.00 551.25\n",
      "Salaries-Indirect 4,000.00 4,200.00\n",
      "Payroll Taxes and Benefits-Indirect 425.00 446.25\n",
      "Subcontractor - -\n",
      "Telephone 500.00 525.00\n",
      "Travel 100.00 105.00\n",
      "Utilities 900.00 945.00\n",
      "Website Development - -\n",
      "Sub-Total Operating Expenses 10,850.00 11,392.50\n",
      "OTHER EXPENSE PAYMENTS\n",
      "Interest Expense 700.00 735.00\n",
      "Income Tax Expense 5,200.00 5,460.00\n",
      "Cash Disbursements to Owners - -\n",
      "Sub-Total Other Expense Payments 5,900.00 6,195.00\n",
      "TOTAL CASH PAYMENTS 32,325.00 33,941.25\n",
      "NET CASH CHANGE - Inflow (Outflow) 14,975.00 15,708.75\n",
      "CASH POSITION (end of month) 39,085.00 47,593.75\n",
      "Evaluation Only. Created with Aspose.Cells for Python via Java.Copyright 2003 - 2024 Aspose Pty Ltd.\n",
      "Income Statement\n",
      "Lilias Active\n",
      "Income Statement\n",
      "12/31/2023\n",
      "Current Period Prior Period\n",
      "09/30/23 to 12/31/23 07/01/23 to 09/30/23\n",
      "REVENUES\n",
      "Core Style $ 14,000.00 $ 11,689.00\n",
      "Crop Tops 55,000.00 72,111.00\n",
      "Services 8,000.00 3,899.00\n",
      "Other Revenue 400.00 203.00\n",
      "TOTAL REVENUES 77,400.00 87,902.00\n",
      "COST OF GOODS SOLD\n",
      "Core Style 4,000.00 3,506.70\n",
      "Services 13,000.00 11,055.50\n",
      "Crop Tops 3,200.00 1,559.60\n",
      "Salaries-Direct 1,900.00 1,766.00\n",
      "Payroll Taxes and Benefits-Direct 225.00 215.00\n",
      "Depreciation-Direct 612.00 612.00\n",
      "Supplies 400.00 310.00\n",
      "Other Direct Costs 50.00 24.00\n",
      "TOTAL COST OF GOODS SOLD 23,387.00 19,048.80\n",
      "GROSS PROFIT (LOSS) 54,013.00 68,853.20\n",
      "OPERATING EXPENSES\n",
      "Advertising and Promotion 300.00 206.00\n",
      "Automobile/Transportation 50.00 45.00\n",
      "Bad Debts/Losses and Thefts 25.00 -\n",
      "Bank Service Charges 30.00 25.00\n",
      "Business Licenses and Permits 50.00 -\n",
      "Charitable Contributions - 100.00\n",
      "Computer and Internet 125.00 129.00\n",
      "Continuing Education - -\n",
      "Depreciation-Indirect 1,277.00 1,277.00\n",
      "Dues and Subscriptions 25.00 -\n",
      "Insurance 600.00 600.00\n",
      "Meals and Entertainment 200.00 185.00\n",
      "Merchant Account Fees 600.00 502.00\n",
      "Miscellaneous Expense 25.00 30.00\n",
      "Office Supplies 50.00 40.00\n",
      "Payroll Processing 200.00 201.00\n",
      "Postage and Delivery 20.00 18.00\n",
      "Printing and Reproduction 50.00 33.00\n",
      "Professional Services - Legal, Accounting 250.00 207.00\n",
      "Occupancy 1,600.00 1,600.00\n",
      "Rental Payments 525.00 514.00\n",
      "Salaries-Indirect 4,000.00 3,805.00\n",
      "Payroll Taxes and Benefits-Indirect 425.00 411.00\n",
      "Subcontractor - -\n",
      "Telephone 500.00 488.00\n",
      "Travel 100.00 56.00\n",
      "Utilities 900.00 790.00\n",
      "Website Development - -\n",
      "TOTAL OPERATING EXPENSES 11,927.00 11,262.00\n",
      "OPERATING PROFIT (LOSS) 42,086.00 57,591.20\n",
      "INTEREST (INCOME), EXPENSE & TAXES\n",
      "Interest (Income) (5,000.00) (3,200.00)\n",
      "Interest Expense 700.00 689.00\n",
      "Income Tax Expense 5,200.00 3,200.00\n",
      "TOTAL INTEREST (INCOME), EXPENSE & TAXES 900.00 689.00\n",
      "NET INCOME (LOSS) $ 41,186.00 $ 56,902.20\n",
      "L I L I A S A C T I V E\n",
      "R o o t e d I n Q u a l i t y W i t h Ve r s a t i l i t y At I t ’s C o r e .\n",
      "M AY 2 0 2 1\n",
      "C o v i d - 1 9 h a s c h a n g e d t h e f a c e\n",
      "o f m a n y i n d u s t r i e s – a n d f a s h i o n\n",
      "b e c a m e o n e o f t h e m .\n",
      "The role of activewear in customers’ lives has drastically changed.\n",
      "Today, activewear is not just about sports – it’s an integral part\n",
      "of women’s everyday lives.\n",
      "LILIAS ACTIVE PAGE 2\n",
      "T H E P R O B L E M\n",
      "01. Not accessible with high pricing.\n",
      "Most activewear of today\n",
      "does not meet the rapidly\n",
      "evolving customer needs.\n",
      "02. Lacks comfort and versatility.\n",
      "03. Limited ranges in size.\n",
      "Standardized performance wear\n",
      "04.\n",
      "that doesn’t reflect one’s personality.\n",
      "LILIAS ACTIVE PAGE 3\n",
      "L I L I A S A C T I V E\n",
      "A N E W L EA D I N G AC T I V E W EA R B R A N D R O OT E D I N Q U A L I T Y.\n",
      "Activewear that supports, Designed with your adventures\n",
      "Quality over everything\n",
      "inspires, and is inspired by in mind to provide support,\n",
      "and versatility at our core.\n",
      "amazing women. flexibility, fashion, and freedom.\n",
      "LILIAS ACTIVE PAGE 4\n",
      "O U R P R O D U C T S\n",
      "CORE STYLES OUR COLLECTIONS\n",
      "LEGGINGS\n",
      "THE SEAMLESS THE SEAMLESS\n",
      "SCRUNCH RIBBED KNIT\n",
      "SPORTS BRAS\n",
      "CROP TOPS & TEES\n",
      "THE SEAMLESS THE SEAMLESS\n",
      "SIGNATURE PERFORMANCE\n",
      "SHORTS\n",
      "LILIAS ACTIVE PAGE 5\n",
      "S T R O N G VA L U E P R O P O S I T I O N\n",
      "A B R A N D C R EAT E D FO R E V E R YO N E – LO O K G O O D A N D F E E L G R EAT A L L DAY LO N G , N O M AT T E R T H E AC T I V I T Y.\n",
      "01. Exceptional quality & comfort 04. Sustainability in our DNA\n",
      "Inclusive community of\n",
      "02. Innovative, on-trend styles and cuts 05.\n",
      "like-minded people\n",
      "Versatility with cloths fit for any Fashion accessible to all – both\n",
      "03. 06.\n",
      "activity, any time of the day in terms of pricing and sizes\n",
      "LILIAS ACTIVE PAGE 6\n",
      "L O V E D B Y C U S T O M E R S\n",
      "Worn by celebrities like Cara Santana,\n",
      "Cassie Randolph, Teala Dunn &\n",
      "Victoria Fuller among others.\n",
      "3 2 k 2 5 k 1 8 %\n",
      "Instagram Followers + website visitors returning visitors and “Obsessed. I need “Love these! I would “Incredible product\n",
      "per month – up 110%+ growing fast more! True to size and buy all!” quality!”\n",
      "in 6 months didn’t have to adjust\n",
      "at all during my\n",
      "workout.”\n",
      "LILIAS ACTIVE PAGE 7\n",
      "T R A C T I O N T O D AT E : E X E C U T I N G F A S T\n",
      "$350k+ in gross sales in 6 months\n",
      "5 , 0 0 0 & consistently growing average order value.\n",
      "Customers & growing by 100,000 140\n",
      "86,054\n",
      "90,000\n",
      "10% every month\n",
      "120\n",
      "80,000\n",
      "70,000 100\n",
      "60,469\n",
      "58,200\n",
      "60,000 54,228\n",
      "80\n",
      "48,728\n",
      "50,000\n",
      "42,141\n",
      "60\n",
      "40,000\n",
      "30,000 40\n",
      "20,000\n",
      "3 , 2 0 0\n",
      "20\n",
      "10,000\n",
      "0 0\n",
      "Orders to date Nov Dec Jan Feb Mar Apr\n",
      "Gross Sales $ AOV $\n",
      "LILIAS ACTIVE PAGE 8\n",
      "T H E M A R K E T\n",
      "H U G E A N D P R OV E N A D D R E S S A B L E M A R K ET\n",
      "Global activewear market is expected to gain K E Y T R E N D S 2 0 2 1\n",
      "market growth to reach $439b by 2026.\n",
      "Rising health and self-consciousness, desire\n",
      "to lead active lifestyles and to appear fit. $ 3 5 4 B Global sportswear\n",
      "4% CARG\n",
      "market in 2020\n",
      "TAM\n",
      "$ 4 3 9 B\n",
      "Rising participation of women in professional\n",
      "and domestic sports and fitness activities.\n",
      "$ 3 5 4 B\n",
      "Increasing consumer desire to wear sports\n",
      "U.S. sportswear\n",
      "wear as casual wear fueled by rising crossover $ 4 8 B\n",
      "market in 2020\n",
      "between sports and fashion. SAM\n",
      "Increasing demand for essentials like\n",
      "leggings and sports bras.\n",
      "$ 4 . 8 B 1% share of U.S.\n",
      "SOM sportswear market\n",
      "Inclusive sizing and sustainability.\n",
      "2 0 2 0 2 0 2 3\n",
      "Source: Statista, Mordor Intelligence\n",
      "LILIAS ACTIVE PAGE 9\n",
      "W H Y N O W ?\n",
      "R A P I D LY I N C R EA S I N G D E M A N D FO R AC T I V E W EA R .\n",
      "Covid pandemic has accelerated the momentum in The demand is now outstripping supply – with sportswear sector\n",
      "athleisure sales globally. poised for exceptional growth & profitability going forward.\n",
      "“The UK could face\n",
      "shortages of sports footwear,\n",
      "8 4 % 4 0 %\n",
      "jogging bottoms and stretchy\n",
      "“Over the coming five years,\n",
      "leggings as retailers and suppli-\n",
      "Since the start of the pandemic The already successful category the sportswear sector will\n",
      "ers struggle to keep up with a\n",
      "in 2020, the order volumes of appeared to be pandemic-proof, outperform all other major\n",
      "pandemic-related surge in\n",
      "women’s athleisure apparel rose accounting for a record high 40% retail sectors.”\n",
      "demand from the online\n",
      "by a whopping 84%. of all online sales last year,\n",
      "channel.”\n",
      "according to NPD.\n",
      "LILIAS ACTIVE PAGE 10\n",
      "O U R B U S I N E S S M O D E L\n",
      "Solid unit economics with\n",
      "O N L I N E ( D I R E C T-TO - CO N S U M E R )\n",
      "As the main sales channel\n",
      "business ready to scale:\n",
      "F O C U S O N A F F O R DA B L E P R I C I N G\n",
      "With items ranging in sales price from $40-70 per item\n",
      "S T R O N G M A R G I N S\n",
      "6 9 %\n",
      "With gross margin profitability at 47-74% per item\n",
      "Gross Profit Margin\n",
      "CO M P E T I T I V E C U S TO M E R ACQ U I S I T I O N CO S T\n",
      "At c.$30 and continuing to decrease as we grow conversions, expand\n",
      "% of returning customers and increase brand awareness\n",
      "3 x\n",
      "H I G H LY A U TO M AT E D O P E R AT I O N S\n",
      "With optimized our fulfilment and delivery channels\n",
      "CAC: Average Order Value\n",
      "LILIAS ACTIVE PAGE 11\n",
      "G O - T O - M A R K E T S T R AT E G Y\n",
      "2 0 2 0 – 2 0 2 1 2 0 2 2 – 2 0 2 3 2 0 2 4 +\n",
      "OUR 2025 VISION\n",
      "Build a leading activewear brand that defines the\n",
      "PHASE LAUNCH & PRODUCT MARKET FIT INTERNATIONAL SCALING HYPER-GROWTH\n",
      "trends in the fashion industry and makes women\n",
      "feel great every day.\n",
      "PRODUCTS ACTIVEWEAR\n",
      "KEY PROMOTION & MARKETING CHANNELS\n",
      "LOUNGEWEAR\n",
      "Brand ambassadors and influencers to help establish\n",
      "awareness across our core target audiences & drive\n",
      "XXXX\n",
      "word-of-mouth advertising.\n",
      "GEOGRAPHIES Building a community around the brand to drive\n",
      "U.S.\n",
      "brand loyalty as well as predictable and growing rev-\n",
      "enues, effectively setting us aside from other brands.\n",
      "MIDDLE EAST\n",
      "High investment in content marketing and curated\n",
      "REST OF THE WORLD purchase experience.\n",
      "Social media marketing and digital advertising.\n",
      "REVENUE 1M 3M 6M 11M 16M\n",
      "LILIAS ACTIVE PAGE 12\n",
      "T H E C O M P E T I T I O N & H O W W E P L A N T O W I N\n",
      "LILIAS ACTIVE PAGE 13\n",
      "ELYTSEFIL\n",
      "/\n",
      "NOIHSAF\n",
      "HIGH PRICE\n",
      "LOW PRICE\n",
      "PERFORMANCE\n",
      "UNIQUE COMPETITIVE POSITIONING –\n",
      "BUILDING AN INCLUSIVE BRAND\n",
      "FIT FOR EVERY WOMAN:\n",
      "Accessible – trend-forward products at an affordable cost\n",
      "Adaptable – functional and perfectly fit for any activity\n",
      "Innovative – unique styles and cuts combining\n",
      "fashion and comfort\n",
      "T H E T E A M\n",
      "OSMAN M. CARRIE DOYLE SHELBY CHAMBERS\n",
      "FOUNDER, CEO CO-FOUNDER, CREATIVE & ART DIRECTOR MARKETING DIRECTOR\n",
      "Proven business leader with over a decade Experienced design professional with 9+ years Seasoned Marketing Director with 10+ years of\n",
      "of experience of professional experience experience across every area of digital market-\n",
      "ing from social media to content strategy and\n",
      "Previously scaled a for-profit education organ- Demonstrated history of successful project\n",
      "brand launches\n",
      "isation from 200 to 5,000 users and hundreds execution with leading retail fashion brands\n",
      "of million dollars of revenue including Victoria’s Secret, Abercrombie & Expertise in leading marketing teams in enter-\n",
      "Fitch, Wella Koleston, Procter & Gamble and tainment, fashion, beauty, CPG, fitness, b2b,\n",
      "Successfully negotiated contacts\n",
      "American Eagle. and lifestyle industries.\n",
      "with a value of over $20m.\n",
      "LILIAS ACTIVE PAGE 14\n",
      "F I N A N C I A L F O R E C A S T & T H E U S E O F F U N D S\n",
      "Financial Outlook & Highlights: The Ask & Use of Funds:\n",
      "Q 2 2 0 2 1 3 3 % 5 0 %\n",
      "13K 30K 57K 98K 114400KK Cash Break Even Production Of New Marketing To Scale\n",
      "Line Of Products: Proven Growth\n",
      "1166,,009911,,995599\n",
      "Channels:\n",
      "$ 1 2 M\n",
      "1100,,772277,,997722\n",
      "Revenue in 2025\n",
      "55,,995599,,998855\n",
      "6 8 %\n",
      "3355%%\n",
      "3322%%\n",
      "22,,997799,,999922 2288%% CAGR\n",
      "1177%%\n",
      "11,,119911,,999977\n",
      "1 7 %\n",
      "22%%\n",
      "$ 6 0 0 K\n",
      "22002211 22002222 22002233 22002244 22002255\n",
      "Other Corporate\n",
      "Funding Required Purposes\n",
      "Revenue EBITDA margin XX Orders\n",
      "LILIAS ACTIVE PAGE 15\n",
      "W H Y I N V E S T W I T H U S\n",
      "01. 02. 03.\n",
      "Exceptional opportunity to tap into a huge Visionary founder-led team with the Strong value proposition with exceptional\n",
      "and growing athleisure market with a new, expertise of launching world class brands product quality at affordable prices,\n",
      "innovative brand that is effectively and proven ability to deliver strong top innovative cuts and fashion forward styles,\n",
      "positioned against other market players and bottom line performance. and relatable brand grounded in the\n",
      "& already loved by customers and community around it.\n",
      "celebrities alike.\n",
      "04. 05. 06.\n",
      "Profitable, scalable business model with Strong traction-to-date with exceptional Well-defined strategy to achieve market\n",
      "optimized production and operations, proven results in a mere 6 months since launch; leadership and clear path to quickly scale\n",
      "ability to deliver strong gross margin $340k in gross sales and business on track the business to success – leveraging\n",
      "performance as well as positive trend to $1.5M+ in revenue by end of 2021; with influencer marketing, content strategy\n",
      "towards falling CAC. continuously increasing AOV & percent & Lilias community to grow the brand\n",
      "of returning customers. internationally.\n",
      "LILIAS ACTIVE PAGE 16\n",
      "O U R CO N TAC TS :\n",
      "I N FO @ L I L I A S AC T I V E .CO M\n",
      "L I L I A S AC T I V E .CO M\n",
      "R o o t e d I n Q u a l i t y W i t h Ve r s a t i l i t y At I t ’s C o r e .\n",
      "LILIAS ACTIVE M AY 2 0 2 1 PAGE 17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def text_to_pdf(pdf_paths):\n",
    "    combined_text = \"\"\n",
    "    for pdf_path in pdf_paths:\n",
    "        with pdfplumber.open(pdf_path) as pdf:\n",
    "            for page in pdf.pages:\n",
    "                page_text = page.extract_text()\n",
    "                if page_text:\n",
    "                    combined_text += page_text + \"\\n\"\n",
    "    return combined_text\n",
    "\n",
    "pdf_text =text_to_pdf([pdf_path1,pdf_path2,pdf_path3,pdf_path4,pdf_path5,pdf_path6])\n",
    "print (pdf_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a8e37b32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total Fixed Assets is $233,537.00 and the total Current Assets is $48,804.00. Let me know if you need any more assistance."
     ]
    }
   ],
   "source": [
    "# Function to handle starting a conversation and streaming responses\n",
    "def start_conversation_and_stream_responses(initial_message):\n",
    "    stream = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=[{\"role\": \"user\", \"content\": initial_message}],\n",
    "        stream=True\n",
    "    )\n",
    "\n",
    "    # Iterate through the response stream\n",
    "    for chunk in stream:\n",
    "        if chunk.choices[0].delta.content is not None:\n",
    "            print(chunk.choices[0].delta.content, end=\"\")\n",
    "\n",
    "# Function to continue the conversation with new user inputs\n",
    "def continue_conversation(user_message):\n",
    "    # Append the new message from the user\n",
    "    messages.append({\"role\": \"user\", \"content\": user_message})\n",
    "\n",
    "    # Continue the conversation using the updated messages list\n",
    "    stream = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "\n",
    "    # Iterate through the response stream\n",
    "    for chunk in stream:\n",
    "        if chunk.choices[0].delta.content is not None:\n",
    "            print(chunk.choices[0].delta.content, end=\"\")\n",
    "\n",
    "# Initial conversation setup\n",
    "initial_message = pdf_text\n",
    "messages = [{\"role\": \"user\", \"content\": initial_message}]\n",
    "# start_conversation_and_stream_responses(initial_message)\n",
    "\n",
    "# Example to continue conversation - this can be triggered by user input\n",
    "user_input = 'Can you send me  the total Fixed Assets and Current Assets?'\n",
    "continue_conversation(user_input)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eb76f997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The company investors for Lilias Active have not been mentioned in the provided documents. If you need specific information about the company's investors, it would be best to reach out directly to the company or refer to their official investor information or financial statements."
     ]
    }
   ],
   "source": [
    "def continue_conversation(user_message):\n",
    "    # Append the new message from the user\n",
    "    messages.append({\"role\": \"user\", \"content\": user_message})\n",
    "\n",
    "    # Continue the conversation using the updated messages list\n",
    "    stream = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "\n",
    "    # Iterate through the response stream\n",
    "    for chunk in stream:\n",
    "        if chunk.choices[0].delta.content is not None:\n",
    "            print(chunk.choices[0].delta.content, end=\"\")\n",
    "\n",
    "initial_message = pdf_text\n",
    "messages = [{\"role\": \"user\", \"content\": initial_message}]\n",
    "\n",
    "user_input = 'Who are all  the  company investors?'\n",
    "continue_conversation(user_input)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "032cab3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "'8', 'Who are all  the  company\\'s investors?', 'Your role as a Business Profile Analyst.', 'BusinessProfile', 'String', 'False'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d456476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory = ConversationBufferMemory()\n",
    "# memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f7386ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory.load_memory_variables({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0d28ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory = ConversationBufferMemory(return_messages=True)\n",
    "# memory.save_context({\"input\": \"hi\"}, {\"output\": \"whats up\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5846be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory.load_memory_variables({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87365a08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d0e377b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"D:\\Development\\LoanAi\\lendiq\\uploads\\Manickam\\Lilias Active - Balance Sheet.pdf\"\n",
    "pdf_path1 =( r\"C:\\Users\\murugan\\Downloads\\ResumeAmalkrishnaAsokankv.pdf\")\n",
    "pdf_path2 = (r\"C:\\Users\\murugan\\Downloads\\ResumeAneenaT.pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c268765c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1c55b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from langchain_community.llms import Ollama\n",
    "# import pdfplumber\n",
    "# from langchain.chains import ConversationChain\n",
    "\n",
    "\n",
    "# llm = Ollama(model=\"llama3\")\n",
    "# conversation = ConversationChain(\n",
    "#     llm=llm,\n",
    "#     verbose=False,\n",
    "#     memory=ConversationBufferMemory()\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5945513a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMAL KRISHNA ASOKAN KV\n",
      "PYTHON DEVELOPER\n",
      "SUMMARY\n",
      "CONTACT\n",
      "Highly Motivated And Detail-Oriented Python Developer With A\n",
      "7510477037\n",
      "Passion For Developing Innovative Solutions. Skilled in Building,\n",
      "Maintaining And Testing Back-end & Front-end Features And\n",
      "amalkrish58@gmail.com\n",
      "Integrating Them Into Applications. Collaborate With Cross-\n",
      "Kakkanad-Kochi-Kerala Functional Teams To Design, Develop & Deploy Software\n",
      "Solutions. I Am Exited About The Opportunity To Shape The\n",
      "https://github.com/AmalJerry/ Electronics Revolution With My Skills.\n",
      "http://linkedin.com/in/amal-krishna-\n",
      "EXPERIENCE\n",
      "5211a7285\n",
      "Luminar Technolab - Kakkanad\n",
      "HARD SKILLS (07/2023 - Present)\n",
      "Python Django Intern\n",
      "Designed robust, scalable, secure, and globalized web-based\n",
      "Python\n",
      "applications to ensure the continuity of all business\n",
      "Django processes and client satisfaction\n",
      "Have Hands-On Experience In Python, Django, HTML5, CSS,\n",
      "HTML 5\n",
      "Bootstrap, JavaScript, React, MySQL, SQLite & Git.\n",
      "CSS\n",
      "Used the Python language to develop 3 web applications.\n",
      "Bootstrap\n",
      "MySQL PROJECTS\n",
      "SQLite\n",
      "ToDo Application : Basic Project Built On Django Rest\n",
      "JavaScript Framework Using Python. Front-End Designed Using\n",
      "HTML,CSS And Bootstrap.\n",
      "React.js\n",
      "JOB PORTAL : A Job Portal Website That Specializes In\n",
      "Job Seeking. Done With DJANGO Framework, HTML, CSS &\n",
      "LANGUAGE\n",
      "Bootstrap, Which Completed With Login & Registration\n",
      "Pages.\n",
      "ENGLISH\n",
      "NIEPCE : Niepce Is A Photography Page Which Is Done\n",
      "MALAYALAM\n",
      "With Python Django, HTML, CSS & Bootstrap.\n",
      "HINDI\n",
      "EDUCATION\n",
      "INTERESTS\n",
      "2020- 2023\n",
      "UG in B.COM\n",
      "Univercity Of Calicut\n",
      "VOLUNTEERING\n",
      "2018-2020\n",
      "HIGHER SECONDARY\n",
      "PERSONAL DEVELOPMENT\n",
      "D.H.O.H.S.S Pookkarathara\n",
      "SPORTS 2015-2018\n",
      "HIGH SCHOOL\n",
      "MOVIES\n",
      "D.H.O.H.S.S Pookkarathara\n",
      "ANEENA T\n",
      "+91 7025549521 ⋄ Kozhikode\n",
      "aneenathoduvail@gmail.com ⋄ linkedin ⋄ Github\n",
      "OBJECTIVE\n",
      "As a dedicated Software Developer, I am seeking a role which allows me to continue learning and perfecting my skills\n",
      "as i provide high-quality work, and encourages me to flourish as a software developer.\n",
      "EDUCATION\n",
      "Python Django Full stack development, Luminar Technolab 2022-2024\n",
      "Bachelor of Computer Science, Pondicherry University 2019-2022\n",
      "Computer Science, GVHSS Madappally 2016-2018\n",
      "SKILLS\n",
      "Backend Technologies Python, Django, Rest Api, ORM, Oops, C++, Git, Docker, JWT, AWS\n",
      "Frond-end Technologies HTML5 , CSS3, JavaScript, SQL, Bootstrap\n",
      "Tools/ Applications Visual studio code, Pycharm, Git, MySQL, Docker, Ms-office\n",
      "EXPERIENCE\n",
      "Python developer Internship April 2023 - Dec 2023\n",
      "Luminar Technolab Ernakulam\n",
      "PROJECTS\n",
      "Social Media Application : It is a social media application with few features of Instagram. Developed a dynamic\n",
      "and interactive social media application using Python that allows users to connect, share content, and interact with\n",
      "each others. Technologies used here are Python, Django, HTML, CSS, bootstrap, ORM etc (Try it here)\n",
      "E-commerce Application : It is a back-end E-commerce applicatin platform. It is using Restful Api architecture\n",
      "and integrated JWT authentication for secure access. Its involved the creation of a user-friendly website where\n",
      "customers could purchase products. (Try it here)\n",
      "E-learning Application : I led a creation of a E-learning platform that served both instructors and students\n",
      "effectively. This project makes it easy for instructors to manage course content and offers students an engaging and\n",
      "interactive learning experience. Technologies used here are Python , Django, HTML, CSS, Bootstrap and SQLite\n",
      "(Try it here)\n",
      "Weather Application : It provides, you can track weather in users current location and you can also search to\n",
      "find the current weather at a city. Technologies used here are JavScript, HTML, CSS, Bootstrap. (Try it here)\n",
      "CERTIFICATES\n",
      "• NACTET(Python) - National Council for technology and training\n",
      "• Python Django web development course certificate\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.llms import Ollama\n",
    "import pdfplumber\n",
    "llm = Ollama(model=\"llama3\")\n",
    "\n",
    "def text_to_pdf(pdf_paths):\n",
    "    combined_text = \"\"\n",
    "    for pdf_path in pdf_paths:\n",
    "        with pdfplumber.open(pdf_path) as pdf:\n",
    "            for page in pdf.pages:\n",
    "                page_text = page.extract_text()\n",
    "                if page_text:\n",
    "                    combined_text += page_text + \"\\n\"\n",
    "    return combined_text\n",
    "\n",
    "\n",
    "pdf_text =text_to_pdf([pdf_path1, pdf_path2])\n",
    "print (pdf_text)\n",
    "# conversation_history=\"\"\n",
    "# while True:\n",
    "#     question = input(\"Enter the questions:\")\n",
    "# #     print(question)\n",
    "#     if question ==\"exit\":\n",
    "#         print(\"The End\")\n",
    "#         break\n",
    "#     if question == \"\":\n",
    "#         continue \n",
    "#     full_prompt = f\"Question: {question}\\n\\nDocuments: {pdf_text}\"\n",
    "#     result = llm.invoke(full_prompt)\n",
    "# #     print(\"Answer:\", result)\n",
    "#     conversation_history += f\"Question: {question}\\nAnswer: {result}\\n\"\n",
    "#     print(\"Current conversation history:\")\n",
    "#     print(conversation_history)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efbbcb0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9e904e1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Hi there!\n",
      "AI:\u001b[0m\n"
     ]
    },
    {
     "ename": "AuthenticationError",
     "evalue": "Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-s8b2J***************************************h2xP. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, '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[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m conversation\u001b[38;5;241m.\u001b[39mpredict(\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHi there!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\llm.py:316\u001b[0m, in \u001b[0;36mLLMChain.predict\u001b[1;34m(self, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, callbacks: Callbacks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m    302\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Format prompt with kwargs and pass to LLM.\u001b[39;00m\n\u001b[0;32m    303\u001b[0m \n\u001b[0;32m    304\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[38;5;124;03m            completion = llm.predict(adjective=\"funny\")\u001b[39;00m\n\u001b[0;32m    315\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs, callbacks\u001b[38;5;241m=\u001b[39mcallbacks)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain_core\\_api\\deprecation.py:148\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    146\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    147\u001b[0m     emit_warning()\n\u001b[1;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\base.py:378\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    346\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Execute the chain.\u001b[39;00m\n\u001b[0;32m    347\u001b[0m \n\u001b[0;32m    348\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    369\u001b[0m \u001b[38;5;124;03m        `Chain.output_keys`.\u001b[39;00m\n\u001b[0;32m    370\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    371\u001b[0m config \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    372\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    373\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    374\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    375\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    376\u001b[0m }\n\u001b[1;32m--> 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minvoke(\n\u001b[0;32m    379\u001b[0m     inputs,\n\u001b[0;32m    380\u001b[0m     cast(RunnableConfig, {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m config\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m}),\n\u001b[0;32m    381\u001b[0m     return_only_outputs\u001b[38;5;241m=\u001b[39mreturn_only_outputs,\n\u001b[0;32m    382\u001b[0m     include_run_info\u001b[38;5;241m=\u001b[39minclude_run_info,\n\u001b[0;32m    383\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\base.py:163\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    161\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    162\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[1;32m--> 163\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    164\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[0;32m    166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\base.py:153\u001b[0m, in \u001b[0;36mChain.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    151\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[0;32m    152\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 153\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs, run_manager\u001b[38;5;241m=\u001b[39mrun_manager)\n\u001b[0;32m    154\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    155\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    156\u001b[0m     )\n\u001b[0;32m    158\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    159\u001b[0m         inputs, outputs, return_only_outputs\n\u001b[0;32m    160\u001b[0m     )\n\u001b[0;32m    161\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",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\llm.py:126\u001b[0m, in \u001b[0;36mLLMChain._call\u001b[1;34m(self, inputs, run_manager)\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call\u001b[39m(\n\u001b[0;32m    122\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    123\u001b[0m     inputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any],\n\u001b[0;32m    124\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    125\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m--> 126\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate([inputs], run_manager\u001b[38;5;241m=\u001b[39mrun_manager)\n\u001b[0;32m    127\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_outputs(response)[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain\\chains\\llm.py:138\u001b[0m, in \u001b[0;36mLLMChain.generate\u001b[1;34m(self, input_list, run_manager)\u001b[0m\n\u001b[0;32m    136\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m run_manager\u001b[38;5;241m.\u001b[39mget_child() \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    137\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm, BaseLanguageModel):\n\u001b[1;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m    139\u001b[0m         prompts,\n\u001b[0;32m    140\u001b[0m         stop,\n\u001b[0;32m    141\u001b[0m         callbacks\u001b[38;5;241m=\u001b[39mcallbacks,\n\u001b[0;32m    142\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_kwargs,\n\u001b[0;32m    143\u001b[0m     )\n\u001b[0;32m    144\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    145\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm\u001b[38;5;241m.\u001b[39mbind(stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_kwargs)\u001b[38;5;241m.\u001b[39mbatch(\n\u001b[0;32m    146\u001b[0m         cast(List, prompts), {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks}\n\u001b[0;32m    147\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\llms.py:633\u001b[0m, in \u001b[0;36mBaseLLM.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m    626\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    627\u001b[0m     prompts: List[PromptValue],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    630\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    631\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m    632\u001b[0m     prompt_strings \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_string() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate(prompt_strings, 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;32m~\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\llms.py:803\u001b[0m, in \u001b[0;36mBaseLLM.generate\u001b[1;34m(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m    788\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m get_llm_cache() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m    789\u001b[0m     run_managers \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    790\u001b[0m         callback_manager\u001b[38;5;241m.\u001b[39mon_llm_start(\n\u001b[0;32m    791\u001b[0m             dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    801\u001b[0m         )\n\u001b[0;32m    802\u001b[0m     ]\n\u001b[1;32m--> 803\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_helper(\n\u001b[0;32m    804\u001b[0m         prompts, stop, run_managers, \u001b[38;5;28mbool\u001b[39m(new_arg_supported), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    805\u001b[0m     )\n\u001b[0;32m    806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[0;32m    807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing_prompts) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\llms.py:670\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[1;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[0;32m    668\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n\u001b[0;32m    669\u001b[0m         run_manager\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--> 670\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m    671\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m    672\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager, flattened_output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(run_managers, flattened_outputs):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain_core\\language_models\\llms.py:657\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[1;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[0;32m    647\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_generate_helper\u001b[39m(\n\u001b[0;32m    648\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    649\u001b[0m     prompts: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    653\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m    654\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m    655\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    656\u001b[0m         output \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 657\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(\n\u001b[0;32m    658\u001b[0m                 prompts,\n\u001b[0;32m    659\u001b[0m                 stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[0;32m    660\u001b[0m                 \u001b[38;5;66;03m# TODO: support multiple run managers\u001b[39;00m\n\u001b[0;32m    661\u001b[0m                 run_manager\u001b[38;5;241m=\u001b[39mrun_managers[\u001b[38;5;241m0\u001b[39m] \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    662\u001b[0m                 \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    663\u001b[0m             )\n\u001b[0;32m    664\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[0;32m    665\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(prompts, stop\u001b[38;5;241m=\u001b[39mstop)\n\u001b[0;32m    666\u001b[0m         )\n\u001b[0;32m    667\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    668\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\langchain_openai\\llms\\base.py:350\u001b[0m, in \u001b[0;36mBaseOpenAI._generate\u001b[1;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    334\u001b[0m     choices\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m    335\u001b[0m         {\n\u001b[0;32m    336\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m: generation\u001b[38;5;241m.\u001b[39mtext,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    347\u001b[0m         }\n\u001b[0;32m    348\u001b[0m     )\n\u001b[0;32m    349\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 350\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(prompt\u001b[38;5;241m=\u001b[39m_prompts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m    351\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m    352\u001b[0m         \u001b[38;5;66;03m# V1 client returns the response in an PyDantic object instead of\u001b[39;00m\n\u001b[0;32m    353\u001b[0m         \u001b[38;5;66;03m# dict. For the transition period, we deep convert it to dict.\u001b[39;00m\n\u001b[0;32m    354\u001b[0m         response \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mmodel_dump()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\openai\\_utils\\_utils.py:277\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    275\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\openai\\resources\\completions.py:528\u001b[0m, in \u001b[0;36mCompletions.create\u001b[1;34m(self, model, prompt, best_of, echo, frequency_penalty, logit_bias, logprobs, max_tokens, n, presence_penalty, seed, stop, stream, stream_options, suffix, temperature, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m    499\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[0;32m    501\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    526\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    527\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Completion \u001b[38;5;241m|\u001b[39m Stream[Completion]:\n\u001b[1;32m--> 528\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    529\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/completions\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    530\u001b[0m         body\u001b[38;5;241m=\u001b[39mmaybe_transform(\n\u001b[0;32m    531\u001b[0m             {\n\u001b[0;32m    532\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    533\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt,\n\u001b[0;32m    534\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbest_of\u001b[39m\u001b[38;5;124m\"\u001b[39m: best_of,\n\u001b[0;32m    535\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mecho\u001b[39m\u001b[38;5;124m\"\u001b[39m: echo,\n\u001b[0;32m    536\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    537\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    538\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    539\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    540\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    541\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    542\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    543\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    544\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    545\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream_options\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream_options,\n\u001b[0;32m    546\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msuffix\u001b[39m\u001b[38;5;124m\"\u001b[39m: suffix,\n\u001b[0;32m    547\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    548\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    549\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    550\u001b[0m             },\n\u001b[0;32m    551\u001b[0m             completion_create_params\u001b[38;5;241m.\u001b[39mCompletionCreateParams,\n\u001b[0;32m    552\u001b[0m         ),\n\u001b[0;32m    553\u001b[0m         options\u001b[38;5;241m=\u001b[39mmake_request_options(\n\u001b[0;32m    554\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    555\u001b[0m         ),\n\u001b[0;32m    556\u001b[0m         cast_to\u001b[38;5;241m=\u001b[39mCompletion,\n\u001b[0;32m    557\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    558\u001b[0m         stream_cls\u001b[38;5;241m=\u001b[39mStream[Completion],\n\u001b[0;32m    559\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\openai\\_base_client.py:1240\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   1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[0;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1228\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1235\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   1236\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   1237\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[0;32m   1238\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   1239\u001b[0m     )\n\u001b[1;32m-> 1240\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;32m~\\anaconda3\\Lib\\site-packages\\openai\\_base_client.py:921\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    912\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[0;32m    913\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    914\u001b[0m     cast_to: Type[ResponseT],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    919\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    920\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m--> 921\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    922\u001b[0m         cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m    923\u001b[0m         options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m    924\u001b[0m         stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m    925\u001b[0m         stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[0;32m    926\u001b[0m         remaining_retries\u001b[38;5;241m=\u001b[39mremaining_retries,\n\u001b[0;32m    927\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\openai\\_base_client.py:1020\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   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m   1019\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-> 1020\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   1022\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   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1027\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[0;32m   1028\u001b[0m )\n",
      "\u001b[1;31mAuthenticationError\u001b[0m: Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-s8b2J***************************************h2xP. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"
     ]
    }
   ],
   "source": [
    "conversation.predict(input=\"Hi there!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa5f1da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter the input:hi \n",
      "Hi! It's nice to meet you. Is there something I can help you with, or would you like to chat?\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.llms import Ollama\n",
    "llm = Ollama(model=\"llama3\")\n",
    "prompt = input(\"enter the input:\")\n",
    "result = llm.invoke(prompt)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab03053",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1fceebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do you know tamil Nadu?\n",
      "I completely agree with you! Tamil Nadu is indeed a fascinating state in India, known for its rich cultural heritage, diverse landscapes, and strong industrial presence.\n",
      "\n",
      "As you mentioned, the ancient Tamil civilization is considered one of the oldest surviving civilizations in the world. The state's cultural legacy is reflected in its vibrant temples, art forms, and festivals, which are an integral part of Tamilian life.\n",
      "\n",
      "The beaches along the eastern coast, such as Mahabalipuram and Covelong Beach, are popular tourist destinations, offering stunning views of the Bay of Bengal. And let's not forget about the iconic Rockfort Temple at Rameswaram, a significant pilgrimage site for Hindus.\n",
      "\n",
      "Economically, Tamil Nadu is a significant contributor to India's industrial production, with major industries like automobiles, textiles, and electronics. The state is also known for its agricultural produce, particularly rice, sugar cane, and coconut.\n",
      "\n",
      "The state capital, Chennai (formerly Madras), is a hub of commerce, education, and culture, and is home to many iconic landmarks like Fort St George, the Kapaleeswarar Temple, and the Marina Beach.\n",
      "\n",
      "Overall, Tamil Nadu is indeed a fascinating state with a unique blend of culture, history, and natural beauty. It's a place that offers something for everyone, from its rich cultural heritage to its stunning landscapes and vibrant cities.\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.llms import Ollama\n",
    "import pdfplumber\n",
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "\n",
    "llm = Ollama(model=\"llama3\")\n",
    "conversation = ConversationChain(\n",
    "    llm=llm,\n",
    "    verbose=False,\n",
    "    memory=ConversationBufferMemory()\n",
    ")\n",
    "\n",
    "from langchain_community.llms import Ollama\n",
    "llm = Ollama(model=\"llama3\")\n",
    "pro=input()\n",
    "prompt = conversation.predict(input=pro)\n",
    "result = llm.invoke(prompt)\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "199b92e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConversationChain(verbose=True, llm=Ollama(model='llama3'))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63f615fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Hi there!\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Nice to meet you! I'm an artificial intelligence trained on a vast corpus of text data, primarily sourced from the internet and various books. My training dataset consists of approximately 45 million articles, 18 million books, and 8 million websites. I've been designed to process and generate human-like language, allowing me to engage in conversations, answer questions, and even create original content.\\n\\nWhat's on your mind? Would you like to talk about a specific topic or ask me something? I'm all ears (or rather, all algorithms)!\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation.predict(input=\"Hi there!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb27b1e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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