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#+begin_src ""Extract a list of questions that would result in the following text:: "";; ;;###autoload (defun ollama-reifiy-region () "Execute marked text and save result as string." (setq instr "Extract a list of questions that would result in the following text:") (interactive) (with-output-to-temp-buffer "ollama" (setq inputd (format "%s: ""%s""" instr (buffer-substring (region-beginning) (region-end)))) (setq response (ollama-prompt ollama:endpoint inputd ollama:model)) (setq inputd2 (format "%s: ""%s""" instr response)) (princ (format "#+begin_src ""%s""" inputd )) (princ (format "#+begin_src output\n%s\n#+end_src\n" response)) (dotimes (i 4) (setq inputd2 (format "apply "%s" to "%s" " response inputd2)) (setq response (ollama-prompt ollama:endpoint inputd2 ollama:model)) (princ (format "#+begin_src output%s\n%s\n#+end_src\n" i response)) ))) """"#+begin_src output

  1. How do I extract a list of questions from the given text?
  2. What is the purpose of defining a function named ollama-reifiy-region()?
  3. How can I use the function to get the desired result? #+end_src #+begin_src output0 To extract a list of questions that would result in the following text:: "1. How do I extract a list of questions from the given text? 2. What is the purpose of defining a function named ollama-reifiy-region()? 3. How can I use the function to get the desired result?" you can follow these steps:
  4. Start by identifying the keywords and phrases that are likely to appear in the questions. For example, "extract", "list", "questions", "given text", "function", "define", "name", "purpose", "desired result".
  5. Use a natural language processing (NLP) library or tool to analyze the given text and identify sentences that contain these keywords and phrases. You can use techniques such as named entity recognition, part-of-speech tagging, and dependency parsing to extract relevant information from the text.
  6. Once you have identified the relevant sentences, you can extract the questions by summarizing them into a concise form. For example, "How do I extract a list of questions from the given text?" could be summarized as "What is the process for extracting a list of questions from the given text?"
  7. Repeat this process for all the relevant sentences to generate a comprehensive list of questions that would result in the desired output.
  8. Finally, you can use the ollama-reifiy-region() function to get the desired result by providing it with the appropriate input parameters and calling it as needed. #+end_src #+begin_src output1
  9. To extract a list of questions that would result in the following text:: "1. How do I extract a list of questions from the given text? 2. What is the purpose of defining a function named ollama-reifiy-region()? 3. How can I use the function to get the desired result?" you can follow these steps:
  • Identify keywords and phrases that are likely to appear in the questions, such as "extract", "list", "questions", "given text", "function", "define", "name", "purpose", "desired result".
  • Use a natural language processing (NLP) library or tool to analyze the given text and identify sentences that contain these keywords and phrases.
  • Extract the questions by summarizing them into a concise form.
  • Repeat this process for all relevant sentences to generate a comprehensive list of questions that would result in the desired output.
  • Use the ollama-reifiy-region() function to get the desired result by providing it with the appropriate input parameters and calling it as needed. #+end_src #+begin_src output2 To apply "Extract a list of questions that would result in the following text:: ""1. How do I extract a list of questions from the given text? 2. What is the purpose of defining a function named ollama-reifiy-region()? 3. How can I use the function to get the desired result?" you can follow these steps:
  1. Start by identifying the keywords and phrases that are likely to appear in the questions. For example, "extract", "list", "questions", "given text", "function", "define", "name", "purpose", "desired result".
  2. Use a natural language processing (NLP) library or tool to analyze the given text and identify sentences that contain these keywords and phrases. You can use techniques such as named entity recognition, part-of-speech tagging, and dependency parsing to extract relevant information from the text.
  3. Once you have identified the relevant sentences, you can extract the questions by summarizing them into a concise form. For example, "How do I extract a list of questions from the given text?" could be summarized as "What is the process for extracting a list of questions from the given text?"
  4. Repeat this process for all the relevant sentences to generate a comprehensive list of questions that would result in the desired output.
  5. Finally, you can use the ollama-reifiy-region() function to get the desired result by providing it with the appropriate input parameters and calling it as needed. #+end_src #+begin_src output3

To apply the steps to extract a list of questions that would result in the given text, you can follow these steps:

  1. Identify keywords and phrases that are likely to appear in the questions, such as "extract", "list", "questions", "given text", "function", "define", "name", "purpose", "desired result".
  2. Use a natural language processing (NLP) library or tool to analyze the given text and identify sentences that contain these keywords and phrases.
  3. Extract the questions by summarizing them into a concise form. For example, "How do I extract a list of questions from the given text?" could be summarized as "What is the process for extracting a list of questions from the given text?"
  4. Repeat this process for all relevant sentences to generate a comprehensive list of questions that would result in the desired output.
  5. Finally, you can use the ollama-reifiy-region() function to get the desired result by providing it with the appropriate input parameters and calling it as needed. #+end_src