I came up with this idea by reading "how to read a book".
I hope AI can follow some of the rules and apply methodology to read a novel using sequential timeline.
This project is for fun.
I used this project to improve my code skills to build infrastructure from scratch.
These classes are built with detailed logging and error checking system (where I want to improve for my code skills)
The current methods for reading PDFs using ChatGPT or GPT3 are limited to vector indexing or summarization, which can fall short when dealing with longer texts such as novels.
In my opinion, novels and movies are art forms that require a time-sequenced approach to fully appreciate them, rather than relying on relevant indexing or summarization techniques to understand chunks of information.
To address this issue, I propose using a reading theoretical methodology, inspired by "How to Read a Book" by the American philosopher Mortimer J. Adler, which would guide ChatGPT to read a book in a time sequence.
This approach would enable ChatGPT to construct an individual understanding of each chapter and accumulate both structural and interpretive knowledge about the book.
While this method can also incorporate previous methods such as embeddings, I believe it is preferable as literature should be read in sequence for a more complete understanding.
This project may continue to be constructed. Will switch to Langchain
Book is a Python class designed to read a book from a text file and split it into chunks of a specified length. Book also has a get_info() method that uses a GPT-based chatbot to extract information about the book, such as its title, author, genre, and theme.
To use Book, first create an instance of the class and specify the path to the book's text file:
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book = Book("path/to/book.txt")
You can then split the book into chunks of a specified length (in words) using the split_book() method:
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book_chunks = book.split_book(word_length=1000, start_page=1)
By default, split_book() splits the book into chunks of 1000 words, starting from the first page. You can specify a different length and starting page by passing values to the word_length and start_page parameters, respectively.
You can also use the get_info() method to extract information about the book:
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book.get_info()
This method uses a GPT-based chatbot to extract information about the book, such as its title, author, genre, and theme.
Book reads its configuration from a config.json file in the same directory as the Book class. The following options are available:
logging_file_path: The file path to use for logging.structure_file_path: The file path to use for the structure prompts.
Book logs its activity to a file specified in the logging_file_path option of the config.json file. The log file contains information about the book loading and splitting, as well as any errors that occur.
ChatGPT is a Python class designed to facilitate interactions with OpenAI's GPT-based chatbots. This class handles session management and provides a high-level function to send a message and receive a response.
To use ChatGPT, first create an instance of the class:
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chatbot = ChatGPT()
By default, ChatGPT uses the GPT-3.5 Turbo 0301 model, but you can specify a different model by passing the model name to the model parameter:
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chatbot = ChatGPT(model="gpt-3")
You can then send a message and receive a response using the send_message() method:
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response = chatbot.send_message("Hello, how are you?")
By default, send_message() returns a string containing the response. However, you can also set the make_json parameter to True to return the response as a JSON object:
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response_json = chatbot.send_message("Hello, how are you?", make_json=True)
You can also specify a custom system message to use instead of the default prompt:
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response = chatbot.send_message("Hello, how are you?", system_message="What can I do for you?")
ChatGPT reads its configuration from a config.json file in the same directory as the ChatGPT class. The following options are available:
default_system_prompt: The default prompt to use if a custom system message is not specified.model: The name of the GPT-based chatbot model to use.prompt_failure_times: The maximum number of times to retry getting a response as JSON.log_conversation: Whether to log the conversation to a file.conversation_file_path: The file path to use for logging the conversation.
ChatGPT can log conversation history to a file if the log_conversation option is set to True. The conversation is logged in the following format:
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Conversation: <conversation number>
System: <system message>
User: <user message>
Response: <response message>
Usage: <total number of tokens used>