94 lines
3.9 KiB
Plaintext
94 lines
3.9 KiB
Plaintext
###################################################
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#
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# Define Blog Content charateristics:
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# This is the main config file which drives the code.
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# This config will restrict code modifications and hence ease of usuability.
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#
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###################################################
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[blog_characteristics]
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# Length of blogs Or word count. Note: It wont be exact and depends on GPT providers and Max token count.
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blog_length = 2000
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# professional, how-to, begginer, research, programming, casual, etc
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blog_tone = "professional"
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# Target Audience, Gen-Z, Tech-savvy, Working professional, students, kids etc
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blog_demographic = "All"
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# informational, commercial, company, news, finance, competitor, programming, scholar etc
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blog_type = "Informational"
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# German, Chinese, Arabic, Nepali, Hindi, Hindustani etc
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blog_language = "Spanish"
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# Specify the output format of the blog as: HTML, markdown, plaintext. Defaults to markdown.
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blog_output_format = "markdown"
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# Specify full path to folder where the final blog should be stored. ex: _posts
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blog_output_folder = ""
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# Specify full path to folder where blog images will be stored. ex: assets
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blog_image_output_folder = ""
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############################################################
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#
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# Blog Images details.
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# Note: The images are created from the blog content. Blog title is used,
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# the title is modified for image generation prompt.
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#
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############################################################
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# Options are dalle2, dalle3, stable-diffusion.
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image_gen_model = "stable-diffusion"
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# Number of blog images to include.
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num_images = 1
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###########################################################
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#
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# Define LLM and its charateristics for fine control on output
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# Note:
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###########################################################
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# Choose one of following: Openai, Google, Minstral
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gpt_provider = "openai"
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# Mention which model of the above provider to use.
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model = "gpt-3.5-turbo-0125"
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# Temperature is a parameter that controls the “creativity” or randomness of the text generated by GPT.
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# greater determinism and higher values indicating more randomness.
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# while a lower temperature (e.g., 0.2) makes the output more deterministic and focused (thus, getting flagged as AI content).
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temperature = 0.6
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# Top-p sampling is particularly useful in scenarios where you want to control the level of diversity in the generated text.
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# By adjusting the threshold p, you can influence the diversity of the generated sequences.
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# A lower top_p will lead to more diverse but potentially less coherent outputs,
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# while a higher top_p will produce more conservative outputs with higher probability tokens.
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top_p = 0.9
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# "Max tokens" is a parameter that determines the maximum length of the output sequence generated by a model,
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# usually measured in the number of tokens (words or subwords).
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# It helps control the length of generated text and manage computational resources during text generation tasks.
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max_tokens = 4096
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# "n" represents the number of words or characters grouped together in a sequence when analyzing text.
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# For example, if "n" is 2, we're looking at pairs of words (bigrams),
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# if "n" is 3, we're looking at groups of three words (trigrams), and so on.
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# It helps us understand patterns and relationships between words in a piece of text.
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n = 1
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# The frequency penalty parameter, ranging from -1 to 1, influences word selection during text generation.
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# Higher values favor less common words, promoting diversity, while lower values favor common words, leading to more predictable text.
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frequency_penalty = 1
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# Presence Penalty encourages the use of diverse words by discouraging repetition.
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# It encourages the model to avoid using the same words repeatedly and prompts it to generate varied text by suggesting,
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# "Try using different words instead of repeating the same ones."
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# from -2 (more flexible while generating text) to 2 (strong discouragement in repetition).
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presence_penalty = 1
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