166 lines
7.3 KiB
Plaintext
166 lines
7.3 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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# company/brand-name
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# professional, how-to, begginer, research, programming, casual, etc
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blog_tone = "Casual"
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# Target Audience, Gen-Z, Tech-savvy, Working professional, students, kids etc
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blog_demographic = "Content creators & Digital marketing"
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# informational, commercial, company, news, finance, competitor, programming, scholar etc
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blog_type = "Informational"
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# Spanish, German, Chinese, Arabic, Nepali, Hindi, Hindustani etc
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blog_language = "English"
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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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############################################################
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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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[img_details]
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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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[llm_options]
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# Choose one of following: Openai, Google, Minstral
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gpt_provider = google
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# Mention which model of the above provider to use.
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model = gemini-1.5-flash-latest
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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 = 0.7
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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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######################################################
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#
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# Search Engine Paramters.
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# Alwrity does comprehensive web research for given content topic.
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# Choose search engine parameters below, this finetunes search results
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# and makes the generated content more accurate.
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#
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######################################################
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# Visit https://serper.dev/playground and provide values from there.
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# https://api.serper.dev/locations
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[web_research]
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# Geographic location(gl): This values restricts the web search to given country.
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# Examples are us for United States, in for India, fr for france, cn for china etc
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geo_location = us
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# Locale:hl:language : Define the language you want to search results in.
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# Example: en for english, zn-cn for chinese, de for german, hi for hindi etc
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search_language: en
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# num_results: Default 10 - Number of google search results to fetch.
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num_results = 10
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# time_range: Acceptable values, past day, past week, past month, past year
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# This limits the search results for given time duration, from today.
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time_range = anytime
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# include_domains (Give Full URLs, separate by comma): A list of domains to specifically include in the search results.
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# Default is None, which includes all domains. Example: https://wikipedia.com,https://stackoverflow.com,google schalor,reddit etc
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include_domains =
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# similar_url : A single URL, this will instruct search engines to give results similar to the given URL.
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similar_url =
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###########################################################
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#
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# Creating Your Virtual content writing Team.
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#
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# One can choose from the following roles and make a virtual team.
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#
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# Chief Editor - Oversees the research process and manages the team.
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# Researcher (gpt-researcher) - A specialized autonomous agent that conducts in depth research on a given topic.
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# Editor - Responsible for planning the research outline and structure.
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# Reviewer - Validates the correctness of the research results given a set of criteria.
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# Revisor - Revises the research results based on the feedback from the reviewer.
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# Writer - Responsible for compiling and writing the final report.
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# Publisher - Responsible for publishing the final report in various formats.
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#
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###########################################################
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# Choose multi-agent framework, for now its default to Crewai. More options for langgraph, agentgpt, autogpt etc.
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# Step1: Choose the team members from this list: chief_editor, researcher, editor, reviewer, writer, publisher
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# Only the mentioned team members/agents will be included in your dream team.
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your_content_team_members = chief_editor, researcher, editor, reviewer, writer
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# Step2: Edit team members for your need in workspace/my_content_team folder.
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# Personlization of AI team members for your needs/requirements. Imagine a real team and think along those lines.
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# To get you started, refer these files in workspace folder, these are template Agents, one can modify for their company's content needs.
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# Tip: Start small, define roles, goals and give backstory which makes Agents work for you.
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# Run the example and keep a quality control. Change Persona of the agent, tone of voice, personality etc.
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# Check if researcher is giving right results and check out of each Agent. Iterate and refine each agents, until they write to your
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# requirements. This will take time but you will end up with a AI Agents content writing team for your own needs(Free, Free, Free).
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# where_your_team_at = Provide the directory location where Team members are defined.
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# Check with template files in workspace/my_content_team folder.
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# where_your_team_at =
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