################################################### # # Define Blog Content charateristics: # This is the main config file which drives the code. # This config will restrict code modifications and hence ease of usuability. # ################################################### [blog_characteristics] # Length of blogs Or word count. Note: It wont be exact and depends on GPT providers and Max token count. blog_length = 2000 # company/brand-name # professional, how-to, begginer, research, programming, casual, etc blog_tone = "Casual" # Target Audience, Gen-Z, Tech-savvy, Working professional, students, kids etc blog_demographic = "Content creators & Digital marketing" # informational, commercial, company, news, finance, competitor, programming, scholar etc blog_type = "Informational" # Spanish, German, Chinese, Arabic, Nepali, Hindi, Hindustani etc blog_language = "English" # Specify the output format of the blog as: HTML, markdown, plaintext. Defaults to markdown. blog_output_format = "markdown" ############################################################ # # Blog Images details. # Note: The images are created from the blog content. Blog title is used, # the title is modified for image generation prompt. # ############################################################ [img_details] # Options are dalle2, dalle3, stable-diffusion. image_gen_model = "stable-diffusion" # Number of blog images to include. num_images = 1 ########################################################### # # Define LLM and its charateristics for fine control on output # Note: ########################################################### [llm_options] # Choose one of following: Openai, Google, Minstral gpt_provider = google # Mention which model of the above provider to use. model = gemini-1.5-flash-latest # Temperature is a parameter that controls the “creativity” or randomness of the text generated by GPT. # greater determinism and higher values indicating more randomness. # while a lower temperature (e.g., 0.2) makes the output more deterministic and focused (thus, getting flagged as AI content). temperature = 0.7 # Top-p sampling is particularly useful in scenarios where you want to control the level of diversity in the generated text. # By adjusting the threshold p, you can influence the diversity of the generated sequences. # A lower top_p will lead to more diverse but potentially less coherent outputs, # while a higher top_p will produce more conservative outputs with higher probability tokens. top_p = 0.9 # "Max tokens" is a parameter that determines the maximum length of the output sequence generated by a model, # usually measured in the number of tokens (words or subwords). # It helps control the length of generated text and manage computational resources during text generation tasks. max_tokens = 4096 # "n" represents the number of words or characters grouped together in a sequence when analyzing text. # For example, if "n" is 2, we're looking at pairs of words (bigrams), # if "n" is 3, we're looking at groups of three words (trigrams), and so on. # It helps us understand patterns and relationships between words in a piece of text. n = 1 # The frequency penalty parameter, ranging from -1 to 1, influences word selection during text generation. # Higher values favor less common words, promoting diversity, while lower values favor common words, leading to more predictable text. frequency_penalty = 1 # Presence Penalty encourages the use of diverse words by discouraging repetition. # It encourages the model to avoid using the same words repeatedly and prompts it to generate varied text by suggesting, # "Try using different words instead of repeating the same ones." # from -2 (more flexible while generating text) to 2 (strong discouragement in repetition). presence_penalty = 1 ###################################################### # # Search Engine Paramters. # Alwrity does comprehensive web research for given content topic. # Choose search engine parameters below, this finetunes search results # and makes the generated content more accurate. # ###################################################### # Visit https://serper.dev/playground and provide values from there. # https://api.serper.dev/locations [web_research] # Geographic location(gl): This values restricts the web search to given country. # Examples are us for United States, in for India, fr for france, cn for china etc geo_location = us # Locale:hl:language : Define the language you want to search results in. # Example: en for english, zn-cn for chinese, de for german, hi for hindi etc search_language: en # num_results: Default 10 - Number of google search results to fetch. num_results = 10 # time_range: Acceptable values, past day, past week, past month, past year # This limits the search results for given time duration, from today. time_range = anytime # include_domains (Give Full URLs, separate by comma): A list of domains to specifically include in the search results. # Default is None, which includes all domains. Example: https://wikipedia.com,https://stackoverflow.com,google schalor,reddit etc include_domains = https://alwrity.com # similar_url : A single URL, this will instruct search engines to give results similar to the given URL. similar_url = ########################################################### # # Creating Your Virtual content writing Team. # # One can choose from the following roles and make a virtual team. # # Chief Editor - Oversees the research process and manages the team. # Researcher (gpt-researcher) - A specialized autonomous agent that conducts in depth research on a given topic. # Editor - Responsible for planning the research outline and structure. # Reviewer - Validates the correctness of the research results given a set of criteria. # Revisor - Revises the research results based on the feedback from the reviewer. # Writer - Responsible for compiling and writing the final report. # Publisher - Responsible for publishing the final report in various formats. # ########################################################### # Choose multi-agent framework, for now its default to Crewai. More options for langgraph, agentgpt, autogpt etc. # Step1: Choose the team members from this list: chief_editor, researcher, editor, reviewer, writer, publisher # Only the mentioned team members/agents will be included in your dream team. your_content_team_members = chief_editor, researcher, editor, reviewer, writer # Step2: Edit team members for your need in workspace/my_content_team folder. # Personlization of AI team members for your needs/requirements. Imagine a real team and think along those lines. # To get you started, refer these files in workspace folder, these are template Agents, one can modify for their company's content needs. # Tip: Start small, define roles, goals and give backstory which makes Agents work for you. # Run the example and keep a quality control. Change Persona of the agent, tone of voice, personality etc. # Check if researcher is giving right results and check out of each Agent. Iterate and refine each agents, until they write to your # requirements. This will take time but you will end up with a AI Agents content writing team for your own needs(Free, Free, Free). # where_your_team_at = Provide the directory location where Team members are defined. # Check with template files in workspace/my_content_team folder. # where_your_team_at =