The KB Microservice is a Python-based application that provides a simple and efficient way to manage a knowledge base (KB) of articles. It allows users to create, search, and update KB articles through a RESTful API. The service uses OpenAI's GPT model to process and generate the content of the articles.

1. Create key_openai.txt and place your API key within.
2. Create kb/ directory for your KB articles.
3. Install all requirements in requirements.txt.

1. Run kb_microservice.py - this is a Flask app that runs on 999 by default.
2. Test it using test_kb_service.py to create, search, and update KB articles.

The KB Microservice uses Flask, a lightweight web framework for Python, to expose endpoints for creating, searching, and updating KB articles. The service uses YAML files to store the articles, and a directory text file to keep track of all the articles in the knowledge base.

The service uses OpenAI's GPT model to process user inputs and generate the content of the articles. The GPT model is a powerful language model that can generate human-like text based on the input it receives.

To create a KB article, a POST request is made to the /create endpoint with a JSON payload containing the text for the article. The service then uses the GPT model to process the text and generate a JSON object containing the title, description, keywords, and body of the article. The article is then saved as a YAML file in the knowledge base directory.

To search for KB articles, a POST request is made to the /search endpoint with a JSON payload containing the search query. The service first updates the directory of articles, then uses the GPT model to process the query and return a list of relevant article filenames. The service then opens each file, converts the YAML content to JSON, and returns the list of articles as a JSON response.

To update a KB article, a POST request is made to the /update endpoint with a JSON payload containing the title of the article to update and the new text for the article. The service first opens the existing article, then uses the GPT model to process the new text and generate an updated JSON object for the article. The updated article is then saved back to the knowledge base directory.

1. Daily Journal (episodic memory)
   • Prioritize based on relevance, or temporal proximity
2. Tasks (like internal Jira or Trello)
   • Prioritize based on ROI or heuristic imperatives (e.g. which tasks will reduce suffering the most, increase prosperity the most, and increase understanding the most)
3. Dossiers (basically KB article on users)