npm start loads an OpenAI API Key and Organization, creates the OpenAI agent and then loads a Chat object that you can use in the REPL to send prompts to ChatGPT and store answers.

Start a Deno REPL with a Chat object loaded that allows you to:

• Set a configuration
  • Set a model: > Chat.setModel(<model>)
  • Set a temperature: > Chat.setTemp(0.1)
  • Set message memory: > Chat.setMemory(20)
• Ask a question in the repl:
  • > var code = Chat.getPrompt('./examples/main.go', 'utf8')
  • > var answer = await Chat.complete('What does the following program do?\n' + code)
• Get history:
  • > Chat.getHistory()
  • > Chat.getLast()
• Convert web pages to smaller docs that GPT models can use for context
  • var doc = await Chat.makeDoc('https://code.visualstudio.com/api/extension-guides/webview')
• Do anything else you can do in the Node.js REPL:
  • Read/write files: modules fs and path loaded on initialization Chat.saveFile(./completions/vscode-ext-docs/guides/webview.md, doc)
  • Import and use modules
  • Feed computational output into prompts

1. An OpenAI API Key and Organization Id
2. A recent version of Deno

1. Clone this repository
2. $ cd gpt-my-repl
3. Create a .env file with the following lines:

OPENAI_API_KEY=<your API key>
ORGANIZATION=<your organization id>

3. $ npm install
4. $ npm start

• Chat.setDefaultConfig(): Reset to the initial configuration:
  • Model: gpt-3.5-turbo
  • Temperature: 0
  • Top P: 1
  • Frequency Penalty: 0
  • Presence Penalty: 0
• Chat.getConfig(): Print the current configurations
• Chat.setModel(<model: string>): Set a valid model
• Chat.setTemp(<temperature: float>): Set a valid temperature
• Chat.setMemory(<steps: integer>): Set the number of messages from history sent with the new prompt.
• Chat.getMemory(): Since memory is not part of the API params, this is a separate getter to return the current memory size in messages

• await Chat.complete(<prompt: string>): Returns a completion with the current configuration.
• Chat.getLast() -> object: Returns the last completion in history.
• Chat.getHistory() -> object[]: Returns an array of the history of completions in reverse-chronological order.
• Chat.saveHistory(<filePath: string>): Save all chat history to a file.
• Chat.loadHistory(<filePath: string>): Load messages from a JSON file into the history array.
• Chat.clearHistory(): Clear the messages in history.
• Chat.tokenUsage(<messages: object[]) -> object: Calculates tokens used in the provided messag history array. Return an object.

• Chat.help(): Print the available methods on the Chat object
• Chat.setWorkDir(<dirPath: string>): Set the working directory in the REPL
• Chat.saveFile(<filePath: string>, <completion: string | array | object>): Save data as string to a file.
• Chat.loadFile(<filePath: string>): Load utf8 string data (text, JSON) in from a file.
• await Chat.fetch(<url: string>) -> string: Fetch a web page's html.
• await Chat.extractFromHtml(<html: string>, <url: string>) -> object: Uses the npm package @extractus/article-extractor to extract content from a web page and creates stripped-down article html (like in Safari's Reader mode). Returns an object with some properties (title, description, content, etc).
• Chat.htmlToMarkdown(<html: string>) -> string: Translates html to markdown and returns the markdown.
• await Chat.makeDoc(<url: string>, <type: string> = 'markdown) -> string: Fetches the contents of a web page, extracts article (text content) and converts content to the specified type (i.e. markdown, text). Returns article in final format.
• await Chat.concatFiles(<dir: string>, <extensions: Array<string>) -> Promise<string>: Asynchronously reads and concatenates the contents of all files with specified extensions in a given directory and its subdirectories into a single string.