package.json

{
  "name": "review-gpt",
  "displayName": "review-gpt",
  "description": "An automated code review tool powered by gpt3",
  "license": "GNU GPLv3.0",
  "publisher": "reviewgpt",
  "icon": "assets/logo/shrinked_logo.png",
  "repository": {
    "type": "git",
    "url": "https://github.com/vibovenkat123/rgpt-vscode"
  },
  "version": "1.2.8",
  "engines": {
    "vscode": "^1.75.0"
  },
  "categories": [
    "Other"
  ],
  "activationEvents": [
    "review-gpt.review"
  ],
  "main": "./out/extension.js",
  "keywords": [
    "reviewgpt",
    "gpt",
    "code review",
    "review",
    "vibovenkat",
    "vibovenkat123"
  ],
  "contributes": {
    "configuration": {
      "title": "rgpt",
      "properties": {
        "rgpt.model": {
          "type": "string",
          "default": "turbo",
          "description": "The model for GPT to use"
        },
        "rgpt.max_tokens": {
          "type": "integer",
          "default": 500,
          "description": "The maximum tokens to use (go to OpenAI's tokenizer to convert your text to tokens). More tokens = More expensive."
        },
        "rgpt.temperature" : {
          "type": "number",
          "default": 0.2,
          "description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic."
        },
        "rgpt.top_p": {
            "type": "number",
            "default": 1,
            "description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered."
        },
        "rgpt.frequence_penalty": {
          "type": "number",
          "default": 1.2,
          "description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
        },
        "rgpt.presence_penalty": {
          "type": "number",
          "default": 0.3, 
          "description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics."
        },
        "rgpt.best_of": {
          "type": "integer",
          "default": 1,
          "description": "Generates best_of completions server-side and returns the 'best' (the one with the highest log probability per token). Results cannot be streamed."
        }
      }
    },
    "commands": [
      {
        "command": "review-gpt.review",
        "title": "RGPT: Review Code"
      }
    ]
  },
  "scripts": {
    "vscode:prepublish": "npm run compile",
    "compile": "tsc -p ./",
    "watch": "tsc -watch -p ./",
    "pretest": "npm run compile && npm run lint",
    "lint": "eslint src --ext ts",
    "test": "node ./out/test/runTest.js"
  },
  "devDependencies": {
    "@types/vscode": "^1.75.0",
    "@types/glob": "^8.0.1",
    "@types/mocha": "^10.0.1",
    "@types/node": "16.x",
    "@typescript-eslint/eslint-plugin": "^5.49.0",
    "@typescript-eslint/parser": "^5.49.0",
    "eslint": "^8.33.0",
    "glob": "^8.1.0",
    "mocha": "^10.1.0",
    "typescript": "^4.9.4",
    "@vscode/test-electron": "^2.2.2"
  }
}