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lightweight interface to OpenAI's Chat-based LLMs","author":{"name":"Adrian Cooney","email":"cooney.adrian@gmail.com"},"license":"MIT","readme":"# `prompt`\n\nA lightweight interface to OpenAI's Chat-based LLMs\n\n#### Features\n\n- Edge compatible\n- Streaming support (via `ReadableStream`)\n  - Includes accurate token count estimation.\n  - `onToken` and `onComplete` callbacks.\n\n---\n\nInput your prompts as a series of messages from the 3 possible roles: `system`, `ai` (\"assistant\") or `user`. `prompt` will read your OpenAI API key from `process.env.OPENAI_API_KEY`.\n\n```ts\nimport { prompt, system, user } from \"@adriancooney/prompt\";\n\nconst response = await prompt([\n  system(\"You are a helpful AI assistant\"),\n  user(\"If you drop a bowl of detergent, is it really a mess?\"),\n]);\n\nconsole.log(response);\n\n// {\n//   model: 'gpt-4',\n//   output: 'If you drop a bowl of detergent, it can be considered a mess in the sense that it requires cleaning up. Although detergent is a cleaning agent, it can create slippery surfaces, pose a risk to children or pets who may ingest it, and potentially damage certain materials. It is important to clean up any spilled detergent promptly to avoid potential hazards.',\n//   prompts: [\n//     { role: 'system', content: 'You are a helpful AI assistant' },\n//     {\n//       role: 'user',\n//       content: 'If you drop a bowl of detergent, is it really a mess?'\n//     },\n//     {\n//       role: 'ai',\n//       content: 'If you drop a bowl of detergent, it can be considered a mess in the sense that it requires cleaning up. Although detergent is a cleaning agent, it can create slippery surfaces, pose a risk to children or pets who may ingest it, and potentially damage certain materials. It is important to clean up any spilled detergent promptly to avoid potential hazards.'\n//     }\n//   ],\n//   timestamp: 1684781024503,\n//   duration: 13577,\n//   estimatedTokens: 100\n// }\n```\n\nThe response object contains the following properties:\n\n- `output` - The full text output receive from the model. (`string`)\n- `model` - The OpenAI Chat Completion model used. (`gpt-4` or `gpt-3.5-turbo` (default))\n- `prompts` - The input array of messages and the output `ai` message. (`ChatMessage[]`)\n- `timestamp` - The time the completion operation started at. (`number` ms)\n- `duration` - The time it took to complete the operation. (`number` ms)\n- `estimatedTokens` - The amount of tokens the operation used. Exact value when called via `prompt`, an accurate estimate when called via `promptStream`. (`number`)\n\nSending another message to the chat is a matter of appending the `prompts` returned from previous calls:\n\n```ts\nimport { prompt, system, user } from \"@adriancooney/prompt\";\n\nconst { prompts } = await prompt([\n  system(\"You are a helpful AI assistant\"),\n  user(\"If you drop a bowl of detergent, is it really a mess?\"),\n]);\n\nconst { output } = await prompt([\n  ...prompts,\n  user(\"Can you repeat your answer in a Batman monologue?\"),\n]);\n\nconsole.log(output);\n\n// *dramatic Batman voice* In the shadows of the night, a fallen bowl of detergent\n// lurks. Though it cleans, it brings chaos, danger to unsuspecting victims. We must\n// act swiftly to vanquish this foe, for the sake of Gotham's children and creatures.\n// The darkness awaits, but justice will prevail..\n```\n\nTo stream your response from an Edge function in Next.js, use the `promptStream` method. It's options are the same with the addition of two callbacks, `onToken` and `onComplete`.\n\n```ts\nimport { promptStream, system, user } from \"@adriancooney/prompt\";\n\nexport const runtime = \"edge\";\n\nexport async function POST(request: Request) {\n  return await promptStream(\n    [\n      system(\"You are a helpful AI assistant\"),\n      user(\"If you drop a bowl of detergent, is it really a mess?\"),\n    ],\n    {\n      onToken(token, response) {\n        console.log(token);\n      },\n\n      onComplete(response) {\n        await sql`\n          INSERT INTO messages(content, estimated_tokens) VALUES(${response.output}, ${response.estimatedTokens});\n        `;\n      },\n    }\n  );\n}\n```\n\nConfigure the prompt:\n\n```ts\nawait prompt(\n  [\n    system(\"You are a helpful AI assistant\"),\n    user(\"If you drop a bowl of detergent, is it really a mess?\"),\n  ],\n  {\n    apiKey: \"...\", // defaults process.env.OPENAI_API_KEY\n    model: \"gpt-3.5-turbo\", // defaults process.env.OPENAI_MODEL\n    frequencyPenalty: 1, // defaults process.env.OPENAI_FREQUENCY_PENALTY\n    presencePenalty: 1, // defaults process.env.OPENAI_PRESENCE_PENALTY\n    maxTokens: 7, // defaults process.env.OPENAI_MAX_TOKENS\n    temperature: 0, // defaults process.env.OPENAI_TEMPERATURE\n    topP: 1, // defaults process.env.OPENAI_TOP_P\n  }\n);\n```\n\nIf an error is called, an `OpenAIError` will be thrown and a `OpenAIModelOverloadedError` when the server is overloaded.\n\n```ts\nimport {\n  prompt,\n  system,\n  user,\n  OpenAIError,\n  OpenAIModelOverloadedError,\n} from \"@adriancooney/prompt\";\n\ntry {\n  await prompt([\n    // ...\n  ]);\n} catch (err) {\n  if (err instanceof OpenAIModelOverloadedError) {\n    console.log(\n      \"Open AI is overloaded and ignoring us! Please try again later.\"\n    );\n  }\n\n  if (err instanceof OpenAIError) {\n    console.log(err);\n  }\n\n  throw err;\n}\n```\n\n### Interface\n\n```ts\ntype ChatCompletionOptions = {\n  apiKey: string;\n  model: \"gpt-3.5-turbo\" | \"gpt-4\";\n  frequencyPenalty?: number;\n  maxTokens?: number;\n  presencePenalty?: number;\n  temperature?: number;\n  topP?: number;\n};\n\ntype Message = {\n  content: string;\n  metadata?: any;\n};\n\ntype SystemMessage = Message & {\n  role: \"system\";\n};\n\ntype UserMessage = Message & {\n  role: \"user\";\n};\n\ntype AIMessage = Message & {\n  role: \"ai\";\n};\n\ntype ChatMessage = SystemMessage | UserMessage | AIMessage;\n\ntype PromptResponse = {\n  prompts: ChatMessage[];\n  output: string;\n  timestamp: number;\n  duration: number;\n  estimatedTokens: number;\n  model: ChatCompletionOptions[\"model\"];\n};\n\ndeclare function prompt(\n  prompts: (string | ChatMessage)[],\n  options?: Partial<ChatCompletionOptions>\n): Promise<PromptResponse>;\n\ndeclare function promptStream(\n  prompts: (string | ChatMessage)[],\n  options?: {\n    onToken?: (token: string, res: PromptResponse) => void | Promise<void>;\n    onComplete?: (res: PromptResponse) => void | Promise<void>;\n  } & Partial<ChatCompletionOptions>\n): Promise<ReadableStream>;\n\ndeclare function system(content: string, metadata?: any): SystemMessage;\ndeclare function user(content: string, metadata?: string): UserMessage;\ndeclare function ai(content: string, metadata?: any): AIMessage;\n\ndeclare class OpenAIError extends Error {\n  type: string;\n  code: string | null;\n  constructor(type: string, code: string | null, message: string);\n}\n\ndeclare class OpenAIModelOverloadedError extends OpenAIError {}\n\nexport {\n  AIMessage,\n  ChatMessage,\n  OpenAIError,\n  OpenAIModelOverloadedError,\n  SystemMessage,\n  UserMessage,\n  ai,\n  prompt,\n  promptStream,\n  system,\n  user,\n};\n```\n","readmeFilename":"README.md"}