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with token checking and retries","homepage":"https://github.com/dzhng/llm-api","keywords":["typescript","gpt","chatgpt","llama","llm","ai","ml","prompt","prompt engineering","openai"],"repository":{"type":"git","url":"git+ssh://git@github.com/dzhng/llm-api.git"},"author":{"name":"David Zhang","email":"david@aomni.com"},"bugs":{"url":"https://github.com/dzhng/llm-api/issues"},"license":"MIT","readme":"# ✨ LLM API\n\n[![test](https://github.com/dzhng/llm-api/actions/workflows/test.yml/badge.svg?branch=main&event=push)](https://github.com/dzhng/llm-api/actions/workflows/test.yml)\n\nFully typed chat APIs for OpenAI, Anthropic, and Azure's chat models for browser, edge, and node environments.\n\n- [Introduction](#-introduction)\n- [Usage](#-usage)\n- [Azure](#-azure)\n- [Anthropic](#-anthropic)\n- [Groq](#-groq)\n- [Amazon Bedrock](#-amazon-bedrock)\n- [Debugging](#-debugging)\n\n## 👋 Introduction\n\n- Clean interface for text and chat completion for OpenAI, Anthropic, and Azure models\n- Catch token overflow errors automatically on the client side\n- Handle rate limit and any other API errors as gracefully as possible (e.g. exponential backoff for rate-limit)\n- Support for browser, edge, and node environments\n- Works great with [zod-gpt](https://github.com/dzhng/zod-gpt) for outputting structured data\n\n```typescript\nimport { OpenAIChatApi } from 'llm-api';\n\nconst openai = new OpenAIChatApi({ apiKey: 'YOUR_OPENAI_KEY' });\n\nconst resText = await openai.textCompletion('Hello');\n\nconst resChat = await openai.chatCompletion({\n  role: 'user',\n  content: 'Hello world',\n});\n```\n\n## 🔨 Usage\n\n### Install\n\nThis package is hosted on npm:\n\n```\nnpm i llm-api\n```\n\n```\nyarn add llm-api\n```\n\n### Model Config\n\nTo configure a new model endpoint:\n\n```typescript\nconst openai = new OpenAIChatApi(params: OpenAIConfig, config: ModelConfig);\n```\n\nThese model config map to OpenAI's config directly, see doc:\nhttps://platform.openai.com/docs/api-reference/chat/create\n\n```typescript\ninterface ModelConfig {\n  model?: string;\n  contextSize?: number;\n  maxTokens?: number;\n  temperature?: number;\n  topP?: number;\n  stop?: string | string[];\n  presencePenalty?: number;\n  frequencyPenalty?: number;\n  logitBias?: Record<string, number>;\n  user?: string;\n\n  // use stream mode for API response, the streamed tokens will be sent to `events in `ModelRequestOptions`\n  stream?: boolean;\n}\n```\n\n### Request\n\nTo send a completion request to a model:\n\n```typescript\nconst text: ModelResponse = await openai.textCompletion(api: CompletionApi, prompt: string, options: ModelRequestOptions);\n\nconst completion: ModelResponse = await openai.chatCompletion(api: CompletionApi, messages: ChatCompletionRequestMessage, options: ModelRequestOptions);\n\n// respond to existing chat session, preserving the past messages\nconst response: ModelResponse = await completion.respond(message: ChatCompletionRequestMessage, options: ModelRequestOptions);\n```\n\n**options**\nYou can override the default request options via this parameter. A request will automatically be retried if there is a ratelimit or server error.\n\n```typescript\ntype ModelRequestOptions = {\n  // set to automatically add system message (only relevant when using textCompletion)\n  systemMessage?: string | (() => string);\n\n  // send a prefix to the model response so the model can continue generating from there, useful for steering the model towards certain output structures.\n  // the response prefix WILL be appended to the model response.\n  // for Anthropic's models ONLY\n  responsePrefix?: string;\n\n  // function related parameters are for OpenAI's models ONLY\n  functions?: ModelFunction[];\n  // force the model to call the following function\n  callFunction?: string;\n\n  // default: 3\n  retries?: number;\n  // default: 30s\n  retryInterval?: number;\n  // default: 60s\n  timeout?: number;\n\n  // the minimum amount of tokens to allocate for the response. if the request is predicted to not have enough tokens, it will automatically throw a 'TokenError' without sending the request\n  // default: 200\n  minimumResponseTokens?: number;\n\n  // the maximum amount of tokens to use for response\n  // NOTE: in OpenAI models, setting this option also requires contextSize in ModelConfig to be set\n  maximumResponseTokens?: number;\n};\n```\n\n### Response\n\nCompletion responses are in the following format:\n\n```typescript\ninterface ModelResponse {\n  content?: string;\n\n  // used to parse function responses\n  name?: string;\n  arguments?: JsonValue;\n\n  usage?: {\n    promptTokens: number;\n    completionTokens: number;\n    totalTokens: number;\n  };\n\n  // function to send another message in the same 'chat', this will automatically append a new message to the messages array\n  respond: (\n    message: ChatCompletionRequestMessage,\n    opt?: ModelRequestOptions,\n  ) => Promise<ModelResponse>;\n}\n```\n\n### 📃 Token Errors\n\nA common error with LLM APIs is token usage - you are only allowed to fit a certain amount of data in the context window.\n\nIf you set a `contextSize` key, `llm-api` will automatically determine if the request will breach the token limit BEFORE sending the actual request to the model provider (e.g. OpenAI). This will save one network round-trip call and let you handle these type of errors in a responsive manner.\n\n```typescript\nconst openai = new OpenAIChatApi(\n  { apiKey: 'YOUR_OPENAI_KEY' },\n  { model: 'gpt-4-0613', contextSize: 8129 },\n);\n\ntry {\n  const res = await openai.textCompletion(...);\n} catch (e) {\n  if (e instanceof TokenError) {\n    // handle token errors...\n  }\n}\n```\n\n## 🔷 Azure\n\n`llm-api` also comes with support for Azure's OpenAI models. The Azure version is usually much faster and more reliable than OpenAI's own API endpoints. In order to use the Azure endpoints, you must include 2 Azure specific options when initializing the OpenAI model, `azureDeployment` and `azureEndpoint`. The `apiKey` field will also now be used for the Azure API key.\n\nYou can find the Azure API key and endpoint in the [Azure Portal](https://portal.azure.com/). The Azure Deployment must be created under the [Azure AI Portal](https://oai.azure.com/).\n\nNote that the `model` parameter in `ModelConfig` will be ignored when using Azure. This is because in the Azure system, the `model` is selected on deployment creation, not on run time.\n\n```typescript\nconst openai = new OpenAIChatApi({\n  apiKey: 'AZURE_OPENAI_KEY',\n  azureDeployment: 'AZURE_DEPLOYMENT_NAME',\n  azureEndpoint: 'AZURE_ENDPOINT',\n\n  // optional, defaults to 2023-06-01-preview\n  azureApiVersion: 'YYYY-MM-DD',\n});\n```\n\n## 🔶 Anthropic\n\nAnthropic's models have the unique advantage of a large 100k context window and extremely fast performance. If no explicit model is specified, `llm-api` will default to the Claude Sonnet model.\n\n```typescript\nconst anthropic = new AnthropicChatApi(params: AnthropicConfig, config: ModelConfig);\n```\n\n## 🔶 Groq\n\nGroq is a new LLM inference provider that provides the fastest inference speed on the market. They currently support Meta's Llama 2 and Mistral's Mixtral models.\n\n```typescript\nconst groq = new GroqChatApi(params: GroqConfig, config: ModelConfig);\n```\n\n## ❖ Amazon Bedrock\n\n```typescript\nconst conf = {\n  accessKeyId: 'AWS_ACCESS_KEY',\n  secretAccessKey: 'AWS_SECRET_KEY',\n};\n\nconst bedrock = new AnthropicBedrockChatApi(params: BedrockConfig, config: ModelConfig);\n```\n\n## 🤓 Debugging\n\n`llm-api` usese the `debug` module for logging & error messages. To run in debug mode, set the `DEBUG` env variable:\n\n`DEBUG=llm-api:* yarn playground`\n\nYou can also specify different logging types via:\n\n`DEBUG=llm-api:error yarn playground`\n`DEBUG=llm-api:log yarn playground`\n","readmeFilename":"README.md"}