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[Initialization](#initialization)\n    - [Default](#default)\n    - [With Custom Embeddings Manager](#with-custom-embeddings-manager)\n    - [With Custom Chat Completions Manager](#with-custom-chat-completions-manager)\n    - [With Custom Chat Completion Tools](#with-custom-chat-completion-tools)\n    - [With Custom Text Splitter](#with-custom-text-splitter)\n    - [Additional Notes](#additional-notes)\n  - [Embeddings](#embeddings)\n    - [Get Embedding Models](#get-embedding-models)\n    - [Create Embeddings](#create-embeddings)\n      - [Create Single Embedding](#create-single-embedding)\n      - [Create Multiple Embeddings](#create-multiple-embeddings)\n      - [Additional Notes](#additional-notes)\n  - [Chat Completions](#chat-completions)\n    - [Get Chat Completion Models](#get-chat-completion-models)\n    - [Create Chat Completion](#create-chat-completion)\n      - [As String](#as-string)\n      - [As Stream](#as-stream)\n      - [Additional Notes](#additional-notes-1)\n  - [Text Splitter](#text-splitter)\n    - [Split Text](#split-text)\n\n</details>\n\n## Installation\n\n```bash\nnpm install @singlestore/ai\n```\n\n## Usage\n\n### Initialization\n\nThe `AI` class can be initialized in various ways depending on your requirements. You can start with the default setup, or extend it with custom managers for embeddings and chat completions, or even add custom tools.\n\n#### Default\n\nThis is the simplest way to initialize the `AI` class, using an OpenAI API key.\n\n```ts\nimport { AI } from \"@singlestore/ai\";\n\nconst ai = new AI({ openAIApiKey: \"<OPENAI_API_KEY>\" });\n```\n\n#### With Custom Embeddings Manager\n\nYou can define a custom embeddings manager by extending the `EmbeddingsManager` class to handle how embeddings are created and models are selected.\n\n```ts\nimport { type CreateEmbeddingsParams, type Embedding, EmbeddingsManager } from \"@singlestore/ai\";\n\nclass CustomEmbeddingsManager extends EmbeddingsManager {\n  getModels(): string[] {\n    return [\"<MODEL_NAME>\"];\n  }\n\n  async create(input: string | string[], params?: CreateEmbeddingsParams): Promise<Embedding[]> {\n    const embeddings: Embedding[] = await customFnCall();\n    return embeddings;\n  }\n}\n\nconst ai = new AI({\n  openAIApiKey: \"<OPENAI_API_KEY>\",\n  embeddingsManager: new CustomEmbeddingsManager(),\n});\n```\n\n#### With Custom Chat Completions Manager\n\nYou can define a custom chat completions manager by extending the `ChatCompletionsManager` class. This allows you to modify how chat completions are handled, whether in a streaming or non-streaming fashion.\n\n```ts\nimport {\n  type AnyChatCompletionTool,\n  ChatCompletionsManager,\n  type CreateChatCompletionParams,\n  type CreateChatCompletionResult,\n  type MergeChatCompletionTools,\n} from \"@singlestore/ai\";\n\ntype ChatCompletionTools = undefined; // If an array of custom tools is created, use `typeof tools`.\n\nclass CustomChatCompletionsManager extends ChatCompletionsManager<ChatCompletionTools> {\n  getModels(): Promise<string[]> | string[] {\n    return [\"<MODEL_NAME>\"];\n  }\n\n  create<TStream extends boolean, TTools extends AnyChatCompletionTool[] | undefined>(\n    params: CreateChatCompletionParams<TStream, MergeChatCompletionTools<ChatCompletionTools, TTools>>,\n  ): Promise<CreateChatCompletionResult<TStream>> {\n    if (params.stream) {\n      const stream = customFnCall();\n      return stream as Promise<CreateChatCompletionResult<TStream>>;\n    }\n\n    const chatCompletion = await customFnCall();\n\n    return chatCompletion as Promise<CreateChatCompletionResult<TStream>>;\n  }\n}\n\nconst ai = new AI({\n  openAIApiKey: \"<OPENAI_API_KEY>\",\n  chatCompletionsManager: new CustomChatCompletionsManager(),\n});\n```\n\n#### With Custom Chat Completion Tools\n\nYou can also create custom tools to extend the functionality of the chat completions by defining them with the `ChatCompletionTool` class.\n\n```ts\nimport { ChatCompletionTool } from \"@singlestore/ai\";\nimport { z } from \"zod\";\n\nconst customTool = new ChatCompletionTool({\n  name: \"<TOOL_NAME>\",\n  description: \"<TOOL_DESCRIPTION>\",\n  params: z.object({ paramName: z.string().describe(\"<PARAM_DESCRIPTION>\") }),\n  call: async (params) => {\n    const value = await anyFnCall(params);\n    return { name: \"<TOOL_NAME>\", params, value: JSON.stringify(value) };\n  },\n});\n\nconst ai = new AI({\n  tools: [customTool],\n  ...\n});\n```\n\n#### With Custom Text Splitter\n\nYou can define a custom text splitter by extending the `TextSplitter` class to handle how text is split.\n\n```ts\nimport { AI, TextSplitter, TextSplitterSplitOptions } from \"@singlestore/ai\";\n\nclass CustomTextSplitter extends TextSplitter {\n  split(text: string, options?: TextSplitterSplitOptions): string[] {\n    return [];\n  }\n}\n\nconst ai = new AI({ textSplitter: new CustomTextSplitter() });\n```\n\n#### Additional Notes\n\n- If you declare a custom embeddings manager and a custom chat completions manager, the `openAIApiKey` parameter is not required.\n- Custom managers and tools allow for extensive customization, giving you the flexibility to integrate AI functionality tailored to your specific needs.\n\n---\n\n### Embeddings\n\n#### Get Embedding Models\n\n```ts\nconst models = ai.embeddings.getModels();\n```\n\n---\n\n#### Create Embeddings\n\n##### Create Single Embedding\n\n```ts\nconst embeddings = await ai.embeddings.create(\"<INPUT>\", {\n  model: \"<MODEL_NAME>\", // Optional\n  dimensions: \"<DIMENSION>\", // Optional\n});\n```\n\n##### Create Multiple Embeddings\n\n```ts\nconst embeddings = await ai.embeddings.create([\"<INPUT>\", \"<INPUT_2>\"], ...);\n```\n\n##### Additional Notes\n\n- If a custom `EmbeddingsManager` is provided, all the parameters can still be passed to the `ai.embeddings.create` method, allowing for custom handling and logic while preserving the same interface.\n\n---\n\n### Chat Completions\n\n#### Get Chat Completion Models\n\n```ts\nconst models = ai.chatCompletions.getModels();\n```\n\n#### Create Chat Completion\n\nThe `create` method allows you to generate chat completions either as a complete string or in a streamed fashion, depending on the `stream` option.\n\n##### As String\n\nPerforms a chat completion and returns the result as a complete string.\n\n```ts\nconst chatCompletion = await ai.chatCompletions.create({\n  stream: false,\n  prompt: \"<PROMPT>\",\n  model: \"<MODEL_NAME>\", // Optional\n  systemRole: \"<SYSTEM_ROLE>\", // Optional\n  messages: [{ role: \"user\", content: \"<CONTENT>\" }], // Optional\n});\n```\n\n##### As Stream\n\nPerforms a chat completion and returns the result as a stream of data chunks.\n\n```ts\nconst stream = await ai.chatCompletions.create({\n  stream: true,\n  prompt: \"<PROMPT>\",\n  model: \"<MODEL_NAME>\", // Optional\n  systemRole: \"<SYSTEM_ROLE>\", // Optional\n  messages: [{ role: \"user\", content: \"<CONTENT>\" }], // Optional\n  tools: [...] // Optional\n});\n\nconst chatCompletion = await ai.chatCompletions.handleStream(stream, async (chunk) => {\n  await customFnCall(chunk);\n});\n```\n\n##### Additional Notes\n\n- When using `stream: true`, the `handleStream` function processes the stream and accepts a callback function as the second argument. The callback handles each new chunk of data as it arrives.\n- You can use the messages array to provide additional context for the chat completion, such as user messages or system instructions.\n- If a custom `ChatCompletionsManager` is provided, all the parameters can still be passed to the `ai.chatCompletions.create` method, allowing for custom handling and logic while preserving the same interface.\n\n---\n\n### Text Splitter\n\n#### Split Text\n\nBreaks a given text into smaller chunks, making it easier to handle for tasks like generating embeddings. By default, it splits text by sentences, but you can customize it to use a different delimiter or set the maximum chunk size.\n\n```ts\nconst chunks = ai.textSplitter.split(\n  text,\n  {\n    chunkSize: 1024, // Optional; 1024 by deafult\n    delimiter: \" \", // Optional; Sentence splitting by default\n  }, // Optional\n);\n```\n\n---\n","readmeFilename":"README.md"}