@langchain/community/dist/llms/friendli.cjs.map

Third-party integrations for LangChain.js

{"version":3,"file":"friendli.cjs","names":["LLM","convertEventStreamToIterableReadableDataStream","GenerationChunk"],"sources":["../../src/llms/friendli.ts"],"sourcesContent":["import { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n  type BaseLLMCallOptions,\n  type BaseLLMParams,\n  LLM,\n} from \"@langchain/core/language_models/llms\";\nimport { GenerationChunk } from \"@langchain/core/outputs\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport { convertEventStreamToIterableReadableDataStream } from \"../utils/event_source_parse.js\";\n\n/**\n * The FriendliParams interface defines the input parameters for\n * the Friendli class.\n */\nexport interface FriendliParams extends BaseLLMParams {\n  /**\n   * Model name to use.\n   */\n  model?: string;\n  /**\n   * Base endpoint url.\n   */\n  baseUrl?: string;\n  /**\n   * Friendli personal access token to run as.\n   */\n  friendliToken?: string;\n  /**\n   * Friendli team ID to run as.\n   */\n  friendliTeam?: string;\n  /**\n   * Number between -2.0 and 2.0. Positive values penalizes tokens that have been\n   * sampled, taking into account their frequency in the preceding text. This\n   * penalization diminishes the model's tendency to reproduce identical lines\n   * verbatim.\n   */\n  frequencyPenalty?: number;\n  /**\n   * Number between -2.0 and 2.0. Positive values penalizes tokens that have been\n   * sampled at least once in the existing text.\n   * presence_penalty: Optional[float] = None\n   * The maximum number of tokens to generate. The length of your input tokens plus\n   * `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI\n   * GPT-3)\n   */\n  maxTokens?: number;\n  /**\n   * When one of the stop phrases appears in the generation result, the API will stop\n   * generation. The phrase is included in the generated result. If you are using\n   * beam search, all of the active beams should contain the stop phrase to terminate\n   * generation. Before checking whether a stop phrase is included in the result, the\n   * phrase is converted into tokens.\n   */\n  stop?: string[];\n  /**\n   * Sampling temperature. Smaller temperature makes the generation result closer to\n   * greedy, argmax (i.e., `top_k = 1`) sampling. If it is `None`, then 1.0 is used.\n   */\n  temperature?: number;\n  /**\n   * Tokens comprising the top `top_p` probability mass are kept for sampling. Numbers\n   * between 0.0 (exclusive) and 1.0 (inclusive) are allowed. If it is `None`, then 1.0\n   * is used by default.\n   */\n  topP?: number;\n  /**\n   * Additional kwargs to pass to the model.\n   */\n  modelKwargs?: Record<string, unknown>;\n}\n\n/**\n * The Friendli class is used to interact with Friendli inference Endpoint models.\n * This requires your Friendli Token and Friendli Team which is autoloaded if not specified.\n */\nexport class Friendli extends LLM<BaseLLMCallOptions> {\n  lc_serializable = true;\n\n  static lc_name() {\n    return \"Friendli\";\n  }\n\n  get lc_secrets(): { [key: string]: string } | undefined {\n    return {\n      friendliToken: \"FRIENDLI_TOKEN\",\n      friendliTeam: \"FRIENDLI_TEAM\",\n    };\n  }\n\n  model = \"mixtral-8x7b-instruct-v0-1\";\n\n  baseUrl = \"https://inference.friendli.ai\";\n\n  friendliToken?: string;\n\n  friendliTeam?: string;\n\n  frequencyPenalty?: number;\n\n  maxTokens?: number;\n\n  stop?: string[];\n\n  temperature?: number;\n\n  topP?: number;\n\n  modelKwargs?: Record<string, unknown>;\n\n  constructor(fields: FriendliParams) {\n    super(fields);\n\n    this.model = fields?.model ?? this.model;\n    this.baseUrl = fields?.baseUrl ?? this.baseUrl;\n    this.friendliToken =\n      fields?.friendliToken ?? getEnvironmentVariable(\"FRIENDLI_TOKEN\");\n    this.friendliTeam =\n      fields?.friendliTeam ?? getEnvironmentVariable(\"FRIENDLI_TEAM\");\n    this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty;\n    this.maxTokens = fields?.maxTokens ?? this.maxTokens;\n    this.stop = fields?.stop ?? this.stop;\n    this.temperature = fields?.temperature ?? this.temperature;\n    this.topP = fields?.topP ?? this.topP;\n    this.modelKwargs = fields?.modelKwargs ?? {};\n\n    if (!this.friendliToken) {\n      throw new Error(\"Missing Friendli Token\");\n    }\n  }\n\n  _llmType() {\n    return \"friendli\";\n  }\n\n  private constructHeaders(stream: boolean) {\n    return {\n      \"Content-Type\": \"application/json\",\n      Accept: stream ? \"text/event-stream\" : \"application/json\",\n      Authorization: `Bearer ${this.friendliToken}`,\n      \"X-Friendli-Team\": this.friendliTeam ?? \"\",\n    };\n  }\n\n  private constructBody(\n    prompt: string,\n    stream: boolean,\n    _options?: this[\"ParsedCallOptions\"]\n  ) {\n    const body = JSON.stringify({\n      prompt,\n      stream,\n      model: this.model,\n      max_tokens: this.maxTokens,\n      frequency_penalty: this.frequencyPenalty,\n      stop: this.stop,\n      temperature: this.temperature,\n      top_p: this.topP,\n      ...this.modelKwargs,\n    });\n    return body;\n  }\n\n  /**\n   * Calls the Friendli endpoint and retrieves the result.\n   * @param {string} prompt The input prompt.\n   * @returns {Promise<string>} A promise that resolves to the generated string.\n   */\n  /** @ignore */\n  async _call(\n    prompt: string,\n    _options: this[\"ParsedCallOptions\"]\n  ): Promise<string> {\n    interface FriendliResponse {\n      choices: {\n        index: number;\n        seed: number;\n        text: string;\n        tokens: number[];\n      }[];\n      usage: {\n        prompt_tokens: number;\n        completion_tokens: number;\n        total_tokens: number;\n      };\n    }\n\n    const response = (await this.caller.call(async () =>\n      fetch(`${this.baseUrl}/v1/completions`, {\n        method: \"POST\",\n        headers: this.constructHeaders(false),\n        body: this.constructBody(prompt, false, _options),\n      }).then((res) => res.json())\n    )) as FriendliResponse;\n\n    return response.choices[0].text;\n  }\n\n  async *_streamResponseChunks(\n    prompt: string,\n    _options: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): AsyncGenerator<GenerationChunk> {\n    interface FriendliResponse {\n      event: string;\n      index: number;\n      text: string;\n      token: number;\n    }\n\n    interface FriendliCompleteResponse {\n      event: string;\n      choices: {\n        index: number;\n        seed: number;\n        text: string;\n        tokens: number[];\n      }[];\n      usage: {\n        prompt_tokens: number;\n        completion_tokens: number;\n        total_tokens: number;\n      };\n    }\n\n    const response = await this.caller.call(async () =>\n      fetch(`${this.baseUrl}/v1/completions`, {\n        method: \"POST\",\n        headers: this.constructHeaders(true),\n        body: this.constructBody(prompt, true, _options),\n      })\n    );\n\n    if (response.status !== 200 || !response.body) {\n      const errorResponse = await response.json();\n      throw new Error(JSON.stringify(errorResponse));\n    }\n\n    const stream = convertEventStreamToIterableReadableDataStream(\n      response.body\n    );\n\n    for await (const chunk of stream) {\n      if (chunk.event !== \"complete\") {\n        const parsedChunk = JSON.parse(chunk) as FriendliResponse;\n        const generationChunk = new GenerationChunk({\n          text: parsedChunk.text ?? 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