agsamantha/node_modules/@langchain/community/dist/chat_models/togetherai.d.ts

import type { BaseChatModelParams, LangSmithParams } from "@langchain/core/language_models/chat_models";
import { type OpenAIClient, type ChatOpenAICallOptions, type OpenAIChatInput, type OpenAICoreRequestOptions, ChatOpenAI } from "@langchain/openai";
type TogetherAIUnsupportedArgs = "frequencyPenalty" | "presencePenalty" | "logitBias" | "functions";
type TogetherAIUnsupportedCallOptions = "functions" | "function_call";
export interface ChatTogetherAICallOptions extends Omit<ChatOpenAICallOptions, TogetherAIUnsupportedCallOptions> {
    response_format: {
        type: "json_object";
        schema: Record<string, unknown>;
    };
}
export interface ChatTogetherAIInput extends Omit<OpenAIChatInput, "openAIApiKey" | TogetherAIUnsupportedArgs>, BaseChatModelParams {
    /**
     * The TogetherAI API key to use for requests.
     * Alias for `apiKey`
     * @default process.env.TOGETHER_AI_API_KEY
     */
    togetherAIApiKey?: string;
    /**
     * The TogetherAI API key to use for requests.
     * @default process.env.TOGETHER_AI_API_KEY
     */
    apiKey?: string;
}
/**
 * TogetherAI chat model integration.
 *
 * The TogetherAI API is compatible to the OpenAI API with some limitations. View the
 * full API ref at:
 * @link {https://docs.together.ai/reference/chat-completions}
 *
 * Setup:
 * Install `@langchain/community` and set an environment variable named `TOGETHER_AI_API_KEY`.
 *
 * ```bash
 * npm install @langchain/community
 * export TOGETHER_AI_API_KEY="your-api-key"
 * ```
 *
 * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.chat_models_togetherai.ChatTogetherAI.html#constructor)
 *
 * ## [Runtime args](https://api.js.langchain.com/interfaces/_langchain_community.chat_models_togetherai.ChatTogetherAICallOptions.html)
 *
 * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
 * They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
 *
 * ```typescript
 * // When calling `.bind`, call options should be passed via the first argument
 * const llmWithArgsBound = llm.bind({
 *   stop: ["\n"],
 *   tools: [...],
 * });
 *
 * // When calling `.bindTools`, call options should be passed via the second argument
 * const llmWithTools = llm.bindTools(
 *   [...],
 *   {
 *     tool_choice: "auto",
 *   }
 * );
 * ```
 *
 * ## Examples
 *
 * <details open>
 * <summary><strong>Instantiate</strong></summary>
 *
 * ```typescript
 * import { ChatTogetherAI } from '@langchain/community/chat_models/togetherai';
 *
 * const llm = new ChatTogetherAI({
 *   model: "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
 *   temperature: 0,
 *   // other params...
 * });
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Invoking</strong></summary>
 *
 * ```typescript
 * const input = `Translate "I love programming" into French.`;
 *
 * // Models also accept a list of chat messages or a formatted prompt
 * const result = await llm.invoke(input);
 * console.log(result);
 * ```
 *
 * ```txt
 * AIMessage {
 *   "id": "8b23ea7bcc4c924b-MUC",
 *   "content": "\"J'adore programmer\"",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "tokenUsage": {
 *       "completionTokens": 8,
 *       "promptTokens": 19,
 *       "totalTokens": 27
 *     },
 *     "finish_reason": "eos"
 *   },
 *   "tool_calls": [],
 *   "invalid_tool_calls": [],
 *   "usage_metadata": {
 *     "input_tokens": 19,
 *     "output_tokens": 8,
 *     "total_tokens": 27
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Streaming Chunks</strong></summary>
 *
 * ```typescript
 * for await (const chunk of await llm.stream(input)) {
 *   console.log(chunk);
 * }
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "\"",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "J",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "'",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "ad",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "ore",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": " programmer",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "\"",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": null
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "id": "8b23eb602fb19263-MUC",
 *   "content": "",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": "eos"
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": []
 * }
 * AIMessageChunk {
 *   "content": "",
 *   "additional_kwargs": {},
 *   "response_metadata": {},
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": [],
 *   "usage_metadata": {
 *     "input_tokens": 19,
 *     "output_tokens": 8,
 *     "total_tokens": 27
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Aggregate Streamed Chunks</strong></summary>
 *
 * ```typescript
 * import { AIMessageChunk } from '@langchain/core/messages';
 * import { concat } from '@langchain/core/utils/stream';
 *
 * const stream = await llm.stream(input);
 * let full: AIMessageChunk | undefined;
 * for await (const chunk of stream) {
 *   full = !full ? chunk : concat(full, chunk);
 * }
 * console.log(full);
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "id": "8b23ecd42e469236-MUC",
 *   "content": "\"J'adore programmer\"",
 *   "additional_kwargs": {},
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": "eos"
 *   },
 *   "tool_calls": [],
 *   "tool_call_chunks": [],
 *   "invalid_tool_calls": [],
 *   "usage_metadata": {
 *     "input_tokens": 19,
 *     "output_tokens": 8,
 *     "total_tokens": 27
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Bind tools</strong></summary>
 *
 * ```typescript
 * import { z } from 'zod';
 *
 * const GetWeather = {
 *   name: "GetWeather",
 *   description: "Get the current weather in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const GetPopulation = {
 *   name: "GetPopulation",
 *   description: "Get the current population in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);
 * const aiMsg = await llmWithTools.invoke(
 *   "Which city is hotter today and which is bigger: LA or NY? Respond with JSON and use tools."
 * );
 * console.log(aiMsg.tool_calls);
 * ```
 *
 * ```txt
 * [
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'Los Angeles' },
 *     type: 'tool_call',
 *     id: 'call_q8i4zx1udqjjnou2bzbrg8ms'
 *   }
 * ]
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Structured Output</strong></summary>
 *
 * ```typescript
 * import { z } from 'zod';
 *
 * const Joke = z.object({
 *   setup: z.string().describe("The setup of the joke"),
 *   punchline: z.string().describe("The punchline to the joke"),
 *   rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
 * }).describe('Joke to tell user.');
 *
 * const structuredLlm = llm.withStructuredOutput(Joke, { name: "Joke" });
 * const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
 * console.log(jokeResult);
 * ```
 *
 * ```txt
 * {
 *   setup: 'Why did the cat join a band',
 *   punchline: 'Because it wanted to be the purr-cussionist'
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Usage Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForMetadata = await llm.invoke(input);
 * console.log(aiMsgForMetadata.usage_metadata);
 * ```
 *
 * ```txt
 * { input_tokens: 19, output_tokens: 65, total_tokens: 84 }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Response Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForResponseMetadata = await llm.invoke(input);
 * console.log(aiMsgForResponseMetadata.response_metadata);
 * ```
 *
 * ```txt
 * {
 *   tokenUsage: { completionTokens: 91, promptTokens: 19, totalTokens: 110 },
 *   finish_reason: 'eos'
 * }
 * ```
 * </details>
 *
 * <br />
 */
export declare class ChatTogetherAI extends ChatOpenAI<ChatTogetherAICallOptions> {
    static lc_name(): string;
    _llmType(): string;
    get lc_secrets(): {
        [key: string]: string;
    } | undefined;
    lc_serializable: boolean;
    constructor(fields?: Partial<Omit<OpenAIChatInput, "openAIApiKey" | TogetherAIUnsupportedArgs>> & BaseChatModelParams & {
        /**
         * Prefer `apiKey`
         */
        togetherAIApiKey?: string;
        /**
         * The TogetherAI API key to use.
         */
        apiKey?: string;
    });
    getLsParams(options: this["ParsedCallOptions"]): LangSmithParams;
    toJSON(): import("@langchain/core/load/serializable").Serialized;
    completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsStreaming, options?: OpenAICoreRequestOptions): Promise<AsyncIterable<OpenAIClient.Chat.Completions.ChatCompletionChunk>>;
    completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming, options?: OpenAICoreRequestOptions): Promise<OpenAIClient.Chat.Completions.ChatCompletion>;
}
export {};