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docs[patch]: Chat model docstrings (#6512)
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‎docs/core_docs/docs/integrations/chat/mistral.ipynb

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"\n",
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"## Setup\n",
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"\n",
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"To access `ChatMistralAI` models you'll need to create a `ChatMistralAI` account, get an API key, and install the `@langchain/mistralai` integration package.\n",
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"To access Mistral AI models you'll need to create a Mistral AI account, get an API key, and install the `@langchain/mistralai` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",

‎libs/langchain-community/src/chat_models/fireworks.ts

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@@ -413,6 +413,8 @@ export type ChatFireworksCallOptions = Partial<
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*
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* <br />
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*
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* <details>
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*
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* <summary><strong>Usage Metadata</strong></summary>
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*
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* ```typescript

‎libs/langchain-community/src/chat_models/togetherai.ts

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@@ -44,23 +44,371 @@ export interface ChatTogetherAIInput
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}
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/**
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* Wrapper around TogetherAI API for large language models fine-tuned for chat
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* TogetherAI chat model integration.
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*
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* TogetherAI API is compatible to the OpenAI API with some limitations. View the
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* The TogetherAI API is compatible to the OpenAI API with some limitations. View the
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* full API ref at:
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* @link {https://docs.together.ai/reference/chat-completions}
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*
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* To use, you should have the `TOGETHER_AI_API_KEY` environment variable set.
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* @example
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* Setup:
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* Install `@langchain/community` and set an environment variable named `TOGETHER_AI_API_KEY`.
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*
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* ```bash
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* npm install @langchain/community
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* export TOGETHER_AI_API_KEY="your-api-key"
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.chat_models_togetherai.ChatTogetherAI.html#constructor)
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*
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* ## [Runtime args](https://api.js.langchain.com/interfaces/_langchain_community.chat_models_togetherai.ChatTogetherAICallOptions.html)
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*
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* Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
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* They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
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*
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* ```typescript
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* // When calling `.bind`, call options should be passed via the first argument
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* const llmWithArgsBound = llm.bind({
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* stop: ["\n"],
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* tools: [...],
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* });
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*
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* // When calling `.bindTools`, call options should be passed via the second argument
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* const llmWithTools = llm.bindTools(
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* [...],
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* {
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* tool_choice: "auto",
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* }
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* );
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* ```
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*
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* ## Examples
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*
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* <details open>
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* <summary><strong>Instantiate</strong></summary>
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*
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* ```typescript
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* const model = new ChatTogetherAI({
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* temperature: 0.9,
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* apiKey: process.env.TOGETHER_AI_API_KEY,
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* import { ChatTogetherAI } from '@langchain/community/chat_models/togetherai';
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*
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* const llm = new ChatTogetherAI({
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* model: "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
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* temperature: 0,
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* // other params...
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* });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Invoking</strong></summary>
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*
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* ```typescript
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* const input = `Translate "I love programming" into French.`;
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*
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* // Models also accept a list of chat messages or a formatted prompt
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* const result = await llm.invoke(input);
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* console.log(result);
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* ```
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*
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* ```txt
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* AIMessage {
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* "id": "8b23ea7bcc4c924b-MUC",
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* "content": "\"J'adore programmer\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "tokenUsage": {
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* "completionTokens": 8,
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* "promptTokens": 19,
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* "totalTokens": 27
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* },
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* "finish_reason": "eos"
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* },
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* "tool_calls": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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* "input_tokens": 19,
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* "output_tokens": 8,
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* "total_tokens": 27
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Streaming Chunks</strong></summary>
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*
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* ```typescript
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* for await (const chunk of await llm.stream(input)) {
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* console.log(chunk);
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* }
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "J",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "'",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "ad",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "ore",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": " programmer",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": null
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "id": "8b23eb602fb19263-MUC",
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* "content": "",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": "eos"
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": []
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* }
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* AIMessageChunk {
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* "content": "",
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* "additional_kwargs": {},
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* "response_metadata": {},
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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* "input_tokens": 19,
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* "output_tokens": 8,
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* "total_tokens": 27
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Aggregate Streamed Chunks</strong></summary>
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*
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* ```typescript
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* import { AIMessageChunk } from '@langchain/core/messages';
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* import { concat } from '@langchain/core/utils/stream';
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*
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* const stream = await llm.stream(input);
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* let full: AIMessageChunk | undefined;
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* for await (const chunk of stream) {
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* full = !full ? chunk : concat(full, chunk);
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* }
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* console.log(full);
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "8b23ecd42e469236-MUC",
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* "content": "\"J'adore programmer\"",
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* "additional_kwargs": {},
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": "eos"
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* },
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* "tool_calls": [],
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* "tool_call_chunks": [],
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* "invalid_tool_calls": [],
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* "usage_metadata": {
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* "input_tokens": 19,
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* "output_tokens": 8,
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* "total_tokens": 27
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Bind tools</strong></summary>
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*
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* ```typescript
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* import { z } from 'zod';
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*
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* const GetWeather = {
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* name: "GetWeather",
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* description: "Get the current weather in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const GetPopulation = {
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* name: "GetPopulation",
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* description: "Get the current population in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);
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* const aiMsg = await llmWithTools.invoke(
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* "Which city is hotter today and which is bigger: LA or NY? Respond with JSON and use tools."
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* );
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* console.log(aiMsg.tool_calls);
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* ```
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*
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* ```txt
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* [
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* {
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* name: 'GetWeather',
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* args: { location: 'Los Angeles' },
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* type: 'tool_call',
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* id: 'call_q8i4zx1udqjjnou2bzbrg8ms'
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* }
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* ]
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Structured Output</strong></summary>
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*
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* ```typescript
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* import { z } from 'zod';
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*
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* const Joke = z.object({
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* setup: z.string().describe("The setup of the joke"),
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* punchline: z.string().describe("The punchline to the joke"),
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* rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
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* }).describe('Joke to tell user.');
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*
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* const response = await model.invoke([new HumanMessage("Hello there!")]);
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* console.log(response);
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* const structuredLlm = llm.withStructuredOutput(Joke, { name: "Joke" });
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* const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
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* console.log(jokeResult);
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* ```
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*
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* ```txt
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* {
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* setup: 'Why did the cat join a band',
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* punchline: 'Because it wanted to be the purr-cussionist'
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Usage Metadata</strong></summary>
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*
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* ```typescript
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* const aiMsgForMetadata = await llm.invoke(input);
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* console.log(aiMsgForMetadata.usage_metadata);
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* ```
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*
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* ```txt
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* { input_tokens: 19, output_tokens: 65, total_tokens: 84 }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Response Metadata</strong></summary>
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*
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* ```typescript
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* const aiMsgForResponseMetadata = await llm.invoke(input);
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* console.log(aiMsgForResponseMetadata.response_metadata);
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* ```
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*
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* ```txt
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* {
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* tokenUsage: { completionTokens: 91, promptTokens: 19, totalTokens: 110 },
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* finish_reason: 'eos'
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* }
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* ```
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* </details>
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*
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* <br />
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*/
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export class ChatTogetherAI extends ChatOpenAI<ChatTogetherAICallOptions> {
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static lc_name() {

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