docs[patch]: Chat model docstrings

docs/core_docs/docs/integrations/chat/mistral.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "## Setup\n",
     "\n",
-    "To access `ChatMistralAI` models you'll need to create a `ChatMistralAI` account, get an API key, and install the `@langchain/mistralai` integration package.\n",
+    "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",
     "\n",
     "### Credentials\n",
     "\n",

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

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

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

@@ -44,23 +44,371 @@ export interface ChatTogetherAIInput
 }
 
 /**
- * Wrapper around TogetherAI API for large language models fine-tuned for chat
+ * TogetherAI chat model integration.
  *
- * TogetherAI API is compatible to the OpenAI API with some limitations. View the
+ * 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}
  *
- * To use, you should have the `TOGETHER_AI_API_KEY` environment variable set.
- * @example
+ * 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
- * const model = new ChatTogetherAI({
- *   temperature: 0.9,
- *   apiKey: process.env.TOGETHER_AI_API_KEY,
+ * 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 response = await model.invoke([new HumanMessage("Hello there!")]);
- * console.log(response);
+ * 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 class ChatTogetherAI extends ChatOpenAI<ChatTogetherAICallOptions> {
   static lc_name() {