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docs[patch]: Chat model docstrings (#6512)
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@jacoblee93
jacoblee93 authored Aug 12, 2024
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2 changes: 1 addition & 1 deletion docs/core_docs/docs/integrations/chat/mistral.ipynb
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Expand Up @@ -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",
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2 changes: 2 additions & 0 deletions libs/langchain-community/src/chat_models/fireworks.ts
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Expand Up @@ -413,6 +413,8 @@ export type ChatFireworksCallOptions = Partial<
*
* <br />
*
* <details>
*
* <summary><strong>Usage Metadata</strong></summary>
*
* ```typescript
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366 changes: 357 additions & 9 deletions libs/langchain-community/src/chat_models/togetherai.ts
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Expand Up @@ -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() {
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