google-genai[minor]: Update JSDoc with examples (#6561)

libs/langchain-google-genai/src/chat_models.ts

@@ -179,33 +179,339 @@ export interface GoogleGenerativeAIChatInput
 }
 
 /**
- * A class that wraps the Google Palm chat model.
- * @example
+ * Google Generative AI chat model integration.
+ *
+ * Setup:
+ * Install `@langchain/google-genai` and set an environment variable named `GOOGLE_API_KEY`.
+ *
+ * ```bash
+ * npm install @langchain/google-genai
+ * export GOOGLE_API_KEY="your-api-key"
+ * ```
+ *
+ * ## [Constructor args](https://api.js.langchain.com/classes/langchain_google_genai.ChatGoogleGenerativeAI.html#constructor)
+ *
+ * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_google_genai.GoogleGenerativeAIChatCallOptions.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(
+ *   [...],
+ *   {
+ *     stop: ["\n"],
+ *   }
+ * );
+ * ```
+ *
+ * ## Examples
+ *
+ * <details open>
+ * <summary><strong>Instantiate</strong></summary>
+ *
  * ```typescript
- * const model = new ChatGoogleGenerativeAI({
- *   apiKey: "<YOUR API KEY>",
- *   temperature: 0.7,
- *   modelName: "gemini-pro",
- *   topK: 40,
- *   topP: 1,
+ * import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
+ *
+ * const llm = new ChatGoogleGenerativeAI({
+ *   model: "gemini-1.5-flash",
+ *   temperature: 0,
+ *   maxRetries: 2,
+ *   // apiKey: "...",
+ *   // other params...
  * });
- * const questions = [
- *   new HumanMessage({
- *     content: [
+ * ```
+ * </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 {
+ *   "content": "There are a few ways to translate \"I love programming\" into French, depending on the level of formality and nuance you want to convey:\n\n**Formal:**\n\n* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and your intended audience. \n",
+ *   "response_metadata": {
+ *     "finishReason": "STOP",
+ *     "index": 0,
+ *     "safetyRatings": [
+ *       {
+ *         "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+ *         "probability": "NEGLIGIBLE"
+ *       },
  *       {
- *         type: "text",
- *         text: "You are a funny assistant that answers in pirate language.",
+ *         "category": "HARM_CATEGORY_HATE_SPEECH",
+ *         "probability": "NEGLIGIBLE"
  *       },
  *       {
- *         type: "text",
- *         text: "What is your favorite food?",
+ *         "category": "HARM_CATEGORY_HARASSMENT",
+ *         "probability": "NEGLIGIBLE"
  *       },
+ *       {
+ *         "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+ *         "probability": "NEGLIGIBLE"
+ *       }
  *     ]
- *   })
- * ];
- * const res = await model.invoke(questions);
- * console.log({ res });
+ *   },
+ *   "usage_metadata": {
+ *     "input_tokens": 10,
+ *     "output_tokens": 149,
+ *     "total_tokens": 159
+ *   }
+ * }
+ * ```
+ * </details>
+ *
+ * <br />
+ *
+ * <details>
+ * <summary><strong>Streaming Chunks</strong></summary>
+ *
+ * ```typescript
+ * for await (const chunk of await llm.stream(input)) {
+ *   console.log(chunk);
+ * }
+ * ```
+ *
+ * ```txt
+ * AIMessageChunk {
+ *   "content": "There",
+ *   "response_metadata": {
+ *     "index": 0
+ *   }
+ *   "usage_metadata": {
+ *     "input_tokens": 10,
+ *     "output_tokens": 1,
+ *     "total_tokens": 11
+ *   }
+ * }
+ * AIMessageChunk {
+ *   "content": " are a few ways to translate \"I love programming\" into French, depending on",
+ * }
+ * AIMessageChunk {
+ *   "content": " the level of formality and nuance you want to convey:\n\n**Formal:**\n\n",
+ * }
+ * AIMessageChunk {
+ *   "content": "* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This",
+ * }
+ * AIMessageChunk {
+ *   "content": " is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More",
+ * }
+ * AIMessageChunk {
+ *   "content": " specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and",
+ * }
+ * AIMessageChunk {
+ *   "content": " your intended audience. \n",
+ * }
+ * ```
+ * </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 {
+ *   "content": "There are a few ways to translate \"I love programming\" into French, depending on the level of formality and nuance you want to convey:\n\n**Formal:**\n\n* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and your intended audience. \n",
+ *   "usage_metadata": {
+ *     "input_tokens": 10,
+ *     "output_tokens": 277,
+ *     "total_tokens": 287
+ *   }
+ * }
+ * ```
+ * </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?"
+ * );
+ * console.log(aiMsg.tool_calls);
+ * ```
+ *
+ * ```txt
+ * [
+ *   {
+ *     name: 'GetWeather',
+ *     args: { location: 'Los Angeles, CA' },
+ *     type: 'tool_call'
+ *   },
+ *   {
+ *     name: 'GetWeather',
+ *     args: { location: 'New York, NY' },
+ *     type: 'tool_call'
+ *   },
+ *   {
+ *     name: 'GetPopulation',
+ *     args: { location: 'Los Angeles, CA' },
+ *     type: 'tool_call'
+ *   },
+ *   {
+ *     name: 'GetPopulation',
+ *     args: { location: 'New York, NY' },
+ *     type: 'tool_call'
+ *   }
+ * ]
+ * ```
+ * </details>
+ *
+ * <br />
+ *
+ * <details>
+ * <summary><strong>Structured Output</strong></summary>
+ *
+ * ```typescript
+ * 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 don\\'t cats play poker?",
+ *   punchline: "Why don\\'t cats play poker? Because they always have an ace up their sleeve!"
+ * }
+ * ```
+ * </details>
+ *
+ * <br />
+ *
+ * <details>
+ * <summary><strong>Multimodal</strong></summary>
+ *
+ * ```typescript
+ * import { HumanMessage } from '@langchain/core/messages';
+ *
+ * const imageUrl = "https://example.com/image.jpg";
+ * const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
+ * const base64Image = Buffer.from(imageData).toString('base64');
+ *
+ * const message = new HumanMessage({
+ *   content: [
+ *     { type: "text", text: "describe the weather in this image" },
+ *     {
+ *       type: "image_url",
+ *       image_url: { url: `data:image/jpeg;base64,${base64Image}` },
+ *     },
+ *   ]
+ * });
+ *
+ * const imageDescriptionAiMsg = await llm.invoke([message]);
+ * console.log(imageDescriptionAiMsg.content);
+ * ```
+ *
+ * ```txt
+ * The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.
+ * ```
+ * </details>
+ *
+ * <br />
+ *
+ * <details>
+ * <summary><strong>Usage Metadata</strong></summary>
+ *
+ * ```typescript
+ * const aiMsgForMetadata = await llm.invoke(input);
+ * console.log(aiMsgForMetadata.usage_metadata);
+ * ```
+ *
+ * ```txt
+ * { input_tokens: 10, output_tokens: 149, total_tokens: 159 }
+ * ```
+ * </details>
+ *
+ * <br />
+ *
+ * <details>
+ * <summary><strong>Response Metadata</strong></summary>
+ *
+ * ```typescript
+ * const aiMsgForResponseMetadata = await llm.invoke(input);
+ * console.log(aiMsgForResponseMetadata.response_metadata);
+ * ```
+ *
+ * ```txt
+ * {
+ *   finishReason: 'STOP',
+ *   index: 0,
+ *   safetyRatings: [
+ *     {
+ *       category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT',
+ *       probability: 'NEGLIGIBLE'
+ *     },
+ *     {
+ *       category: 'HARM_CATEGORY_HATE_SPEECH',
+ *       probability: 'NEGLIGIBLE'
+ *     },
+ *     { category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
+ *     {
+ *       category: 'HARM_CATEGORY_DANGEROUS_CONTENT',
+ *       probability: 'NEGLIGIBLE'
+ *     }
+ *   ]
+ * }
  * ```
+ * </details>
+ *
+ * <br />
  */
 export class ChatGoogleGenerativeAI
   extends BaseChatModel<GoogleGenerativeAIChatCallOptions, AIMessageChunk>