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@langchain/google-genai - npm Package Compare versions

Comparing version
0.0.26
 to
0.1.0
+325
-19
dist/chat_models.cjs

@@ -13,29 +13,335 @@ "use strict";

/**
* 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
* const model = new ChatGoogleGenerativeAI({
* apiKey: "<YOUR API KEY>",
* temperature: 0.7,
* modelName: "gemini-pro",
* topK: 40,
* topP: 1,
* // When calling `.bind`, call options should be passed via the first argument
* const llmWithArgsBound = llm.bind({
* stop: ["\n"],
* tools: [...],
* });
* const questions = [
* new HumanMessage({
* content: [
*
* // 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
* import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
*
* const llm = new ChatGoogleGenerativeAI({
* model: "gemini-1.5-flash",
* temperature: 0,
* maxRetries: 2,
* // apiKey: "...",
* // 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 {
* "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": [
* {
* type: "text",
* text: "You are a funny assistant that answers in pirate language.",
* "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
* "probability": "NEGLIGIBLE"
* },
* {
* type: "text",
* text: "What is your favorite food?",
* "category": "HARM_CATEGORY_HATE_SPEECH",
* "probability": "NEGLIGIBLE"
* },
* {
* "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 />
*/

@@ -42,0 +348,0 @@ class ChatGoogleGenerativeAI extends chat_models_1.BaseChatModel {

+325
-19
dist/chat_models.d.ts

@@ -120,29 +120,335 @@ import { GenerateContentRequest, SafetySetting, Part as GenerativeAIPart } from "@google/generative-ai";

/**
* 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
* const model = new ChatGoogleGenerativeAI({
* apiKey: "<YOUR API KEY>",
* temperature: 0.7,
* modelName: "gemini-pro",
* topK: 40,
* topP: 1,
* // When calling `.bind`, call options should be passed via the first argument
* const llmWithArgsBound = llm.bind({
* stop: ["\n"],
* tools: [...],
* });
* const questions = [
* new HumanMessage({
* content: [
*
* // 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
* import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
*
* const llm = new ChatGoogleGenerativeAI({
* model: "gemini-1.5-flash",
* temperature: 0,
* maxRetries: 2,
* // apiKey: "...",
* // 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 {
* "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": [
* {
* type: "text",
* text: "You are a funny assistant that answers in pirate language.",
* "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
* "probability": "NEGLIGIBLE"
* },
* {
* type: "text",
* text: "What is your favorite food?",
* "category": "HARM_CATEGORY_HATE_SPEECH",
* "probability": "NEGLIGIBLE"
* },
* {
* "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 />
*/

@@ -149,0 +455,0 @@ export declare class ChatGoogleGenerativeAI extends BaseChatModel<GoogleGenerativeAIChatCallOptions, AIMessageChunk> implements GoogleGenerativeAIChatInput {

+325
-19
dist/chat_models.js

@@ -10,29 +10,335 @@ import { GoogleGenerativeAI as GenerativeAI, } from "@google/generative-ai";

/**
* 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
* const model = new ChatGoogleGenerativeAI({
* apiKey: "<YOUR API KEY>",
* temperature: 0.7,
* modelName: "gemini-pro",
* topK: 40,
* topP: 1,
* // When calling `.bind`, call options should be passed via the first argument
* const llmWithArgsBound = llm.bind({
* stop: ["\n"],
* tools: [...],
* });
* const questions = [
* new HumanMessage({
* content: [
*
* // 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
* import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
*
* const llm = new ChatGoogleGenerativeAI({
* model: "gemini-1.5-flash",
* temperature: 0,
* maxRetries: 2,
* // apiKey: "...",
* // 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 {
* "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": [
* {
* type: "text",
* text: "You are a funny assistant that answers in pirate language.",
* "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
* "probability": "NEGLIGIBLE"
* },
* {
* type: "text",
* text: "What is your favorite food?",
* "category": "HARM_CATEGORY_HATE_SPEECH",
* "probability": "NEGLIGIBLE"
* },
* {
* "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 />
*/

@@ -39,0 +345,0 @@ export class ChatGoogleGenerativeAI extends BaseChatModel {

+7
-4
package.json
{
"name": "@langchain/google-genai",
"version": "0.0.26",
"version": "0.1.0",
"description": "Google Generative AI integration for LangChain.js",

@@ -18,3 +18,3 @@ "type": "module",

"build": "yarn turbo:command build:internal --filter=@langchain/google-genai",
"build:internal": "yarn lc_build_v2 --create-entrypoints --pre --tree-shaking",
"build:internal": "yarn lc_build --create-entrypoints --pre --tree-shaking",
"lint:eslint": "NODE_OPTIONS=--max-old-space-size=4096 eslint --cache --ext .ts,.js src/",

@@ -40,8 +40,11 @@ "lint:dpdm": "dpdm --exit-code circular:1 --no-warning --no-tree src/*.ts src/**/*.ts",

"@google/generative-ai": "^0.7.0",
"@langchain/core": ">=0.2.21 <0.3.0",
"zod-to-json-schema": "^3.22.4"
},
"peerDependencies": {
"@langchain/core": ">=0.2.21 <0.4.0"
},
"devDependencies": {
"@jest/globals": "^29.5.0",
"@langchain/scripts": "~0.0.20",
"@langchain/core": "workspace:*",
"@langchain/scripts": ">=0.1.0 <0.2.0",
"@langchain/standard-tests": "0.0.0",

@@ -48,0 +51,0 @@ "@swc/core": "^1.3.90",

+6
-6
README.md

@@ -8,3 +8,3 @@ # @langchain/google-genai

```bash npm2yarn
npm install @langchain/google-genai
npm install @langchain/google-genai @langchain/core
```

@@ -21,14 +21,14 @@

"dependencies": {
"@langchain/google-genai": "^0.0.0",
"langchain": "0.0.207"
"@langchain/core": "^0.3.0",
"@langchain/google-genai": "^0.0.0"
},
"resolutions": {
"@langchain/core": "0.1.5"
"@langchain/core": "^0.3.0"
},
"overrides": {
"@langchain/core": "0.1.5"
"@langchain/core": "^0.3.0"
},
"pnpm": {
"overrides": {
"@langchain/core": "0.1.5"
"@langchain/core": "^0.3.0"
}

@@ -35,0 +35,0 @@ }