import { removeAdditionalProperties, schemaToGenerativeAIParameters } from "./utils/zod_to_genai_parameters.js";
import { convertBaseMessagesToContent, convertResponseContentToChatGenerationChunk, convertUsageMetadata, mapGenerateContentResultToChatResult } from "./utils/common.js";
import { GoogleGenerativeAIToolsOutputParser } from "./output_parsers.js";
import { convertToolsToGenAI } from "./utils/tools.js";
import PROFILES from "./profiles.js";
import { GoogleGenerativeAI } from "@google/generative-ai";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { BaseChatModel } from "@langchain/core/language_models/chat_models";
import { isInteropZodSchema } from "@langchain/core/utils/types";
import { isSerializableSchema } from "@langchain/core/utils/standard_schema";
import { assembleStructuredOutputPipeline, createContentParser, createFunctionCallingParser } from "@langchain/core/language_models/structured_output";
//#region src/chat_models.ts
/**
* 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 `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:
*
* ```typescript
* // When calling `.withConfig`, call options should be passed via the first argument
* const llmWithArgsBound = llm.withConfig({
* stop: ["\n"],
* });
*
* // When calling `.bindTools`, call options should be passed via the second argument
* const llmWithTools = llm.bindTools(
* [...],
* {
* stop: ["\n"],
* }
* );
* ```
*
* ## Examples
*
*
* Instantiate
*
* ```typescript
* import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
*
* const llm = new ChatGoogleGenerativeAI({
* model: "gemini-1.5-flash",
* temperature: 0,
* maxRetries: 2,
* // apiKey: "...",
* // other params...
* });
* ```
*
*
*
*
*
* Invoking
*
* ```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"
* },
* {
* "category": "HARM_CATEGORY_HATE_SPEECH",
* "probability": "NEGLIGIBLE"
* },
* {
* "category": "HARM_CATEGORY_HARASSMENT",
* "probability": "NEGLIGIBLE"
* },
* {
* "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
* "probability": "NEGLIGIBLE"
* }
* ]
* },
* "usage_metadata": {
* "input_tokens": 10,
* "output_tokens": 149,
* "total_tokens": 159
* }
* }
* ```
*
*
*
*
*
* Streaming Chunks
*
* ```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",
* }
* ```
*
*
*
*
*
* Aggregate Streamed Chunks
*
* ```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
* }
* }
* ```
*
*
*
*
*
* Bind tools
*
* ```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'
* }
* ]
* ```
*
*
*
*
*
* Structured Output
*
* ```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!"
* }
* ```
*
*
*
*
*
* Multimodal
*
* ```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.
* ```
*
*
*
*
*
* Usage Metadata
*
* ```typescript
* const aiMsgForMetadata = await llm.invoke(input);
* console.log(aiMsgForMetadata.usage_metadata);
* ```
*
* ```txt
* { input_tokens: 10, output_tokens: 149, total_tokens: 159 }
* ```
*
*
*
*
*
* Response Metadata
*
* ```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'
* }
* ]
* }
* ```
*
*
*
*
*
* Document Messages
*
* This example will show you how to pass documents such as PDFs to Google
* Generative AI through messages.
*
* ```typescript
* const pdfPath = "/Users/my_user/Downloads/invoice.pdf";
* const pdfBase64 = await fs.readFile(pdfPath, "base64");
*
* const response = await llm.invoke([
* ["system", "Use the provided documents to answer the question"],
* [
* "user",
* [
* {
* type: "application/pdf", // If the `type` field includes a single slash (`/`), it will be treated as inline data.
* data: pdfBase64,
* },
* {
* type: "text",
* text: "Summarize the contents of this PDF",
* },
* ],
* ],
* ]);
*
* console.log(response.content);
* ```
*
* ```txt
* This is a billing invoice from Twitter Developers for X API Basic Access. The transaction date is January 7, 2025,
* and the amount is $194.34, which has been paid. The subscription period is from January 7, 2025 21:02 to February 7, 2025 00:00 (UTC).
* The tax is $0.00, with a tax rate of 0%. The total amount is $194.34. The payment was made using a Visa card ending in 7022,
* expiring in 12/2026. The billing address is Brace Sproul, 1234 Main Street, San Francisco, CA, US 94103. The company being billed is
* X Corp, located at 865 FM 1209 Building 2, Bastrop, TX, US 78602. Terms and conditions apply.
* ```
*
*
*
*/
var ChatGoogleGenerativeAI = class extends BaseChatModel {
static lc_name() {
return "ChatGoogleGenerativeAI";
}
lc_serializable = true;
get lc_secrets() {
return { apiKey: "GOOGLE_API_KEY" };
}
lc_namespace = [
"langchain",
"chat_models",
"google_genai"
];
get lc_aliases() {
return { apiKey: "google_api_key" };
}
model;
temperature;
maxOutputTokens;
topP;
topK;
stopSequences = [];
safetySettings;
apiKey;
streaming = false;
json;
streamUsage = true;
convertSystemMessageToHumanContent;
thinkingConfig;
client;
get _isMultimodalModel() {
return this.model.includes("vision") || this.model.startsWith("gemini-1.5") || this.model.startsWith("gemini-2") || this.model.startsWith("gemma-3-") && !this.model.startsWith("gemma-3-1b") || this.model.startsWith("gemini-3");
}
constructor(modelOrFields, fieldsArg) {
const fields = typeof modelOrFields === "string" ? {
...fieldsArg ?? {},
model: modelOrFields
} : modelOrFields;
super(fields);
this._addVersion("@langchain/google-genai", "2.1.30");
this.model = fields.model.replace(/^models\//, "");
this.maxOutputTokens = fields.maxOutputTokens ?? this.maxOutputTokens;
if (this.maxOutputTokens && this.maxOutputTokens < 0) throw new Error("`maxOutputTokens` must be a positive integer");
this.temperature = fields.temperature ?? this.temperature;
if (this.temperature && (this.temperature < 0 || this.temperature > 2)) throw new Error("`temperature` must be in the range of [0.0,2.0]");
this.topP = fields.topP ?? this.topP;
if (this.topP && this.topP < 0) throw new Error("`topP` must be a positive integer");
if (this.topP && this.topP > 1) throw new Error("`topP` must be below 1.");
this.topK = fields.topK ?? this.topK;
if (this.topK && this.topK < 0) throw new Error("`topK` must be a positive integer");
this.stopSequences = fields.stopSequences ?? this.stopSequences;
this.apiKey = fields.apiKey ?? getEnvironmentVariable("GOOGLE_API_KEY");
if (!this.apiKey) throw new Error("Please set an API key for Google GenerativeAI in the environment variable GOOGLE_API_KEY or in the `apiKey` field of the ChatGoogleGenerativeAI constructor");
this.safetySettings = fields.safetySettings ?? this.safetySettings;
if (this.safetySettings && this.safetySettings.length > 0) {
if (new Set(this.safetySettings.map((s) => s.category)).size !== this.safetySettings.length) throw new Error("The categories in `safetySettings` array must be unique");
}
this.streaming = fields.streaming ?? this.streaming;
this.json = fields.json;
this.thinkingConfig = fields.thinkingConfig ?? this.thinkingConfig;
this.client = new GoogleGenerativeAI(this.apiKey).getGenerativeModel({
model: this.model,
safetySettings: this.safetySettings,
generationConfig: {
stopSequences: this.stopSequences,
maxOutputTokens: this.maxOutputTokens,
temperature: this.temperature,
topP: this.topP,
topK: this.topK,
...this.json ? { responseMimeType: "application/json" } : {},
...this.thinkingConfig ? { thinkingConfig: this.thinkingConfig } : {}
}
}, {
apiVersion: fields.apiVersion,
baseUrl: fields.baseUrl,
customHeaders: fields.customHeaders
});
this.streamUsage = fields.streamUsage ?? this.streamUsage;
}
useCachedContent(cachedContent, modelParams, requestOptions) {
if (!this.apiKey) return;
this.client = new GoogleGenerativeAI(this.apiKey).getGenerativeModelFromCachedContent(cachedContent, modelParams, requestOptions);
}
get useSystemInstruction() {
return typeof this.convertSystemMessageToHumanContent === "boolean" ? !this.convertSystemMessageToHumanContent : this.computeUseSystemInstruction;
}
get computeUseSystemInstruction() {
if (this.model === "gemini-1.0-pro-001") return false;
else if (this.model.startsWith("gemini-pro-vision")) return false;
else if (this.model.startsWith("gemini-1.0-pro-vision")) return false;
else if (this.model === "gemini-pro") return false;
return true;
}
getLsParams(options) {
return {
ls_provider: "google_genai",
ls_model_name: this.model,
ls_model_type: "chat",
ls_temperature: this.client.generationConfig.temperature,
ls_max_tokens: this.client.generationConfig.maxOutputTokens,
ls_stop: options.stop
};
}
_combineLLMOutput() {
return [];
}
_llmType() {
return "googlegenerativeai";
}
bindTools(tools, kwargs) {
return this.withConfig({
tools: convertToolsToGenAI(tools)?.tools,
...kwargs
});
}
invocationParams(options) {
const toolsAndConfig = options?.tools?.length ? convertToolsToGenAI(options.tools, {
toolChoice: options.tool_choice,
allowedFunctionNames: options.allowedFunctionNames
}) : void 0;
if (options?.responseSchema) {
this.client.generationConfig.responseSchema = options.responseSchema;
this.client.generationConfig.responseMimeType = "application/json";
} else {
this.client.generationConfig.responseSchema = void 0;
this.client.generationConfig.responseMimeType = this.json ? "application/json" : void 0;
}
return {
...toolsAndConfig?.tools ? { tools: toolsAndConfig.tools } : {},
...toolsAndConfig?.toolConfig ? { toolConfig: toolsAndConfig.toolConfig } : {}
};
}
async _generate(messages, options, runManager) {
options.signal?.throwIfAborted();
const prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel, this.useSystemInstruction, this.model);
let actualPrompt = prompt;
if (prompt[0].role === "system") {
const [systemInstruction] = prompt;
this.client.systemInstruction = systemInstruction;
actualPrompt = prompt.slice(1);
}
const parameters = this.invocationParams(options);
if (this.streaming) {
const tokenUsage = {};
const stream = this._streamResponseChunks(messages, options, runManager);
const finalChunks = [];
for await (const chunk of stream) {
const index = chunk.generationInfo?.completion ?? 0;
if (finalChunks[index] === void 0) finalChunks[index] = chunk;
else finalChunks[index] = finalChunks[index].concat(chunk);
}
return {
generations: finalChunks.filter((c) => c !== void 0),
llmOutput: { estimatedTokenUsage: tokenUsage }
};
}
const res = await this.completionWithRetry({
...parameters,
contents: actualPrompt
});
let usageMetadata;
if ("usageMetadata" in res.response) usageMetadata = convertUsageMetadata(res.response.usageMetadata, this.model);
const generationResult = mapGenerateContentResultToChatResult(res.response, { usageMetadata });
if (generationResult.generations?.length > 0) await runManager?.handleLLMNewToken(generationResult.generations[0]?.text ?? "");
return generationResult;
}
async *_streamResponseChunks(messages, options, runManager) {
const prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel, this.useSystemInstruction, this.model);
let actualPrompt = prompt;
if (prompt[0].role === "system") {
const [systemInstruction] = prompt;
this.client.systemInstruction = systemInstruction;
actualPrompt = prompt.slice(1);
}
const request = {
...this.invocationParams(options),
contents: actualPrompt
};
const stream = await this.caller.callWithOptions({ signal: options?.signal }, async () => {
const { stream } = await this.client.generateContentStream(request, { signal: options?.signal });
return stream;
});
let usageMetadata;
let prevPromptTokenCount = 0;
let prevCandidatesTokenCount = 0;
let prevTotalTokenCount = 0;
let index = 0;
for await (const response of stream) {
if (options.signal?.aborted) return;
if ("usageMetadata" in response && response.usageMetadata !== void 0 && this.streamUsage !== false && options.streamUsage !== false) {
usageMetadata = convertUsageMetadata(response.usageMetadata, this.model);
const newPromptTokenCount = response.usageMetadata.promptTokenCount ?? 0;
usageMetadata.input_tokens = Math.max(0, newPromptTokenCount - prevPromptTokenCount);
prevPromptTokenCount = newPromptTokenCount;
const newCandidatesTokenCount = response.usageMetadata.candidatesTokenCount ?? 0;
usageMetadata.output_tokens = Math.max(0, newCandidatesTokenCount - prevCandidatesTokenCount);
prevCandidatesTokenCount = newCandidatesTokenCount;
const newTotalTokenCount = response.usageMetadata.totalTokenCount ?? 0;
usageMetadata.total_tokens = Math.max(0, newTotalTokenCount - prevTotalTokenCount);
prevTotalTokenCount = newTotalTokenCount;
}
const chunk = convertResponseContentToChatGenerationChunk(response, {
usageMetadata,
index
});
index += 1;
if (!chunk) continue;
yield chunk;
await runManager?.handleLLMNewToken(chunk.text ?? "");
}
}
async completionWithRetry(request, options) {
return this.caller.callWithOptions({ signal: options?.signal }, async () => {
try {
return await this.client.generateContent(request, { signal: options?.signal });
} catch (e) {
if (e.message?.includes("400 Bad Request")) e.status = 400;
throw e;
}
});
}
/**
* Return profiling information for the model.
*
* Provides information about the model's capabilities and constraints,
* including token limits, multimodal support, and advanced features like
* tool calling and structured output.
*
* @returns {ModelProfile} An object describing the model's capabilities and constraints
*
* @example
* ```typescript
* const model = new ChatGoogleGenerativeAI({ model: "gemini-1.5-flash" });
* const profile = model.profile;
* console.log(profile.maxInputTokens); // 2000000
* console.log(profile.imageInputs); // true
* ```
*/
get profile() {
return PROFILES[this.model] ?? {};
}
withStructuredOutput(outputSchema, config) {
const schema = outputSchema;
const name = config?.name;
const method = config?.method;
const includeRaw = config?.includeRaw;
if (method === "jsonMode") throw new Error(`ChatGoogleGenerativeAI only supports "jsonSchema" or "functionCalling" as a method.`);
let llm;
let outputParser;
if (method === "functionCalling") {
let functionName = name ?? "extract";
let geminiFunctionDeclaration;
if (isInteropZodSchema(schema) || isSerializableSchema(schema)) {
const jsonSchema = schemaToGenerativeAIParameters(schema);
geminiFunctionDeclaration = {
name: functionName,
description: jsonSchema.description ?? "A function available to call.",
parameters: jsonSchema
};
} else if (typeof schema.name === "string" && typeof schema.parameters === "object" && schema.parameters != null) {
geminiFunctionDeclaration = schema;
geminiFunctionDeclaration.parameters = removeAdditionalProperties(schema.parameters);
functionName = schema.name;
} else geminiFunctionDeclaration = {
name: functionName,
description: schema.description ?? "",
parameters: removeAdditionalProperties(schema)
};
const tools = [{ functionDeclarations: [geminiFunctionDeclaration] }];
llm = this.bindTools(tools).withConfig({ allowedFunctionNames: [functionName] });
outputParser = createFunctionCallingParser(schema, functionName, GoogleGenerativeAIToolsOutputParser);
} else {
const jsonSchema = schemaToGenerativeAIParameters(schema);
llm = this.withConfig({ responseSchema: jsonSchema });
outputParser = createContentParser(schema);
}
return assembleStructuredOutputPipeline(llm, outputParser, includeRaw, includeRaw ? "StructuredOutputRunnable" : "ChatGoogleGenerativeAIStructuredOutput");
}
};
//#endregion
export { ChatGoogleGenerativeAI };
//# sourceMappingURL=chat_models.js.map