dtamind-components
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Apps integration for Dtamind. Contain Nodes and Credentials.
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JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.ChatGoogleGenerativeAI = exports.LangchainChatGoogleGenerativeAI = void 0;
const generative_ai_1 = require("@google/generative-ai");
const env_1 = require("@langchain/core/utils/env");
const chat_models_1 = require("@langchain/core/language_models/chat_models");
const runnables_1 = require("@langchain/core/runnables");
const types_1 = require("@langchain/core/utils/types");
const output_parsers_1 = require("@langchain/core/output_parsers");
const zod_to_genai_parameters_js_1 = require("./utils/zod_to_genai_parameters.js");
const common_js_1 = require("./utils/common.js");
const output_parsers_js_1 = require("./utils/output_parsers.js");
const tools_js_1 = require("./utils/tools.js");
/**
* 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
*
* <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": [
* {
* "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
* }
* }
* ```
* </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 />
*
* <details>
* <summary><strong>Document Messages</strong></summary>
*
* 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.
* ```
* </details>
*
* <br />
*/
class LangchainChatGoogleGenerativeAI extends chat_models_1.BaseChatModel {
static lc_name() {
return 'ChatGoogleGenerativeAI';
}
get lc_secrets() {
return {
apiKey: 'GOOGLE_API_KEY'
};
}
get lc_aliases() {
return {
apiKey: 'google_api_key'
};
}
get _isMultimodalModel() {
return this.model.includes('vision') || this.model.startsWith('gemini-1.5') || this.model.startsWith('gemini-2');
}
constructor(fields) {
super(fields);
this.lc_serializable = true;
this.lc_namespace = ['langchain', 'chat_models', 'google_genai'];
this.stopSequences = [];
this.streaming = false;
this.streamUsage = true;
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 ?? (0, env_1.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) {
const safetySettingsSet = new Set(this.safetySettings.map((s) => s.category));
if (safetySettingsSet.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.client = new generative_ai_1.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' } : {})
}
}, {
apiVersion: fields.apiVersion,
baseUrl: fields.baseUrl
});
this.streamUsage = fields.streamUsage ?? this.streamUsage;
}
useCachedContent(cachedContent, modelParams, requestOptions) {
if (!this.apiKey)
return;
this.client = new generative_ai_1.GoogleGenerativeAI(this.apiKey).getGenerativeModelFromCachedContent(cachedContent, modelParams, requestOptions);
}
get useSystemInstruction() {
return typeof this.convertSystemMessageToHumanContent === 'boolean'
? !this.convertSystemMessageToHumanContent
: this.computeUseSystemInstruction;
}
get computeUseSystemInstruction() {
// This works on models from April 2024 and later
// Vertex AI: gemini-1.5-pro and gemini-1.0-002 and later
// AI Studio: gemini-1.5-pro-latest
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') {
// on AI Studio gemini-pro is still pointing at gemini-1.0-pro-001
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: (0, tools_js_1.convertToolsToGenAI)(tools)?.tools,
...kwargs
});
}
invocationParams(options) {
const toolsAndConfig = options?.tools?.length
? (0, tools_js_1.convertToolsToGenAI)(options.tools, {
toolChoice: options.tool_choice,
allowedFunctionNames: options.allowedFunctionNames
})
: undefined;
if (options?.responseSchema) {
this.client.generationConfig.responseSchema = options.responseSchema;
this.client.generationConfig.responseMimeType = 'application/json';
}
else {
this.client.generationConfig.responseSchema = undefined;
this.client.generationConfig.responseMimeType = this.json ? 'application/json' : undefined;
}
return {
...(toolsAndConfig?.tools ? { tools: toolsAndConfig.tools } : {}),
...(toolsAndConfig?.toolConfig ? { toolConfig: toolsAndConfig.toolConfig } : {})
};
}
async _generate(messages, options, runManager) {
const prompt = (0, common_js_1.convertBaseMessagesToContent)(messages, this._isMultimodalModel, this.useSystemInstruction);
let actualPrompt = prompt;
if (prompt[0].role === 'system') {
const [systemInstruction] = prompt;
this.client.systemInstruction = systemInstruction;
actualPrompt = prompt.slice(1);
}
const parameters = this.invocationParams(options);
// Handle streaming
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] === undefined) {
finalChunks[index] = chunk;
}
else {
finalChunks[index] = finalChunks[index].concat(chunk);
}
}
const generations = Object.entries(finalChunks)
.sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))
.map(([_, value]) => value);
return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };
}
const res = await this.completionWithRetry({
...parameters,
contents: actualPrompt
});
let usageMetadata;
if ('usageMetadata' in res.response) {
const genAIUsageMetadata = res.response.usageMetadata;
usageMetadata = {
input_tokens: genAIUsageMetadata.promptTokenCount ?? 0,
output_tokens: genAIUsageMetadata.candidatesTokenCount ?? 0,
total_tokens: genAIUsageMetadata.totalTokenCount ?? 0
};
}
const generationResult = (0, common_js_1.mapGenerateContentResultToChatResult)(res.response, {
usageMetadata
});
// may not have generations in output if there was a refusal for safety reasons, malformed function call, etc.
if (generationResult.generations?.length > 0) {
await runManager?.handleLLMNewToken(generationResult.generations[0]?.text ?? '');
}
return generationResult;
}
async *_streamResponseChunks(messages, options, runManager) {
const prompt = (0, common_js_1.convertBaseMessagesToContent)(messages, this._isMultimodalModel, this.useSystemInstruction);
let actualPrompt = prompt;
if (prompt[0].role === 'system') {
const [systemInstruction] = prompt;
this.client.systemInstruction = systemInstruction;
actualPrompt = prompt.slice(1);
}
const parameters = this.invocationParams(options);
const request = {
...parameters,
contents: actualPrompt
};
const stream = await this.caller.callWithOptions({ signal: options?.signal }, async () => {
const { stream } = await this.client.generateContentStream(request);
return stream;
});
let usageMetadata;
let index = 0;
for await (const response of stream) {
if ('usageMetadata' in response && this.streamUsage !== false && options.streamUsage !== false) {
const genAIUsageMetadata = response.usageMetadata;
if (!usageMetadata) {
usageMetadata = {
input_tokens: genAIUsageMetadata.promptTokenCount ?? 0,
output_tokens: genAIUsageMetadata.candidatesTokenCount ?? 0,
total_tokens: genAIUsageMetadata.totalTokenCount ?? 0
};
}
else {
// Under the hood, LangChain combines the prompt tokens. Google returns the updated
// total each time, so we need to find the difference between the tokens.
const outputTokenDiff = (genAIUsageMetadata.candidatesTokenCount ?? 0) - usageMetadata.output_tokens;
usageMetadata = {
input_tokens: 0,
output_tokens: outputTokenDiff,
total_tokens: outputTokenDiff
};
}
}
const chunk = (0, common_js_1.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);
}
catch (e) {
// TODO: Improve error handling
if (e.message?.includes('400 Bad Request')) {
e.status = 400;
}
throw e;
}
});
}
// eslint-disable-next-line
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 tools;
if ((0, types_1.isInteropZodSchema)(schema)) {
const jsonSchema = (0, zod_to_genai_parameters_js_1.schemaToGenerativeAIParameters)(schema);
tools = [
{
functionDeclarations: [
{
name: functionName,
description: jsonSchema.description ?? 'A function available to call.',
parameters: jsonSchema
}
]
}
];
outputParser = new output_parsers_js_1.GoogleGenerativeAIToolsOutputParser({
returnSingle: true,
keyName: functionName,
zodSchema: schema
});
}
else {
let geminiFunctionDefinition;
if (typeof schema.name === 'string' && typeof schema.parameters === 'object' && schema.parameters != null) {
geminiFunctionDefinition = schema;
geminiFunctionDefinition.parameters = (0, zod_to_genai_parameters_js_1.removeAdditionalProperties)(schema.parameters);
functionName = schema.name;
}
else {
geminiFunctionDefinition = {
name: functionName,
description: schema.description ?? '',
parameters: (0, zod_to_genai_parameters_js_1.removeAdditionalProperties)(schema)
};
}
tools = [
{
functionDeclarations: [geminiFunctionDefinition]
}
];
outputParser = new output_parsers_js_1.GoogleGenerativeAIToolsOutputParser({
returnSingle: true,
keyName: functionName
});
}
llm = this.bindTools(tools).withConfig({
allowedFunctionNames: [functionName]
});
}
else {
const jsonSchema = (0, zod_to_genai_parameters_js_1.schemaToGenerativeAIParameters)(schema);
llm = this.withConfig({
responseSchema: jsonSchema
});
outputParser = new output_parsers_1.JsonOutputParser();
}
if (!includeRaw) {
return llm.pipe(outputParser).withConfig({
runName: 'ChatGoogleGenerativeAIStructuredOutput'
});
}
const parserAssign = runnables_1.RunnablePassthrough.assign({
parsed: (input, config) => outputParser.invoke(input.raw, config)
});
const parserNone = runnables_1.RunnablePassthrough.assign({
parsed: () => null
});
const parsedWithFallback = parserAssign.withFallbacks({
fallbacks: [parserNone]
});
return runnables_1.RunnableSequence.from([
{
raw: llm
},
parsedWithFallback
]).withConfig({
runName: 'StructuredOutputRunnable'
});
}
}
exports.LangchainChatGoogleGenerativeAI = LangchainChatGoogleGenerativeAI;
class ChatGoogleGenerativeAI extends LangchainChatGoogleGenerativeAI {
constructor(id, fields) {
super(fields);
this.id = id;
this.configuredModel = fields?.model ?? '';
this.configuredMaxToken = fields?.maxOutputTokens;
}
revertToOriginalModel() {
this.model = this.configuredModel;
this.maxOutputTokens = this.configuredMaxToken;
}
setMultiModalOption(multiModalOption) {
this.multiModalOption = multiModalOption;
}
setVisionModel() {
// pass
}
}
exports.ChatGoogleGenerativeAI = ChatGoogleGenerativeAI;
//# sourceMappingURL=DtamindChatGoogleGenerativeAI.js.map