dtamind-components
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DTAmindai Components
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TypeScript
import { GenerateContentRequest, SafetySetting, Part as GenerativeAIPart, ModelParams, RequestOptions, type CachedContent, Schema } from '@google/generative-ai';
import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager';
import { AIMessageChunk, BaseMessage } from '@langchain/core/messages';
import { ChatGenerationChunk, ChatResult } from '@langchain/core/outputs';
import { BaseChatModel, type BaseChatModelCallOptions, type LangSmithParams, type BaseChatModelParams } from '@langchain/core/language_models/chat_models';
import { BaseLanguageModelInput, StructuredOutputMethodOptions } from '@langchain/core/language_models/base';
import { Runnable } from '@langchain/core/runnables';
import { InteropZodType } from '@langchain/core/utils/types';
import { GoogleGenerativeAIToolType } from './utils/types.js';
import { IMultiModalOption, IVisionChatModal } from '../../../src';
export type BaseMessageExamplePair = {
input: BaseMessage;
output: BaseMessage;
};
export interface GoogleGenerativeAIChatCallOptions extends BaseChatModelCallOptions {
tools?: GoogleGenerativeAIToolType[];
/**
* Allowed functions to call when the mode is "any".
* If empty, any one of the provided functions are called.
*/
allowedFunctionNames?: string[];
/**
* Whether or not to include usage data, like token counts
* in the streamed response chunks.
* @default true
*/
streamUsage?: boolean;
/**
* JSON schema to be returned by the model.
*/
responseSchema?: Schema;
}
/**
* An interface defining the input to the ChatGoogleGenerativeAI class.
*/
export interface GoogleGenerativeAIChatInput extends BaseChatModelParams, Pick<GoogleGenerativeAIChatCallOptions, 'streamUsage'> {
/**
* Model Name to use
*
* Note: The format must follow the pattern - `{model}`
*/
model: string;
/**
* Controls the randomness of the output.
*
* Values can range from [0.0,2.0], inclusive. A value closer to 2.0
* will produce responses that are more varied and creative, while
* a value closer to 0.0 will typically result in less surprising
* responses from the model.
*
* Note: The default value varies by model
*/
temperature?: number;
/**
* Maximum number of tokens to generate in the completion.
*/
maxOutputTokens?: number;
/**
* Top-p changes how the model selects tokens for output.
*
* Tokens are selected from most probable to least until the sum
* of their probabilities equals the top-p value.
*
* For example, if tokens A, B, and C have a probability of
* .3, .2, and .1 and the top-p value is .5, then the model will
* select either A or B as the next token (using temperature).
*
* Note: The default value varies by model
*/
topP?: number;
/**
* Top-k changes how the model selects tokens for output.
*
* A top-k of 1 means the selected token is the most probable among
* all tokens in the model's vocabulary (also called greedy decoding),
* while a top-k of 3 means that the next token is selected from
* among the 3 most probable tokens (using temperature).
*
* Note: The default value varies by model
*/
topK?: number;
/**
* The set of character sequences (up to 5) that will stop output generation.
* If specified, the API will stop at the first appearance of a stop
* sequence.
*
* Note: The stop sequence will not be included as part of the response.
* Note: stopSequences is only supported for Gemini models
*/
stopSequences?: string[];
/**
* A list of unique `SafetySetting` instances for blocking unsafe content. The API will block
* any prompts and responses that fail to meet the thresholds set by these settings. If there
* is no `SafetySetting` for a given `SafetyCategory` provided in the list, the API will use
* the default safety setting for that category.
*/
safetySettings?: SafetySetting[];
/**
* Google API key to use
*/
apiKey?: string;
/**
* Google API version to use
*/
apiVersion?: string;
/**
* Google API base URL to use
*/
baseUrl?: string;
/** Whether to stream the results or not */
streaming?: boolean;
/**
* Whether or not to force the model to respond with JSON.
* Available for `gemini-1.5` models and later.
* @default false
*/
json?: boolean;
/**
* Whether or not model supports system instructions.
* The following models support system instructions:
* - All Gemini 1.5 Pro model versions
* - All Gemini 1.5 Flash model versions
* - Gemini 1.0 Pro version gemini-1.0-pro-002
*/
convertSystemMessageToHumanContent?: boolean | undefined;
}
/**
* 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 />
*/
export declare class LangchainChatGoogleGenerativeAI extends BaseChatModel<GoogleGenerativeAIChatCallOptions, AIMessageChunk> implements GoogleGenerativeAIChatInput {
static lc_name(): string;
lc_serializable: boolean;
get lc_secrets(): {
[key: string]: string;
} | undefined;
lc_namespace: string[];
get lc_aliases(): {
apiKey: string;
};
model: string;
temperature?: number;
maxOutputTokens?: number;
topP?: number;
topK?: number;
stopSequences: string[];
safetySettings?: SafetySetting[];
apiKey?: string;
streaming: boolean;
json?: boolean;
streamUsage: boolean;
convertSystemMessageToHumanContent: boolean | undefined;
private client;
get _isMultimodalModel(): boolean;
constructor(fields: GoogleGenerativeAIChatInput);
useCachedContent(cachedContent: CachedContent, modelParams?: ModelParams, requestOptions?: RequestOptions): void;
get useSystemInstruction(): boolean;
get computeUseSystemInstruction(): boolean;
getLsParams(options: this['ParsedCallOptions']): LangSmithParams;
_combineLLMOutput(): never[];
_llmType(): string;
bindTools(tools: GoogleGenerativeAIToolType[], kwargs?: Partial<GoogleGenerativeAIChatCallOptions>): Runnable<BaseLanguageModelInput, AIMessageChunk, GoogleGenerativeAIChatCallOptions>;
invocationParams(options?: this['ParsedCallOptions']): Omit<GenerateContentRequest, 'contents'>;
_generate(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;
_streamResponseChunks(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;
completionWithRetry(request: string | GenerateContentRequest | (string | GenerativeAIPart)[], options?: this['ParsedCallOptions']): Promise<import("@google/generative-ai").GenerateContentResult>;
withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;
withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {
raw: BaseMessage;
parsed: RunOutput;
}>;
}
export declare class ChatGoogleGenerativeAI extends LangchainChatGoogleGenerativeAI implements IVisionChatModal {
configuredModel: string;
configuredMaxToken?: number;
multiModalOption: IMultiModalOption;
id: string;
constructor(id: string, fields: GoogleGenerativeAIChatInput);
revertToOriginalModel(): void;
setMultiModalOption(multiModalOption: IMultiModalOption): void;
setVisionModel(): void;
}