LanguageDetector

LanguageDetector 是一个输入处理器，用于识别输入文本的语言，并可选择将其翻译成目标语言以实现统一处理。该处理器通过检测接收消息的语言来帮助保持语言一致性，并提供处理多语言内容的灵活策略，包括自动翻译，以确保所有内容都以目标语言进行处理。

🌐 The LanguageDetector is an input processor that identifies the language of input text and optionally translates it to a target language for consistent processing. This processor helps maintain language consistency by detecting the language of incoming messages and providing flexible strategies for handling multilingual content, including automatic translation to ensure all content is processed in the target language.

使用示例

🌐 Usage example

import { LanguageDetector } from "@mastra/core/processors";

const processor = new LanguageDetector({
  model: "openrouter/openai/gpt-oss-safeguard-20b",
  targetLanguages: ["English", "en"],
  threshold: 0.8,
  strategy: "translate"
});

构造函数参数

🌐 Constructor parameters

options:

Options

Configuration options for language detection and translation

选项

🌐 Options

model:

MastraModelConfig

Model configuration for the detection/translation agent

targetLanguages?:

string[]

Target language(s) for the project. If content is detected in a different language, it may be translated. Can be language name ('English') or ISO code ('en')

threshold?:

number

Confidence threshold for language detection (0-1). Only process when detection confidence exceeds this threshold

strategy?:

'detect' | 'translate' | 'block' | 'warn'

Strategy when non-target language is detected: 'detect' only detects language, 'translate' automatically translates to target language, 'block' rejects content not in target language, 'warn' logs warning but allows through

preserveOriginal?:

boolean

Whether to preserve original content in message metadata. Useful for audit trails and debugging

instructions?:

string

Custom detection instructions for the agent. If not provided, uses default instructions

minTextLength?:

number

Minimum text length to perform detection. Short text is often unreliable for language detection

includeDetectionDetails?:

boolean

Whether to include detailed detection info in logs

translationQuality?:

'speed' | 'quality' | 'balanced'

Translation quality preference: 'speed' prioritizes fast translation, 'quality' prioritizes accuracy, 'balanced' balances between speed and quality

providerOptions?:

ProviderOptions

Provider-specific options passed to the internal detection agent. Use this to control model behavior like reasoning effort for thinking models (e.g., `{ openai: { reasoningEffort: 'low' } }`)

返回

🌐 Returns

id:

string

Processor identifier set to 'language-detector'

name?:

string

Optional processor display name

processInput:

(args: { messages: MastraDBMessage[]; abort: (reason?: string) => never; tracingContext?: TracingContext }) => Promise<MastraDBMessage[]>

Processes input messages to detect language and optionally translate content before sending to LLM

扩展使用示例

🌐 Extended usage example

src/mastra/agents/multilingual-agent.ts

import { Agent } from "@mastra/core/agent";
import { LanguageDetector } from "@mastra/core/processors";

export const agent = new Agent({
  id: "multilingual-agent",
  name: "multilingual-agent",
  instructions: "You are a helpful assistant",
  model: "openai/gpt-5.1",
  inputProcessors: [
    new LanguageDetector({
      model: "openrouter/openai/gpt-oss-safeguard-20b",
      targetLanguages: ["English", "en"],
      threshold: 0.8,
      strategy: "translate",
      preserveOriginal: true,
      instructions: "Detect language and translate non-English content to English while preserving original intent",
      minTextLength: 10,
      includeDetectionDetails: true,
      translationQuality: "quality"
    })
  ]
});

相关

🌐 Related

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