deepl.py

Overview

deepl.py defines a DeepL translation component integrated into the InfiniFlow agent framework. It provides functionality to translate text from a specified source language to a target language using the DeepL API. The file contains two main classes:

• DeepLParam: Defines and validates parameters for the DeepL component, including authentication key and language settings.

• DeepL: Implements the translation logic by invoking the DeepL API and returning translated text.

This module acts as a bridge between the InfiniFlow system’s component architecture and the external DeepL translation service, enabling multilingual translation capabilities within workflows or pipelines managed by InfiniFlow.

Detailed Documentation

Class: DeepLParam

Defines the configuration parameters necessary for the DeepL translation component.

Attributes

• auth_key (str):
  API authentication key for DeepL service. Default is "xxx" (placeholder, should be replaced with a valid key).

• parameters (list):
  An empty list prepared to hold additional parameters if needed. Currently unused.

• source_lang (str):
  Source language code for translation. Default is 'ZH' (Chinese).

• target_lang (str):
  Target language code for translation. Default is 'EN-GB' (British English).

Methods

• init(self)
  Initializes DeepLParam with default values.

• check(self)
  Validates the parameter values to ensure they are appropriate before the component runs.

  • Checks that top_n is a positive integer (inherited property, not explicitly declared here).

  • Validates source_lang against a predefined list of supported ISO language codes.

  • Validates target_lang against a predefined list of supported ISO language codes including region variants (e.g., EN-GB, PT-BR).

Usage Example

param = DeepLParam()
param.auth_key = "your_valid_deepl_auth_key"
param.source_lang = "EN"
param.target_lang = "FR"
param.check()  # Raises error if invalid

Class: DeepL

The main translation component class that interfaces with the DeepL API.

Inherits from:

• ComponentBase (from agent.component.base): Base class for all components in the InfiniFlow framework.

• ABC (Python’s Abstract Base Class): Indicates this class is abstract (although no abstract methods defined here).

Class Attributes

• component_name (str):
  "DeepL" — the identifier used by the system for this component.

Methods

• _run(self, history, **kwargs)
  Executes the translation process.

  Parameters:

  • history: Not used explicitly in this method; typically contains interaction history relevant to the component.

  • **kwargs: Additional optional parameters (not used here).

  Process:

  1. Retrieves input data via self.get_input(), expecting a dictionary with a "content" key containing a list of text segments.

  2. Joins the list of texts with " - " as a separator into a single string ans.

  3. If ans is empty or missing, returns an empty output using DeepL.be_output("").

  4. Instantiates a deepl.Translator object with the stored API key (self._param.auth_key).

  5. Calls translate_text method with the input text, source_lang, and target_lang from parameters.

  6. Returns the translated text wrapped with DeepL.be_output.

  7. Catches exceptions, and returns an error message prefixed with "Error:".

  Returns:

  • Output string containing the translated text or an error message.

Usage Example

deepl_component = DeepL()
deepl_component._param.auth_key = "your_deepl_api_key"
deepl_component._param.source_lang = "EN"
deepl_component._param.target_lang = "DE"

# Mock input setup (depends on ComponentBase implementation)
deepl_component.set_input({"content": ["Hello world", "How are you?"]})

result = deepl_component._run(None)
print(result)  # Should print translated text in German

Implementation Details and Algorithms

• Parameter Validation:
  The check() method in DeepLParam ensures language codes are valid by checking against pre-defined lists of supported languages, preventing runtime errors from invalid codes.

• Translation Workflow:
  The _run() method aggregates multiple content strings into one, then directly uses the DeepL API’s translate_text method to perform the translation in a single request. This approach minimizes API calls and simplifies error handling.

• Error Handling:
  Any exception during the API call is caught, and an error message is returned instead of raising, allowing the system to handle failures gracefully.

• Dependency:
  Uses the official deepl Python library for API communication.

Interaction with Other System Components

• ComponentBase and ComponentParamBase:
  DeepL and DeepLParam inherit from these base classes which provide common interfaces and behaviors for all InfiniFlow components and parameters. These include methods like get_input() and be_output() used for input retrieval and output formatting.

• Agent Framework:
  This component can be plugged into the InfiniFlow agent pipeline as a translation step, receiving input from upstream components and passing translated output downstream.

• DeepL API:
  Acts as a client wrapper around the external DeepL translation service, requiring a valid authentication key.

Mermaid Class Diagram

classDiagram
    class DeepLParam {
        -auth_key: str
        -parameters: list
        -source_lang: str
        -target_lang: str
        +__init__()
        +check()
    }

    class DeepL {
        +component_name: str
        +_run(history, **kwargs)
    }

    DeepLParam <|-- ComponentParamBase
    DeepL --|> ComponentBase
    DeepL --|> ABC

Summary

deepl.py provides a reusable, parameterized DeepL translation component for the InfiniFlow system. It encapsulates configuration validation, API interaction, and error handling within an easy-to-integrate class structure. This module enables automated translation tasks within larger workflows, leveraging DeepL’s powerful language translation capabilities.