Officiële LangChain-integratie
FluentC LangChain Tool
Officiële Python-plugin voor het integreren van de FluentC AI Vertaal-API met LangChain. Schakel realtime- en batchvertaling, taalherkenning en taakpolling in via LangChain-compatibele tools.
LangChain-integratiefuncties
Realtime- en batchvertaling
Kies tussen directe vertalingen of batchverwerking voor grote hoeveelheden inhoud.
Taalherkenning
Automatisch de taal van de invoerinhoud detecteren met betrouwbaarheidsbeoordeling.
Dynamische taalkeuze
Dropdown-menu's gevuld met de talen die zijn ingeschakeld voor uw API-sleutel.
Formaatondersteuning
Behandel zowel platte tekst als HTML-inhoud naadloos.
Foutafhandeling
Uitgebreide foutafhandeling met ondersteuning voor doorgaan bij fout.
Workflow-integratie
Naadloos integreren in elke N8N-automatiseringsworkflow.
Installatie & Instelling
Beschikbaar LangChain-tools
FluentC LangChain Tool Klassen
Gereedschapklasse
Doel
FluentCTranslationTool
Vertaling in realtime of batch indienen
FluentCLanguageDetectorTool
Detecteer de brontaal uit de invoer
FluentCTranslationStatusTool
Controleer de status van batchvertalingsopdrachten
FluentCResultsTool
Peiling voor batchjob vertalingsresultaat
FluentCBatchTranslationTool
Eénmalige batchindiening + polling
Gebruik Voorbeelden
Voer realtime vertaling uit met behulp van de FluentC LangChain-tool:
from fluentc_langchain_tool import FluentCTranslationTool
# Initialize the translation tool
tool = FluentCTranslationTool(api_key="your-api-key")
# Perform real-time translation
response = tool.run({
"text": "Hello, world!",
"target_language": "fr",
"source_language": "en",
"mode": "realtime"
})
print(response) # Output: "Bonjour, le monde !"
# Translation with auto-detection
response = tool.run({
"text": "¿Cómo estás?",
"target_language": "en",
"mode": "realtime"
})
print(response) # Output: "How are you?"
print("Detected source language:", response.get('detected_language', 'Unknown'))
Behandel grote inhoud met batchvertaling en automatische polling:
from fluentc_langchain_tool import FluentCBatchTranslationTool
# Initialize batch translation tool
tool = FluentCBatchTranslationTool(api_key="your-api-key")
# Translate large HTML content
large_html = """
<html>
<head><title>Welcome</title></head>
<body data-rsssl=1>
<h1>Hello, batch world!</h1>
<p>This is a large document that needs translation.</p>
<p>It contains multiple paragraphs and HTML structure.</p>
</body>
</html>
"""
# Submit and automatically poll for results
result = tool.run({
"text": large_html,
"target_language": "de",
"source_language": "en"
})
print("Translated HTML:")
print(result) # Final translated output after polling
# The tool automatically:
# 1. Submits the batch job
# 2. Polls for completion using estimated_wait_seconds
# 3. Returns the final translation result
Controleer de status van batchvertalingsopdrachten:
from fluentc_langchain_tool import (
FluentCTranslationTool,
FluentCTranslationStatusTool
)
# Initialize tools
translation_tool = FluentCTranslationTool(api_key="your-api-key")
status_tool = FluentCTranslationStatusTool(api_key="your-api-key")
# Submit a batch translation job
job_response = translation_tool.run({
"text": "Large document for batch processing...",
"target_language": "es",
"mode": "batch"
})
job_id = job_response.get('job_id')
print(f"Batch job submitted: {job_id}")
# Check job status
status_response = status_tool.run({
"job_id": job_id
})
print(f"Job status: {status_response}")
# Status responses include:
# - "processing": Job is still running
# - "complete": Translation finished
# - "failed": Job encountered an error
# - estimated_wait_seconds: Recommended polling interval
Integreer FluentC-tools met LangChain-agenten voor complexe workflows:
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from fluentc_langchain_tool import (
FluentCTranslationTool,
FluentCBatchTranslationTool,
FluentCLanguageDetectorTool,
FluentCTranslationStatusTool
)
# Initialize FluentC tools
api_key = "your-fluentc-api-key"
translation_tool = FluentCTranslationTool(api_key)
batch_tool = FluentCBatchTranslationTool(api_key)
detector_tool = FluentCLanguageDetectorTool(api_key)
status_tool = FluentCTranslationStatusTool(api_key)
# Create LangChain agent with FluentC tools
agent = initialize_agent(
tools=[
Tool.from_function(
func=translation_tool.run,
name="FluentC_Translation",
description="Translate text in real-time or batch mode"
),
Tool.from_function(
func=batch_tool.run,
name="FluentC_Batch_Translation",
description="Translate large content with auto-polling"
),
Tool.from_function(
func=detector_tool.run,
name="FluentC_Language_Detection",
description="Detect language of input text"
),
Tool.from_function(
func=status_tool.run,
name="FluentC_Status_Check",
description="Check batch translation job status"
)
],
llm=OpenAI(temperature=0),
agent="zero-shot-react-description",
verbose=True
)
# Example agent interactions
responses = [
"Translate 'Hello world' from English to German using FluentC.",
"Detect the language of 'Bonjour tout le monde' and translate it to Spanish.",
"Translate this large HTML document to French using batch processing."
]
for query in responses:
print(f"\nQuery: {query}")
result = agent.run(query)
print(f"Result: {result}")
Klaar om vertalingen toe te voegen aan je N8N-workflows?
Begin vandaag nog met de N8N-integratie van FluentC. Maak je API-sleutel aan, installeer de plugin en begin met het bouwen van meertalige automatiseringsworkflows.