Official LangChain Integration
FluentC LangChain Tool
Official Python plugin for integrating FluentC AI Translation API with LangChain. Enable real-time and batch translation, language detection, and job polling through LangChain-compatible tools.
LangChain Integration Features
Real-time & Batch Translation
Choose between instant translations or batch processing for large content volumes.
Language Detection
Automatically detect the language of input content with confidence scoring.
Dynamic Language Selection
Dropdown menus populated with your API key's enabled languages.
Format Support
Handle both plain text and HTML content seamlessly.
Error Handling
Comprehensive error handling with continue-on-fail support.
Workflow Integration
Seamlessly integrate into any N8N automation workflow.
Installation & Setup
Available LangChain Tools
FluentC LangChain Tool Classes
Tool Class
Purpose
FluentCTranslationTool
Real-time or batch translation submit
FluentCLanguageDetectorTool
Detect source language from input
FluentCTranslationStatusTool
Check status of batch translation jobs
FluentCResultsTool
Poll for batch job translation result
FluentCBatchTranslationTool
One-shot batch submit + polling
Usage Examples
Perform real-time translation using the 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'))
Handle large content with batch translation and automatic 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
Check the status of batch translation jobs:
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
Integrate FluentC tools with LangChain agents for complex 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}")
Ready to Add Translation to Your N8N Workflows?
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