Lemonfox-AI

Integrate LemonFox with Portkey for seamless completions, prompt management, and advanced features like streaming, function calling, and fine-tuning.

Portkey Provider Slug: lemonfox-ai

Overview

Portkey offers native integrations with LemonFox-AI for Node.js, Python, and REST APIs. By combining Portkey with Lemonfox AI, you can create production-grade AI applications with enhanced reliability, observability, and advanced features.

Lemonfox AI Documentation

Explore the official Lemonfox AI documentation for comprehensive details on their APIs and models.

Getting Started

1. Obtain your LemonFox AI API Key

   Visit the LemonFox dashboard to generate your API key.

2. Create a Virtual Key in Portkey

   Portkey’s virtual key vault simplifies your interaction with LemonFox AI. Virtual keys act as secure aliases for your actual API keys, offering enhanced security and easier management through budget limits to control your API usage.

   Use the Portkey app to create a virtual key associated with your Lemonfox AI API key.

3. Initialize the Portkey Client

   Now that you have your virtual key, set up the Portkey client:

   Portkey Hosted App

   Use the Portkey API key and the LemonFox AI virtual key to initialize the client in your preferred programming language.

   Python

   from portkey_ai import Portkey
   
   portkey = Portkey(
       api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
       provider="@PROVIDER"   # Replace with your virtual key for LemonFox AI
   )
   

   Open Source Use

   Alternatively, use Portkey’s Open Source AI Gateway to enhance your app’s reliability with minimal code:

   Python

   from portkey_ai import Portkey, PORTKEY_GATEWAY_URL
   
   portkey = Portkey(
       api_key="dummy",  # Replace with your Portkey API key
       base_url=PORTKEY_GATEWAY_URL,
       Authorization="LEMONFOX_AI_API_KEY", # Replace with your Lemonfox AI API Key
       provider="lemonfox-ai"
   )
   

🔥 That’s it! You’ve integrated Portkey into your application with just a few lines of code. Now let’s explore making requests using the Portkey client.

Supported Models

Supported Lemonfox AI Models

Chat - Mixtral AI, Llama 3.1 8B and Llama 3.1 70B

Speech-To-Text- Whisper large-v3

Vision- Stable Diffusion XL (SDXL)

Supported Endpoints and Parameters

Endpoint	Supported Parameters
chatComplete	messages, max_tokens, temperature, top_p, stream, presence_penalty, frequency_penalty
imageGenerate	prompt, response_format, negative_prompt, size, n
createTranscription	translate, language, prompt, response_format, file

Lemonfox AI Supported Features

Chat Completions

Generate chat completions using Lemonfox AI models through Portkey:

Python

completion = portkey.chat.completions.create(
    messages=[{"role": "user", "content": "Say this is a test"}],
    model="llama-8b-chat"
)

print(completion.choices[0].message.content)

Streaming

Stream responses for real-time output in your applications:

Python

chat_complete = portkey.chat.completions.create(
    model="llama-8b-chat",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=True
)

for chunk in chat_complete:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Image Generate

Here’s how you can generate images using Lemonfox AI

Python

from portkey_ai import Portkey

client = Portkey(
  api_key = "PORTKEY_API_KEY",
  virtual_key = "PROVIDER"
)

client.images.generate(
  prompt="A cute baby sea otter",
  n=1,
  size="1024x1024"
)

Transcription

Portkey supports both Transcription methods for STT models:

Python

audio_file= open("/path/to/file.mp3", "rb")

# Transcription
transcription = portkey.audio.transcriptions.create(
  model="whisper-1",
  file=audio_file
)
print(transcription.text)

Portkey’s Advanced Features

Track End-User IDs

Portkey allows you to track user IDs passed with the user parameter in Lemonfox AI requests, enabling you to monitor user-level costs, requests, and more:

Python

response = portkey.chat.completions.create(
  model="llama-8b-chat",
  messages=[{"role": "user", "content": "Say this is a test"}],
  user="user_123456"
)

When you include the user parameter in your requests, Portkey logs will display the associated user ID, as shown in the image below:

In addition to the user parameter, Portkey allows you to send arbitrary custom metadata with your requests. This powerful feature enables you to associate additional context or information with each request, which can be useful for analysis, debugging, or other custom use cases.

Learn More About Metadata

Explore how to use custom metadata to enhance your request tracking and analysis.

Using The Gateway Config

Here’s a simplified version of how to use Portkey’s Gateway Configuration:

1. Create a Gateway Configuration

   You can create a Gateway configuration using the Portkey Config Dashboard or by writing a JSON configuration in your code. In this example, requests are routed based on the user’s subscription plan (paid or free).

   config = {
     "strategy": {
       "mode": "conditional",
       "conditions": [
         {
           "query": { "metadata.user_plan": { "$eq": "paid" } },
           "then": "llama-8b-chat"
         },
         {
           "query": { "metadata.user_plan": { "$eq": "free" } },
           "then": "gpt-3.5"
         }
       ],
       "default": "base-gpt4"
     },
     "targets": [
       {
         "name": "llama-8b-chat",
         "provider":"@xx"
       },
       {
         "name": "gpt-3.5",
         "provider":"@yy"
       }
     ]
   }
   

2. Process Requests

   When a user makes a request, it will pass through Portkey’s AI Gateway. Based on the configuration, the Gateway routes the request according to the user’s metadata.

   
3. Set Up the Portkey Client

   Pass the Gateway configuration to your Portkey client. You can either use the config object or the Config ID from Portkey’s hosted version.

   Python

   from portkey_ai import Portkey
   
   portkey = Portkey(
       api_key="PORTKEY_API_KEY",
       provider="@PROVIDER",
       config=portkey_config
   )
   

That’s it! Portkey seamlessly allows you to make your AI app more robust using built-in gateway features. Learn more about advanced gateway features:

Load Balancing

Distribute requests across multiple targets based on defined weights.

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Caching

Enable caching of responses to improve performance and reduce costs.

Guardrails

Portkey’s AI gateway enables you to enforce input/output checks on requests by applying custom hooks before and after processing. Protect your user’s/company’s data by using PII guardrails and many more available on Portkey Guardrails:

{
	"provider:"@lemonfox-ai-xxx",
	"before_request_hooks": [{
		"id": "input-guardrail-id-xx"
	}],
	"after_request_hooks": [{
		"id": "output-guardrail-id-xx"
	}]
}

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance the security and reliability of your AI applications.

Next Steps

The complete list of features supported in the SDK are available in our comprehensive documentation:

Portkey SDK Documentation

Explore the full capabilities of the Portkey SDK and how to leverage them in your projects.

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For the most up-to-date information on supported features and endpoints, please refer to our API Reference.