lemonade-integration 1.0.0

A clean interface for interacting with the Lemonade LLM server

🍋 Lemonade Python SDK

A robust, production-grade Python wrapper for the Lemonade C++ Backend.

This SDK provides a clean, pythonic interface for interacting with local LLMs running on Lemonade. It was built to power Sorana (a visual workspace for AI), extracting the core integration logic into a standalone, open-source library for the developer community.

🚀 Key Features

• Auto-Discovery: Automatically scans multiple ports and hosts to find active Lemonade instances.
• Low-Overhead Architecture: Designed as a thin, efficient wrapper to leverage Lemonade's C++ performance with minimal Python latency.
• Health Checks & Recovery: Built-in utilities to verify server status and handle connection drops.
• Type-Safe Client: Full Python type hinting for better developer experience (IDE autocompletion).
• Model Management: Simple API to load, unload, and list models dynamically.
• Embeddings API: Generate text embeddings for semantic search, RAG, and clustering (FLM & llamacpp backends).

📦 Installation

pip install .

Alternatively, you can install it directly from GitHub:

pip install git+[https://github.com/Tetramatrix/lemonade-python-sdk.git](https://github.com/Tetramatrix/lemonade-python-sdk.git)

⚡ Quick Start

1. Connecting to Lemonade

The SDK automatically handles port discovery, so you don't need to hardcode localhost:8000.

from lemonade_integration.client import LemonadeClient
from lemonade_integration.port_scanner import find_available_lemonade_port

# Auto-discover running instance
port = find_available_lemonade_port()
if port:
    client = LemonadeClient(base_url=f"http://localhost:{port}")
    if client.health_check():
        print(f"Connected to Lemonade on port {port}")
else:
    print("No Lemonade instance found.")

2. Chat Completion

response = client.chat_completion(
    model="Llama-3-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Hello World in C++"}
    ],
    temperature=0.7
)

print(response['choices'][0]['message']['content'])

3. Model Management

# List all available models
models = client.list_models()
for m in models:
    print(f"Found model: {m['id']}")

# Load a specific model into memory
client.load_model("Mistral-7B-v0.1")

4. Embeddings (NEW)

Generate text embeddings for semantic search, RAG pipelines, and clustering.

# List available embedding models (filtered by 'embeddings' label)
embedding_models = client.list_embedding_models()
for model in embedding_models:
    print(f"Embedding model: {model['id']}")

# Generate embeddings for single text
response = client.embeddings(
    input="Hello, world!",
    model="nomic-embed-text-v1-GGUF"
)

embedding_vector = response["data"][0]["embedding"]
print(f"Vector length: {len(embedding_vector)}")

# Generate embeddings for multiple texts
texts = ["Text 1", "Text 2", "Text 3"]
response = client.embeddings(
    input=texts,
    model="nomic-embed-text-v1-GGUF"
)

for item in response["data"]:
    print(f"Text {item['index']}: {len(item['embedding'])} dimensions")

Supported Backends:

• ✅ FLM (FastFlowLM) - NPU-accelerated on Windows
• ✅ llamacpp (.GGUF models) - CPU/GPU
• ❌ ONNX/OGA - Not supported

🖼️ Production Showcase: Sorana

This SDK was extracted from the core engine of Sorana, a professional visual workspace for AI. It demonstrates the SDK's capability to handle complex, real-world requirements on AMD Ryzen AI hardware:

• Low Latency: Powers sub-second response times for multi-model chat interfaces.
• Dynamic Workflows: Manages the loading and unloading of 20+ different LLMs based on user activity to optimize local NPU/GPU memory.
• Zero-Config UX: Uses the built-in port scanner to automatically connect the Sorana frontend to the Lemonade backend without user intervention.

🛠️ Project Structure

• client.py: Main entry point for API interactions (chat, embeddings, model management).
• port_scanner.py: Utilities for detecting Lemonade instances across ports (8000-9000).
• model_discovery.py: Logic for fetching and parsing model metadata.
• request_builder.py: Helper functions to construct compliant payloads (chat, embeddings).
• utils.py: Additional utility functions.

📚 Documentation

• Embeddings API - Complete guide for using embeddings
• Lemonade Server Docs - Official Lemonade documentation

🤝 Contributing

Contributions are welcome! This project is intended to help the AMD Ryzen AI and Lemonade community build downstream applications faster.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.