neuredge-sdk 1.0.0b3

Official Python SDK for the Neuredge AI Platform

Neuredge Python SDK

The official Python client for the Neuredge AI Platform.

Installation

pip install neuredge-sdk

Features

• 🤖 OpenAI-compatible chat completions and embeddings
• 📝 Text summarization, sentiment analysis, and translation
• 🎨 Image generation with fast and standard modes
• 🔍 Vector storage with consistency and retry controls
• 🔒 Built-in error handling and retries

Available Models

Chat Models

Llama Models

• Llama 2 Series

  • @cf/meta/llama-2-7b-chat-fp16 - 7B parameter model
  • @cf/meta/llama-2-7b-chat-int8 - 7B parameter quantized model

• Llama 3 Series

  • @cf/meta/llama-3-8b-instruct - Base 8B model
  • @cf/meta/llama-3-8b-instruct-awq - 8B AWQ quantized
  • @cf/meta/llama-3.1-8b-instruct - Latest 8B with JSON support
  • @cf/meta/llama-3.1-8b-instruct-awq - Latest 8B AWQ quantized
  • @cf/meta/llama-3.1-8b-instruct-fp8 - 8B FP8 quantized
  • @cf/meta/llama-3.1-8b-instruct-fast - Optimized for speed
  • @cf/meta/llama-3.1-70b-instruct - Large 70B model
  • @cf/meta/llama-3.2-1b-instruct - Compact 1B model
  • @cf/meta/llama-3.2-3b-instruct - Efficient 3B model

• Vision Models

  • @cf/meta/llama-3.2-11b-vision - 11B multimodal model
  • @cf/meta/llama-3.2-90b-vision - 90B multimodal model

Mistral Models

• @cf/mistral/mistral-7b-instruct-v0.1 - Original 7B model
• @hf/mistral/mistral-7b-instruct-v0.2 - Improved 7B model
• @cf/mistral/mistral-7b-instruct-v0.2-lora - LoRA-enabled version

Qwen Models

• @cf/qwen/qwen1.5-0.5b-chat - Compact 0.5B model
• @cf/qwen/qwen1.5-1.8b-chat - Small 1.8B model
• @cf/qwen/qwen1.5-7b-chat-awq - 7B AWQ quantized
• @cf/qwen/qwen1.5-14b-chat-awq - 14B AWQ quantized

Google Models

• @cf/google/gemma-2b-it-lora - 2B LoRA-enabled
• @cf/google/gemma-7b-it-lora - 7B LoRA-enabled
• @hf/google/gemma-7b-it - Standard 7B model

Specialized Models

• @cf/microsoft/phi-2 - General purpose
• @cf/openchat/openchat-3.5-0106 - ChatGPT-like
• @cf/deepseek-ai/deepseek-math-7b-instruct - Math specialized
• @cf/deepseek-ai/deepseek-r1-distill-qwen-32b - Distilled 32B
• @cf/tinyllama/tinyllama-1.1b-chat-v1.0 - Ultra-compact

Embedding Models

BGE Models

• @cf/baai/bge-base-en-v1.5

  • Dimensions: 768
  • Best for: General purpose
  • Max tokens: 8191

• @cf/baai/bge-large-en-v1.5

  • Dimensions: 1024
  • Best for: High accuracy
  • Max tokens: 8191

• @cf/baai/bge-small-en-v1.5

  • Dimensions: 384
  • Best for: Efficiency
  • Max tokens: 8191

Model Features

Model Type	Context Window	Features
Llama 3.1 70B	8192	JSON mode, Function calling, Streaming
Llama 3.1 8B	8192	Multilingual, Streaming
Llama 3.2 Vision	128000	Image understanding, Multilingual
Mistral 7B	8192	Streaming, LoRA fine-tuning
Qwen 14B	8192	Multilingual, Streaming
BGE Embeddings	8191	Semantic search, Cross-lingual

Quick Start

from neuredge_sdk import Neuredge

client = Neuredge(
    api_key="your_api_key",
    base_url="https://api.neuredge.dev",  # Optional
    max_retries=3,                        # Optional
    retry_delay=1.0                       # Optional
)

Text Processing

from neuredge_sdk import Neuredge

client = Neuredge(api_key="your_api_key")

# Summarization
text = """Workers AI allows you to run machine learning models on the Cloudflare network.
With the launch of Workers AI, Cloudflare is rolling out GPUs globally."""
summary = client.text.summarize(text)
print(summary)

# Sentiment Analysis with actual response format
sentiment = client.text.analyze_sentiment("I love this product!")
print(f"Sentiment: {sentiment['sentiment']}")  # POSITIVE or NEGATIVE
print(f"Confidence: {sentiment['confidence']}")
print(f"Is Confident: {sentiment['is_confident']}")

# Translation
spanish = client.text.translate(
    text="Hello, world!",
    target_lang="es",
    source_lang="en"  # Optional
)
print(spanish)

Chat Completions (OpenAI Compatible)

from neuredge_sdk import Neuredge

client = Neuredge(api_key="your_api_key")

# Basic completion with actual model names
completion = client.openai.chat.create(
    model="@cf/meta/llama-2-7b-chat-fp16",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
)
print(completion['choices'][0]['message']['content'])

# Streaming with actual response format
stream = client.openai.chat.create(
    model="@cf/meta/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "Count to 3"}],
    stream=True
)
for chunk in stream:
    if chunk['choices'][0]['delta'].get('content'):
        print(chunk['choices'][0]['delta']['content'], end='')

Embeddings (OpenAI Compatible)

from neuredge_sdk import Neuredge

client = Neuredge(api_key="your_api_key")

# Using correct model name and dimensions
embedding = client.openai.embeddings.create(
    input="Hello world",
    model="@cf/baai/bge-small-en-v1.5"  # 384 dimensions
)
vector = embedding['data'][0]['embedding']  # 384-dimensional vector
print(vector[:5])  # First 5 dimensions

Vector Store Operations

from neuredge_sdk import Neuredge

client = Neuredge(api_key="your_api_key")

# Create index with correct dimensions
client.vector.create_index({
    "name": "my-vectors",
    "dimension": 384,  # BGE small dimension
    "metric": "cosine"
})

# Store vectors with proper consistency options
result = client.vector.add_vectors(
    "my-vectors",
    vectors=[{
        "id": "1",
        "values": [0.1] * 384  # Match BGE small dimensions
    }],
    options={
        "consistency": {
            "enabled": True,
            "maxRetries": 3,
            "retryDelay": 1000
        }
    }
)

# Search with actual response format
matches = client.vector.search_vector(
    "my-vectors",
    vector=[0.1] * 384,
    options={
        "topK": 10,
        "consistency": {"enabled": True}
    }
)
for match in matches:
    print(f"ID: {match['id']}, Score: {match['score']}")

Image Generation

from neuredge_sdk import Neuredge
from pathlib import Path

client = Neuredge(api_key="your_api_key")

# Fast mode - Quick generation
fast_image = client.image.generate_fast(
    "A simple sketch of a cat"
)  # Returns bytes

# Standard mode with options
standard_image = client.image.generate(
    "A magical forest with glowing mushrooms",
    options={
        "mode": "standard",
        "width": 1024,
        "height": 768,
        "guidance": 8.5,
        "negativePrompt": "dark, scary, spooky"
    }
)  # Returns bytes

# Save the generated images
images_dir = Path("generated_images")
images_dir.mkdir(exist_ok=True)

# Direct file writing (bytes response)
with open(images_dir / "fast.png", 'wb') as f:
    f.write(fast_image)

with open(images_dir / "standard.png", 'wb') as f:
    f.write(standard_image)

Return Types

# All image generation methods return bytes
image: bytes = client.image.generate_fast("prompt")  # Direct bytes response
image: bytes = client.image.generate("prompt", options)  # Direct bytes response

# Image generation options
options = {
    "mode": "standard",    # 'fast' or 'standard'
    "width": 1024,        # 512-1024px
    "height": 1024,       # 512-1024px
    "guidance": 7.5,      # 1-20, controls prompt adherence
    "negativePrompt": ""  # Things to avoid in generation
}

Response Format

# Image generation response format
{
    'images': [
        'data:image/jpeg;base64,<base64-encoded-image-data>',
        # More images if batch generation
    ]
}

Error Handling

from neuredge_sdk import Neuredge, NeuredgeError

try:
    client = Neuredge(api_key="invalid-key")
    summary = client.text.summarize("Some text")
except NeuredgeError as e:
    if e.code == 'AUTHENTICATION_ERROR':
        print("Invalid API key")
    elif e.code == 'QUOTA_EXCEEDED':
        print("Rate limit reached")
    else:
        print(f"Error: {e.code} - {e.message}")

Development

Running Tests

# Install test dependencies
pip install -e ".[test]"

# Run all tests
python -m tests.run_tests

# Run specific suite
python -m tests.integration.text
python -m tests.integration.vector

Project Structure

neuredge_sdk/
├── capabilities/     # Core API capabilities
│   ├── text.py
│   ├── image.py
│   └── vector.py
├── openai/          # OpenAI-compatible interfaces
│   ├── completions.py
│   └── embeddings.py
├── types.py         # Type definitions
└── client.py        # Main client

tests/
├── integration/     # Integration tests
├── utils.py        # Test utilities
└── config.py       # Test configuration

Contributing

Contributions are welcome! Please see our Contribution Guidelines for more information.

License

MIT