elizaOS Cloud plugin - Multi-model AI generation with text, image, and audio support
Project description
elizaos-plugin-elizacloud
Python implementation of the elizaOS Cloud plugin for multi-model AI generation.
Installation
pip install elizaos-plugin-elizacloud
Usage
import asyncio
from elizaos_plugin_elizacloud import (
ElizaCloudClient,
ElizaCloudConfig,
TextGenerationParams,
TextEmbeddingParams,
ImageGenerationParams,
)
async def main():
# Configure the client
config = ElizaCloudConfig(
api_key="eliza_xxxxx", # Get from https://www.elizacloud.ai/dashboard/api-keys
base_url="https://www.elizacloud.ai/api/v1",
)
async with ElizaCloudClient(config) as client:
# Text generation
text = await client.generate_text(
TextGenerationParams(prompt="What is the meaning of life?"),
model_size="large",
)
print(f"Generated text: {text}")
# Embeddings
embedding = await client.generate_embedding(
TextEmbeddingParams(text="Hello, world!")
)
print(f"Embedding dimension: {len(embedding)}")
# Batch embeddings
embeddings = await client.generate_embedding(
TextEmbeddingParams(texts=["Hello", "World", "!"])
)
print(f"Batch embeddings: {len(embeddings)}")
# Image generation
images = await client.generate_image(
ImageGenerationParams(
prompt="A beautiful sunset over the ocean",
count=1,
size="1024x1024",
)
)
print(f"Generated image URL: {images[0]}")
asyncio.run(main())
Features
- Text Generation: Small (fast) and large (powerful) model support
- Embeddings: Single and batch text embedding with rate limit handling
- Image Generation: DALL-E style image generation
- Image Description: Vision model for describing images
- Text-to-Speech: Multiple voice options
- Transcription: Whisper-based audio transcription
Configuration
| Setting | Description | Default |
|---|---|---|
api_key |
elizaOS Cloud API key (required) | - |
base_url |
Base URL for API requests | https://www.elizacloud.ai/api/v1 |
small_model |
Model for quick tasks | gpt-5-mini |
large_model |
Model for complex tasks | gpt-5 |
embedding_model |
Model for embeddings | text-embedding-3-small |
embedding_dimensions |
Embedding vector size | 1536 |
License
MIT
Project details
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