workspacex 0.1.24

workspacex is a Python library for managing AIGC (AI-Generated Content) artifacts. It provides a collaborative workspace environment for handling multiple artifacts with features like version control, update notifications, artifact management, and pluggable storage and embedding backends.

workspacex

workspacex is a Python library for managing AIGC (AI-Generated Content) artifacts. It provides a collaborative workspace environment for handling multiple artifacts with features like version control, update notifications, artifact management, and pluggable storage and embedding backends.

{width=800px height=400px}

Features

• Artifact Management: Create, update, and manage different types of artifacts (text, code, novels, etc.)
• Workspace Organization: Group related artifacts in collaborative workspaces
• Parallel Processing: 🚀 Subartifacts are processed in parallel for improved performance
• Storage Backends: Local file system and S3-compatible storage (via s3fs)
• Embedding Backends: OpenAI-compatible and Ollama embedding support
• Vector Search: Hybrid search combining semantic and keyword-based search
• Full-Text Search: Elasticsearch-based full-text search with Chinese analyzer support
• Reranking: Local reranking using Qwen3-Reranker models
• HTTP Service: FastAPI-based reranking service

Process

{width=400px height=800px}

---

Installation

Basic Installation

pip install workspacex

With Reranker Support

pip install "workspacex[reranker]"  # For using reranker in your code
pip install "workspacex[reranker-server]"  # For running the reranker HTTP service

Using Poetry:

poetry install --extras "reranker-server"  # Installs all features

---

Usage

Basic Example

import asyncio
from workspacex.utils.logger import logger

from workspacex import WorkSpace, ArtifactType

if __name__ == '__main__':
    workspace = WorkSpace.from_local_storages(workspace_id="demo")
    asyncio.run(workspace.create_artifact(ArtifactType.TEXT, "artifact_001"))

Parallel Processing Demo

WorkspaceX now supports high-performance parallel processing of artifacts and subartifacts, providing significant performance improvements:

Key Features:

• 🚀 Full Parallel Processing: Main artifacts and subartifacts processed simultaneously
• ⚡ Thread Pool Optimization: CPU-intensive operations moved to thread pool
• 🎯 Configurable Concurrency: Control concurrent operations with max_concurrent_embeddings
• 🛡️ Error Handling: Robust error handling with detailed logging
• 📊 Performance Monitoring: Real-time performance metrics and logging

import asyncio
from workspacex import WorkSpace, ArtifactType

async def demo_enhanced_parallel_processing():
    workspace = WorkSpace(workspace_id="parallel_demo", clear_existing=True)
    
    # Configure concurrency limits (optional)
    workspace.workspace_config.max_concurrent_embeddings = 10
    
    # Create an artifact with multiple subartifacts
    # All artifacts and subartifacts will be processed in parallel for maximum performance
    await workspace.create_artifact(
        artifact_type=ArtifactType.NOVEL,
        novel_file_path="path/to/novel.txt",
        embedding_flag=True  # Enables parallel embedding processing
    )

# Run the demo
asyncio.run(demo_enhanced_parallel_processing())

Performance Improvements:

• Sequential Processing: ~1.0x baseline
• Parallel Subartifacts Only: ~2-3x faster
• Full Parallel Processing: ~5-10x faster
• Batch Processing: ~10-20x faster

For a complete performance comparison demo, see src/examples/parallel_processing_example.py.

More Examples

For more detailed examples on features like reranking, storage/embedding backends, hybrid search, and Chinese full-text search, please refer to the scripts in the src/examples/ directory.

To run an example:

export PYTHONPATH=src
python src/examples/embeddings/openai_example.py

Running the Reranker Server[Optional]

1. Install server dependencies:

pip install "workspacex[reranker-server]"

2. Start the server:

python -m workspacex.reranker.server.reranker_server

Default model: Qwen/Qwen3-Reranker-0.6B

To download the model first:

# Install huggingface_hub
pip install -U huggingface_hub

# Set mirror for faster download in China
export HF_ENDPOINT=https://hf-mirror.com

# Download the model
huggingface-cli download --resume-download Qwen/Qwen3-Reranker-0.6B --local-dir Qwen/Qwen3-Reranker-0.6B

The server can be configured with these environment variables:

RERANKER_MODEL=Qwen/Qwen3-Reranker-0.6B  # or Qwen/Qwen3-Reranker-8B
RERANKER_PORT=8000
RERANKER_RELOAD=False

The server will start on http://localhost:8000. Interactive API docs are available at /docs and /redoc. It provides endpoints like /rerank and a Dify-compatible /dify/rerank.

---

Changelog