milvus-dataset 1.0.0.post19

Default template for PDM package

Milvus Dataset

Milvus Dataset is a versatile Python library for efficient management and processing of large-scale datasets. While optimized for seamless integration with Milvus vector database, it also serves as a powerful standalone dataset management tool. The library provides a simple yet powerful interface for creating, writing, reading, and managing datasets, particularly excelling in handling large-scale vector data and general-purpose data management tasks.

Key Features

1. Flexible Storage Support

   • Local storage support
   • Object storage support (S3/MinIO)
   • Easy migration between different storage types

2. Rich Data Type Support

   • Basic data types (INT64, VARCHAR, etc.)
   • Vector data types (FLOAT_VECTOR)
   • JSON fields
   • Sparse vectors
   • Binary vectors

3. Dataset Management

   • Training and test set split support
   • Dataset metadata management
   • Dataset statistics and analytics
   • Schema definition and validation

4. Integration Capabilities

   • Import to Milvus database
   • Upload to Hugging Face Hub
   • Seamless pandas DataFrame integration
   • Built-in nearest neighbor computation
   • Built-in mock data generation

Installation

pip install milvus-dataset

Quick Start Guide

1. Basic Configuration

from milvus_dataset import ConfigManager, StorageType

# Initialize local storage
ConfigManager().init_storage(
    root_path="./data/my-dataset",
    storage_type=StorageType.LOCAL,
)

# Initialize S3 storage
ConfigManager().init_storage(
    root_path="s3://bucket/path",
    storage_type=StorageType.S3,
    options={
        "aws_access_key_id": "your_key",
        "aws_secret_access_key": "your_secret",
        "endpoint_url": "your_endpoint"  # Optional, for MinIO
    }
)

2. Creating a Dataset

from pymilvus import CollectionSchema, DataType, FieldSchema
from milvus_dataset import load_dataset

# Define Schema
schema = CollectionSchema(
    fields=[
        FieldSchema("id", DataType.INT64, is_primary=True),
        FieldSchema("text", DataType.VARCHAR, max_length=65535),
        FieldSchema("embedding", DataType.FLOAT_VECTOR, dim=1024)
    ],
    description="Text vector dataset"
)

# Load dataset
dataset = load_dataset("my-dataset", schema=schema)

3. Writing Data

import pandas as pd
import numpy as np

# Prepare data
df = pd.DataFrame({
    "id": range(1000),
    "text": ["text_" + str(i) for i in range(1000)],
    "embedding": [np.random.rand(1024) for _ in range(1000)]
})

# Write to training set
with dataset["train"].get_writer(mode="append") as writer:
    writer.write(df)

4. Dataset Operations

# View dataset information
print(dataset.summary())

# Compute neighbors
dataset.compute_neighbors(
    vector_field_name="embedding",
    pk_field_name="id",
    top_k=100
)

# import to Milvus
dataset.to_milvus(
    milvus_config={
        "host": "localhost",
        "port": 19530
    },
    milvus_storage=StorageConfig(
        root_path="s3://bucket/path",
        storage_type=StorageType.S3,
        options={
            "aws_access_key_id": "your_key",
            "aws_secret_access_key": "your_secret",
            "endpoint_url": "your_endpoint"  # Optional, for MinIO
        }
    )

)

# Upload to Hugging Face
dataset.to_hf(repo_name="username/dataset-name")

Advanced Usage

Performance Optimization

1. File Size Configuration

   with dataset["train"].get_writer(
       mode="append",
       target_file_size_mb=512,  # Adjust file size
       num_buffers=15,           # Adjust buffer number
       queue_size=30             # Adjust queue size
   ) as writer:
       writer.write(df)
   

2. Batch Processing

   # Read in batches
   for batch in dataset["train"].read(mode="batch", batch_size=1000):
       process_batch(batch)
   

Storage Migration

# Move data from local to S3
dataset.to_storage(StorageConfig(
    storage_type=StorageType.S3,
    root_path="s3://bucket/path",
    options={...}
))

Common Issues and Solutions

1. Storage Type Selection

   • Use local storage for development and testing
   • Use object storage for production environments

2. Handling Large-Scale Data

   • Use batch writing
   • Set appropriate buffer size and queue size
   • Consider parallel processing

3. Ensuring Data Quality

   • Define comprehensive schema
   • Enable schema validation
   • Regularly check dataset statistics

4. Performance Optimization Tips

   • Set reasonable file size (target_file_size_mb)
   • Adjust buffer parameters (num_buffers, queue_size)
   • Process data in batches instead of one by one

Contributing

We welcome contributions! Please feel free to submit a Pull Request.