TOS Vectors Skill

Comprehensive skill for managing vector storage, indexing, and similarity search using the TOS Vectors service - a cloud-based vector database optimized for AI applications.

Quick Start

Initialize Client

import os
import tos

# Get credentials from environment
ak = os.getenv('TOS_ACCESS_KEY')
sk = os.getenv('TOS_SECRET_KEY')
account_id = os.getenv('TOS_ACCOUNT_ID')

# Configure endpoint and region
endpoint = 'https://tosvectors-cn-beijing.volces.com'
region = 'cn-beijing'

# Create client
client = tos.VectorClient(ak, sk, endpoint, region)

Basic Workflow

# 1. Create vector bucket (like a database)
client.create_vector_bucket('my-vectors')

# 2. Create vector index (like a table)
client.create_index(
    account_id=account_id,
    vector_bucket_name='my-vectors',
    index_name='embeddings-768d',
    data_type=tos.DataType.DataTypeFloat32,
    dimension=768,
    distance_metric=tos.DistanceMetricType.DistanceMetricCosine
)

# 3. Insert vectors
vectors = [
    tos.models2.Vector(
        key='doc-1',
        data=tos.models2.VectorData(float32=[0.1] * 768),
        metadata={'title': 'Document 1', 'category': 'tech'}
    )
]
client.put_vectors(
    vector_bucket_name='my-vectors',
    account_id=account_id,
    index_name='embeddings-768d',
    vectors=vectors
)

# 4. Search similar vectors
query_vector = tos.models2.VectorData(float32=[0.1] * 768)
results = client.query_vectors(
    vector_bucket_name='my-vectors',
    account_id=account_id,
    index_name='embeddings-768d',
    query_vector=query_vector,
    top_k=5,
    return_distance=True,
    return_metadata=True
)

Core Operations

Vector Bucket Management

Create Bucket

client.create_vector_bucket(bucket_name)

List Buckets

result = client.list_vector_buckets(max_results=100)
for bucket in result.vector_buckets:
    print(bucket.vector_bucket_name)

Delete Bucket (must be empty)

client.delete_vector_bucket(bucket_name, account_id)

Vector Index Management

Create Index

client.create_index(
    account_id=account_id,
    vector_bucket_name=bucket_name,
    index_name='my-index',
    data_type=tos.DataType.DataTypeFloat32,
    dimension=128,
    distance_metric=tos.DistanceMetricType.DistanceMetricCosine
)

List Indexes

result = client.list_indexes(bucket_name, account_id)
for index in result.indexes:
    print(f"{index.index_name}: {index.dimension}d")

Vector Data Operations

Insert Vectors (batch up to 500)

vectors = []
for i in range(100):
    vector = tos.models2.Vector(
        key=f'vec-{i}',
        data=tos.models2.VectorData(float32=[...]),
        metadata={'category': 'example'}
    )
    vectors.append(vector)

client.put_vectors(
    vector_bucket_name=bucket_name,
    account_id=account_id,
    index_name=index_name,
    vectors=vectors
)