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This page explains how to retrieve collection metadata, monitor collection health, and list available collections.

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Get collection metadata

The following example retrieves collection configuration and properties. 
import asyncio
from actian_vectorai import AsyncVectorAIClient

async def main():
    # Connect to VectorAI DB server
    async with AsyncVectorAIClient("localhost:50051") as client:
        # Get collection info
        info = await client.collections.get_info("my_collection")
        
        # Display collection metadata
        print(f"Name: {info.config.params.collection_name if info.config else 'my_collection'}")
        print(f"Status: {info.status}")
        print(f"Vector count: {info.points_count}")

asyncio.run(main())
The response includes the following fields.
  • config.params.collection_name: The name of the collection.
  • config.params.vectors_config: Vector configuration including size and distance metric.
  • status: Current collection state (Ready, Loading, Closed, Error).
  • points_count: Total number of points in the collection.
  • optimizer_status: Status of the indexing optimizer.

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Get collection statistics

The following example monitors collection size, index status, and resource usage. 
import asyncio
from actian_vectorai import AsyncVectorAIClient

async def main():
    # Connect to VectorAI DB server
    async with AsyncVectorAIClient("localhost:50051") as client:
        # Get vector count
        count = await client.points.count("my_collection")
        print(f"Total vectors: {count}")

        # Get detailed info
        info = await client.collections.get_info("my_collection")
        print(f"Total vectors: {info.points_count}")
        print(f"Status: {info.status}")
        print(f"Optimizer status: {info.optimizer_status}")

asyncio.run(main())
The response contains these properties.
  • points_count: Total number of points stored in the collection.
  • status: Current collection state (Ready, Loading, Closed, Error).
  • optimizer_status: Current status of the indexing optimizer.

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Get collection state

Collections exist in one of four states that indicate their availability for operations:
  • Ready. Collection is loaded into memory and available for operations.
  • Loading. Collection is currently loading into memory.
  • Closed. Collection exists on disk but is not loaded into memory.
  • Error. Collection encountered an issue.
The following example checks the current state of a collection by calling get_info() and reading the status field. 
import asyncio
from actian_vectorai import AsyncVectorAIClient

async def main():
    # Connect to VectorAI DB server
    async with AsyncVectorAIClient("localhost:50051") as client:
        # Get collection information
        info = await client.collections.get_info("my_collection")
        print(f"Collection status: {info.status}")

asyncio.run(main())
The response includes the status field, which contains the current state of the collection.

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Run a health check

The health check monitors database connectivity and uptime at the instance level, not for individual collections. Use it to verify that the VectorAI DB server is running and to retrieve version information. 
import asyncio
from actian_vectorai import AsyncVectorAIClient

async def main():
    # Connect to VectorAI DB server
    async with AsyncVectorAIClient("localhost:50051") as client:
        # Get health check information
        health = await client.health_check()
        print(f"Title: {health['title']}")
        print(f"Version: {health['version']}")

asyncio.run(main())
The health check response includes these fields.
  • title: Name of the VectorAI DB service.
  • version: Current version of the VectorAI DB server.

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List all collections

Retrieve a list of all collections in your VectorAI DB instance. This returns information about all collections including their names, vector counts, and basic configuration. Use this to discover available collections or to monitor your database state. 
import asyncio
from actian_vectorai import AsyncVectorAIClient

async def main():
    # Connect to VectorAI DB server
    async with AsyncVectorAIClient("localhost:50051") as client:
        # List all collections
        collections = await client.collections.list()
        
        print(f"Total collections: {len(collections)}")
        for collection_name in collections:
            # Get info for each collection
            info = await client.collections.get_info(collection_name)
            print(f"- {collection_name}: {info.points_count} vectors")

asyncio.run(main())
The response contains the following information.
  • collections: List of collection names (strings).
  • For each collection via get_info():
    • points_count: Number of vectors in the collection.
    • status: Collection state.
    • config: Collection configuration details.