查询 Collection 列表

import tcvectordb
from tcvectordb.model.enum import ReadConsistency
#create a database client object
client = tcvectordb.VectorDBClient(url='http://10.0.X.X', username='root', key='eC4bLRy2va******************************', timeout=30)
# 指定 Base 类数据库
db = client.database('db-test')
# 查询 Base 类数据库下的集合列表        
coll_list = db.list_collections()
for col in coll_list:
        print(vars(col))      

{
    "database": "db-test",
    "collection": "book-emb",
    "replicaNum": 2,
    "shardNum": 1,
    "description": "this is a collection of test embedding",
    "indexes": [
        {
            "fieldName": "id",
            "fieldType": "string",
            "indexType": "primaryKey"
        },
        {
            "fieldName": "author",
            "fieldType": "string",
            "indexType": "filter"
        },
        {
            "fieldName": "vector",
            "fieldType": "vector",
            "indexType": "HNSW",
            "dimension": 768,
            "metricType": "COSINE",
            "params": {
                "M": 16,
                "efConstruction": 200
            },
            "indexedCount": 3
        },
        {
            "fieldName": "bookName",
            "fieldType": "string",
            "indexType": "filter"
        },
        {
            "fieldName": "tags",
            "fieldType": "string",
            "indexType": "filter"
        }
    ],
    "embedding": {
        "status": "enabled",
        "field": "text",
        "model": "bge-base-zh",
        "vectorField": "vector"
    },
    "documentCount": 3,
    "alias": [
        "alias-book-emb"
    ],
    "indexStatus": {
        "status": "ready",
        "startTime": ""
    }
}

{
    "database": "db-test",
    "collection": "book-vector",
    "replicaNum": 2,
    "shardNum": 1,
    "description": "this is a collection of test embedding",
    "indexes": [
        {
            "fieldName": "author",
            "fieldType": "string",
            "indexType": "filter"
        },
        {
            "fieldName": "id",
            "fieldType": "string",
            "indexType": "primaryKey"
        },
        {
            "fieldName": "vector",
            "fieldType": "vector",
            "indexType": "HNSW",
            "dimension": 3,
            "metricType": "COSINE",
            "params": {
                "M": 16,
                "efConstruction": 200
            },
            "indexedCount": 3
        },
        {
            "fieldName": "bookName",
            "fieldType": "string",
            "indexType": "filter"
        }
    ],
    "embedding": {
        "status": "disabled"
    },
    "documentCount": 3,
    "indexStatus": {
        "status": "ready",
        "startTime": ""
    }
}