aiotcvectordb 0.1.1

Tencent VectorDB Python Async SDK

aiotcvectordb — Tencent VectorDB Python Async SDK

An asyncio-first client for Tencent Cloud VectorDB built on top of aiohttp. It mirrors the official tcvectordb SDK’s models and request payloads, while providing non-blocking APIs and REPL-friendly representations.

Looking for Chinese docs? See README_zh.md.

Features

• Fully async HTTP client using aiohttp with connection pooling and proxy support.
• Type parity with tcvectordb (indexes, enums, document types) re-exported under aiotcvectordb.model.
• Convenient async wrappers: AsyncDatabase, AsyncCollection, AsyncCollectionView, AsyncDocumentSet.
• Supports vector search, hybrid search (dense/sparse), text search with server-side embeddings, and full-text search.

Requirements

• Python 3.9+
• Dependencies: tcvectordb, aiohttp, numpy
• Optional: qcloud_cos for AI document upload in CollectionView.upload/load_and_split_text

Install

pip install aiotcvectordb

From source (repo root):

pip install -e .
# or with uv
uv pip install -e .

Quickstart

import asyncio
from aiotcvectordb import AsyncVectorDBClient
from aiotcvectordb.model import (
    Index, VectorIndex, FilterIndex,
    FieldType, IndexType, MetricType,
)

async def main():
    async with AsyncVectorDBClient(
        url="http://127.0.0.1:8081",
        username="root",
        key="<your-api-key>",
    ) as client:
        # Create database if not exists
        await client.create_database_if_not_exists("demo_db")

        # Define indexes for the collection
        idx = Index()
        idx.add(VectorIndex(
            name="vector",
            dimension=3,
            index_type=IndexType.HNSW,
            metric_type=MetricType.COSINE,
            params={"M": 8, "efConstruction": 80},
        ))
        idx.add(FilterIndex(
            name="id",
            field_type=FieldType.String,
            index_type=IndexType.PRIMARY_KEY,
        ))

        # Create collection if not exists
        await client.create_collection_if_not_exists(
            database_name="demo_db",
            collection_name="demo_coll",
            shard=1,
            replicas=1,
            index=idx,
        )

        # Upsert documents
        docs = [
            {"id": "1", "vector": [0.1, 0.2, 0.3], "tag": "hello"},
            {"id": "2", "vector": [0.2, 0.3, 0.1], "tag": "world"},
        ]
        await client.upsert("demo_db", "demo_coll", documents=docs)

        # Vector similarity search
        res = await client.search(
            database_name="demo_db",
            collection_name="demo_coll",
            vectors=[[0.1, 0.2, 0.3]],
            limit=5,
            retrieve_vector=False,
        )
        print(res)

asyncio.run(main())

Common Operations

• Databases: create_database, create_database_if_not_exists, drop_database, list_databases
• Collections: create_collection, create_collection_if_not_exists, describe_collection, list_collections, truncate_collection, set_alias, delete_alias
• Documents: upsert, query, count, update, delete
• Search: search, search_by_id, search_by_text (server-side embedding), hybrid_search, fulltext_search

AI Document Database

from aiotcvectordb.model import SplitterProcess

aidb = await client.create_ai_database("ai_demo")
cv = await aidb.create_collection_view(name="cv1")
ds = await cv.load_and_split_text("./doc.pdf", splitter_process=SplitterProcess())
results = await cv.search("your question", limit=5)

Links

• Repo: https://github.com/alviezhang/aiotcvectordb
• Issues: https://github.com/alviezhang/aiotcvectordb/issues

License

MIT