| Name | aiotcvectordb JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Tencent VectorDB Python Async SDK |
| upload_time | 2025-09-15 08:29:40 |
| maintainer | None |
| docs_url | None |
| author | Alvie Zhang |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Alvie Zhang
Permission is hereby granted, free of charge, to any person obtaining a copy
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
aiohttp
async
database
tencent
vector
vectordb
|
| VCS |
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# aiotcvectordb — Tencent VectorDB Python Async SDK
[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/ci.yml)
[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/testpypi.yml)
[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/release.yml)
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
```bash
pip install aiotcvectordb
```
From source (repo root):
```bash
pip install -e .
# or with uv
uv pip install -e .
```
## Quickstart
```python
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
```python
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
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"description": "# aiotcvectordb \u2014 Tencent VectorDB Python Async SDK\n\n[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/ci.yml)\n[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/testpypi.yml)\n[](https://github.com/alviezhang/aiotcvectordb/actions/workflows/release.yml)\n\nAn asyncio-first client for Tencent Cloud VectorDB built on top of `aiohttp`. It mirrors the official `tcvectordb` SDK\u2019s models and request payloads, while providing non-blocking APIs and REPL-friendly representations.\n\nLooking for Chinese docs? See README_zh.md.\n\n## Features\n\n- Fully async HTTP client using `aiohttp` with connection pooling and proxy support.\n- Type parity with `tcvectordb` (indexes, enums, document types) re-exported under `aiotcvectordb.model`.\n- Convenient async wrappers: `AsyncDatabase`, `AsyncCollection`, `AsyncCollectionView`, `AsyncDocumentSet`.\n- Supports vector search, hybrid search (dense/sparse), text search with server-side embeddings, and full-text search.\n\n## Requirements\n\n- Python 3.9+\n- Dependencies: `tcvectordb`, `aiohttp`, `numpy`\n- Optional: `qcloud_cos` for AI document upload in `CollectionView.upload/load_and_split_text`\n\n## Install\n\n```bash\npip install aiotcvectordb\n```\n\nFrom source (repo root):\n\n```bash\npip install -e .\n# or with uv\nuv pip install -e .\n```\n\n## Quickstart\n\n```python\nimport asyncio\nfrom aiotcvectordb import AsyncVectorDBClient\nfrom aiotcvectordb.model import (\n Index, VectorIndex, FilterIndex,\n FieldType, IndexType, MetricType,\n)\n\nasync def main():\n async with AsyncVectorDBClient(\n url=\"http://127.0.0.1:8081\",\n username=\"root\",\n key=\"<your-api-key>\",\n ) as client:\n # Create database if not exists\n await client.create_database_if_not_exists(\"demo_db\")\n\n # Define indexes for the collection\n idx = Index()\n idx.add(VectorIndex(\n name=\"vector\",\n dimension=3,\n index_type=IndexType.HNSW,\n metric_type=MetricType.COSINE,\n params={\"M\": 8, \"efConstruction\": 80},\n ))\n idx.add(FilterIndex(\n name=\"id\",\n field_type=FieldType.String,\n index_type=IndexType.PRIMARY_KEY,\n ))\n\n # Create collection if not exists\n await client.create_collection_if_not_exists(\n database_name=\"demo_db\",\n collection_name=\"demo_coll\",\n shard=1,\n replicas=1,\n index=idx,\n )\n\n # Upsert documents\n docs = [\n {\"id\": \"1\", \"vector\": [0.1, 0.2, 0.3], \"tag\": \"hello\"},\n {\"id\": \"2\", \"vector\": [0.2, 0.3, 0.1], \"tag\": \"world\"},\n ]\n await client.upsert(\"demo_db\", \"demo_coll\", documents=docs)\n\n # Vector similarity search\n res = await client.search(\n database_name=\"demo_db\",\n collection_name=\"demo_coll\",\n vectors=[[0.1, 0.2, 0.3]],\n limit=5,\n retrieve_vector=False,\n )\n print(res)\n\nasyncio.run(main())\n```\n\n## Common Operations\n\n- Databases: `create_database`, `create_database_if_not_exists`, `drop_database`, `list_databases`\n- Collections: `create_collection`, `create_collection_if_not_exists`, `describe_collection`, `list_collections`, `truncate_collection`, `set_alias`, `delete_alias`\n- Documents: `upsert`, `query`, `count`, `update`, `delete`\n- Search: `search`, `search_by_id`, `search_by_text` (server-side embedding), `hybrid_search`, `fulltext_search`\n\n## AI Document Database\n\n```python\nfrom aiotcvectordb.model import SplitterProcess\n\naidb = await client.create_ai_database(\"ai_demo\")\ncv = await aidb.create_collection_view(name=\"cv1\")\nds = await cv.load_and_split_text(\"./doc.pdf\", splitter_process=SplitterProcess())\nresults = await cv.search(\"your question\", limit=5)\n```\n\n## Links\n\n- Repo: https://github.com/alviezhang/aiotcvectordb\n- Issues: https://github.com/alviezhang/aiotcvectordb/issues\n\n## License\n\nMIT\n",
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