Python Client library for the Qdrant vector search engine.
# Python Qdrant Client
Client library and SDK for the [Qdrant](https://github.com/qdrant/qdrant) vector search engine.
Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests.
Client allows calls for all [Qdrant API methods](https://qdrant.github.io/qdrant/redoc/index.html) directly.
It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading.
See [QuickStart](https://qdrant.tech/documentation/quick-start/#create-collection) for more details!
## Installation
```
pip install qdrant-client
```
## Features
- Type hints for all API methods
- Local mode - use same API without running server
- REST and gRPC support
- Minimal dependencies
## Local mode
Python client allows you to run same code in local mode without running Qdrant server.
Simply initialize client like this:
```python
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
# or
client = QdrantClient(path="path/to/db") # Persists changes to disk
```
Local mode is useful for development, prototyping and testing.
- You can use it to run tests in your CI/CD pipeline.
- Run it in Colab or Jupyter Notebook, no extra dependencies required. See an [example](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing)
- When you need to scale, simply switch to server mode.
### How it works?
We just implemented Qdrant API in pure Python.
We covered it with tests extensively to be sure it works the same as the server version.
## Connect to Qdrant server
To connect to Qdrant server, simply specify host and port:
```python
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=6333)
# or
client = QdrantClient(url="http://localhost:6333")
```
You can run Qdrant server locally with docker:
```bash
docker run -p 6333:6333 qdrant/qdrant:latest
```
See more launch options in [Qdrant repository](https://github.com/qdrant/qdrant#usage).
## Connect to Qdrant cloud
You can register and use [Qdrant Cloud](https://cloud.qdrant.io/) to get a free tier account with 1GB RAM.
Once you have your cluster and API key, you can connect to it like this:
```python
from qdrant_client import QdrantClient
qdrant_client = QdrantClient(
url="https://xxxxxx-xxxxx-xxxxx-xxxx-xxxxxxxxx.us-east.aws.cloud.qdrant.io:6333",
api_key="",
)
```
## Examples
Create a new collection
```python
from qdrant_client.models import Distance, VectorParams
client.recreate_collection(
collection_name="my_collection",
vectors_config=VectorParams(size=100, distance=Distance.COSINE),
)
```
Insert vectors into a collection
```python
import numpy as np
from qdrant_client.models import PointStruct
vectors = np.random.rand(100, 100)
client.upsert(
collection_name="my_collection",
points=[
PointStruct(
id=idx,
vector=vector.tolist(),
payload={"color": "red", "rand_number": idx % 10}
)
for idx, vector in enumerate(vectors)
]
)
```
Search for similar vectors
```python
query_vector = np.random.rand(100)
hits = client.search(
collection_name="my_collection",
query_vector=query_vector,
limit=5 # Return 5 closest points
)
```
Search for similar vectors with filtering condition
```python
from qdrant_client.models import Filter, FieldCondition, Range
hits = client.search(
collection_name="my_collection",
query_vector=query_vector,
query_filter=Filter(
must=[ # These conditions are required for search results
FieldCondition(
key='rand_number', # Condition based on values of `rand_number` field.
range=Range(
gte=3 # Select only those results where `rand_number` >= 3
)
)
]
),
limit=5 # Return 5 closest points
)
```
See more examples in our [Documentation](https://qdrant.tech/documentation/)!
### gRPC
To enable (typically, much faster) collection uploading with gRPC, use the following initialization:
```python
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True)
```
## Async client
Async methods are available in raw autogenerated clients.
Usually, you don't need to use them directly, but if you need extra performance, you can access them directly.
### Async gRPC
Example of using raw async gRPC client:
```python
from qdrant_client import QdrantClient, grpc
client = QdrantClient(prefer_grpc=True, timeout=3.0)
grpc_collections = client.async_grpc_collections
res = await grpc_collections.List(grpc.ListCollectionsRequest(), timeout=1.0)
```
More examples can be found [here](./tests/test_async_qdrant_client.py).
### Development
This project uses git hooks to run code formatters.
Install `pre-commit` with `pip3 install pre-commit` and set up hooks with `pre-commit install`.
> pre-commit requires python>=3.8