| name | qdrant-clients-sdk |
| description | Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments. |
| allowed-tools | ["Read","Grep","Glob"] |
Qdrant Clients SDK
Qdrant has the following officially supported client SDKs:
API Reference
All interaction with Qdrant takes place via the REST API. We recommend using REST API if you are using Qdrant for the first time or if you are working on a
prototype.
Code examples
To obtain code examples for a specific client and use-case, you can make a search request to library of curated code snippets for Qdrant client.
curl -X GET "https://snippets.qdrant.tech/search?language=python&query=how+to+upload+points"
Available languages: python, typescript, rust, java, go, csharp
Response example:
## Snippet 1
*qdrant-client* (vlatest) — https://qdrant.tech/documentation/concepts/points/
Uploads multiple vector-embedded points to a Qdrant collection using the Python qdrant_client (PointStruct) with id, payload (e.g., color), and a 3D-like vector for similarity search. It supports parallel uploads (parallel=4) and a retry policy (max_retries=3) for robust indexing. The operation is idempotent: re-uploading with the same id overwrites existing points; if ids aren’t provided, Qdrant auto-generates UUIDs.
client.upload_points(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
payload={
"color": "red",
},
vector=[0.9, 0.1, 0.1],
),
models.PointStruct(
id=2,
payload={
"color": "green",
},
vector=[0.1, 0.9, 0.1],
),
],
parallel=4,
max_retries=3,
)
If snippet output is required in json format, you can add &format=json to the query string
