qdrant-clients-sdk

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:

• Python — qdrant-client · Installation: pip install qdrant-client[fastembed]
• JavaScript / TypeScript — qdrant-js · Installation: npm install @qdrant/js-client-rest
• Rust — rust-client · Installation: cargo add qdrant-client
• Go — go-client · Installation: go get github.com/qdrant/go-client
• .NET — qdrant-dotnet · Installation: dotnet add package Qdrant.Client
• Java — java-client · Available on Maven Central: https://central.sonatype.com/artifact/io.qdrant/client

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.

• REST API - OpenAPI Reference - GitHub
• gRPC API - gRPC protobuf definitions

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