awesome-copilot/skills/qdrant-clients-sdk/SKILL.md

name, description, allowed-tools

name	description	allowed-tools
qdrant-clients-sdk	Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.	Read Grep Glob Bash

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 can happen through the REST API or gRPC API. We recommend using the REST API if you are using Qdrant for the first time or 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 send a search request to the library of curated code snippets for the 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://search.qdrant.tech/md/documentation/manage-data/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,
)

Default response format is markdown, if snippet output is required in JSON format, you can add &format=json to the query string.