Qdrant clients sdk

Skill Pyfagorass/bookofspells/skills/qdrant/qdrant-clients-sdk

📖 The Book of Spells: a curated, enchanted index of real LLM tooling — and a pipeline that gathers SKILL.md skills from many houses into one searchable shelf.

Install

npx -y skills add Pyfagorass/bookofspells --skill qdrant-clients-sdk

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Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.

SKILL.md

3.0 KB, as published. Nobody here has run it

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://skills.qdrant.tech/snippets/search?language=python&query=how+to+upload+points"

Available languages: python, typescript, rust, java, go, csharp

Response example:


## Snippet 1

*qdrant-client* (vlatest) — https://skills.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,
)