Qdrant Clients SDK

From the author

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

— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a centralized reference for Qdrant's officially supported client SDKs across multiple programming languages, including Python, JavaScript/TypeScript, Rust, Go, .NET, and Java. It outlines installation methods for each language and provides access to REST and gRPC API documentation. Additionally, it includes a mechanism to retrieve curated code snippets for specific use cases, assisting developers in implementing vector search and data management tasks within their applications.

What it does

Lists installation commands for official Qdrant client SDKs.
Provides links to REST and gRPC API documentation.
Offers a search interface for retrieving language-specific code snippets.
Supports multiple programming languages including Python, Go, and Java.
Allows retrieval of code examples in Markdown or JSON formats.

When to use it

When you need to integrate Qdrant vector database capabilities into a new project.
When you are looking for specific code examples for data operations like uploading points.
When you need to verify the installation method for a specific language SDK.
When you are deciding between using the REST API or a language-specific client library.

Advantages

Centralized access to official SDK installation instructions.
Direct access to curated, language-specific code snippets.
Supports both REST and gRPC integration paths.
Flexible output formats for code snippets.

Usage examples

“How do I install the Qdrant client for Python?”: The agent provides the pip installation command: pip install qdrant-client[fastembed].
“Show me a Python example for uploading points to Qdrant.”: The agent retrieves a code snippet demonstrating the use of PointStruct and the upload_points method with parallel processing settings.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-clients-sdk/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-clients-sdk.zip -d .agents/skills

Documentation by Qdrant

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,
)

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

Files

ATTRIBUTION.mdAdded by AutoGineer848 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md3.0 KiB

SKILL.md