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Qdrant Clients SDK

GitHubOfficialFirst committed by anush008Distributed by GitHub

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • Low risk
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From the author

Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
— the description by anush008

AutoGineer summary

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

This skill provides a centralized reference for integrating Qdrant vector database deployments into applications using official client SDKs. It covers supported programming languages including Python, JavaScript/TypeScript, Rust, Go, .NET, and Java. Additionally, it offers guidance on accessing REST and gRPC API documentation and provides a mechanism to retrieve curated code snippets for specific use cases.

What it does

  • Lists installation commands for official Qdrant client SDKs across six programming languages.
  • Provides links to REST and gRPC API documentation for direct interaction with Qdrant.
  • Enables searching for curated code snippets based on language and specific tasks.
  • Supports output formats in both markdown and JSON for code snippets.

When to use it

  • When you need to connect your application to a Qdrant vector database instance.
  • When you are looking for the correct installation package for a specific programming language.
  • When you need practical code examples for common operations like uploading points.
  • When you need to reference the OpenAPI or gRPC definitions for custom integration.

Advantages

  • Centralizes access to all official SDKs and API documentation.
  • Provides searchable, language-specific code snippets to accelerate development.
  • Supports multiple output formats for integration into different workflows.

Usage examples

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

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/qdrant-clients-sdk/download

  2. 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 anush008

Qdrant Clients SDK

Qdrant has the following officially supported client SDKs:

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.

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.

Files

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs Python, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of anush008anush008
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/qdrant-clients-sdk · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
anush008

AutoGineer is not affiliated with or endorsed by anush008 or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download qdrant-clients-sdk.zip