القائمة
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/qdrant/skills --skill qdrant-clients-sdkيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.
Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed and efficiency. For diagnosing an active production slowdown or analyzing live metrics, use qdrant-monitoring instead.
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | qdrant-clients-sdk |
| description | Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
Qdrant has the following officially supported client SDKs:
pip install qdrant-client[fastembed]npm install @qdrant/js-client-restcargo add qdrant-clientgo get github.com/qdrant/go-clientdotnet add package Qdrant.ClientAll 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.
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.