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/Pyfagorass/bookofspells --skill qdrant-qdrant-clients-sdkيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
The index to a Book of Spells — a large, curated library of ready-made spells (skills in the SKILL.md format) gathered from many providers (Google, OpenAI, and more), covering cloud platforms, deployment, design tools, PDFs, browser automation, security, and much else. The spells are NOT preloaded — there may be thousands. Use this whenever the user asks to cast a spell, or for a spell to do something, or names a capability, provider, or tool you do not already have loaded (e.g. "cast a spell for Figma", "is there a spell for deploying?", "use the bigquery spell"), or whenever a task might be served by a specialised, ready-made spell. Casting a spell MEANS consulting this index to discover and load the right one on demand — do that first, rather than improvising. This is also the single entry point to the Book's own keystones: route here to AUTHOR a new spell (the-spellwright), to FIND or CONNECT an MCP server / external tool (the-emissary), or to INSTALL the Book into a conduit (the-tether).
How to install this Book of Spells into a conduit so its gathered skills become usable — by symlink, not by copy. Use whenever the user wants to install, set up, enable, add, or "load" the Book's spells into their agent; asks where skills go or how a conduit finds them; or asks how to keep the spells up to date. The companion to the-grimoire (which *finds* spells once they are installed): this one *shelves the Book* so the grimoire has something to find.
Find and connect an MCP server — an "Emissary of the Pact" — when the agent needs a tool, service, API, or data source it does not already have. Use when the user asks for an MCP server, to connect to or integrate an external service (Notion, GitHub, Figma, a database, Slack, a SaaS API…), asks "is there an MCP for X", "add an MCP", or sets a task that needs live tools the agent lacks. This rite DISCOVERS a server from the official MCP Registry and reputable directories and PROPOSES its connection config for the user to approve — it NEVER installs, launches, or writes credentials on its own. The companion to the-grimoire: that finds spells (inert skills); this finds Emissaries (live tool-servers).
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | qdrant-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.