From qdrant
Lists Qdrant's official client SDKs (Python, JS/TS, Rust, Go, .NET, Java) with install commands and links to REST/gRPC API references. Can search curated code snippets by language and query.
How this skill is triggered — by the user, by Claude, or both
Slash command
/qdrant:qdrant-clients-sdkThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Qdrant has the following officially supported client SDKs:
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.
npx claudepluginhub qdrant/skills --plugin qdrantIntegrates Qdrant vector database with LangChain4j in Java/Spring Boot apps for embedding storage, similarity search, and vector management in RAG, semantic search, or recommendations.
Guides selection among Qdrant deployment options: local mode, Docker, self-hosted, Qdrant Cloud (zero-ops), and Qdrant EDGE for lowest latency.
Guides vector database selection for embeddings and semantic search, compares managed options like Pinecone and self-hosted like pgvector/Milvus, explains ANN algorithms like HNSW.