From qdrant
Lists Qdrant client SDKs for Python, JavaScript/TypeScript, Rust, Go, .NET, and Java with installation commands, REST/gRPC API references, and code snippet search endpoint.
npx claudepluginhub qdrant/skills --plugin qdrantThis skill is limited to using the following tools:
Qdrant has the following officially supported client SDKs:
Integrates 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 Qdrant deployment selection: local mode for prototyping, Docker/self-hosted for control, Cloud for zero-ops production, EDGE for lowest latency.
Implements vector, hybrid, semantic search, indexing, and AI enrichment with Azure AI Search Python SDK. Covers authentication, clients, and vector field indexes.
Share bugs, ideas, or general feedback.
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://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.