qdrant-clients-sdk — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited qdrant-clients-sdk (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.