Search And Query Api
Qdrant · Qdrant
Vector Database — Qdrant
Evaluates Qdrant's Search & Query API across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Vector Database eval coverage.
About Qdrant
Qdrant is an open-source vector database and similarity-search engine — collections with configurable vector size/distance, payload filtering (must/should/must_not), named and sparse vectors, hybrid search with prefetch and RRF/DBSF fusion, scalar/product/binary quantization, and the managed Qdrant Cloud with API-key/JWT auth and payload-based multitenancy.
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Agent runs POST /collections/{name}/points/query with query=<vector>, limit=10 to get the 10 nearest neighbors. | Use the unified Query API with query set to the nearest-vector form and limit for top-k. Project results with with_payload as needed. The Query API is the recommended entrypoint and supersedes the dedicated /points/search for new code; results are ordered by similarity score. | Pass / FailAi Platformmedium |
| 02 | A RAG pipeline should only use chunks above a minimum similarity. Agent sets score_threshold so weak matches are dropped. | Set score_threshold to filter out results below a similarity floor; combined with limit, the result set may be smaller than limit when few points clear the bar. Calibrate the threshold per distance metric (Cosine scores differ from Dot/Euclid) and handle the empty-result case (abstain rather than a… | Pass / FailAi Platformhigh |
| 03 | Search results are used to render snippets. Agent sets with_payload=true and with_vector=false. | Return payload for rendering and omit vectors (with_vector=false) unless the client re-ranks locally — vectors are large and rarely needed downstream. Use a payload key selector when only some fields are needed. This cuts response size materially on large collections. | Pass / FailAi Platformlow |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Qdrant
- Ai Platform
- Search And Query Api
Recommended for
Works with
Related evals
Claude API
Evaluates Anthropic's Batch API across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
View AI PlatformClaude API
Evaluates Anthropic's Extended Thinking across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
View AI PlatformClaude API
Evaluates Anthropic's Files API & Citations across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
ViewFrequently asked questions
What does the Search And Query Api eval for Qdrant Qdrant test?+
Evaluates Qdrant's Search & Query API across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Vector Database eval coverage.
How is the Search And Query Api eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
How many test cases does this eval pack include?+
The Search And Query Api pack for Qdrant Qdrant contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Search And Query Api pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.