Index Management
Pinecone · Pinecone
Vector Database — Pinecone
Evaluates Pinecone's Index Management 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 Pinecone
Pinecone is a managed vector database for AI applications — serverless and pod-based indexes, namespaces for multi-tenant isolation, hybrid sparse-dense search, integrated inference (embed + rerank), and Pinecone Assistant for retrieval-augmented generation with citations.
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Operator must choose between serverless and pod-based for a workload with spiky read traffic, ~10M vectors at 1536 dim, and unpredictable namespace count. | Pick serverless via spec.serverless{cloud, region}: auto-scales, pays per read/write/storage unit, supports unbounded namespaces. Pod-based (p1/p2/s1) fits steady QPS with predictable size — do not default to pods just because they look 'production'. Document the dimensional and metric choice (immu… | Pass / FailAi Platformhigh |
| 02 | Operator creates an index with metric=euclidean for vectors produced by an OpenAI text-embedding-3-small model (normalized — cosine-appropriate). Recall is poor. | Pinecone index metric is set at create time and immutable. The fix is to create a NEW index with metric=cosine (or dotproduct on normalized vectors) and reupsert, not to mutate. Verify embedder docs for the expected metric before create. | Pass / FailAi Platformcritical |
| 03 | Index dimension=1536. Upsert payload contains a vector with 768 values (operator switched embedder mid-pipeline without recreating the index). | Pinecone rejects with 400 dimension mismatch. The fix is to recreate the index at the new dimension (or use a separate index per embedder family) and reupsert; never pad/truncate vectors to fit a different dimension — that destroys embedding geometry. | Pass / FailAi Platformcritical |
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
- Pinecone
- Ai Platform
- Index Management
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ViewFrequently asked questions
What does the Index Management eval for Pinecone Pinecone test?+
Evaluates Pinecone's Index Management 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 Index Management 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 Index Management pack for Pinecone Pinecone 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 Index Management 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.