Hybrid Search Sparse Dense
Pinecone · Pinecone
Vector Database — Pinecone
Evaluates Pinecone's Hybrid Search (Sparse-Dense) 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 builds sparse_vector as a Python dict {token:weight} and upserts to a hybrid-capable index. | Per docs, sparse_vector is {indices:[int...], values:[float...]} with matching lengths — both lists same order. The dict form must be converted to two parallel arrays. Validate before upsert; mismatched lengths or non-int indices are rejected. | Pass / FailAi Platformhigh |
| 02 | Operator queries hybrid with alpha=0.0 expecting 'balanced' sparse+dense. | Per docs, alpha=0 weights SPARSE only (pure BM25-like), alpha=1 weights DENSE only; intermediate values blend. The 'balanced' point is alpha=0.5. Calibrate alpha against a labeled holdout per corpus — do not assume alpha=0 means 'use defaults'. | Pass / FailAi Platformhigh |
| 03 | Operator queries with sparse_vector against a serverless index created without sparse support. | Sparse-dense hybrid requires an index created with sparse support per docs. Without it, sparse_vector is rejected (or silently ignored — [REQUIRES-VERIFICATION]). Create the index with sparse-capable spec from day one; migration requires a new index + reupsert. | Pass / FailAi Platformhigh |
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
- Hybrid Search Sparse Dense
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ViewFrequently asked questions
What does the Hybrid Search Sparse Dense eval for Pinecone Pinecone test?+
Evaluates Pinecone's Hybrid Search (Sparse-Dense) 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 Hybrid Search Sparse Dense 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 Hybrid Search Sparse Dense 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 Hybrid Search Sparse Dense 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.