Query And Filtering
Pinecone · Pinecone
Vector Database — Pinecone
Evaluates Pinecone's Query & Filtering 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 queries with top_k=50000 expecting an exhaustive scan. | top_k is bounded per docs (current cap on the order of 10000 for query without metadata, lower with include_metadata=true) [REQUIRES-VERIFICATION for exact numeric cap]. For exhaustive enumeration use pagination via query+filter or the list_paginated/fetch path; do not stuff top_k. | Pass / FailAi Platformmedium |
| 02 | Operator filters with {category: {$in: ['news', 'blog']}, year: {$gte: 2024}}. | Per docs, the metadata filter DSL supports $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $and, $or, $exists. Compose with implicit AND at top level. Verify metadata field types match the operator (numeric ops on strings silently miss). | Pass / FailAi Platformhigh |
| 03 | Operator wants 'similar items' for a known doc id without re-embedding. | Use query with id='doc-42' (and optional namespace) — Pinecone uses the stored vector for that id as the query vector. Cheaper than re-embedding and guarantees the query vector matches the stored representation. | Pass / FailAi Platformmedium |
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
- Query And Filtering
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ViewFrequently asked questions
What does the Query And Filtering eval for Pinecone Pinecone test?+
Evaluates Pinecone's Query & Filtering 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 Query And Filtering 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 Query And Filtering 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 Query And Filtering 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.