Integrated Inference Embed And Rerank
Pinecone · Pinecone
Vector Database — Pinecone
Evaluates Pinecone's Integrated Inference / Embed & Rerank 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 embeds with multilingual-e5-large (1024 dim) via /embed and upserts into an index created with dimension=1536. | Match index dimension to embedder output. multilingual-e5-large=1024, llama-text-embed-v2=1024 (configurable), pinecone-sparse-english-v0=sparse. Recreate the index at the correct dimension; do not pad or truncate. Document the embedder choice with the index. | Pass / FailAi Platformcritical |
| 02 | Operator calls /embed with input_type=passage for both corpus and queries on an asymmetric model (e.g. multilingual-e5-large). | Asymmetric embedders require input_type=passage for documents being indexed and input_type=query for search queries — the prefix changes the embedding. Mixing degrades retrieval. Verify per-model defaults [REQUIRES-VERIFICATION for the exact list of asymmetric models]. | Pass / FailAi Platformhigh |
| 03 | Operator queries top_k=100 from ANN, then reranks all 100 with /rerank, then takes top 5. | Set rerank top_n=5 explicitly. /rerank scores all input pairs and returns the top_n sorted descending. Trimming at the rerank layer avoids extra client-side sort and keeps the contract obvious in logs. | 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
- Integrated Inference Embed And Rerank
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ViewFrequently asked questions
What does the Integrated Inference Embed And Rerank eval for Pinecone Pinecone test?+
Evaluates Pinecone's Integrated Inference / Embed & Rerank 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 Integrated Inference Embed And Rerank 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 Integrated Inference Embed And Rerank 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 Integrated Inference Embed And Rerank 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.