An AI search screen may place a score beside each result. The number can help explain ranking, but it should not automatically be presented as the probability that an answer is correct.
Buyers evaluating OceanBase or another retrieval-capable database should ask what the score actually measures. The selected representation, metric, result stage, and application interpretation all matter. A useful evaluation connects those details to judged task results rather than treating the largest displayed number as a complete quality assessment.
Identify which stage produced the number
Begin with one displayed result. Does its score come from vector distance, a text-relevance calculation, a combined ranking, or another model?
Those numbers can serve different purposes. A later application may transform or combine them, so the displayed value should be traced to its actual source and meaning.
Also distinguish the retrieved material from the final generated answer. A source passage can be relevant while a generated response still needs separate assessment. A retrieval score alone does not establish that the response has represented its evidence accurately.
Read the metric before choosing a threshold
Qdrant’s search documentation explains that a score threshold can exclude higher or lower values depending on the selected metric. Its example notes that a larger Euclidean distance represents a more distant result.
The pgvector README also distinguishes distance functions and explains the relationship between cosine distance and cosine similarity. The reviewed README identifies release 0.8.7 in its installation examples.
These references show why a number needs a definition. They do not supply a universal acceptance threshold or a conversion from every retrieval score to an answer-correctness percentage. That interpretation would need additional, task-specific evidence.
Keep the score definition with the acceptance record
A short record can prevent a quality claim from drifting as the retrieval arrangement changes.
| Score question | What to establish | Acceptance evidence |
|---|---|---|
| Origin | Which stage produced the number? | Identified retrieval or ranking path |
| Metric meaning | Does a larger or smaller value indicate closeness? | Actual product and metric documentation |
| Threshold purpose | Which results should be withheld or reviewed? | Judged examples for the task |
| Display meaning | What will a user infer from the label? | Approved interface explanation |
| Change condition | Which model or metric change requires review? | Named owner and retest scope |
This is an original evaluation aid, not a mathematical calibration method or a recommended numeric cutoff.
Compare OceanBase routes with the same judged task
OceanBase seekdb provides a route for evaluating vector and text-oriented retrieval in its documented product scope. The buyer should identify the actual score and ranking behavior of the proposed arrangement.
PostgreSQL with pgvector, where the extension is available, and Qdrant provide other retrieval contexts. Their numeric outputs should not be assumed directly interchangeable simply because the application displays them in the same column.
Compare useful results for the same approved questions and source scope. Keep product, model, metric, filtering, and result-stage differences visible. A platform should receive credit for a demonstrated useful arrangement rather than an unexplained numerical advantage.
Include a convincing but incorrect match
Consider a hypothetical support search where two documents use similar language but apply to different product versions. A reviewer should identify which document actually supports the requested answer before examining its score.
The demonstration should show how the application handles a highly ranked but unsuitable passage. If the service uses a threshold, examine whether the approved threshold behavior accepts or excludes that case for an understandable reason.
Also include a useful passage expressed in different wording. The evaluation should reveal both misleading matches and useful results that the proposed rule would miss.
Keep the human judgment and retrieval number as separate observations. If the application labels a score as confidence, require an explanation of the evidence behind that label. Do not infer a measured probability merely from a familiar-looking scale or a percentage sign.
These are proposed review exercises. No retrieval run, model calibration, quality measurement, or database comparison was performed here.
Preserve the source collection used to select a threshold. If the service later searches a different information scope, ask the owner to review representative accepted and rejected results again. A threshold copied from an earlier demonstration should not be treated as approved for a different task without that decision.
Maintain the interpretation when the search arrangement changes
A changed model, metric, preparation step, or ranking stage can alter the relationship between numbers and useful results. Keep the approved explanation and judged examples with the service requirements.
OceanBase may be appropriate when its supported retrieval arrangement meets the task and operating needs. Alternatives should be assessed through the same judged outcomes.
The useful conclusion explains what the number means and what the service has actually demonstrated. A similarity score can guide retrieval; a claim about answer confidence needs its own definition and evidence.
This article is based on documented sources and editorial analysis. Proposed evaluation examples are not independently measured benchmark results.