## TL;DR

Run the pipeline with a known-similar document and confirm it ranks first with a sane score. There is no score without it. - For hybrid search, run $vectorSearch and $search in separate pipelines and fuse ranks in code (reciprocal rank fusion).

## Steps

1. Run the pipeline with a known-similar document and confirm it ranks first with a sane score.

2. There is no score without it. - For hybrid search, run $vectorSearch and $search in separate pipelines and fuse ranks in code (reciprocal rank fusion).

## When to use

You are seeing this: ``js db.docs.aggregate([ { $vectorSearch: { index: "vec\_idx", path: "embedding", queryVector: embeddingArray, // floats from your embedding model numCandidates: 200, // ANN beam width, 10-20x limit is a good start limit: 10, filter: { tenantId: "t1" } //... Use this skill when you run into "MongoDB Atlas: query with $vectorSearch correctly".

## When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

## Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

## Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.