I’m unhappy with MongoDB Atlas Vector Search accuracy

If Atlas Vector Search feels inaccurate, the issue is usually one of these:

1. Embeddings aren’t a good fit

   • The model may not capture your domain well.
   • Try a stronger embedding model, or one tuned for your content type.
   • Make sure query and document embeddings come from the same model.

2. Chunking is off

   • Too large: embeddings become vague.
   • Too small: you lose context.
   • A common fix is smaller, semantically coherent chunks with overlap.

3. You’re relying on vectors alone

   • Pure vector search often misses exact terms, IDs, product names, or rare entities.
   • Use hybrid retrieval: combine vector search with keyword/metadata filtering.

4. Index configuration is not ideal

   • Check similarity metric: cosine vs dot product vs Euclidean.
   • Ensure the indexed field is the correct vector field and dimensions match exactly.

5. Top-k / reranking

   • Vector search often benefits from retrieving more candidates, then reranking.
   • Try larger candidate pools and a reranker or post-filtering step.

6. Metadata filtering

   • If you have category, date, language, tenant, or type fields, use them.
   • Without filters, the search space can be too broad.

7. Data quality

   • Duplicates, noisy text, boilerplate, or OCR errors can hurt relevance.
   • Clean the text before embedding.

8. Expectation mismatch

   • Vector search is semantic, not exact. It can be “right” semantically but not return the exact item you expected.

Quick debugging checklist

• Verify embedding model consistency
• Inspect a few query/document embedding pairs manually
• Test multiple chunk sizes
• Increase candidate count and rerank
• Add keyword + metadata filtering
• Compare against a baseline BM25/full-text search

If you want, I can help you diagnose this systematically. Send me:

• your embedding model
• chunk size/overlap
• Atlas vector search index config
• a few example queries and the bad results you’re seeing