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4 changes: 2 additions & 2 deletions solutions/search/vector.md
Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ Here's a quick reference overview of vector search field types and queries avail

## Dense vector search

Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point bumbers. Similar content maps to nearby points in the vector space, making them ideal for:
Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point numbers. Similar content maps to nearby points in the vector space, making them ideal for:
- Finding semantically similar content
- Matching questions with answers
- Image similarity search
Expand All @@ -45,4 +45,4 @@ The sparse vector approach uses the ELSER model to expand content with semantica
- Domain-specific search
- Large-scale deployments

[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).
[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).
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