{"id":9837,"date":"2025-04-01T05:00:09","date_gmt":"2025-04-01T12:00:09","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/cosmosdb\/?p=9837"},"modified":"2025-04-01T09:14:50","modified_gmt":"2025-04-01T16:14:50","slug":"exact-nearest-neighbor-enn-vector-search","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/cosmosdb\/exact-nearest-neighbor-enn-vector-search\/","title":{"rendered":"Exact Nearest Neighbor (ENN) Vector Search for Precise Retrieval"},"content":{"rendered":"

In applications such as personalized recommendations and scientific research, accurately identifying the closest data points to a given query is crucial. Traditional methods like Approximate Nearest Neighbor (ANN) search offer faster retrieval times by approximating results but may sacrifice accuracy, leading to potential errors in scenarios where precision is essential. This trade-off between speed and accuracy poses a significant challenge when exact results are necessary.<\/p>\n

Exact Vector Search (ENN)<\/a> is a search method that compares your input vector to every single vector in the dataset to find the closest matches. It\u2019s the most accurate<\/strong> way to find nearest neighbors because it doesn’t skip any candidates. In vCore-based Azure Cosmos DB for MongoDB<\/strong>, ENN is supported as part of the vector search capabilities for no additional cost<\/strong>. It allows you to run high-precision searches across your vector-indexed collections without needing to rebuild existing indexes.<\/p>\n

\"enn<\/a><\/p>\n

ANN vs ENN: What\u2019s the Difference?<\/h2>\n\n\n\n\n\n\n\n
Feature<\/th>\nANN (Approximate)<\/th>\nENN (Exact)<\/th>\n<\/tr>\n<\/thead>\n
Speed (on large dataset)<\/td>\nFast<\/td>\nSlower<\/td>\n<\/tr>\n
Accuracy<\/td>\nClose (not always 100%)<\/td>\nAlways correct<\/td>\n<\/tr>\n
Scales well?<\/td>\nYes (great for millions of vectors)<\/td>\nOnly if data is small or filtered<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

When to Use ENN<\/strong><\/h2>\n

You should use Exact Vector Search<\/strong> when:<\/p>\n

    \n
  1. You need perfect accuracy<\/strong> \u2014 e.g., in recommendations, scientific work, legal\/financial scenarios.<\/li>\n
  2. You have a small dataset<\/strong> \u2014 or performance isn’t a concern.<\/li>\n
  3. You\u2019re using filters that return a small subset of data<\/strong> \u2014 even in big datasets!<\/li>\n<\/ol>\n

    Practical Example: Why ENN Can Be the Better Choice?<\/h3>\n

    A customer was working with ~300K documents across thousands of tenants (using tenant_id<\/code>). They originally used ANN vector search with a tenant_id<\/code> filter but ran into slow query performance and inconsistent accuracy.<\/p>\n

    After switching to Exact Nearest Neighbor (ENN)<\/strong> search with the same filter, they saw:<\/p>\n