Search Config

Vald Search Service

Vald provides the four types of search services.

  1. Search

    • Search is the ANN(Approximate Nearest Neighbor) search with query vector. It is a fast search even though the vector consists large dimension. The search duration is quick but less accurate than LinearSearch. The search algorithm depends on each core algorithm.
  2. SearchById

    • SearchById is the ANN(Approximate Nearest Neighbor) search with the stored vector’s id. The id should already exist in the NGT indexes before the search process. The search algorithm is the same as Search.
  3. LinearSearch

    • LinearSearch is the primary search algorithm with a query vector. It searches all indexed vectors and calculates the distance between the query. Its accuracy is exact, but the search time requires more than Search (ANN search) and increases the amount of indexed vector.
  4. LinearSearchById

    • LinearSearchById is the primary search algorithm with the vector’s id. The id should already exist in the NGT indexes before the search process. The search algorithm is the same as LinearSearch.
Linear Search service is available from Vald v1.4 or later.

For more details, please refer to the Search API document.

Configuration

Here is the current search config parameters.

// Represent search configuration.
message Config {
  // Unique request ID.
  string request_id = 1;
  // Maximum number of result to be returned.
  uint32 num = 2 [ (validate.rules).uint32.gte = 1 ];
  // Search radius.
  float radius = 3;
  // Search coefficient.
  float epsilon = 4;
  // Search timeout in nanoseconds.
  int64 timeout = 5;
  // Ingress filter configurations.
  Filter.Config ingress_filters = 6;
  // Egress filter configurations.
  Filter.Config egress_filters = 7;
  // Minimum number of result to be returned.
  uint32 min_num = 8 [ (validate.rules).uint32.gte = 0 ];
}

request_id

request_id is a unique request ID. It is NOT indexed vector’s id. Users can use it for, e.g., the error handling process.

num

num is the maximum number of search results you’d like to get. num should be a positive integer.

radius

radius, the specific parameter for NGT, specifies the search range centered on the query vector in terms of the radius of a sphere. The number of search target vectors increases along with the radius is large. There is a trade-off between accuracy and search speed. It is hard to set it depending on the dataset in many cases.

The default value is infinity. When setting a negative number as radius, NGT applies the radius as infinity.

NGT will self-update the radius during the search process.

epsilon

epsilon, the specific parameter for NGT, specifies the search range’s magnification coefficient (epsilon). NGT will use radius*(1+epsilon) as the search range. The number of search target vectors increases along with the epsilon being large.

The default value is 0.1, and it may work in most cases. However, the appropriate value may vary depending on the dataset. While it is desirable to adjust this value within 0 - 0.3, it can also set a negative value (over than -1).

ingress_filters

ingress_filters is required when using the ingress filter component. It requires the ingress filter component’s hostname and port.

egress_filters

egress_filters is required when using the egress filter component. It requires the egress filter component’s hostname and port.

min_num

min_num is the minimum number of search result you’d like to get at least. It helps you avoid the timeout error when the search process requires more time. min_num should be a positive integer and smaller than num.