qdrant-client 1.18.0

Rust client for Qdrant Vector Search Engine
Documentation 
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syntax = "proto3";

import "points.proto";

package qdrant;
option csharp_namespace = "Qdrant.Client.Grpc";

service Points {
  // Perform insert + updates on points.
  // If a point with a given ID already exists - it will be overwritten.
  rpc Upsert(UpsertPoints) returns (PointsOperationResponse) {}
  // Delete points
  rpc Delete(DeletePoints) returns (PointsOperationResponse) {}
  // Retrieve points
  rpc Get(GetPoints) returns (GetResponse) {}
  // Update named vectors for point
  rpc UpdateVectors(UpdatePointVectors) returns (PointsOperationResponse) {}
  // Delete named vectors for points
  rpc DeleteVectors(DeletePointVectors) returns (PointsOperationResponse) {}
  // Set payload for points
  rpc SetPayload(SetPayloadPoints) returns (PointsOperationResponse) {}
  // Overwrite payload for points
  rpc OverwritePayload(SetPayloadPoints) returns (PointsOperationResponse) {}
  // Delete specified key payload for points
  rpc DeletePayload(DeletePayloadPoints) returns (PointsOperationResponse) {}
  // Remove all payload for specified points
  rpc ClearPayload(ClearPayloadPoints) returns (PointsOperationResponse) {}
  // Create index for field in collection
  rpc CreateFieldIndex(CreateFieldIndexCollection)
      returns (PointsOperationResponse) {}
  // Delete field index for collection
  rpc DeleteFieldIndex(DeleteFieldIndexCollection)
      returns (PointsOperationResponse) {}
  // Create a new named vector on the collection
  rpc CreateVectorName(CreateVectorNameRequest) returns (PointsOperationResponse) {}
  // Delete a named vector from the collection
  rpc DeleteVectorName(DeleteVectorNameRequest) returns (PointsOperationResponse) {}
  // Retrieve closest points based on vector similarity and given filtering
  // conditions
  rpc Search(SearchPoints) returns (SearchResponse) {}
  // Retrieve closest points based on vector similarity and given filtering
  // conditions
  rpc SearchBatch(SearchBatchPoints) returns (SearchBatchResponse) {}
  // Retrieve closest points based on vector similarity and given filtering
  // conditions, grouped by a given field
  rpc SearchGroups(SearchPointGroups) returns (SearchGroupsResponse) {}
  // Iterate over all or filtered points
  rpc Scroll(ScrollPoints) returns (ScrollResponse) {}
  // Look for the points which are closer to stored positive examples and at
  // the same time further to negative examples.
  rpc Recommend(RecommendPoints) returns (RecommendResponse) {}
  // Look for the points which are closer to stored positive examples and at
  // the same time further to negative examples.
  rpc RecommendBatch(RecommendBatchPoints) returns (RecommendBatchResponse) {}
  // Look for the points which are closer to stored positive examples and at
  // the same time further to negative examples, grouped by a given field
  rpc RecommendGroups(RecommendPointGroups) returns (RecommendGroupsResponse) {}
  // Use context and a target to find the most similar points to the target,
  // constrained by the context.
  //
  // When using only the context (without a target), a special search - called
  // context search - is performed where pairs of points are used to generate a
  // loss that guides the search towards the zone where most positive examples
  // overlap. This means that the score minimizes the scenario of finding a
  // point closer to a negative than to a positive part of a pair.
  //
  // Since the score of a context relates to loss, the maximum score a point
  // can get is 0.0, and it becomes normal that many points can have a score of
  // 0.0.
  //
  // When using target (with or without context), the score behaves a little
  // different: The integer part of the score represents the rank with respect
  // to the context, while the decimal part of the score relates to the
  // distance to the target. The context part of the score for each pair is
  // calculated +1 if the point is closer to a positive than to a negative part
  // of a pair, and -1 otherwise.
  rpc Discover(DiscoverPoints) returns (DiscoverResponse) {}
  // Batch request points based on { positive, negative } pairs of examples, and/or a target
  rpc DiscoverBatch(DiscoverBatchPoints) returns (DiscoverBatchResponse) {}
  // Count points in collection with given filtering conditions
  rpc Count(CountPoints) returns (CountResponse) {}

  // Perform multiple update operations in one request
  rpc UpdateBatch(UpdateBatchPoints) returns (UpdateBatchResponse) {}
  // Universally query points.
  // This endpoint covers all capabilities of search, recommend, discover, filters.
  // But also enables hybrid and multi-stage queries.
  rpc Query(QueryPoints) returns (QueryResponse) {}
  // Universally query points in a batch fashion.
  // This endpoint covers all capabilities of search, recommend, discover, filters.
  // But also enables hybrid and multi-stage queries.
  rpc QueryBatch(QueryBatchPoints) returns (QueryBatchResponse) {}
  // Universally query points in a group fashion.
  // This endpoint covers all capabilities of search, recommend, discover, filters.
  // But also enables hybrid and multi-stage queries.
  rpc QueryGroups(QueryPointGroups) returns (QueryGroupsResponse) {}
  // Perform facet counts.
  // For each value in the field, count the number of points that have this
  // value and match the conditions.
  rpc Facet(FacetCounts) returns (FacetResponse) {}
  // Compute distance matrix for sampled points with a pair based output format
  rpc SearchMatrixPairs(SearchMatrixPoints)
      returns (SearchMatrixPairsResponse) {}
  // Compute distance matrix for sampled points with an offset based output format
  rpc SearchMatrixOffsets(SearchMatrixPoints)
      returns (SearchMatrixOffsetsResponse) {}
}