Add distance and similarity metric as output in KNN search

python/hello_infinity.py

@@ -58,8 +58,8 @@ def test_english():
 
         res = (
             table.output(["num", "body"])
-            .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
-            .to_pl()
+                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
+                .to_pl()
         )
 
         pds_df = pds.DataFrame(res)
@@ -70,8 +70,8 @@ def test_english():
         table_obj = db.get_table("my_table")
         qb_result = (
             table_obj.output(["num", "body"])
-            .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
-            .to_pl()
+                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
+                .to_pl()
         )
         print("------tabular -------")
         print("------vector-------")
@@ -85,10 +85,10 @@ def test_english():
 
         qb_result2 = (
             table_obj.output(["num", "body"])
-            .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
-            .match("body", "blooms", "topn=1")
-            .fusion("rrf")
-            .to_pl()
+                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
+                .match("body", "blooms", "topn=1")
+                .fusion("rrf")
+                .to_pl()
         )
         print("------vector+fulltext-------")
         print(qb_result2)
@@ -171,8 +171,8 @@ def test_chinese():
         print("------json-------")
         res = (
             table.output(["num", "body"])
-            .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
-            .to_pl()
+                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2)
+                .to_pl()
         )
         pds_df = pds.DataFrame(res)
         json_data = pds_df.to_json()
@@ -183,8 +183,8 @@ def test_chinese():
         print("------vector-------")
         qb_result = (
             table_obj.output(["num", "body"])
-            .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
-            .to_pl()
+                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 3)
+                .to_pl()
         )
         print(qb_result)
 
@@ -199,8 +199,8 @@ def test_chinese():
         for question in questions:
             qb_result = (
                 table_obj.output(["num", "body", "_score"])
-                .match("body", question, "topn=10")
-                .to_pl()
+                    .match("body", question, "topn=10")
+                    .to_pl()
             )
             print(f"question: {question}")
             print(qb_result)
@@ -209,10 +209,10 @@ def test_chinese():
         for question in questions:
             qb_result = (
                 table_obj.output(["num", "body", "_score"])
-                .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 10)
-                .match("body", question, "topn=10")
-                .fusion("rrf")
-                .to_pl()
+                    .knn("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 10)
+                    .match("body", question, "topn=10")
+                    .fusion("rrf")
+                    .to_pl()
             )
             print(f"question: {question}")
             print(qb_result)

python/infinity/remote_thrift/query_builder.py

@@ -223,6 +223,11 @@ def output(self, columns: Optional[list]) -> InfinityThriftQueryBuilder:
                     expr_type = ParsedExprType(function_expr=func_expr)
                     parsed_expr = ParsedExpr(type=expr_type)
                     select_list.append(parsed_expr)
+                case "_similarity":
+                    func_expr = FunctionExpr(function_name="similarity", arguments=[])
+                    expr_type = ParsedExprType(function_expr=func_expr)
+                    parsed_expr = ParsedExpr(type=expr_type)
+                    select_list.append(parsed_expr)
                 case "_distance":
                     func_expr = FunctionExpr(function_name="distance", arguments=[])
                     expr_type = ParsedExprType(function_expr=func_expr)

python/infinity/remote_thrift/table.py

@@ -213,7 +213,7 @@ def insert(self, data: Union[INSERT_DATA, list[INSERT_DATA]]):
                                     literal_type=ttypes.LiteralType.DoubleTensorArray,
                                     f64_tensor_array_value=value)
                 else:
-                    raise InfinityException(3069, "Invalid constant expression")
+                    raise InfinityException(3069, f"Invalid constant expression: {type(value)}")
 
                 expr_type = ttypes.ParsedExprType(
                     constant_expr=constant_expression)

python/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "infinity_sdk"
-version = "0.2.0.dev2"
+version = "0.2.0.dev3"
 dependencies = [
     "sqlglot~=11.7.1",
     "pydantic~=2.7.1",

python/test/test_knn.py

@@ -152,7 +152,7 @@ def test_knn_on_vector_column(self, get_infinity_db, check_data, column_name):
             copy_data("tmp_20240116.csv")
         test_csv_dir = "/var/infinity/test_data/tmp_20240116.csv"
         table_obj.import_data(test_csv_dir, None)
-        res = table_obj.output(["variant_id", "_row_id", "_distance"]).knn(
+        res = table_obj.output(["variant_id", "_row_id", "_similarity"]).knn(
             column_name, [1.0] * 4, "float", "ip", 2).to_pl()
         print(res)
 
@@ -302,12 +302,12 @@ def test_valid_embedding_data_type(self, get_infinity_db, check_data, embedding_
         test_csv_dir = "/var/infinity/test_data/tmp_20240116.csv"
         table_obj.import_data(test_csv_dir, None)
         if embedding_data_type[1]:
-            res = table_obj.output(["variant_id"]).knn("gender_vector", embedding_data, embedding_data_type[0],
+            res = table_obj.output(["variant_id", "_distance"]).knn("gender_vector", embedding_data, embedding_data_type[0],
                                                        "l2",
                                                        2).to_pl()
             print(res)
         else:
-            res = table_obj.output(["variant_id"]).knn("gender_vector", embedding_data, embedding_data_type[0],
+            res = table_obj.output(["variant_id", "_similarity"]).knn("gender_vector", embedding_data, embedding_data_type[0],
                                                        "ip",
                                                        2).to_pl()
 

src/function/builtin_functions.cpp

@@ -118,11 +118,14 @@ void BuiltinFunctions::RegisterSpecialFunction() {
     SharedPtr<SpecialFunction> row_function = MakeShared<SpecialFunction>("ROW_ID", DataType(LogicalType::kBigInt), 1, SpecialType::kRowID);
     Catalog::AddSpecialFunction(catalog_ptr_.get(), row_function);
 
-    SharedPtr<SpecialFunction> create_ts_function = MakeShared<SpecialFunction>("DISTANCE", DataType(LogicalType::kFloat), 2, SpecialType::kDistance);
-    Catalog::AddSpecialFunction(catalog_ptr_.get(), create_ts_function);
+    SharedPtr<SpecialFunction> distance_function = MakeShared<SpecialFunction>("DISTANCE", DataType(LogicalType::kFloat), 2, SpecialType::kDistance);
+    Catalog::AddSpecialFunction(catalog_ptr_.get(), distance_function);
 
-    SharedPtr<SpecialFunction> delete_ts_function = MakeShared<SpecialFunction>("SCORE", DataType(LogicalType::kFloat), 3, SpecialType::kScore);
-    Catalog::AddSpecialFunction(catalog_ptr_.get(), delete_ts_function);
+    SharedPtr<SpecialFunction> similarity_function = MakeShared<SpecialFunction>("SIMILARITY", DataType(LogicalType::kFloat), 3, SpecialType::kSimilarity);
+    Catalog::AddSpecialFunction(catalog_ptr_.get(), similarity_function);
+
+    SharedPtr<SpecialFunction> score_function = MakeShared<SpecialFunction>("SCORE", DataType(LogicalType::kFloat), 4, SpecialType::kScore);
+    Catalog::AddSpecialFunction(catalog_ptr_.get(), score_function);
 }
 
 } // namespace infinity

src/function/special_function.cppm

@@ -26,6 +26,7 @@ namespace infinity {
 export enum class SpecialType {
     kRowID,
     kDistance,
+    kSimilarity,
     kScore,
     kCreateTs,
     kDeleteTs,

src/parser/type/data_type.cpp

@@ -345,6 +345,7 @@ std::shared_ptr<DataType> DataType::Deserialize(const nlohmann::json &data_type_
             }
             case LogicalType::kSparse: {
                 type_info = SparseInfo::Deserialize(type_info_json);
+                break;
             }
             default:
                 // There's no type_info for other types

src/planner/bind_context.cpp

@@ -29,6 +29,7 @@ import column_identifer;
 import block_index;
 import column_expr;
 import logger;
+import knn_expr;
 
 namespace infinity {
 
@@ -368,6 +369,44 @@ const Binding *BindContext::GetBindingFromCurrentOrParentByName(const String &bi
     return binding_iter->second.get();
 }
 
+void BindContext::BoundSearch(ParsedExpr *expr) {
+    if (expr == nullptr) {
+        return;
+    }
+    auto search_expr = (SearchExpr *)expr;
+
+    if(!search_expr->knn_exprs_.empty() && search_expr->fusion_exprs_.empty()) {
+        SizeT expr_count = search_expr->knn_exprs_.size();
+        KnnExpr* first_knn = search_expr->knn_exprs_[0];
+        KnnDistanceType first_distance_type = first_knn->distance_type_;
+        for(SizeT idx = 1; idx < expr_count; ++ idx) {
+            if(search_expr->knn_exprs_[idx]->distance_type_ != first_distance_type) {
+                // Mixed distance type
+                return ;
+            }
+        }
+        switch(first_distance_type) {
+            case KnnDistanceType::kL2:
+            case KnnDistanceType::kHamming: {
+                allow_distance = true;
+                break;
+            }
+            case KnnDistanceType::kInnerProduct:
+            case KnnDistanceType::kCosine: {
+                allow_similarity = true;
+                break;
+            }
+            default: {
+                String error_message = "Invalid KNN metric type";
+                LOG_ERROR(error_message);
+                UnrecoverableError(error_message);
+            }
+        }
+    }
+
+    allow_score = !search_expr->match_exprs_.empty() || !search_expr->match_tensor_exprs_.empty() || !(search_expr->fusion_exprs_.empty());
+}
+
 // void
 // BindContext::AddChild(const SharedPtr<BindContext>& child) {
 //     child->binding_context_id_ = GenerateBindingContextIndex();

src/planner/bind_context.cppm

@@ -139,6 +139,7 @@ public:
     bool single_row = false;
 
     bool allow_distance = false;
+    bool allow_similarity = false;
     bool allow_score = false;
 
 public:
@@ -166,15 +167,7 @@ public:
 
     void BoundTable(const String &table_name) { bound_table_set_.insert(table_name); }
 
-    void BoundSearch(ParsedExpr *expr) {
-        if (expr == nullptr) {
-            return;
-        }
-        auto search_expr = (SearchExpr *)expr;
-
-        allow_distance = !search_expr->knn_exprs_.empty() && search_expr->fusion_exprs_.empty();
-        allow_score = !search_expr->match_exprs_.empty() || !search_expr->match_tensor_exprs_.empty() || !(search_expr->fusion_exprs_.empty());
-    }
+    void BoundSearch(ParsedExpr *expr);
 
     void AddSubqueryBinding(const String &name,
                             u64 table_index,

src/planner/expression_binder.cpp

@@ -730,7 +730,15 @@ Optional<SharedPtr<BaseExpression>> ExpressionBinder::TryBuildSpecialFuncExpr(co
         switch (special_function_ptr->special_type()) {
             case SpecialType::kDistance: {
                 if (!bind_context_ptr->allow_distance) {
-                    Status status = Status::SyntaxError("DISTANCE() needs to be allowed only when there is only MATCH VECTOR");
+                    Status status = Status::SyntaxError("DISTANCE() needs to be allowed only when there is only MATCH VECTOR with distance metrics, like L2");
+                    LOG_ERROR(status.message());
+                    RecoverableError(status);
+                }
+                break;
+            }
+            case SpecialType::kSimilarity: {
+                if (!bind_context_ptr->allow_similarity) {
+                    Status status = Status::SyntaxError("SIMILARITY() needs to be allowed only when there is only MATCH VECTOR with similarity metrics, like Inner product");
                     LOG_ERROR(status.message());
                     RecoverableError(status);
                 }

src/planner/optimizer/column_remapper.cpp

@@ -80,6 +80,7 @@ SharedPtr<BaseExpression> BindingRemapper::VisitReplace(const SharedPtr<ColumnEx
                                                  column_cnt_ - 1);
             }
             case SpecialType::kScore:
+            case SpecialType::kSimilarity:
             case SpecialType::kDistance: {
                 return ReferenceExpression::Make(expression->Type(),
                                                  expression->table_name(),

test/sql/dql/knn/test_knn_ip.slt

@@ -29,12 +29,15 @@ SELECT c2 FROM test_knn_ip SEARCH MATCH VECTOR (c2, [0.3, 0.3, 0.2, 0.2], 'float
 0.2,0.1,0.3,0.4
 
 query II
-SELECT c1, ROW_ID(), DISTANCE() FROM test_knn_ip SEARCH MATCH VECTOR (c2, [0.3, 0.3, 0.2, 0.2], 'float', 'ip', 3);
+SELECT c1, ROW_ID(), SIMILARITY() FROM test_knn_ip SEARCH MATCH VECTOR (c2, [0.3, 0.3, 0.2, 0.2], 'float', 'ip', 3);
 ----
 8 3 0.270000
 6 2 0.250000
 4 1 0.230000
 
+statement error
+SELECT c1, ROW_ID(), DISTANCE() FROM test_knn_l2 SEARCH MATCH VECTOR (c2, [0.3, 0.3, 0.2, 0.2], 'float', 'ip', 3);
+
 # copy to create another new block
 # there will has 2 knn_scan operator to scan the blocks, and one merge_knn to merge
 statement ok

test/sql/dql/knn/test_knn_l2.slt

@@ -35,6 +35,9 @@ SELECT c1, ROW_ID(), DISTANCE() FROM test_knn_l2 SEARCH MATCH VECTOR (c2, [0.3,
 6 2 0.060000
 4 1 0.100000
 
+statement error
+SELECT c1, ROW_ID(), SIMILARITY() FROM test_knn_l2 SEARCH MATCH VECTOR (c2, [0.3, 0.3, 0.2, 0.2], 'float', 'l2', 3);
+
 # copy to create another new block
 # there will has 2 knn_scan operator to scan the blocks, and one merge_knn to merge
 statement ok