Support int8 embedding and uint8 embedding

docs/references/pysdk_api_reference.md

@@ -357,19 +357,21 @@ Create an index by `IndexInfo` list.
   A IndexInfo struct contains three fields,`column_name`, `index_type`, and `index_param_list`.
     - **column_name : str** Name of the column to build index on.
     - **index_type : IndexType**
-      enum type: `IVFFlat` , `Hnsw`, `HnswLVQ`, `FullText`, or `BMP`. Defined in `infinity.index`.
-      `Note: The difference between Hnsw and HnswLVQ is only adopting different clustering method. The former uses K-Means while the later uses LVQ(Learning Vector Quantization)`
+      enum type: `IVFFlat` , `Hnsw`, `FullText`, or `BMP`. Defined in `infinity.index`.
+      `Note: For Hnsw index, add encode=lvq in index_param_list to use LVQ(Locally-adaptive vector quantization)`
     - **index_param_list**
       A list of InitParameter. The InitParameter struct is like a key-value pair, with two string fields named param_name and param_value. The optional parameters of each type of index are listed below:
         - `IVFFlat`: `'centroids_count'`(default:`'128'`), `'metric'`(required)
-        - `Hnsw`: `'M'`(default:`'16'`), `'ef_construction'`(default:`'50'`), `'ef'`(default:`'50'`), `'metric'`(required)
-        - `HnswLVQ`: 
+        - `Hnsw`: 
           - `'M'`(default:`'16'`)
           - `'ef_construction'`(default:`'50'`)
           - `'ef'`(default:`'50'`)
           - `'metric'`(required)
              - `ip`: Inner product
              - `l2`: Euclidean distance
+          - `'encode'`(optional)
+              - `plain`: Plain encoding (default)
+              - `lvq`: LVQ(Locally-adaptive vector quantization)
         - `FullText`: `'ANALYZER'`(default:`'standard'`)
         - `BMP`: 
           - `block_size=1~256`(default: 16): The size of the block in BMP index

example/ColBERT_reranker_example/helper.py

@@ -13,8 +13,12 @@
 # limitations under the License.
 
 from typing import Union
-import torch
-from infinity.remote_thrift.types import make_match_tensor_expr
+# NOTICE: please check which infinity you are using, local or remote
+# this statement is for local infinity
+from infinity.local_infinity.types import make_match_tensor_expr
+# enable the following import statement to use remote infinity
+# from infinity.remote_thrift.types import make_match_tensor_expr
+# from infinity.common import LOCAL_HOST
 
 
 class InfinityHelperForColBERT:
@@ -40,9 +44,9 @@ def create_test_env(self, schema: dict):
         # append two hidden columns: "INNER_HIDDEN_ColBERT_TENSOR_ARRAY_FLOAT", "INNER_HIDDEN_ColBERT_TENSOR_ARRAY_BIT"
         self.test_db_name = "colbert_test_db"
         self.test_table_name = "colbert_test_table"
-        self.inner_col_txt = "INNER_HIDDEN_ColBERT_TEXT_FOR_BM25"
-        self.inner_col_float = "INNER_HIDDEN_ColBERT_TENSOR_ARRAY_FLOAT"
-        self.inner_col_bit = "INNER_HIDDEN_ColBERT_TENSOR_ARRAY_BIT"
+        self.inner_col_txt = "inner_hidden_colbert_text_for_bm25"
+        self.inner_col_float = "inner_hidden_colbert_tensor_array_float"
+        self.inner_col_bit = "inner_hidden_colbert_tensor_array_bit"
         if self.inner_col_txt in schema:
             raise ValueError(f"Column name {self.inner_col_txt} is reserved for internal use.")
         if self.inner_col_float in schema:
@@ -56,13 +60,22 @@ def create_test_env(self, schema: dict):
         from infinity import NetworkAddress
         import infinity.index as index
         from infinity.common import ConflictType
-        self.infinity_obj = infinity.connect(NetworkAddress("127.0.0.1", 23817))
+        # NOTICE: the following statement is for local infinity
+        self.infinity_obj = infinity.connect("/var/infinity")
+        # enable the following statement to use remote infinity
+        # self.infinity_obj = infinity.connect(LOCAL_HOST)
         self.infinity_obj.drop_database(self.test_db_name, ConflictType.Ignore)
         self.colbert_test_db = self.infinity_obj.create_database(self.test_db_name)
         self.colbert_test_table = self.colbert_test_db.create_table(self.test_table_name, schema, ConflictType.Error)
+        # NOTICE: the following statement is for english text
         self.colbert_test_table.create_index("test_ft_index",
                                              [index.IndexInfo(self.inner_col_txt, index.IndexType.FullText, [])],
                                              ConflictType.Error)
+        # please enable the following statement to use chinese text
+        # self.colbert_test_table.create_index("test_ft_index",
+        #                                      [index.IndexInfo(self.inner_col_txt, index.IndexType.FullText,
+        #                                                       [infinity.index.InitParameter("ANALYZER", "chinese")])],
+        #                                      ConflictType.Error)
 
     # clear the test environment for ColBERT
     def clear_test_env(self):

python/benchmark/flat_knn/flat_knn_interactive_benchmark.py

@@ -90,7 +90,7 @@ def faiss_import(self, data_set):
     def infinity_benchmark_flat_knn(self, data_set):
         threads = int(input("Enter number of threads:\n"))
         rounds = int(input("Enter number of rounds:\n"))
-        benchmark(threads, rounds, data_set, self.dataset_path_map[data_set])
+        benchmark(threads, rounds, data_set, 200, True, self.dataset_path_map[data_set])
 
     def faiss_benchmark_flat_knn_batch_query(self, data_set):
         xq = fvecs_read(self.dataset_path_map[data_set] + self.query_suffix[data_set])

python/infinity/index.py

@@ -24,19 +24,16 @@
 
 class IndexType(Enum):
     IVFFlat = 1
-    HnswLVQ = 2
-    Hnsw = 3
-    FullText = 4
-    Secondary = 5
-    EMVB = 6
-    BMP = 7
+    Hnsw = 2
+    FullText = 3
+    Secondary = 4
+    EMVB = 5
+    BMP = 6
 
     def to_ttype(self):
         match self:
             case IndexType.IVFFlat:
                 return ttypes.IndexType.IVFFlat
-            case IndexType.HnswLVQ:
-                return ttypes.IndexType.HnswLVQ
             case IndexType.Hnsw:
                 return ttypes.IndexType.Hnsw
             case IndexType.FullText:
@@ -54,8 +51,6 @@ def to_local_type(self):
         match self:
             case IndexType.IVFFlat:
                 return LocalIndexType.kIVFFlat
-            case IndexType.HnswLVQ:
-                return LocalIndexType.kHnswLVQ
             case IndexType.Hnsw:
                 return LocalIndexType.kHnsw
             case IndexType.FullText:

python/infinity/local_infinity/db.py

@@ -85,6 +85,10 @@ def get_ordinary_info(column_info, column_defs, column_name, index):
                         proto_column_type.logical_type = LogicalType.kFloat
                     case "double" | "float64":
                         proto_column_type.logical_type = LogicalType.kDouble
+                    case "float16":
+                        proto_column_type.logical_type = LogicalType.kFloat16
+                    case "bfloat16":
+                        proto_column_type.logical_type = LogicalType.kBFloat16
                     case "varchar":
                         proto_column_type.logical_type = LogicalType.kVarchar
                     case "bool":
@@ -157,6 +161,8 @@ def get_embedding_info(column_info, column_defs, column_name, index):
         embedding_type.element_type = EmbeddingDataType.kElemFloat
     elif element_type == "float64" or element_type == "double":
         embedding_type.element_type = EmbeddingDataType.kElemDouble
+    elif element_type == "uint8":
+        embedding_type.element_type = EmbeddingDataType.kElemUInt8
     elif element_type == "int8":
         embedding_type.element_type = EmbeddingDataType.kElemInt8
     elif element_type == "int16":
@@ -205,6 +211,8 @@ def get_sparse_info(column_info, column_defs, column_name, index):
         sparse_type.element_type = EmbeddingDataType.kElemFloat
     elif element_type == "float64" or element_type == "double":
         sparse_type.element_type = EmbeddingDataType.kElemDouble
+    elif element_type == "uint8":
+        sparse_type.element_type = EmbeddingDataType.kElemUInt8
     elif element_type == "int8":
         sparse_type.element_type = EmbeddingDataType.kElemInt8
     elif element_type == "int16":

python/infinity/local_infinity/query_builder.py

@@ -98,35 +98,34 @@ def knn(
                 f"Invalid embedding data, type should be embedded, but get {type(embedding_data)}",
             )
 
-        if (
-            embedding_data_type == "tinyint"
-            or embedding_data_type == "smallint"
-            or embedding_data_type == "int"
-            or embedding_data_type == "bigint"
-        ):
+        if embedding_data_type in ["int", "uint8", "int8", "int16", "int32", "int64", "tinyint", "unsigned tinyint",
+                                   "smallint", "bigint"]:
             embedding_data = [int(x) for x in embedding_data]
 
         data = EmbeddingData()
         elem_type = EmbeddingDataType.kElemFloat
         if embedding_data_type == "bit":
             elem_type = EmbeddingDataType.kElemBit
             raise Exception(f"Invalid embedding {embedding_data[0]} type")
-        elif embedding_data_type == "tinyint":
+        elif embedding_data_type in ["unsigned tinyint", "uint8"]:
+            elem_type = EmbeddingDataType.kElemUInt8
+            data.u8_array_value = embedding_data
+        elif embedding_data_type in ["tinyint", "int8"]:
             elem_type = EmbeddingDataType.kElemInt8
             data.i8_array_value = embedding_data
-        elif embedding_data_type == "smallint":
+        elif embedding_data_type in ["smallint", "int16"]:
             elem_type = EmbeddingDataType.kElemInt16
             data.i16_array_value = embedding_data
-        elif embedding_data_type == "int":
+        elif embedding_data_type in ["int", "int32"]:
             elem_type = EmbeddingDataType.kElemInt32
             data.i32_array_value = embedding_data
-        elif embedding_data_type == "bigint":
+        elif embedding_data_type in ["bigint", "int64"]:
             elem_type = EmbeddingDataType.kElemInt64
             data.i64_array_value = embedding_data
-        elif embedding_data_type == "float":
+        elif embedding_data_type in ["float", "float32"]:
             elem_type = EmbeddingDataType.kElemFloat
             data.f32_array_value = embedding_data
-        elif embedding_data_type == "double":
+        elif embedding_data_type in ["double", "float64"]:
             elem_type = EmbeddingDataType.kElemDouble
             data.f64_array_value = embedding_data
         else:

python/infinity/local_infinity/table.py

@@ -168,6 +168,9 @@ def insert(self, data: Union[INSERT_DATA, list[INSERT_DATA]]):
                 if isinstance(value, str):
                     constant_expression.literal_type = LiteralType.kString
                     constant_expression.str_value = value
+                elif isinstance(value, bool):
+                    constant_expression.literal_type = LiteralType.kBoolean
+                    constant_expression.bool_value = value
                 elif isinstance(value, int):
                     constant_expression.literal_type = LiteralType.kInteger
                     constant_expression.i64_value = value

python/infinity/local_infinity/types.py

@@ -38,11 +38,17 @@ def logic_type_to_dtype(ttype: WrapDataType):
             return dtype('float32')
         case LogicalType.kDouble:
             return dtype('float64')
+        case LogicalType.kFloat16:
+            return dtype('float32')
+        case LogicalType.kBFloat16:
+            return dtype('float32')
         case LogicalType.kVarchar:
             return dtype('str')
         case LogicalType.kEmbedding:
             if ttype.embedding_type is not None:
                 match ttype.embedding_type.element_type:
+                    case EmbeddingDataType.kElemUInt8:
+                        return object
                     case EmbeddingDataType.kElemInt8:
                         return object
                     case EmbeddingDataType.kElemInt16:
@@ -81,6 +87,9 @@ def tensor_to_list(column_data_type, binary_data) -> list[list[Any]]:
                 mid_res_int = mid_res_int * 256 + j
             result.append([f"\u007b0:0{dimension}b\u007d".format(mid_res_int)[::-1]])
         return result
+    elif column_data_type.embedding_type.element_type == EmbeddingDataType.kElemUInt8:
+        all_list = list(struct.unpack('<{}B'.format(len(binary_data)), binary_data))
+        return [all_list[i:i + dimension] for i in range(0, len(all_list), dimension)]
     elif column_data_type.embedding_type.element_type == EmbeddingDataType.kElemInt8:
         all_list = list(struct.unpack('<{}b'.format(len(binary_data)), binary_data))
         return [all_list[i:i + dimension] for i in range(0, len(all_list), dimension)]
@@ -144,6 +153,14 @@ def column_vector_to_list(column_type, column_data_type, column_vectors) -> \
             return list(struct.unpack('<{}f'.format(len(column_vector) // 4), column_vector))
         case LogicalType.kDouble:
             return list(struct.unpack('<{}d'.format(len(column_vector) // 8), column_vector))
+        case LogicalType.kFloat16:
+            return list(struct.unpack('<{}e'.format(len(column_vector) // 2), column_vector))
+        case LogicalType.kBFloat16:
+            tmp_u16 = np.frombuffer(column_vector, dtype='<i2')
+            result_arr = np.zeros(2 * len(tmp_u16), dtype='<i2')
+            result_arr[1::2] = tmp_u16
+            view_float32 = result_arr.view('<f4')
+            return list(view_float32)
         case LogicalType.kVarchar:
             return list(parse_bytes(column_vector))
         case LogicalType.kBoolean:
@@ -158,7 +175,10 @@ def column_vector_to_list(column_type, column_data_type, column_vectors) -> \
         case LogicalType.kEmbedding:
             dimension = column_data_type.embedding_type.dimension
             element_type = column_data_type.embedding_type.element_type
-            if element_type == EmbeddingDataType.kElemInt8:
+            if element_type == EmbeddingDataType.kElemUInt8:
+                all_list = list(struct.unpack('<{}B'.format(len(column_vector)), column_vector))
+                return [all_list[i:i + dimension] for i in range(0, len(all_list), dimension)]
+            elif element_type == EmbeddingDataType.kElemInt8:
                 all_list = list(struct.unpack('<{}b'.format(len(column_vector)), column_vector))
                 return [all_list[i:i + dimension] for i in range(0, len(all_list), dimension)]
             elif element_type == EmbeddingDataType.kElemInt16:
@@ -225,6 +245,9 @@ def parse_sparse_bytes(column_data_type, column_vector):
             case _:
                 raise NotImplementedError(f"Unsupported type {index_type}")
         match element_type:
+            case EmbeddingDataType.kElemUInt8:
+                values = struct.unpack('<{}B'.format(nnz), column_vector[offset:offset + nnz])
+                offset += nnz
             case EmbeddingDataType.kElemInt8:
                 values = struct.unpack('<{}b'.format(nnz), column_vector[offset:offset + nnz])
                 offset += nnz
@@ -277,22 +300,25 @@ def make_match_tensor_expr(vector_column_name: str, embedding_data: VEC, embeddi
     elem_type = EmbeddingDataType.kElemFloat
     if embedding_data_type == 'bit':
         raise InfinityException(ErrorCode.INVALID_EMBEDDING_DATA_TYPE, f"Invalid embedding {embedding_data[0]} type")
-    elif embedding_data_type == 'tinyint' or embedding_data_type == 'int8' or embedding_data_type == 'i8':
+    elif embedding_data_type in ['unsigned tinyint', 'uint8', 'u8']:
+        elem_type = EmbeddingDataType.kElemUInt8
+        data.u8_array_value = np.asarray(embedding_data, dtype=np.uint8).flatten()
+    elif embedding_data_type in ['tinyint', 'int8', 'i8']:
         elem_type = EmbeddingDataType.kElemInt8
         data.i8_array_value = np.asarray(embedding_data, dtype=np.int8).flatten()
-    elif embedding_data_type == 'smallint' or embedding_data_type == 'int16' or embedding_data_type == 'i16':
+    elif embedding_data_type in ['smallint', 'int16', 'i16']:
         elem_type = EmbeddingDataType.kElemInt16
         data.i16_array_value = np.asarray(embedding_data, dtype=np.int16).flatten()
-    elif embedding_data_type == 'int' or embedding_data_type == 'int32' or embedding_data_type == 'i32':
+    elif embedding_data_type in ['int', 'int32', 'i32']:
         elem_type = EmbeddingDataType.kElemInt32
         data.i32_array_value = np.asarray(embedding_data, dtype=np.int32).flatten()
-    elif embedding_data_type == 'bigint' or embedding_data_type == 'int64' or embedding_data_type == 'i64':
+    elif embedding_data_type in ['bigint', 'int64', 'i64']:
         elem_type = EmbeddingDataType.kElemInt64
         data.i64_array_value = np.asarray(embedding_data, dtype=np.int64).flatten()
-    elif embedding_data_type == 'float' or embedding_data_type == 'float32' or embedding_data_type == 'f32':
+    elif embedding_data_type in ['float', 'float32', 'f32']:
         elem_type = EmbeddingDataType.kElemFloat
         data.f32_array_value = np.asarray(embedding_data, dtype=np.float32).flatten()
-    elif embedding_data_type == 'double' or embedding_data_type == 'float64' or embedding_data_type == 'f64':
+    elif embedding_data_type in ['double', 'float64', 'f64']:
         elem_type = EmbeddingDataType.kElemDouble
         data.f64_array_value = np.asarray(embedding_data, dtype=np.float64).flatten()
     else:

python/infinity/local_infinity/utils.py

@@ -60,6 +60,15 @@ def traverse_conditions(cons, fn=None):
 
         return parsed_expr
 
+    elif isinstance(cons, exp.Boolean):
+        parsed_expr = WrapParsedExpr()
+        constant_expr = WrapConstantExpr()
+        constant_expr.literal_type = LiteralType.kBoolean
+        constant_expr.bool_value = cons.this
+        parsed_expr.type = ParsedExprType.kConstant
+        parsed_expr.constant_expr = constant_expr
+        return parsed_expr
+
     elif isinstance(cons, exp.Literal):
         parsed_expr = WrapParsedExpr()
         constant_expr = WrapConstantExpr()