Fix checking data_type before converting queries to bytes

engine/base_client/search.py

@@ -7,8 +7,10 @@
 import numpy as np
 import tqdm
 import os
+from ml_dtypes import bfloat16
 
 from dataset_reader.base_reader import Query
+from engine.base_client.utils import check_data_type
 
 DEFAULT_TOP = 10
 MAX_QUERIES = int(os.getenv("MAX_QUERIES", -1))
@@ -66,6 +68,11 @@ def search_all(
     ):
         parallel = self.search_params.get("parallel", 1)
         top = self.search_params.get("top", None)
+        single_search_params = self.search_params.get("search_params", None)
+        if single_search_params:
+            data_type = check_data_type(single_search_params.get("data_type", "FLOAT32").upper())
+        else:
+            data_type = np.float32  # Default data type if not specified
         # setup_search may require initialized client
         self.init_client(
             self.host, distance, self.connection_params, self.search_params
@@ -78,7 +85,7 @@ def search_all(
         # Also, converts query vectors to bytes beforehand, preparing them for sending to client without affecting search time measurements
         queries_list = []
         for query in queries:
-            query.vector = np.array(query.vector).astype(np.float32).tobytes()
+            query.vector = np.array(query.vector).astype(data_type).tobytes()
             queries_list.append(query)
 
         # Handle MAX_QUERIES environment variable

engine/base_client/utils.py

@@ -1,5 +1,8 @@
 from typing import Any, Iterable
 
+from ml_dtypes import bfloat16
+import numpy as np
+
 from dataset_reader.base_reader import Record
 
 
@@ -18,3 +21,19 @@ def iter_batches(records: Iterable[Record], n: int) -> Iterable[Any]:
             ids, vectors, metadata = [], [], []
     if len(ids) > 0:
         yield [ids, vectors, metadata]
+
+
+def check_data_type(data_type: str):
+    valid_data_types = ["FLOAT32", "FLOAT64", "FLOAT16", "BFLOAT16"]
+    if data_type.upper() not in valid_data_types:
+        raise ValueError(
+            f"Invalid data type: {data_type}. Valid options are: {valid_data_types}"
+        )
+    if data_type == "FLOAT32":
+        return np.float32
+    if data_type == "FLOAT64":
+        return np.float64
+    if data_type == "FLOAT16":
+        return np.float16
+    if data_type == "BFLOAT16":
+        return bfloat16

engine/clients/redis/search.py

@@ -1,9 +1,9 @@
 import random
 from typing import List, Tuple
-from ml_dtypes import bfloat16
 import numpy as np
 from redis import Redis, RedisCluster
 from redis.commands.search.query import Query
+from engine.base_client.utils import check_data_type
 from engine.base_client.search import BaseSearcher
 from engine.clients.redis.config import (
     REDIS_PORT,
@@ -44,13 +44,7 @@ def init_client(cls, host, distance, connection_params: dict, search_params: dic
             cls.data_type = (
                 cls.search_params["search_params"].get("data_type", "FLOAT32").upper()
             )
-        cls.np_data_type = np.float32
-        if cls.data_type == "FLOAT64":
-            cls.np_data_type = np.float64
-        if cls.data_type == "FLOAT16":
-            cls.np_data_type = np.float16
-        if cls.data_type == "BFLOAT16":
-            cls.np_data_type = bfloat16
+        cls.np_data_type = check_data_type(cls.data_type)
         cls._is_cluster = True if REDIS_CLUSTER else False
 
         # In the case of CLUSTER API enabled we randomly select the starting primary shard

engine/clients/redis/upload.py

@@ -7,6 +7,7 @@
 import numpy as np
 from redis import Redis, RedisCluster
 from engine.base_client.upload import BaseUploader
+from engine.base_client.utils import check_data_type
 from engine.clients.redis.config import (
     REDIS_PORT,
     REDIS_AUTH,
@@ -42,13 +43,7 @@ def init_client(cls, host, distance, connection_params, upload_params):
         cls.upload_params = upload_params
         cls.algorithm = cls.upload_params.get("algorithm", "hnsw").upper()
         cls.data_type = cls.upload_params.get("data_type", "FLOAT32").upper()
-        cls.np_data_type = np.float32
-        if cls.data_type == "FLOAT64":
-            cls.np_data_type = np.float64
-        if cls.data_type == "FLOAT16":
-            cls.np_data_type = np.float16
-        if cls.data_type == "BFLOAT16":
-            cls.np_data_type = bfloat16
+        cls.np_data_type = check_data_type(cls.data_type)
         cls._is_cluster = True if REDIS_CLUSTER else False
 
     @classmethod