sets the default datatype in our vectorizers to float32 if not specified by users

docs/user_guide/vectorizers_04.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,33 +305,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/tyler.hutcherson/redis/redis-vl-python/.venv/lib/python3.9/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return self.fget.__get__(instance, owner)()\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[0.00037810884532518685,\n",
-       " -0.05080341175198555,\n",
-       " -0.03514723479747772,\n",
-       " -0.02325104922056198,\n",
-       " -0.044158220291137695,\n",
-       " 0.020487844944000244,\n",
-       " 0.0014617963461205363,\n",
-       " 0.031261757016181946,\n",
+       "[0.0003780885017476976,\n",
+       " -0.05080340430140495,\n",
+       " -0.035147231072187424,\n",
+       " -0.02325103059411049,\n",
+       " -0.04415831342339516,\n",
+       " 0.02048780582845211,\n",
+       " 0.0014618589775636792,\n",
+       " 0.03126184269785881,\n",
        " 0.05605152249336243,\n",
-       " 0.018815357238054276]"
+       " 0.018815429881215096]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,14 +524,14 @@
     }
    ],
    "source": [
-    "# from redisvl.utils.vectorize import MistralAITextVectorizer\n",
+    "from redisvl.utils.vectorize import MistralAITextVectorizer\n",
     "\n",
-    "# mistral = MistralAITextVectorizer()\n",
+    "mistral = MistralAITextVectorizer()\n",
     "\n",
-    "# # embed a sentence using their asyncronous method\n",
-    "# test = await mistral.aembed(\"This is a test sentence.\")\n",
-    "# print(\"Vector dimensions: \", len(test))\n",
-    "# print(test[:10])"
+    "# embed a sentence using their asyncronous method\n",
+    "test = await mistral.aembed(\"This is a test sentence.\")\n",
+    "print(\"Vector dimensions: \", len(test))\n",
+    "print(test[:10])"
    ]
   },
   {
@@ -588,9 +580,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector dimensions: 1024\n"
+     ]
+    }
+   ],
    "source": [
     "from redisvl.utils.vectorize import BedrockTextVectorizer\n",
     "\n",
@@ -823,6 +823,43 @@
     "    print(doc[\"text\"], doc[\"vector_distance\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting your float data type\n",
+    "When embedding text as byte arrays RedisVL supports 4 different floating point data types, `float16`, `float32`, `float64` and `bfloat16`.\n",
+    "Your dtype set for your vectorizer must match what is defined in your search index. If one is not explicitly set the default is `float32`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vectorizer = HFTextVectorizer(dtype=\"float16\")\n",
+    "\n",
+    "# subsequent calls to embed('', as_buffer=True) and embed_many('', as_buffer=True) will now encode as float16\n",
+    "float16_bytes = vectorizer.embed('test sentence', as_buffer=True)\n",
+    "\n",
+    "# you can override this setting on each individual method call\n",
+    "float64_bytes = vectorizer.embed('test sentence', as_buffer=True, dtype=\"float64\")\n",
+    "\n",
+    "float16_bytes != float64_bytes"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -836,7 +873,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 ('redisvl2')",
+   "display_name": "redisvl-dev",
    "language": "python",
    "name": "python3"
   },
@@ -852,12 +889,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.12.2"
   },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "9b1e6e9c2967143209c2f955cb869d1d3234f92dc4787f49f155f3abbdfb1316"
-   }
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

pyproject.toml

@@ -32,7 +32,7 @@ sentence-transformers = { version = ">=2.2.2", optional = true }
 google-cloud-aiplatform = { version = ">=1.26", optional = true }
 protobuf = { version = ">=5.29.1,<6.0.0.dev0", optional = true }
 cohere = { version = ">=4.44", optional = true }
-mistralai = { version = ">=0.2.0", optional = true }
+mistralai = { version = ">=1.0.0", optional = true }
 boto3 = { version = ">=1.34.0", optional = true }
 
 [tool.poetry.extras]

redisvl/utils/vectorize/base.py

@@ -5,6 +5,7 @@
 from pydantic.v1 import BaseModel, validator
 
 from redisvl.redis.utils import array_to_buffer
+from redisvl.schema.fields import VectorDataType
 
 
 class Vectorizers(Enum):
@@ -19,11 +20,22 @@ class Vectorizers(Enum):
 class BaseVectorizer(BaseModel, ABC):
     model: str
     dims: int
+    dtype: str
 
     @property
     def type(self) -> str:
         return "base"
 
+    @validator("dtype")
+    def check_dtype(dtype):
+        try:
+            VectorDataType(dtype.upper())
+        except ValueError:
+            raise ValueError(
+                f"Invalid data type: {dtype}. Supported types are: {[t.lower() for t in VectorDataType]}"
+            )
+        return dtype
+
     @validator("dims")
     @classmethod
     def check_dims(cls, value):
@@ -81,13 +93,7 @@ def batchify(self, seq: list, size: int, preprocess: Optional[Callable] = None):
             else:
                 yield seq[pos : pos + size]
 
-    def _process_embedding(
-        self, embedding: List[float], as_buffer: bool, dtype: Optional[str]
-    ):
+    def _process_embedding(self, embedding: List[float], as_buffer: bool, dtype: str):
         if as_buffer:
-            if not dtype:
-                raise RuntimeError(
-                    "dtype is required if converting from float to byte string."
-                )
             return array_to_buffer(embedding, dtype)
         return embedding

redisvl/utils/vectorize/text/azureopenai.py

@@ -52,7 +52,10 @@ class AzureOpenAITextVectorizer(BaseVectorizer):
     _aclient: Any = PrivateAttr()
 
     def __init__(
-        self, model: str = "text-embedding-ada-002", api_config: Optional[Dict] = None
+        self,
+        model: str = "text-embedding-ada-002",
+        api_config: Optional[Dict] = None,
+        dtype: str = "float32",
     ):
         """Initialize the AzureOpenAI vectorizer.
 
@@ -63,13 +66,17 @@ def __init__(
             api_config (Optional[Dict], optional): Dictionary containing the
                 API key, API version, Azure endpoint, and any other API options.
                 Defaults to None.
+            dtype (str): the default datatype to use when embedding text as byte arrays.
+                Used when setting `as_buffer=True` in calls to embed() and embed_many().
+                Defaults to 'float32'.
 
         Raises:
             ImportError: If the openai library is not installed.
             ValueError: If the AzureOpenAI API key, version, or endpoint are not provided.
+            ValueError: If an invalid dtype is provided.
         """
         self._initialize_clients(api_config)
-        super().__init__(model=model, dims=self._set_model_dims(model))
+        super().__init__(model=model, dims=self._set_model_dims(model), dtype=dtype)
 
     def _initialize_clients(self, api_config: Optional[Dict]):
         """
@@ -190,7 +197,7 @@ def embed_many(
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
 
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
 
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
@@ -234,7 +241,7 @@ def embed(
         if preprocess:
             text = preprocess(text)
 
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
 
         result = self._client.embeddings.create(input=[text], model=self.model)
         return self._process_embedding(result.data[0].embedding, as_buffer, dtype)
@@ -274,7 +281,7 @@ async def aembed_many(
         if len(texts) > 0 and not isinstance(texts[0], str):
             raise TypeError("Must pass in a list of str values to embed.")
 
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
 
         embeddings: List = []
         for batch in self.batchify(texts, batch_size, preprocess):
@@ -320,7 +327,7 @@ async def aembed(
         if preprocess:
             text = preprocess(text)
 
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
 
         result = await self._aclient.embeddings.create(input=[text], model=self.model)
         return self._process_embedding(result.data[0].embedding, as_buffer, dtype)

redisvl/utils/vectorize/text/bedrock.py

@@ -49,6 +49,7 @@ def __init__(
         self,
         model: str = "amazon.titan-embed-text-v2:0",
         api_config: Optional[Dict[str, str]] = None,
+        dtype: str = "float32",
     ) -> None:
         """Initialize the AWS Bedrock Vectorizer.
 
@@ -57,10 +58,14 @@ def __init__(
             api_config (Optional[Dict[str, str]]): AWS credentials and config.
                 Can include: aws_access_key_id, aws_secret_access_key, aws_region
                 If not provided, will use environment variables.
+            dtype (str): the default datatype to use when embedding text as byte arrays.
+                Used when setting `as_buffer=True` in calls to embed() and embed_many().
+                Defaults to 'float32'.
 
         Raises:
             ValueError: If credentials are not provided in config or environment.
             ImportError: If boto3 is not installed.
+            ValueError: If an invalid dtype is provided.
         """
         try:
             import boto3  # type: ignore
@@ -94,7 +99,7 @@ def __init__(
             region_name=aws_region,
         )
 
-        super().__init__(model=model, dims=self._set_model_dims(model))
+        super().__init__(model=model, dims=self._set_model_dims(model), dtype=dtype)
 
     def _set_model_dims(self, model: str) -> int:
         """Initialize model and determine embedding dimensions."""
@@ -145,7 +150,7 @@ def embed(
         response_body = json.loads(response["body"].read())
         embedding = response_body["embedding"]
 
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
         return self._process_embedding(embedding, as_buffer, dtype)
 
     @retry(
@@ -181,7 +186,7 @@ def embed_many(
             raise TypeError("Texts must be a list of strings")
 
         embeddings: List[List[float]] = []
-        dtype = kwargs.pop("dtype", None)
+        dtype = kwargs.pop("dtype", self.dtype)
 
         for batch in self.batchify(texts, batch_size, preprocess):
             # Process each text in the batch individually since Bedrock