diff --git a/chromadb/api/__init__.py b/chromadb/api/__init__.py
index 60865835cc4..138049aaa36 100644
--- a/chromadb/api/__init__.py
+++ b/chromadb/api/__init__.py
@@ -370,7 +370,7 @@ def create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
         get_or_create: bool = False,
     ) -> Collection:
@@ -407,7 +407,7 @@ def get_collection(
         name: str,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> Collection:
         """Get a collection with the given name.
@@ -439,7 +439,7 @@ def get_or_create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> Collection:
         """Get or create a collection with the given name and metadata.
diff --git a/chromadb/api/async_api.py b/chromadb/api/async_api.py
index f3eb365cc7c..81a1db1ea41 100644
--- a/chromadb/api/async_api.py
+++ b/chromadb/api/async_api.py
@@ -363,7 +363,7 @@ async def create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
         get_or_create: bool = False,
     ) -> AsyncCollection:
@@ -400,7 +400,7 @@ async def get_collection(
         name: str,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> AsyncCollection:
         """Get a collection with the given name.
@@ -432,7 +432,7 @@ async def get_or_create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> AsyncCollection:
         """Get or create a collection with the given name and metadata.
diff --git a/chromadb/api/async_client.py b/chromadb/api/async_client.py
index 9bdf59af330..0758720e40c 100644
--- a/chromadb/api/async_client.py
+++ b/chromadb/api/async_client.py
@@ -180,7 +180,7 @@ async def create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
         get_or_create: bool = False,
     ) -> AsyncCollection:
@@ -219,7 +219,7 @@ async def get_collection(
         name: str,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> AsyncCollection:
         model = await self._server.get_collection(
@@ -248,7 +248,7 @@ async def get_or_create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> AsyncCollection:
         if configuration is None:
diff --git a/chromadb/api/client.py b/chromadb/api/client.py
index 4bc0e7d755a..003a1ad17fd 100644
--- a/chromadb/api/client.py
+++ b/chromadb/api/client.py
@@ -156,7 +156,7 @@ def create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
         get_or_create: bool = False,
     ) -> Collection:
@@ -195,7 +195,7 @@ def get_collection(
         name: str,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> Collection:
         model = self._server.get_collection(
@@ -224,7 +224,7 @@ def get_or_create_collection(
         metadata: Optional[CollectionMetadata] = None,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ) -> Collection:
         if configuration is None:
diff --git a/chromadb/api/collection_configuration.py b/chromadb/api/collection_configuration.py
index 943415d2e67..24783aeb5e9 100644
--- a/chromadb/api/collection_configuration.py
+++ b/chromadb/api/collection_configuration.py
@@ -1,6 +1,7 @@
-from typing import TypedDict, Dict, Any, Optional, cast, get_args
+from typing import Type, TypedDict, Dict, Any, Optional, cast, get_args
 import json
 from chromadb.api.types import (
+    Embeddable,
     Space,
     CollectionMetadata,
     UpdateMetadata,
@@ -40,7 +41,7 @@ class SpannConfiguration(TypedDict, total=False):
 class CollectionConfiguration(TypedDict, total=True):
     hnsw: Optional[HNSWConfiguration]
     spann: Optional[SpannConfiguration]
-    embedding_function: Optional[EmbeddingFunction]  # type: ignore
+    embedding_function: Optional[EmbeddingFunction[Embeddable]]
 
 
 def load_collection_configuration_from_json_str(
@@ -88,13 +89,13 @@ def load_collection_configuration_from_json(
                     f"Embedding function name not found in config: {ef_config}"
                 )
             try:
-                ef = known_embedding_functions[ef_name]
+                ef_class = known_embedding_functions[ef_name]
             except KeyError:
                 raise ValueError(
                     f"Embedding function {ef_name} not found. Add @register_embedding_function decorator to the class definition."
                 )
             try:
-                ef = ef.build_from_config(ef_config["config"])  # type: ignore
+                ef = ef_class.build_from_config(ef_config["config"])
             except Exception as e:
                 raise ValueError(
                     f"Could not build embedding function {ef_config['name']} from config {ef_config['config']}: {e}"
@@ -106,7 +107,7 @@ def load_collection_configuration_from_json(
     return CollectionConfiguration(
         hnsw=hnsw_config,
         spann=spann_config,
-        embedding_function=ef,  # type: ignore
+        embedding_function=ef,
     )
 
 
@@ -257,7 +258,7 @@ def json_to_create_spann_configuration(
 class CreateCollectionConfiguration(TypedDict, total=False):
     hnsw: Optional[CreateHNSWConfiguration]
     spann: Optional[CreateSpannConfiguration]
-    embedding_function: Optional[EmbeddingFunction]  # type: ignore
+    embedding_function: Optional[EmbeddingFunction[Embeddable]]
 
 
 def load_collection_configuration_from_create_collection_configuration(
@@ -381,7 +382,7 @@ def create_collection_configuration_to_json(
         }
 
     try:
-        ef = cast(EmbeddingFunction, config.get("embedding_function"))  # type: ignore
+        ef = cast(EmbeddingFunction[Embeddable], config.get("embedding_function"))
         if ef.is_legacy():
             ef_config = {"type": "legacy"}
         else:
@@ -456,7 +457,7 @@ def create_collection_configuration_to_json(
 
 
 def populate_create_hnsw_defaults(
-    config: CreateHNSWConfiguration, ef: Optional[EmbeddingFunction] = None  # type: ignore
+    config: CreateHNSWConfiguration, ef: Optional[EmbeddingFunction[Embeddable]] = None
 ) -> CreateHNSWConfiguration:
     """Populate a CreateHNSW configuration with default values"""
     if config.get("space") is None:
@@ -522,7 +523,7 @@ def json_to_update_spann_configuration(
 class UpdateCollectionConfiguration(TypedDict, total=False):
     hnsw: Optional[UpdateHNSWConfiguration]
     spann: Optional[UpdateSpannConfiguration]
-    embedding_function: Optional[EmbeddingFunction]  # type: ignore
+    embedding_function: Optional[EmbeddingFunction[Embeddable]]
 
 
 def update_collection_configuration_from_legacy_collection_metadata(
@@ -697,9 +698,9 @@ def overwrite_spann_configuration(
 
 # TODO: make warnings prettier and add link to migration docs
 def overwrite_embedding_function(
-    existing_embedding_function: EmbeddingFunction,  # type: ignore
-    update_embedding_function: EmbeddingFunction,  # type: ignore
-) -> EmbeddingFunction:  # type: ignore
+    existing_embedding_function: EmbeddingFunction[Embeddable],
+    update_embedding_function: EmbeddingFunction[Embeddable],
+) -> EmbeddingFunction[Embeddable]:
     """Overwrite an EmbeddingFunction with a new configuration"""
     # Check for legacy embedding functions
     if existing_embedding_function.is_legacy() or update_embedding_function.is_legacy():
@@ -768,8 +769,8 @@ def overwrite_collection_configuration(
 
 
 def validate_embedding_function_conflict_on_create(
-    embedding_function: Optional[EmbeddingFunction],  # type: ignore
-    configuration_ef: Optional[EmbeddingFunction],  # type: ignore
+    embedding_function: Optional[EmbeddingFunction[Embeddable]],
+    configuration_ef: Optional[EmbeddingFunction[Embeddable]],
 ) -> None:
     """
     Validates that there are no conflicting embedding functions between function parameter
@@ -800,7 +801,7 @@ def validate_embedding_function_conflict_on_create(
 # if there is an issue with deserializing the config, an error shouldn't be raised
 # at get time. CollectionCommon.py will raise an error at _embed time if there is an issue deserializing.
 def validate_embedding_function_conflict_on_get(
-    embedding_function: Optional[EmbeddingFunction],  # type: ignore
+    embedding_function: Optional[EmbeddingFunction[Embeddable]],
     persisted_ef_config: Optional[Dict[str, Any]],
 ) -> None:
     """
diff --git a/chromadb/api/models/CollectionCommon.py b/chromadb/api/models/CollectionCommon.py
index 70d3f6cf7a3..44497302775 100644
--- a/chromadb/api/models/CollectionCommon.py
+++ b/chromadb/api/models/CollectionCommon.py
@@ -117,7 +117,7 @@ def __init__(
         model: CollectionModel,
         embedding_function: Optional[
             EmbeddingFunction[Embeddable]
-        ] = ef.DefaultEmbeddingFunction(),  # type: ignore
+        ] = ef.DefaultEmbeddingFunction(),
         data_loader: Optional[DataLoader[Loadable]] = None,
     ):
         """Initializes a new instance of the Collection class."""
diff --git a/chromadb/test/property/test_persist.py b/chromadb/test/property/test_persist.py
index ccc72ba45c2..2db94ec4bef 100644
--- a/chromadb/test/property/test_persist.py
+++ b/chromadb/test/property/test_persist.py
@@ -413,7 +413,7 @@ def test_delete_add_after_persist(settings: Settings) -> None:
                 "hnsw:batch_size": 3,
                 "hnsw:sync_threshold": 3,
             },
-            embedding_function=DefaultEmbeddingFunction(),  # type: ignore[arg-type]
+            embedding_function=DefaultEmbeddingFunction(),
             id=UUID("0851f751-2f11-4424-ab23-4ae97074887a"),
             dimension=2,
             dtype=None,
diff --git a/chromadb/utils/__init__.py b/chromadb/utils/__init__.py
index fe6bb81853b..281d34538d4 100644
--- a/chromadb/utils/__init__.py
+++ b/chromadb/utils/__init__.py
@@ -1,6 +1,8 @@
 import importlib
 from typing import Type, TypeVar, cast
 
+from chromadb.api.types import Document, Documents, Embeddable
+
 C = TypeVar("C")
 
 
@@ -10,3 +12,16 @@ def get_class(fqn: str, type: Type[C]) -> Type[C]:
     module = importlib.import_module(module_name)
     cls = getattr(module, class_name)
     return cast(Type[C], cls)
+
+
+def text_only_embeddable_check(input: Embeddable, embedding_function_name: str) -> Documents:
+    """
+    Helper function to determine if a given Embeddable is text-only.
+
+    Once the minimum supported python version is bumped up to 3.10, this should
+    be replaced with TypeGuard:
+    https://docs.python.org/3.10/library/typing.html#typing.TypeGuard
+    """
+    if not all(isinstance(item, Document) for item in input):
+        raise ValueError(f"{embedding_function_name} only supports text documents, not images")
+    return cast(Documents, input)
diff --git a/chromadb/utils/embedding_functions/__init__.py b/chromadb/utils/embedding_functions/__init__.py
index 28984f139cf..a80114177cc 100644
--- a/chromadb/utils/embedding_functions/__init__.py
+++ b/chromadb/utils/embedding_functions/__init__.py
@@ -1,11 +1,14 @@
-from typing import Dict, Any, Type, Set
+from typing import Dict, Any, Type, Set, cast
 from chromadb.api.types import (
+    Document,
+    Embeddable,
     EmbeddingFunction,
     Embeddings,
     Documents,
 )
 
 # Import all embedding functions
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.cohere_embedding_function import (
     CohereEmbeddingFunction,
 )
@@ -106,13 +109,13 @@ def get_builtins() -> Set[str]:
     return _all_classes
 
 
-class DefaultEmbeddingFunction(EmbeddingFunction[Documents]):
+class DefaultEmbeddingFunction(EmbeddingFunction[Embeddable]):
     def __init__(self) -> None:
         if is_thin_client:
             return
 
-    def __call__(self, input: Documents) -> Embeddings:
-        # Delegate to ONNXMiniLM_L6_V2
+    def __call__(self, input: Embeddable) -> Embeddings:
+         # Delegate to ONNXMiniLM_L6_V2
         return ONNXMiniLM_L6_V2()(input)
 
     @staticmethod
@@ -136,7 +139,7 @@ def validate_config(config: Dict[str, Any]) -> None:
 
 
 # Dictionary of supported embedding functions
-known_embedding_functions: Dict[str, Type[EmbeddingFunction]] = {  # type: ignore
+known_embedding_functions: Dict[str, Type[EmbeddingFunction[Embeddable]]] = {
     "cohere": CohereEmbeddingFunction,
     "openai": OpenAIEmbeddingFunction,
     "huggingface": HuggingFaceEmbeddingFunction,
@@ -197,7 +200,7 @@ def _register(cls):  # type: ignore
 
 
 # Function to convert config to embedding function
-def config_to_embedding_function(config: Dict[str, Any]) -> EmbeddingFunction:  # type: ignore
+def config_to_embedding_function(config: Dict[str, Any]) -> EmbeddingFunction[Embeddable]:
     """Convert a config dictionary to an embedding function.
 
     Args:
diff --git a/chromadb/utils/embedding_functions/amazon_bedrock_embedding_function.py b/chromadb/utils/embedding_functions/amazon_bedrock_embedding_function.py
index 886701b0dca..97d211a7576 100644
--- a/chromadb/utils/embedding_functions/amazon_bedrock_embedding_function.py
+++ b/chromadb/utils/embedding_functions/amazon_bedrock_embedding_function.py
@@ -1,11 +1,12 @@
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction
 from typing import Dict, Any, cast
 import json
 import numpy as np
 
 
-class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Documents]):
+class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to generate embeddings for a list of texts using Amazon Bedrock.
     """
@@ -51,7 +52,7 @@ def __init__(
             **kwargs,
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
@@ -65,6 +66,7 @@ def __call__(self, input: Documents) -> Embeddings:
         content_type = "application/json"
         embeddings = []
 
+        input = text_only_embeddable_check(input, "Amazon Bedrock")
         for text in input:
             input_body = {"inputText": text}
             body = json.dumps(input_body)
@@ -86,7 +88,7 @@ def name() -> str:
         return "amazon_bedrock"
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         try:
             import boto3
         except ImportError:
diff --git a/chromadb/utils/embedding_functions/chroma_langchain_embedding_function.py b/chromadb/utils/embedding_functions/chroma_langchain_embedding_function.py
index 5f3f8029d89..915e74e7f0c 100644
--- a/chromadb/utils/embedding_functions/chroma_langchain_embedding_function.py
+++ b/chromadb/utils/embedding_functions/chroma_langchain_embedding_function.py
@@ -1,12 +1,10 @@
 from chromadb.api.types import (
-    Documents,
     Embeddings,
-    Images,
     Embeddable,
     EmbeddingFunction,
 )
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
-from typing import List, Dict, Any, Union, cast, Sequence
+from typing import List, Dict, Any, cast, Sequence
 import numpy as np
 
 
@@ -100,7 +98,7 @@ def embed_image(self, uris: List[str]) -> List[List[float]]:
                 "The provided embedding function does not support image embeddings."
             )
 
-    def __call__(self, input: Union[Documents, Images]) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts or images.
 
@@ -134,7 +132,7 @@ def name() -> str:
     @staticmethod
     def build_from_config(
         config: Dict[str, Any]
-    ) -> "EmbeddingFunction[Union[Documents, Images]]":
+    ) -> "EmbeddingFunction[Embeddable]":
         # This is a placeholder implementation since we can't easily serialize and deserialize
         # langchain embedding functions. Users will need to recreate the langchain embedding function
         # and pass it to create_langchain_embedding.
diff --git a/chromadb/utils/embedding_functions/cloudflare_workers_ai_embedding_function.py b/chromadb/utils/embedding_functions/cloudflare_workers_ai_embedding_function.py
index b48cf22ebf8..71368048d63 100644
--- a/chromadb/utils/embedding_functions/cloudflare_workers_ai_embedding_function.py
+++ b/chromadb/utils/embedding_functions/cloudflare_workers_ai_embedding_function.py
@@ -1,4 +1,5 @@
 from chromadb.api.types import (
+    Embeddable,
     Embeddings,
     Documents,
     EmbeddingFunction,
@@ -6,6 +7,7 @@
 )
 from typing import List, Dict, Any, Optional
 import os
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import cast
 import warnings
@@ -14,7 +16,7 @@
 GATEWAY_BASE_URL = "https://gateway.ai.cloudflare.com/v1"
 
 
-class CloudflareWorkersAIEmbeddingFunction(EmbeddingFunction[Documents]):
+class CloudflareWorkersAIEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to get embeddings for a list of texts using the Cloudflare Workers AI API.
     It requires an API key and a model name.
@@ -70,7 +72,7 @@ def __init__(
             {"Authorization": f"Bearer {self.api_key}", "Accept-Encoding": "identity"}
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
@@ -80,10 +82,7 @@ def __call__(self, input: Documents) -> Embeddings:
         Returns:
             Embeddings for the documents.
         """
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError(
-                "Cloudflare Workers AI only supports text documents, not images"
-            )
+        input = text_only_embeddable_check(input, "Cloudflare Workers AI")
 
         payload: Dict[str, Any] = {
             "text": input,
@@ -107,7 +106,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
         account_id = config.get("account_id")
diff --git a/chromadb/utils/embedding_functions/google_embedding_function.py b/chromadb/utils/embedding_functions/google_embedding_function.py
index b08e1f5dd6f..bb96b5fc62a 100644
--- a/chromadb/utils/embedding_functions/google_embedding_function.py
+++ b/chromadb/utils/embedding_functions/google_embedding_function.py
@@ -1,13 +1,14 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
 from typing import List, Dict, Any, cast, Optional
 import os
 import numpy as np
 import numpy.typing as npt
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 import warnings
 
 
-class GooglePalmEmbeddingFunction(EmbeddingFunction[Documents]):
+class GooglePalmEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """To use this EmbeddingFunction, you must have the google.generativeai Python package installed and have a PaLM API key."""
 
     def __init__(
@@ -48,19 +49,18 @@ def __init__(
         palm.configure(api_key=self.api_key)
         self._palm = palm
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
         Args:
-            input: Documents or images to generate embeddings for.
+            input: Documents to generate embeddings for.
 
         Returns:
             Embeddings for the documents.
         """
         # Google PaLM only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Google PaLM only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Google PaLM")
 
         return [
             np.array(
@@ -83,7 +83,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
 
@@ -119,7 +119,7 @@ def validate_config(config: Dict[str, Any]) -> None:
         validate_config_schema(config, "google_palm")
 
 
-class GoogleGenerativeAiEmbeddingFunction(EmbeddingFunction[Documents]):
+class GoogleGenerativeAiEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """To use this EmbeddingFunction, you must have the google.generativeai Python package installed and have a Google API key."""
 
     def __init__(
@@ -165,21 +165,18 @@ def __init__(
         genai.configure(api_key=self.api_key)
         self._genai = genai
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
         Args:
-            input: Documents or images to generate embeddings for.
+            input: Documents to generate embeddings for.
 
         Returns:
             Embeddings for the documents.
         """
         # Google Generative AI only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError(
-                "Google Generative AI only supports text documents, not images"
-            )
+        input = text_only_embeddable_check(input, "Google Generative AI")
 
         embeddings_list: List[npt.NDArray[np.float32]] = []
         for text in input:
@@ -206,7 +203,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
         task_type = config.get("task_type")
@@ -251,7 +248,7 @@ def validate_config(config: Dict[str, Any]) -> None:
         validate_config_schema(config, "google_generative_ai")
 
 
-class GoogleVertexEmbeddingFunction(EmbeddingFunction[Documents]):
+class GoogleVertexEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """To use this EmbeddingFunction, you must have the vertexai Python package installed and have Google Cloud credentials configured."""
 
     def __init__(
@@ -301,19 +298,18 @@ def __init__(
         vertexai.init(project=project_id, location=region)
         self._model = TextEmbeddingModel.from_pretrained(model_name)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
         Args:
-            input: Documents or images to generate embeddings for.
+            input: Documents to generate embeddings for.
 
         Returns:
             Embeddings for the documents.
         """
         # Google Vertex only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Google Vertex only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Google Vertex")
 
         embeddings_list: List[npt.NDArray[np.float32]] = []
         for text in input:
@@ -336,7 +332,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
         project_id = config.get("project_id")
diff --git a/chromadb/utils/embedding_functions/huggingface_embedding_function.py b/chromadb/utils/embedding_functions/huggingface_embedding_function.py
index 193a74b0eda..3d6c5171856 100644
--- a/chromadb/utils/embedding_functions/huggingface_embedding_function.py
+++ b/chromadb/utils/embedding_functions/huggingface_embedding_function.py
@@ -1,12 +1,13 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
 from typing import List, Dict, Any, Optional
 import os
 import numpy as np
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 import warnings
 
 
-class HuggingFaceEmbeddingFunction(EmbeddingFunction[Documents]):
+class HuggingFaceEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to get embeddings for a list of texts using the HuggingFace API.
     It requires an API key and a model name. The default model name is "sentence-transformers/all-MiniLM-L6-v2".
@@ -51,12 +52,12 @@ def __init__(
         self._session = httpx.Client()
         self._session.headers.update({"Authorization": f"Bearer {self.api_key}"})
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts.
 
         Args:
-            input (Documents): A list of texts to get embeddings for.
+            input (Embeddable): A list of texts to get embeddings for.
 
         Returns:
             Embeddings: The embeddings for the texts.
@@ -66,6 +67,8 @@ def __call__(self, input: Documents) -> Embeddings:
             >>> texts = ["Hello, world!", "How are you?"]
             >>> embeddings = hugging_face(texts)
         """
+        input = text_only_embeddable_check(input, "HuggingFace")
+
         # Call HuggingFace Embedding API for each document
         response = self._session.post(
             self._api_url,
@@ -86,7 +89,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
 
@@ -122,7 +125,7 @@ def validate_config(config: Dict[str, Any]) -> None:
         validate_config_schema(config, "huggingface")
 
 
-class HuggingFaceEmbeddingServer(EmbeddingFunction[Documents]):
+class HuggingFaceEmbeddingServer(EmbeddingFunction[Embeddable]):
     """
     This class is used to get embeddings for a list of texts using the HuggingFace Embedding server
     (https://github.com/huggingface/text-embeddings-inference).
@@ -171,12 +174,12 @@ def __init__(
         if self.api_key is not None:
             self._session.headers.update({"Authorization": f"Bearer {self.api_key}"})
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts.
 
         Args:
-            input (Documents): A list of texts to get embeddings for.
+            input (Embeddable): A list of texts to get embeddings for.
 
         Returns:
             Embeddings: The embeddings for the texts.
@@ -186,6 +189,9 @@ def __call__(self, input: Documents) -> Embeddings:
             >>> texts = ["Hello, world!", "How are you?"]
             >>> embeddings = hugging_face(texts)
         """
+
+        input = text_only_embeddable_check(input, "HuggingFace Server")
+
         # Call HuggingFace Embedding Server API for each document
         response = self._session.post(self._api_url, json={"inputs": input}).json()
 
@@ -203,7 +209,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         url = config.get("url")
         api_key_env_var = config.get("api_key_env_var")
         if url is None:
diff --git a/chromadb/utils/embedding_functions/instructor_embedding_function.py b/chromadb/utils/embedding_functions/instructor_embedding_function.py
index 0d6b053c640..4b23a8f6f8d 100644
--- a/chromadb/utils/embedding_functions/instructor_embedding_function.py
+++ b/chromadb/utils/embedding_functions/instructor_embedding_function.py
@@ -1,10 +1,11 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any, Optional
 import numpy as np
 
 
-class InstructorEmbeddingFunction(EmbeddingFunction[Documents]):
+class InstructorEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to generate embeddings for a list of texts using the Instructor embedding model.
     """
@@ -41,19 +42,18 @@ def __init__(
 
         self._model = INSTRUCTOR(model_name_or_path=model_name, device=device)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
         Args:
-            input: Documents or images to generate embeddings for.
+            input: Documents to generate embeddings for.
 
         Returns:
             Embeddings for the documents.
         """
         # Instructor only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Instructor only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Instructor")
 
         if self.instruction is None:
             embeddings = self._model.encode(input, convert_to_numpy=True)
@@ -77,7 +77,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         model_name = config.get("model_name")
         device = config.get("device")
         instruction = config.get("instruction")
diff --git a/chromadb/utils/embedding_functions/jina_embedding_function.py b/chromadb/utils/embedding_functions/jina_embedding_function.py
index b627295259b..3fffbe73cca 100644
--- a/chromadb/utils/embedding_functions/jina_embedding_function.py
+++ b/chromadb/utils/embedding_functions/jina_embedding_function.py
@@ -1,4 +1,5 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any, Union, Optional
 import os
@@ -6,7 +7,7 @@
 import warnings
 
 
-class JinaEmbeddingFunction(EmbeddingFunction[Documents]):
+class JinaEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to get embeddings for a list of texts using the Jina AI API.
     It requires an API key and a model name. The default model name is "jina-embeddings-v2-base-en".
@@ -81,12 +82,12 @@ def __init__(
             {"Authorization": f"Bearer {self.api_key}", "Accept-Encoding": "identity"}
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts.
 
         Args:
-            input (Documents): A list of texts to get embeddings for.
+            input (Embeddable): A list of texts to get embeddings for.
 
         Returns:
             Embeddings: The embeddings for the texts.
@@ -96,8 +97,7 @@ def __call__(self, input: Documents) -> Embeddings:
             >>> input = ["Hello, world!", "How are you?"]
         """
         # Jina AI only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Jina AI only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Jina AI")
 
         payload: Dict[str, Any] = {
             "input": input,
@@ -150,7 +150,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
         task = config.get("task")
diff --git a/chromadb/utils/embedding_functions/mistral_embedding_function.py b/chromadb/utils/embedding_functions/mistral_embedding_function.py
index b4d67712354..177930400f4 100644
--- a/chromadb/utils/embedding_functions/mistral_embedding_function.py
+++ b/chromadb/utils/embedding_functions/mistral_embedding_function.py
@@ -1,11 +1,12 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any
 import os
 import numpy as np
 
 
-class MistralEmbeddingFunction(EmbeddingFunction[Documents]):
+class MistralEmbeddingFunction(EmbeddingFunction[Embeddable]):
     def __init__(
         self,
         model: str,
@@ -31,15 +32,14 @@ def __init__(
             raise ValueError(f"The {api_key_env_var} environment variable is not set.")
         self.client = Mistral(api_key=self.api_key)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts.
 
         Args:
-            input (Documents): A list of texts to get embeddings for.
+            input (Embeddable): A list of texts to get embeddings for.
         """
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Mistral only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Mistral")
         output = self.client.embeddings.create(
             model=self.model,
             inputs=input,
@@ -59,7 +59,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         model = config.get("model")
         api_key_env_var = config.get("api_key_env_var")
 
diff --git a/chromadb/utils/embedding_functions/morph_embedding_function.py b/chromadb/utils/embedding_functions/morph_embedding_function.py
index ac8e569714d..0ce04e7a0a6 100644
--- a/chromadb/utils/embedding_functions/morph_embedding_function.py
+++ b/chromadb/utils/embedding_functions/morph_embedding_function.py
@@ -1,12 +1,13 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, Documents, EmbeddingFunction, Space
 from typing import List, Dict, Any, Optional
 import os
 import numpy as np
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 import warnings
 
 
-class MorphEmbeddingFunction(EmbeddingFunction[Documents]):
+class MorphEmbeddingFunction(EmbeddingFunction[Embeddable]):
     def __init__(
         self,
         api_key: Optional[str] = None,
@@ -60,7 +61,7 @@ def __init__(
             base_url=self.api_base,
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
@@ -70,6 +71,8 @@ def __call__(self, input: Documents) -> Embeddings:
         Returns:
             Embeddings for the documents.
         """
+        input = text_only_embeddable_check(input, type(self).__name__)
+
         # Handle empty input
         if not input:
             return []
@@ -99,7 +102,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         # Extract parameters from config
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
@@ -144,4 +147,4 @@ def validate_config(config: Dict[str, Any]) -> None:
         Raises:
             ValidationError: If the configuration does not match the schema
         """
-        validate_config_schema(config, "morph")
\ No newline at end of file
+        validate_config_schema(config, "morph")
diff --git a/chromadb/utils/embedding_functions/ollama_embedding_function.py b/chromadb/utils/embedding_functions/ollama_embedding_function.py
index 4e0ebe3b005..7164c168e76 100644
--- a/chromadb/utils/embedding_functions/ollama_embedding_function.py
+++ b/chromadb/utils/embedding_functions/ollama_embedding_function.py
@@ -1,4 +1,5 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any
 import numpy as np
@@ -7,7 +8,7 @@
 DEFAULT_MODEL_NAME = "chroma/all-minilm-l6-v2-f32"
 
 
-class OllamaEmbeddingFunction(EmbeddingFunction[Documents]):
+class OllamaEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to generate embeddings for a list of texts using the Ollama Embedding API
     (https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings).
@@ -47,12 +48,12 @@ def __init__(
 
         self._client = Client(host=self._base_url, timeout=timeout)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Get the embeddings for a list of texts.
 
         Args:
-            input (Documents): A list of texts to get embeddings for.
+            input (Embeddable): A list of texts to get embeddings for.
 
         Returns:
             Embeddings: The embeddings for the texts.
@@ -62,6 +63,8 @@ def __call__(self, input: Documents) -> Embeddings:
             >>> texts = ["Hello, world!", "How are you?"]
             >>> embeddings = ollama_ef(texts)
         """
+        input = text_only_embeddable_check(input, "Ollama")
+
         # Call Ollama client
         response = self._client.embed(model=self.model_name, input=input)
 
@@ -82,7 +85,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         url = config.get("url")
         model_name = config.get("model_name")
         timeout = config.get("timeout")
diff --git a/chromadb/utils/embedding_functions/onnx_mini_lm_l6_v2.py b/chromadb/utils/embedding_functions/onnx_mini_lm_l6_v2.py
index 4084121cd6e..79a1994cebb 100644
--- a/chromadb/utils/embedding_functions/onnx_mini_lm_l6_v2.py
+++ b/chromadb/utils/embedding_functions/onnx_mini_lm_l6_v2.py
@@ -13,7 +13,8 @@
 import httpx
 from tenacity import retry, retry_if_exception, stop_after_attempt, wait_random
 
-from chromadb.api.types import Documents, Embeddings, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 
 logger = logging.getLogger(__name__)
@@ -34,7 +35,7 @@ def _verify_sha256(fname: str, expected_sha256: str) -> bool:
 # implements the same functionality as "all-MiniLM-L6-v2" from sentence-transformers.
 # visit https://github.com/chroma-core/onnx-embedding for the source code to generate
 # and verify the ONNX model.
-class ONNXMiniLM_L6_V2(EmbeddingFunction[Documents]):
+class ONNXMiniLM_L6_V2(EmbeddingFunction[Embeddable]):
     MODEL_NAME = "all-MiniLM-L6-v2"
     DOWNLOAD_PATH = Path.home() / ".cache" / "chroma" / "onnx_models" / MODEL_NAME
     EXTRACTED_FOLDER_NAME = "onnx"
@@ -255,7 +256,7 @@ def model(self) -> Any:
             sess_options=so,
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
@@ -266,6 +267,8 @@ def __call__(self, input: Documents) -> Embeddings:
             Embeddings for the documents.
         """
 
+        input = text_only_embeddable_check(input, type(self).__name__)
+
         # Only download the model when it is actually used
         self._download_model_if_not_exists()
 
@@ -336,7 +339,7 @@ def max_tokens(self) -> int:
         return 256
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         preferred_providers = config.get("preferred_providers")
 
         return ONNXMiniLM_L6_V2(preferred_providers=preferred_providers)
diff --git a/chromadb/utils/embedding_functions/open_clip_embedding_function.py b/chromadb/utils/embedding_functions/open_clip_embedding_function.py
index 6c6ef9cd454..ad087a76e6a 100644
--- a/chromadb/utils/embedding_functions/open_clip_embedding_function.py
+++ b/chromadb/utils/embedding_functions/open_clip_embedding_function.py
@@ -142,7 +142,7 @@ def supported_spaces(self) -> List[Space]:
     @staticmethod
     def build_from_config(
         config: Dict[str, Any]
-    ) -> "EmbeddingFunction[Union[Documents, Images]]":
+    ) -> "EmbeddingFunction[Embeddable]":
         model_name = config.get("model_name")
         checkpoint = config.get("checkpoint")
         device = config.get("device")
diff --git a/chromadb/utils/embedding_functions/openai_embedding_function.py b/chromadb/utils/embedding_functions/openai_embedding_function.py
index b34babd57ff..74df0ddba41 100644
--- a/chromadb/utils/embedding_functions/openai_embedding_function.py
+++ b/chromadb/utils/embedding_functions/openai_embedding_function.py
@@ -1,12 +1,13 @@
-from chromadb.api.types import Embeddings, Documents, EmbeddingFunction, Space
+from chromadb.api.types import Embeddable, Embeddings, EmbeddingFunction, Space
 from typing import List, Dict, Any, Optional
 import os
 import numpy as np
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 import warnings
 
 
-class OpenAIEmbeddingFunction(EmbeddingFunction[Documents]):
+class OpenAIEmbeddingFunction(EmbeddingFunction[Embeddable]):
     def __init__(
         self,
         api_key: Optional[str] = None,
@@ -102,7 +103,7 @@ def __init__(
                 default_headers=self.default_headers,
             )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
         Args:
@@ -110,6 +111,8 @@ def __call__(self, input: Documents) -> Embeddings:
         Returns:
             Embeddings for the documents.
         """
+        input = text_only_embeddable_check(input, type(self).__name__)
+
         # Handle batching
         if not input:
             return []
@@ -141,7 +144,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         # Extract parameters from config
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
diff --git a/chromadb/utils/embedding_functions/sentence_transformer_embedding_function.py b/chromadb/utils/embedding_functions/sentence_transformer_embedding_function.py
index c5b2cfb47b1..9f683a6cafb 100644
--- a/chromadb/utils/embedding_functions/sentence_transformer_embedding_function.py
+++ b/chromadb/utils/embedding_functions/sentence_transformer_embedding_function.py
@@ -1,10 +1,11 @@
-from chromadb.api.types import EmbeddingFunction, Space, Embeddings, Documents
+from chromadb.api.types import Embeddable, EmbeddingFunction, Space, Embeddings, Documents
 from typing import List, Dict, Any
 import numpy as np
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 
 
-class SentenceTransformerEmbeddingFunction(EmbeddingFunction[Documents]):
+class SentenceTransformerEmbeddingFunction(EmbeddingFunction[Embeddable]):
     # Since we do dynamic imports we have to type this as Any
     models: Dict[str, Any] = {}
 
@@ -46,7 +47,7 @@ def __init__(
             )
         self._model = self.models[model_name]
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """Generate embeddings for the given documents.
 
         Args:
@@ -55,6 +56,7 @@ def __call__(self, input: Documents) -> Embeddings:
         Returns:
             Embeddings for the documents.
         """
+        input = text_only_embeddable_check(input, "Sentence Transformers")
         embeddings = self._model.encode(
             list(input),
             convert_to_numpy=True,
@@ -75,7 +77,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         model_name = config.get("model_name")
         device = config.get("device")
         normalize_embeddings = config.get("normalize_embeddings")
diff --git a/chromadb/utils/embedding_functions/text2vec_embedding_function.py b/chromadb/utils/embedding_functions/text2vec_embedding_function.py
index 4c9838f55ac..594374165e9 100644
--- a/chromadb/utils/embedding_functions/text2vec_embedding_function.py
+++ b/chromadb/utils/embedding_functions/text2vec_embedding_function.py
@@ -1,10 +1,11 @@
-from chromadb.api.types import EmbeddingFunction, Space, Embeddings, Documents
+from chromadb.api.types import Embeddable, EmbeddingFunction, Space, Embeddings
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any
 import numpy as np
 
 
-class Text2VecEmbeddingFunction(EmbeddingFunction[Documents]):
+class Text2VecEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to generate embeddings for a list of texts using the Text2Vec model.
     """
@@ -27,19 +28,18 @@ def __init__(self, model_name: str = "shibing624/text2vec-base-chinese"):
         self.model_name = model_name
         self._model = SentenceModel(model_name_or_path=model_name)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
         Args:
-            input: Documents or images to generate embeddings for.
+            input: Documents to generate embeddings for.
 
         Returns:
             Embeddings for the documents.
         """
         # Text2Vec only works with text documents
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("Text2Vec only supports text documents, not images")
+        input = text_only_embeddable_check(input, "Text2Vec")
 
         embeddings = self._model.encode(list(input), convert_to_numpy=True)
 
@@ -57,7 +57,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         model_name = config.get("model_name")
 
         if model_name is None:
diff --git a/chromadb/utils/embedding_functions/together_ai_embedding_function.py b/chromadb/utils/embedding_functions/together_ai_embedding_function.py
index d258ca22a64..d69c08074d1 100644
--- a/chromadb/utils/embedding_functions/together_ai_embedding_function.py
+++ b/chromadb/utils/embedding_functions/together_ai_embedding_function.py
@@ -1,11 +1,12 @@
 from chromadb.api.types import (
+    Embeddable,
     Embeddings,
-    Documents,
     EmbeddingFunction,
     Space,
 )
 from typing import List, Dict, Any, Optional
 import os
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import cast
 import warnings
@@ -13,7 +14,7 @@
 ENDPOINT = "https://api.together.xyz/v1/embeddings"
 
 
-class TogetherAIEmbeddingFunction(EmbeddingFunction[Documents]):
+class TogetherAIEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to get embeddings for a list of texts using the Together AI API.
     """
@@ -68,7 +69,7 @@ def __init__(
             }
         )
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Embed a list of texts using the Together AI API.
 
@@ -82,8 +83,7 @@ def __call__(self, input: Documents) -> Embeddings:
         if not isinstance(input, list):
             raise ValueError("Input must be a list")
 
-        if not all(isinstance(item, str) for item in input):
-            raise ValueError("All items in input must be strings")
+        input = text_only_embeddable_check(input, "Together AI")
 
         response = self._session.post(
             ENDPOINT,
@@ -109,7 +109,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
 
diff --git a/chromadb/utils/embedding_functions/voyageai_embedding_function.py b/chromadb/utils/embedding_functions/voyageai_embedding_function.py
index 94a7051e46e..80f789fd3f4 100644
--- a/chromadb/utils/embedding_functions/voyageai_embedding_function.py
+++ b/chromadb/utils/embedding_functions/voyageai_embedding_function.py
@@ -1,4 +1,5 @@
-from chromadb.api.types import EmbeddingFunction, Space, Embeddings, Documents
+from chromadb.api.types import Embeddable, EmbeddingFunction, Space, Embeddings
+from chromadb.utils import text_only_embeddable_check
 from chromadb.utils.embedding_functions.schemas import validate_config_schema
 from typing import List, Dict, Any, Optional
 import os
@@ -6,7 +7,7 @@
 import warnings
 
 
-class VoyageAIEmbeddingFunction(EmbeddingFunction[Documents]):
+class VoyageAIEmbeddingFunction(EmbeddingFunction[Embeddable]):
     """
     This class is used to generate embeddings for a list of texts using the VoyageAI API.
     """
@@ -57,7 +58,7 @@ def __init__(
         self.truncation = truncation
         self._client = voyageai.Client(api_key=self.api_key)
 
-    def __call__(self, input: Documents) -> Embeddings:
+    def __call__(self, input: Embeddable) -> Embeddings:
         """
         Generate embeddings for the given documents.
 
@@ -67,6 +68,7 @@ def __call__(self, input: Documents) -> Embeddings:
         Returns:
             Embeddings for the documents.
         """
+        input = text_only_embeddable_check(input, "VoyageAI")
         embeddings = self._client.embed(
             texts=input,
             model=self.model_name,
@@ -90,7 +92,7 @@ def supported_spaces(self) -> List[Space]:
         return ["cosine", "l2", "ip"]
 
     @staticmethod
-    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Documents]":
+    def build_from_config(config: Dict[str, Any]) -> "EmbeddingFunction[Embeddable]":
         api_key_env_var = config.get("api_key_env_var")
         model_name = config.get("model_name")
         input_type = config.get("input_type")