diff --git a/examples/apps/documents_chat.py b/examples/apps/documents_chat.py
index 8f7f24a4..576d6e37 100644
--- a/examples/apps/documents_chat.py
+++ b/examples/apps/documents_chat.py
@@ -125,7 +125,9 @@ async def _handle_message(
         if not self._documents_ingested:
             yield self.NO_DOCUMENTS_INGESTED_MESSAGE
         results = await self.document_search.search(message[-1])
-        prompt = RAGPrompt(QueryWithContext(query=message, context=[i.text_representation for i in results]))
+        prompt = RAGPrompt(
+            QueryWithContext(query=message, context=[i.text_representation for i in results if i.text_representation])
+        )
         response = await self._llm.generate(prompt)
         yield response.answer
 
diff --git a/packages/ragbits-core/src/ragbits/core/embeddings/__init__.py b/packages/ragbits-core/src/ragbits/core/embeddings/__init__.py
index 15e47529..5ad6c218 100644
--- a/packages/ragbits-core/src/ragbits/core/embeddings/__init__.py
+++ b/packages/ragbits-core/src/ragbits/core/embeddings/__init__.py
@@ -2,10 +2,10 @@
 
 from ragbits.core.utils.config_handling import get_cls_from_config
 
-from .base import Embeddings
+from .base import Embeddings, EmbeddingType
 from .noop import NoopEmbeddings
 
-__all__ = ["Embeddings", "NoopEmbeddings"]
+__all__ = ["EmbeddingType", "Embeddings", "NoopEmbeddings"]
 
 module = sys.modules[__name__]
 
diff --git a/packages/ragbits-core/src/ragbits/core/embeddings/base.py b/packages/ragbits-core/src/ragbits/core/embeddings/base.py
index 66c2716d..e03087b6 100644
--- a/packages/ragbits-core/src/ragbits/core/embeddings/base.py
+++ b/packages/ragbits-core/src/ragbits/core/embeddings/base.py
@@ -1,4 +1,20 @@
 from abc import ABC, abstractmethod
+from enum import Enum
+
+
+class EmbeddingType(Enum):
+    """
+    Indicates the type of embedding in regard to what kind of data has been embedded.
+
+    Used to specify the embedding type for a given element. Unlike `Element` type,
+    which categorizes the element itself, `EmbeddingType` determines how the
+    element's data is represented. For example, an image element can support
+    multiple embedding types, such as a description, OCR output, or raw bytes,
+    allowing for the creation of different embeddings for the same element.
+    """
+
+    TEXT: str = "text"
+    IMAGE: str = "image"
 
 
 class Embeddings(ABC):
diff --git a/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py b/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
index 2c167330..9896d6a9 100644
--- a/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
+++ b/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
@@ -3,6 +3,7 @@
 import pytest
 from pydantic import computed_field
 
+from ragbits.core.embeddings import EmbeddingType
 from ragbits.core.vector_stores.base import VectorStoreOptions
 from ragbits.core.vector_stores.in_memory import InMemoryVectorStore
 from ragbits.document_search.documents.document import DocumentMeta, DocumentType
@@ -48,7 +49,9 @@ async def store_fixture() -> InMemoryVectorStore:
         ),
     ]
 
-    entries = [element[0].to_vector_db_entry(vector=element[1]) for element in elements]
+    entries = [
+        element[0].to_vector_db_entry(vector=element[1], embedding_type=EmbeddingType.TEXT) for element in elements
+    ]
 
     store = InMemoryVectorStore()
     await store.store(entries)
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/_main.py b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
index 706e1e86..db432eba 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/_main.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
@@ -5,7 +5,7 @@
 from pydantic import BaseModel, Field
 
 from ragbits.core.audit import traceable
-from ragbits.core.embeddings import Embeddings, get_embeddings
+from ragbits.core.embeddings import Embeddings, EmbeddingType, get_embeddings
 from ragbits.core.vector_stores import VectorStore, get_vector_store
 from ragbits.core.vector_stores.base import VectorStoreOptions
 from ragbits.document_search.documents.document import Document, DocumentMeta
@@ -150,23 +150,31 @@ async def insert_elements(self, elements: list[Element]) -> None:
         Args:
             elements: The list of Elements to insert.
         """
-        vectors = await self.embedder.embed_text([element.key for element in elements])
+        elements_with_text = [element for element in elements if element.key]
+        images_with_text = [element for element in elements_with_text if isinstance(element, ImageElement)]
+        vectors = await self.embedder.embed_text([element.key for element in elements_with_text])  # type: ignore
 
         image_elements = [element for element in elements if isinstance(element, ImageElement)]
-        entries = [element.to_vector_db_entry(vector) for element, vector in zip(elements, vectors, strict=False)]
+
+        entries = [
+            element.to_vector_db_entry(vector, EmbeddingType.TEXT)
+            for element, vector in zip(elements_with_text, vectors, strict=False)
+        ]
+        not_embedded_image_elements = [
+            image_element for image_element in image_elements if image_element not in images_with_text
+        ]
 
         if image_elements and self.embedder.image_support():
             image_vectors = await self.embedder.embed_image([element.image_bytes for element in image_elements])
             entries.extend(
                 [
-                    element.to_vector_db_entry(vector)
+                    element.to_vector_db_entry(vector, EmbeddingType.IMAGE)
                     for element, vector in zip(image_elements, image_vectors, strict=False)
                 ]
             )
-        elif image_elements:
-            warnings.warn(
-                f"Image elements are not supported by the embedder {self.embedder}. "
-                f"Skipping {len(image_elements)} image elements."
-            )
+            not_embedded_image_elements = []
+
+        for image_element in not_embedded_image_elements:
+            warnings.warn(f"Image: {image_element.id} could not be embedded")
 
         await self.vector_store.store(entries)
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py b/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
index 63bf82bd..d049d349 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
@@ -1,9 +1,11 @@
+import hashlib
 import uuid
 from abc import ABC, abstractmethod
 from typing import Any, ClassVar
 
 from pydantic import BaseModel, computed_field
 
+from ragbits.core.embeddings import EmbeddingType
 from ragbits.core.vector_stores.base import VectorStoreEntry
 from ragbits.document_search.documents.document import DocumentMeta
 
@@ -28,29 +30,37 @@ class Element(BaseModel, ABC):
 
     _elements_registry: ClassVar[dict[str, type["Element"]]] = {}
 
-    @computed_field  # type: ignore[prop-decorator]
+    # type: ignore[prop-decorator]
     @property
     def id(self) -> str:
         """
-        Get the ID of the element. The id is primarly used as a key in the vector store.
-        The current representation is a UUID5 hash of various element metadata, including
-        its contents and location where it was sourced from.
+        Retrieve the ID of the element, primarily used to represent the element's data.
 
         Returns:
-            The ID in the form of a UUID5 hash.
+            str: string representing element
         """
-        id_components = [
-            self.document_meta.id,
-            self.element_type,
-            self.key,
-            self.text_representation,
-            str(self.location),
-        ]
-        return str(uuid.uuid5(uuid.NAMESPACE_OID, ";".join(id_components)))
+        id_components = self.get_id_components()
+        return "&".join(f"{k}={v}" for k, v in id_components.items())
+
+    def get_id_components(self) -> dict[str, str]:
+        """
+        Creates a dictionary of key value pairs of id components
+
+        Returns:
+            dict: a dictionary
+        """
+        id_components = {
+            "meta": self.document_meta.id,
+            "type": self.element_type,
+            "key": str(self.key),
+            "text": str(self.text_representation),
+            "location": str(self.location),
+        }
+        return id_components
 
     @computed_field  # type: ignore[prop-decorator]
     @property
-    def key(self) -> str:
+    def key(self) -> str | None:
         """
         Get the representation of the element for embedding.
 
@@ -62,7 +72,7 @@ def key(self) -> str:
     @computed_field  # type: ignore[prop-decorator]
     @property
     @abstractmethod
-    def text_representation(self) -> str:
+    def text_representation(self) -> str | None:
         """
         Get the text representation of the element.
 
@@ -90,24 +100,28 @@ def from_vector_db_entry(cls, db_entry: VectorStoreEntry) -> "Element":
         """
         element_type = db_entry.metadata["element_type"]
         element_cls = Element._elements_registry[element_type]
+        if "embedding_type" in db_entry.metadata:
+            del db_entry.metadata["embedding_type"]
         return element_cls(**db_entry.metadata)
 
-    def to_vector_db_entry(self, vector: list[float]) -> VectorStoreEntry:
+    def to_vector_db_entry(self, vector: list[float], embedding_type: EmbeddingType) -> VectorStoreEntry:
         """
         Create a vector database entry from the element.
 
         Args:
             vector: The vector.
-
+            embedding_type: EmbeddingTypes
         Returns:
             The vector database entry
         """
-        return VectorStoreEntry(
-            id=self.id,
-            key=self.key,
-            vector=vector,
-            metadata=self.model_dump(exclude={"id", "key"}),
-        )
+        id_components = [
+            self.id,
+            str(embedding_type),
+        ]
+        vector_store_entry_id = str(uuid.uuid5(uuid.NAMESPACE_OID, ";".join(id_components)))
+        metadata = self.model_dump(exclude={"id", "key"})
+        metadata["embedding_type"] = str(embedding_type)
+        return VectorStoreEntry(id=vector_store_entry_id, key=str(self.key), vector=vector, metadata=metadata)
 
 
 class TextElement(Element):
@@ -142,11 +156,29 @@ class ImageElement(Element):
 
     @computed_field  # type: ignore[prop-decorator]
     @property
-    def text_representation(self) -> str:
+    def text_representation(self) -> str | None:
         """
         Get the text representation of the element.
 
         Returns:
             The text representation.
         """
-        return f"Description: {self.description}\nExtracted text: {self.ocr_extracted_text}"
+        if not self.description and not self.ocr_extracted_text:
+            return None
+        repr = ""
+        if self.description:
+            repr += f"Description: {self.description}\n"
+        if self.ocr_extracted_text:
+            repr += f"Extracted text: {self.ocr_extracted_text}"
+        return repr
+
+    def get_id_components(self) -> dict[str, str]:
+        """
+        Creates a dictionary of key value pairs of id components
+
+        Returns:
+            dict: a dictionary
+        """
+        id_components = super().get_id_components()
+        id_components["image_hash"] = hashlib.sha256(self.image_bytes).hexdigest()
+        return id_components