diff --git a/docs/docs/Components/components-embedding-models.mdx b/docs/docs/Components/components-embedding-models.mdx
index 49f49d435..f3c9525c6 100644
--- a/docs/docs/Components/components-embedding-models.mdx
+++ b/docs/docs/Components/components-embedding-models.mdx
@@ -8,9 +8,7 @@ import Icon from "@site/src/components/icon";
 **Embedding Model** components in Langflow generate text embeddings using a specified Large Language Model (LLM).
 
 Langflow includes an **Embedding Model** core component that has built-in support for some LLMs.
-Alternatively, you can use any [additional **Embedding Model** component](#additional-embedding-model-components) in place of the core **Embedding Model** component.
-
-The built-in LLMs are appropriate for most text-based embedding model use cases in Langflow.
+Alternatively, you can use [additional embedding models](#additional-embedding-model-components) in place of the core **Embedding Model** component.
 
 ## Use Embedding Model components in a flow
 
@@ -21,22 +19,16 @@ This flow loads a text file, splits the text into chunks, generates embeddings f
 
 ![A semantic search flow that uses Embedding Model, File, Split Text, Chroma DB, Chat Input, and Chat Output components](/img/component-embedding-models-add-chat.png)
 
-:::tip
-This example uses the **Embedding Model** core component.
-
-To use another model, you can replace the **Embedding Model** core component with any [additional **Embedding Model** component](#additional-embedding-model-components) in these steps.
-However, your component might have different parameters than the **Embedding Model** core component.
-:::
-
 1. Create a flow, add a **File** component, and then select a file containing text data, such as a PDF, that you can use to test the flow.
 
 2. Add an **Embedding Model** component, and then provide a valid OpenAI API key.
+You can enter component API keys directly or use Langflow global variables to reference your API keys.
 
-    By default, the **Embedding Model** component uses an OpenAI model.
-    If you want to use a different model, edit the **Model Name**, and **API Key** fields accordingly.
-    Or, see [Additional Embedding Model components](#additional-embedding-model-components) for other components that you can use in place of the **Embedding Model** core component.
+    :::tip
+    If your preferred embedding model provider or model isn't supported by the **Embedding Model** core component, you can use [additional embedding models](#additional-embedding-model-components) in place of the core component.
 
-    You can enter component API keys directly or use Langflow global variables to reference your API keys.
+    Search the **Components** menu for your preferred provider to find additional embedding models, such as the [**Hugging Face Embeddings Inference** component](/bundles-huggingface#hugging-face-embeddings-inference).
+    :::
 
 3. Add a [**Split Text** component](/components-processing#split-text) to your flow.
 This component splits text input into smaller chunks to be processed into embeddings.
@@ -76,9 +68,9 @@ You can toggle parameters through the <Icon name="SlidersHorizontal" aria-hidden
 | model_kwargs | Model Kwargs | Dictionary | Input parameter. Additional keyword arguments to pass to the model. |
 | embeddings | Embeddings | Embeddings | Output parameter. An instance for generating embeddings using the selected provider. |
 
-## Additional Embedding Model components
+## Additional embedding models {#additional-embedding-model-components}
 
-If your provider or model isn't supported by the **Embedding Model** core component, additional single-provider **Embedding Model** components are available in the [**Bundles**](/components-bundle-components) section of the **Components** menu.
+If your provider or model isn't supported by the **Embedding Model** core component, additional provider-specific **Embedding Model** components are available in the [**Bundles**](/components-bundle-components) section of the **Components** menu.
 
 ## Legacy embedding components
 
diff --git a/docs/docs/Components/components-models.mdx b/docs/docs/Components/components-models.mdx
index dcad38225..24cffa26b 100644
--- a/docs/docs/Components/components-models.mdx
+++ b/docs/docs/Components/components-models.mdx
@@ -7,8 +7,8 @@ import Icon from "@site/src/components/icon";
 
 **Language Model** components in Langflow generate text using a specified Large Language Model (LLM).
 
-Langflow includes a **Language Model** core component that has built-in support for many LLMs, as well as an interface to connect any [additional **Language Model** component](#additional-language-model-components).
-The built-in LLMs are appropriate for most text-based language model use cases in Langflow.
+Langflow includes a **Language Model** core component that has built-in support for many LLMs.
+Alternatively, you can use any [additional language model](#additional-language-models) in place of the core **Language Model** component.
 
 ## Use Language Model components in a flow
 
@@ -18,19 +18,20 @@ These components accept inputs like chat messages, files, and instructions in or
 The flow must include [**Chat Input and Output** components](/components-io#chat-io) to allow chat-based interactions with the LLM.
 However, you can also use the **Language Model** component for actions that don't emit chat output directly, such as the **Smart Function** component.
 
-The following example uses the **Language Model** core component and a built-in LLM to create a chatbot flow similar to the **Basic Prompting** template.
-The example focuses on using the built-in models, but it also indicates where you can integrate another model.
+The following example uses the **Language Model** core component to create a chatbot flow similar to the **Basic Prompting** template.
+It also explains how you can replace the core component with another LLM.
 
 1. Add the **Language Model** component to your flow.
 
 2. In the **OpenAI API Key** field, enter your OpenAI API key.
 
     This example uses the default OpenAI model and a built-in Anthropic model to compare responses from different providers.
-
     If you want to use a different provider, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
 
-    If you want to use provider or model that isn't built-in to the **Language Model** core component, see [Additional Language Model components](#additional-language-model-components) to learn how to connect a **Custom** model provider to the **Language Model** component.
+    :::tip My preferred provider or model isn't listed
+    If you want to use a provider or model that isn't built-in to the **Language Model** core component, you can replace this component with another compatible component, as explained in [Additional language models](#additional-language-models).
     Then, you can continue following these steps to build your flow.
+    :::
 
 3. In the [component's header menu](/concepts-components#component-menus), click <Icon name="SlidersHorizontal" aria-hidden="true"/> **Controls**, enable the **System Message** parameter, and then click **Close**.
 
@@ -118,24 +119,27 @@ This is a specific data type that is only required by certain components, such a
     With this configuration, the **Language Model** component is meant to support an action completed by another component, rather than producing a text response for a standard chat-based interaction.
     For an example, the **Smart Function** component uses an LLM to create a function from natural language input.
 
-## Additional Language Model components
+## Additional language models
 
-If your provider or model isn't supported by the **Language Model** core component, additional single-provider **Language Model** components are available in the [**Bundles**](/components-bundle-components) section of the **Components** menu.
+If your provider or model isn't supported by the **Language Model** core component, additional provider-specific models are available in the [**Bundles**](/components-bundle-components) section of the **Components** menu.
 
-You can use bundled components directly in your flows or you can connect them to other components that accept a [`LanguageModel`](/data-types#languagemodel) input, such as the **Language Model** and **Agent** components.
+You can use these provider-specific components directly in your flows in the same place that you would use the **Language Model** core component.
+Or, you can connect them to other components that accept a [`LanguageModel`](/data-types#languagemodel) input, such as the **Smart Function** and **Agent** components.
 
-For example, to connect bundled components to the **Language Model** core component, do the following:
+For example, to connect a provider-specific component to the **Agent** component, do the following:
 
-1. In the **Language Model** component, set **Model Provider** to **Custom**.
+1. In the **Components** menu, search for your preferred model provider, and then add the provider's LLM component to your flow.
+The component may not have `model` in the name.
+For example, Azure OpenAI LLMs are in the [**Azure OpenAI** component](/bundles-azure#azure-openai).
 
-    The field name changes to **Language Model** and the input port changes to a `LanguageModel` port.
+2. Configure the LLM component as needed to connect to your preferred model.
 
-2. Add a compatible bundled component to your flow, such as the [**Vertex AI** component for text generation](/bundles-vertexai).
-
-3. Change the bundled component's output type to `LanguageModel`.
-To do this, click **Model Response** near the component's output port, and then select **Language Model**.
+3. Change the LLM component's output type from **Model Response** to **Language Model**.
+The output port changes to a `LanguageModel` port.
 For more information, see [Language Model output types](#language-model-output-types).
 
-4. Connect the bundled component's output to the **Language Model** component's `LanguageModel` input port.
+2. Add an **Agent** component to the flow, and then set **Model Provider** to **Custom**.
+The **Model Provider** field changes to a **Language Model** field with a `LanguageModel` port.
 
-    The bundled component now provides the LLM configuration for the component that it is connected to, and you can continue building your flow as needed.
\ No newline at end of file
+4. Connect the LLM component's output to the **Agent** component's **Language Model** input.
+The **Agent** component now inherits the LLM settings from the connected LLM component instead of using any of the built-in models.
\ No newline at end of file
diff --git a/docs/docs/Support/release-notes.mdx b/docs/docs/Support/release-notes.mdx
index a23ffc08a..8faed6160 100644
--- a/docs/docs/Support/release-notes.mdx
+++ b/docs/docs/Support/release-notes.mdx
@@ -61,7 +61,7 @@ For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/rel
     The [**Language Model** component](/components-models) and [**Embedding Model** component](/components-embedding-models) are now core components for your LLM and embeddings flows. They support multiple models and model providers, and allow you to experiment with different models without swapping out single-provider components.
     Find them in the **Components** menu in the **Models** category.
 
-    The single-provider components are still available for your flows in the **Components** menu in the [**Bundles**](/components-bundle-components) section, and you can connect them to the **Language Model** and **Embedding Model** components with the **Custom** provider option.
+    The single-provider components are still available for your flows in the **Components** menu in the [**Bundles**](/components-bundle-components) section, and you can use them to replace the **Language Model** and **Embedding Model** core components, or connect them to the **Agent** component with the **Custom** provider option.
 
 - MCP server one-click installation