docs: watsonx embeddings component (#7621)

* initial-content

* docs: Update IBM watsonx embeddings component

* add-back-text-embedder

* explanation-of-flow

* Apply suggestions from code review

Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>

---------

Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>

docs/docs/Components/components-embedding-models.md

@@ -239,6 +239,51 @@ There are two embeddings models in this flow that you can replace with **Hugging
 4. In the **Hugging Face** components, set the **Inference Endpoint** field to the URL of your local inference model. **The **API Key** field is not required for local inference.**
 5. Run the flow. The local inference models generate embeddings for the input text.
 
+## IBM watsonx embeddings
+
+This component generates text using [IBM watsonx.ai](https://www.ibm.com/watsonx) foundation models.
+
+To use **IBM watsonx.ai** embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow.
+
+An example document processing flow looks like the following:
+
+![IBM watsonx embeddings model loading a chroma-db with split text](/img/component-watsonx-embeddings-chroma.png)
+
+This flow loads a PDF file from local storage and splits the text into chunks.
+
+The **IBM watsonx** embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store.
+
+The values for **API endpoint**, **Project ID**, **API key**, and **Model Name** are found in your IBM watsonx.ai deployment.
+For more information, see the [Langchain documentation](https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/).
+
+### Default models
+
+The component supports several default models with the following vector dimensions:
+
+- `sentence-transformers/all-minilm-l12-v2`: 384-dimensional embeddings
+- `ibm/slate-125m-english-rtrvr-v2`: 768-dimensional embeddings
+- `ibm/slate-30m-english-rtrvr-v2`: 768-dimensional embeddings
+- `intfloat/multilingual-e5-large`: 1024-dimensional embeddings
+
+The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials.
+
+### Inputs
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| url | watsonx API Endpoint | The base URL of the API.|
+| project_id | watsonx project id | The project ID for your watsonx.ai instance. |
+| api_key | API Key | The API Key to use for the model.|
+| model_name | Model Name | The name of the embedding model to use.|
+| truncate_input_tokens | Truncate Input Tokens | The maximum number of tokens to process. Default: `200`. |
+| input_text | Include the original text in the output | Determines if the original text is included in the output. Default: `True`. |
+
+### Outputs
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| embeddings | Embeddings | An instance for generating embeddings using watsonx.ai |
+
 ## LM Studio Embeddings
 
 This component generates embeddings using [LM Studio](https://lmstudio.ai/docs) models.
@@ -258,7 +303,6 @@ This component generates embeddings using [LM Studio](https://lmstudio.ai/docs)
 |------|--------------|------|
 | embeddings | Embeddings | The generated embeddings |
 
-
 ## MistralAI
 
 This component generates embeddings using [MistralAI](https://docs.mistral.ai/) models.