Doc : Nvidia embedding models

docs/my-website/docs/embedding/supported_embedding.md

@@ -310,9 +310,25 @@ import os
 os.environ['NVIDIA_NIM_API_KEY'] = ""
 response = embedding(
     model='nvidia_nim/<model_name>', 
-    input=["good morning from litellm"]
+    input=["good morning from litellm"],
+    input_type="query"
 )
 ```
+## `input_type` Parameter for Embedding Models
+
+Certain embedding models, such as `nvidia/embed-qa-4` and the E5 family, operate in **dual modes**—one for **indexing documents (passages)** and another for **querying**. To maintain high retrieval accuracy, it's essential to specify how the input text is being used by setting the `input_type` parameter correctly.
+
+### Usage
+
+Set the `input_type` parameter to one of the following values:
+
+- `"passage"` – for embedding content during **indexing** (e.g., documents).
+- `"query"` – for embedding content during **retrieval** (e.g., user queries).
+
+> **Warning:** Incorrect usage of `input_type` can lead to a significant drop in retrieval performance.
+
+
+
 All models listed [here](https://build.nvidia.com/explore/retrieval) are supported:
 
 | Model Name         | Function Call                                         |
@@ -327,6 +343,7 @@ All models listed [here](https://build.nvidia.com/explore/retrieval) are support
 | snowflake/arctic-embed-l | `embedding(model="nvidia_nim/snowflake/arctic-embed-l", input)` |
 | baai/bge-m3 | `embedding(model="nvidia_nim/baai/bge-m3", input)` |
 
+
 ## HuggingFace Embedding Models
 LiteLLM supports all Feature-Extraction + Sentence Similarity Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction