From b3bca98561f7573c31bdd7ba2f3e4bced23175fb Mon Sep 17 00:00:00 2001
From: ishaan-jaff <ishaanjaffer0324@gmail.com>
Date: Wed, 22 Nov 2023 14:03:41 -0800
Subject: [PATCH] (feat) embedding() remove junk params

---
 litellm/main.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/litellm/main.py b/litellm/main.py
index 8dbdb820c..5b29e62a2 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1672,11 +1672,7 @@ def embedding(
     model, 
     input=[], 
     # Optional params
-    azure=False, 
-    force_timeout=60, 
-    litellm_call_id=None, 
-    litellm_logging_obj=None,
-    logger_fn=None, 
+    timeout=600, # default to 10 minutes 
     # set api_base, api_version, api_key
     api_base: Optional[str] = None,
     api_version: Optional[str] = None,
@@ -1684,6 +1680,9 @@ def embedding(
     api_type: Optional[str] = None,
     caching: bool=False,
     custom_llm_provider=None,
+    litellm_call_id=None, 
+    litellm_logging_obj=None,
+    logger_fn=None, 
     **kwargs
 ):
     """
@@ -1692,8 +1691,7 @@ def embedding(
     Parameters:
     - model: The embedding model to use.
     - input: The input for which embeddings are to be generated.
-    - azure: A boolean indicating whether to use the Azure API for embedding.
-    - force_timeout: The timeout value for the API call.
+    - timeout: The timeout value for the API call, default 10 mins
     - litellm_call_id: The call ID for litellm logging.
     - litellm_logging_obj: The litellm logging object.
     - logger_fn: The logger function.
@@ -1710,11 +1708,12 @@ def embedding(
     Raises:
     - exception_type: If an exception occurs during the API call.
     """
+    azure = kwargs.get("azure", None)
     model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base)
     try:
         response = None
         logging = litellm_logging_obj
-        logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"force_timeout": force_timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
+        logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
         if azure == True or custom_llm_provider == "azure":
             # azure configs
             api_type = get_secret("AZURE_API_TYPE") or "azure"
@@ -1751,8 +1750,9 @@ def embedding(
                 api_version=api_version,
                 azure_ad_token=azure_ad_token,
                 logging_obj=logging,
+                timeout=timeout,
                 model_response=EmbeddingResponse(), 
-                optional_params=kwargs
+                optional_params=kwargs,
             )
         elif model in litellm.open_ai_embedding_models:
             api_base = (
@@ -1784,6 +1784,7 @@ def embedding(
                 api_base=api_base,
                 api_key=api_key,
                 logging_obj=logging,
+                timeout=timeout,
                 model_response=EmbeddingResponse(), 
                 optional_params=kwargs
             )