From 07cb41ea9e532c1c9af7fb589119d255204db0b1 Mon Sep 17 00:00:00 2001
From: Florian Chappaz <41546602+florianchappaz@users.noreply.github.com>
Date: Wed, 19 Mar 2025 14:07:28 +0100
Subject: [PATCH] community: aligning ChatLiteLLM default parameters with
 litellm (#30360)

**Description:**
Since `ChatLiteLLM` is forwarding most parameters to
`litellm.completion(...)`, there is no reason to set other default
values than the ones defined by `litellm`.

In the case of parameter 'n', it also provokes an issue when trying to
call a serverless endpoint on Azure, as it is considered an extra
parameter. So we need to keep it optional.

We can debate about backward compatibility of this change: in my
opinion, there should not be big issues since from my experience,
calling `litellm.completion()` without these parameters works fine.

**Issue:**
- #29679

**Dependencies:** None
---
 libs/community/langchain_community/chat_models/litellm.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/libs/community/langchain_community/chat_models/litellm.py b/libs/community/langchain_community/chat_models/litellm.py
index 818988e31af..9b12f0c2095 100644
--- a/libs/community/langchain_community/chat_models/litellm.py
+++ b/libs/community/langchain_community/chat_models/litellm.py
@@ -259,7 +259,7 @@ class ChatLiteLLM(BaseChatModel):
     organization: Optional[str] = None
     custom_llm_provider: Optional[str] = None
     request_timeout: Optional[Union[float, Tuple[float, float]]] = None
-    temperature: Optional[float] = 1
+    temperature: Optional[float] = None
     """Run inference with this temperature. Must be in the closed
        interval [0.0, 1.0]."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
@@ -270,12 +270,12 @@ class ChatLiteLLM(BaseChatModel):
     top_k: Optional[int] = None
     """Decode using top-k sampling: consider the set of top_k most probable tokens.
        Must be positive."""
-    n: int = 1
+    n: Optional[int] = None
     """Number of chat completions to generate for each prompt. Note that the API may
        not return the full n completions if duplicates are generated."""
     max_tokens: Optional[int] = None
 
-    max_retries: int = 6
+    max_retries: int = 1
 
     @property
     def _default_params(self) -> Dict[str, Any]: