From b150a0e504d6d38563903d539b2bd1091e702194 Mon Sep 17 00:00:00 2001
From: Harrison Chase <hw.chase.17@gmail.com>
Date: Thu, 24 Nov 2022 06:43:24 -0800
Subject: [PATCH] flexible model args

---
 langchain/llms/openai.py | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/langchain/llms/openai.py b/langchain/llms/openai.py
index 2affb86d..8bd857c0 100644
--- a/langchain/llms/openai.py
+++ b/langchain/llms/openai.py
@@ -1,7 +1,7 @@
 """Wrapper around OpenAI APIs."""
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.llms.base import LLM
 from langchain.utils import get_from_dict_or_env
@@ -37,6 +37,7 @@ class OpenAI(LLM, BaseModel):
     """How many completions to generate for each prompt."""
     best_of: int = 1
     """Generates best_of completions server-side and returns the "best"."""
+    model_kwargs: dict = Field(default_factory=dict)
 
     openai_api_key: Optional[str] = None
 
@@ -63,10 +64,29 @@ class OpenAI(LLM, BaseModel):
             )
         return values
 
+    @root_validator()
+    def validate_model_kwargs(cls, values: Dict) -> Dict:
+        named_params = {
+            "temperature",
+            "max_tokens",
+            "top_p",
+            "frequency_penalty",
+            "presence_penalty",
+            "n",
+            "best_of",
+        }
+        overlap = named_params.intersection(values["model_kwargs"])
+        if overlap:
+            raise ValueError(
+                "Found named params in model_kwargs, "
+                f"should be specified separately: {overlap}"
+            )
+        return values
+
     @property
     def _default_params(self) -> Mapping[str, Any]:
         """Get the default parameters for calling OpenAI API."""
-        return {
+        named_params = {
             "temperature": self.temperature,
             "max_tokens": self.max_tokens,
             "top_p": self.top_p,
@@ -75,6 +95,7 @@ class OpenAI(LLM, BaseModel):
             "n": self.n,
             "best_of": self.best_of,
         }
+        return {**named_params, **self.model_kwargs}
 
     @property
     def _identifying_params(self) -> Mapping[str, Any]: