add llm kwargs

libs/langchain/langchain/chat_models/base.py

@@ -419,7 +419,9 @@ class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
         llm_kwargs = prompts[0].llm_kwargs
         for prompt in prompts:
             if prompt.llm_kwargs != llm_kwargs:
-                raise ValueError("All prompt kwargs must be the same when calling in batch")
+                raise ValueError(
+                    "All prompt kwargs must be the same when calling in batch"
+                )
         kwargs = {**llm_kwargs, **kwargs}
         prompt_messages = [p.to_messages() for p in prompts]
         return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
@@ -434,7 +436,9 @@ class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
         llm_kwargs = prompts[0].llm_kwargs
         for prompt in prompts:
             if prompt.llm_kwargs != llm_kwargs:
-                raise ValueError("All prompt kwargs must be the same when calling in batch")
+                raise ValueError(
+                    "All prompt kwargs must be the same when calling in batch"
+                )
         kwargs = {**llm_kwargs, **kwargs}
         prompt_messages = [p.to_messages() for p in prompts]
         return await self.agenerate(

libs/langchain/langchain/llms/base.py

@@ -470,7 +470,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         llm_kwargs = prompts[0].llm_kwargs
         for prompt in prompts:
             if prompt.llm_kwargs != llm_kwargs:
-                raise ValueError("All prompt kwargs must be the same when calling in batch")
+                raise ValueError(
+                    "All prompt kwargs must be the same when calling in batch"
+                )
         kwargs = {**llm_kwargs, **kwargs}
         prompt_strings = [p.to_string() for p in prompts]
         return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
@@ -485,7 +487,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         llm_kwargs = prompts[0].llm_kwargs
         for prompt in prompts:
             if prompt.llm_kwargs != llm_kwargs:
-                raise ValueError("All prompt kwargs must be the same when calling in batch")
+                raise ValueError(
+                    "All prompt kwargs must be the same when calling in batch"
+                )
         kwargs = {**llm_kwargs, **kwargs}
         prompt_strings = [p.to_string() for p in prompts]
         return await self.agenerate(

libs/langchain/langchain/prompts/chat.py

@@ -298,6 +298,7 @@ class ChatPromptValue(PromptValue):
 
 class BaseChatPromptTemplate(BasePromptTemplate, ABC):
     """Base class for chat prompt templates."""
+
     llm_kwargs: dict = Field(default_factory=dict)
 
     @property
@@ -488,9 +489,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     @classmethod
     def from_messages(
-        cls,
-        messages: Sequence[MessageLikeRepresentation],
-        **kwargs: Any
+        cls, messages: Sequence[MessageLikeRepresentation], **kwargs: Any
     ) -> ChatPromptTemplate:
         """Create a chat prompt template from a variety of message formats.
 

libs/langchain/langchain/schema/prompt.py

@@ -3,9 +3,8 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import List
 
-from langchain.pydantic_v1 import Field
-
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field
 from langchain.schema.messages import BaseMessage
 
 
@@ -15,6 +14,7 @@ class PromptValue(Serializable, ABC):
     PromptValues can be converted to both LLM (pure text-generation) inputs and
         ChatModel inputs.
     """
+
     llm_kwargs: dict = Field(default_factory=dict)
 
     @property