community[patch]: Update root_validators to use pre=True or pre=False (#23731)

Update root_validators in preparation for pydantic 2 migration.

libs/community/langchain_community/agents/openai_assistant/base.py

@@ -209,7 +209,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
     as_agent: bool = False
     """Use as a LangChain agent, compatible with the AgentExecutor."""
 
-    @root_validator()
+    @root_validator(pre=False, skip_on_failure=True)
     def validate_async_client(cls, values: dict) -> dict:
         if values["async_client"] is None:
             import openai

libs/community/langchain_community/chains/llm_requests.py

@@ -59,7 +59,7 @@ class LLMRequestsChain(Chain):
         """
         return [self.output_key]
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         try:

libs/community/langchain_community/chat_models/anyscale.py

@@ -101,7 +101,7 @@ class ChatAnyscale(ChatOpenAI):
 
         return {model["id"] for model in models_response.json()["data"]}
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: dict) -> dict:
         """Validate that api key and python package exists in environment."""
         values["anyscale_api_key"] = convert_to_secret_str(

libs/community/langchain_community/chat_models/coze.py

@@ -116,7 +116,7 @@ class ChatCoze(BaseChatModel):
 
         allow_population_by_field_name = True
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         values["coze_api_base"] = get_from_dict_or_env(
             values,

libs/community/langchain_community/chat_models/dappier.py

@@ -75,7 +75,7 @@ class ChatDappierAI(BaseChatModel):
 
         extra = Extra.forbid
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key exists in environment."""
         values["dappier_api_key"] = convert_to_secret_str(

libs/community/langchain_community/chat_models/deepinfra.py

@@ -278,8 +278,8 @@ class ChatDeepInfra(BaseChatModel):
 
         return await _completion_with_retry(**kwargs)
 
-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:
+    @root_validator(pre=True)
+    def init_defaults(cls, values: Dict) -> Dict:
         """Validate api key, python package exists, temperature, top_p, and top_k."""
         # For compatibility with LiteLLM
         api_key = get_from_dict_or_env(
@@ -294,7 +294,10 @@ class ChatDeepInfra(BaseChatModel):
             "DEEPINFRA_API_TOKEN",
             default=api_key,
         )
+        return values
 
+    @root_validator(pre=False, skip_on_failure=True)
+    def validate_environment(cls, values: Dict) -> Dict:
         if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
             raise ValueError("temperature must be in the range [0.0, 1.0]")
 

libs/community/langchain_community/chat_models/ernie.py

@@ -108,7 +108,7 @@ class ErnieBotChat(BaseChatModel):
 
     _lock = threading.Lock()
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         values["ernie_api_base"] = get_from_dict_or_env(
             values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com"

libs/community/langchain_community/chat_models/fireworks.py

@@ -112,7 +112,7 @@ class ChatFireworks(BaseChatModel):
         """Get the namespace of the langchain object."""
         return ["langchain", "chat_models", "fireworks"]
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key in environment."""
         try:

libs/community/langchain_community/chat_models/huggingface.py

@@ -76,7 +76,7 @@ class ChatHuggingFace(BaseChatModel):
             else self.tokenizer
         )
 
-    @root_validator()
+    @root_validator(pre=False, skip_on_failure=True)
     def validate_llm(cls, values: dict) -> dict:
         if not isinstance(
             values["llm"],