revoke serialization (#14456)

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -28,6 +28,10 @@ class OpenAIAssistantFinish(AgentFinish):
     run_id: str
     thread_id: str
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
 
 class OpenAIAssistantAction(AgentAction):
     """AgentAction with info needed to submit custom tool output to existing run."""
@@ -36,6 +40,10 @@ class OpenAIAssistantAction(AgentAction):
     run_id: str
     thread_id: str
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
 
 def _get_openai_client() -> openai.OpenAI:
     try:

libs/langchain/langchain/agents/schema.py

@@ -7,6 +7,10 @@ from langchain_core.prompts.chat import ChatPromptTemplate
 class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
     """Chat prompt template for the agent scratchpad."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     def _construct_agent_scratchpad(
         self, intermediate_steps: List[Tuple[AgentAction, str]]
     ) -> str:

libs/langchain/langchain/chains/api/openapi/requests_chain.py

@@ -40,6 +40,10 @@ class APIRequesterOutputParser(BaseOutputParser):
 class APIRequesterChain(LLMChain):
     """Get the request parser."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @classmethod
     def from_llm_and_typescript(
         cls,

libs/langchain/langchain/chains/api/openapi/response_chain.py

@@ -40,6 +40,10 @@ class APIResponderOutputParser(BaseOutputParser):
 class APIResponderChain(LLMChain):
     """Get the response parser."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @classmethod
     def from_llm(
         cls, llm: BaseLanguageModel, verbose: bool = True, **kwargs: Any

libs/langchain/langchain/chains/conversation/base.py

@@ -36,6 +36,10 @@ class ConversationChain(LLMChain):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def input_keys(self) -> List[str]:
         """Use this since so some prompt vars come from history."""

libs/langchain/langchain/chains/flare/base.py

@@ -29,6 +29,10 @@ class _ResponseChain(LLMChain):
 
     prompt: BasePromptTemplate = PROMPT
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def input_keys(self) -> List[str]:
         return self.prompt.input_variables
@@ -77,6 +81,10 @@ class QuestionGeneratorChain(LLMChain):
     prompt: BasePromptTemplate = QUESTION_GENERATOR_PROMPT
     """Prompt template for the chain."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def input_keys(self) -> List[str]:
         """Input keys for the chain."""

libs/langchain/langchain/chat_models/anyscale.py

@@ -57,6 +57,10 @@ class ChatAnyscale(ChatOpenAI):
     def lc_secrets(self) -> Dict[str, str]:
         return {"anyscale_api_key": "ANYSCALE_API_KEY"}
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     anyscale_api_key: SecretStr
     """AnyScale Endpoints API keys."""
     model_name: str = Field(default=DEFAULT_MODEL, alias="model")

libs/langchain/langchain/chat_models/everlyai.py

@@ -51,6 +51,10 @@ class ChatEverlyAI(ChatOpenAI):
     def lc_secrets(self) -> Dict[str, str]:
         return {"everlyai_api_key": "EVERLYAI_API_KEY"}
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     everlyai_api_key: Optional[str] = None
     """EverlyAI Endpoints API keys."""
     model_name: str = Field(default=DEFAULT_MODEL, alias="model")

libs/langchain/langchain/chat_models/jinachat.py

@@ -165,7 +165,7 @@ class JinaChat(BaseChatModel):
     @classmethod
     def is_lc_serializable(cls) -> bool:
         """Return whether this model can be serialized by Langchain."""
-        return True
+        return False
 
     client: Any  #: :meta private:
     temperature: float = 0.7

libs/langchain/langchain/chat_models/konko.py

@@ -57,7 +57,7 @@ class ChatKonko(BaseChatModel):
     @classmethod
     def is_lc_serializable(cls) -> bool:
         """Return whether this model can be serialized by Langchain."""
-        return True
+        return False
 
     client: Any = None  #: :meta private:
     model: str = Field(default=DEFAULT_MODEL, alias="model")

libs/langchain/langchain/chat_models/ollama.py

@@ -50,7 +50,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
     @classmethod
     def is_lc_serializable(cls) -> bool:
         """Return whether this model can be serialized by Langchain."""
-        return True
+        return False
 
     def _format_message_as_text(self, message: BaseMessage) -> str:
         if isinstance(message, ChatMessage):

libs/langchain/langchain/chat_models/promptlayer_openai.py

@@ -39,6 +39,10 @@ class PromptLayerChatOpenAI(ChatOpenAI):
     pl_tags: Optional[List[str]]
     return_pl_id: Optional[bool] = False
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     def _generate(
         self,
         messages: List[BaseMessage],

libs/langchain/langchain/chat_models/volcengine_maas.py

@@ -76,7 +76,7 @@ class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase):
     @classmethod
     def is_lc_serializable(cls) -> bool:
         """Return whether this model can be serialized by Langchain."""
-        return True
+        return False
 
     @property
     def _identifying_params(self) -> Dict[str, Any]:

libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py

@@ -15,6 +15,10 @@ class _DocumentWithState(Document):
     state: dict = Field(default_factory=dict)
     """State associated with the document."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     def to_document(self) -> Document:
         """Convert the DocumentWithState to a Document."""
         return Document(page_content=self.page_content, metadata=self.metadata)

libs/langchain/langchain/evaluation/comparison/eval_chain.py

@@ -188,6 +188,10 @@ class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
         default_factory=PairwiseStringResultOutputParser
     )
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     class Config:
         """Configuration for the PairwiseStringEvalChain."""
 

libs/langchain/langchain/evaluation/criteria/eval_chain.py

@@ -232,6 +232,10 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
     """The name of the criterion being evaluated."""
     output_key: str = "results"  #: :meta private:
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     class Config:
         """Configuration for the QAEvalChain."""
 
@@ -508,6 +512,10 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
 class LabeledCriteriaEvalChain(CriteriaEvalChain):
     """Criteria evaluation chain that requires references."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def requires_reference(self) -> bool:
         """Whether the evaluation requires a reference text."""

libs/langchain/langchain/evaluation/qa/eval_chain.py

@@ -77,6 +77,10 @@ class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
 
         extra = Extra.ignore
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def evaluation_name(self) -> str:
         return "correctness"
@@ -204,6 +208,10 @@ class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
 class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
     """LLM Chain for evaluating QA w/o GT based on context"""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def requires_reference(self) -> bool:
         """Whether the chain requires a reference string."""
@@ -328,6 +336,10 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
 class CotQAEvalChain(ContextQAEvalChain):
     """LLM Chain for evaluating QA using chain of thought reasoning."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def evaluation_name(self) -> str:
         return "COT Contextual Accuracy"

libs/langchain/langchain/evaluation/qa/generate_chain.py

@@ -22,6 +22,10 @@ class QAGenerateChain(LLMChain):
     output_parser: BaseLLMOutputParser = Field(default=_QA_OUTPUT_PARSER)
     output_key: str = "qa_pairs"
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @classmethod
     def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> QAGenerateChain:
         """Load QA Generate Chain from LLM."""

libs/langchain/langchain/evaluation/scoring/eval_chain.py

@@ -185,6 +185,10 @@ class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
 
         extra = Extra.ignore
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def requires_reference(self) -> bool:
         """Return whether the chain requires a reference.

libs/langchain/langchain/indexes/_api.py

@@ -58,6 +58,10 @@ class _HashedDocument(Document):
     metadata_hash: str
     """The hash of the document metadata."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @root_validator(pre=True)
     def calculate_hashes(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Root validator to calculate content and metadata hash."""

libs/langchain/langchain/llms/anyscale.py

@@ -90,6 +90,10 @@ class Anyscale(BaseOpenAI):
 
     prefix_messages: List = Field(default_factory=list)
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""

libs/langchain/langchain/llms/openlm.py

@@ -8,6 +8,10 @@ from langchain.llms.openai import BaseOpenAI
 class OpenLM(BaseOpenAI):
     """OpenLM models."""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def _invocation_params(self) -> Dict[str, Any]:
         return {**{"model": self.model_name}, **super()._invocation_params}

libs/langchain/langchain/llms/promptlayer_openai.py

@@ -37,6 +37,10 @@ class PromptLayerOpenAI(OpenAI):
     pl_tags: Optional[List[str]]
     return_pl_id: Optional[bool] = False
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     def _generate(
         self,
         prompts: List[str],

libs/langchain/langchain/llms/tongyi.py

@@ -106,7 +106,7 @@ class Tongyi(LLM):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        return True
+        return False
 
     client: Any  #: :meta private:
     model_name: str = "qwen-plus-v1"

libs/langchain/langchain/llms/vllm.py

@@ -147,6 +147,10 @@ class VLLM(BaseLLM):
 class VLLMOpenAI(BaseOpenAI):
     """vLLM OpenAI-compatible API client"""
 
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return False
+
     @property
     def _invocation_params(self) -> Dict[str, Any]:
         """Get the parameters used to invoke the model."""

libs/langchain/langchain/llms/watsonxllm.py

@@ -97,7 +97,7 @@ class WatsonxLLM(BaseLLM):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        return True
+        return False
 
     @property
     def lc_secrets(self) -> Dict[str, str]: