experimental[major]: upgrade pydantic (#26228)

libs/experimental/langchain_experimental/llms/anthropic_functions.py

@@ -18,8 +18,7 @@ from langchain_core.messages import (
     BaseMessage,
     SystemMessage,
 )
-
-from langchain_experimental.pydantic_v1 import root_validator
+from pydantic import model_validator
 
 prompt = """In addition to responding, you can use tools. \
 You have access to the following tools.
@@ -134,8 +133,9 @@ class AnthropicFunctions(BaseChatModel):
 
     llm: BaseChatModel
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["llm"] = values.get("llm") or ChatAnthropic(**values)
         return values
 

libs/experimental/langchain_experimental/llms/jsonformer_decoder.py

@@ -7,8 +7,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, cast
 
 from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain_core.callbacks.manager import CallbackManagerForLLMRun
-
-from langchain_experimental.pydantic_v1 import Field, root_validator
+from pydantic import Field, model_validator
 
 if TYPE_CHECKING:
     import jsonformer
@@ -38,10 +37,9 @@ class JsonFormer(HuggingFacePipeline):
     )
     debug: bool = Field(default=False, description="Debug mode.")
 
-    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
-    #       and causes mypy type-checking failures (hence the `type: ignore`)
-    @root_validator  # type: ignore[call-overload]
-    def check_jsonformer_installation(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def check_jsonformer_installation(cls, values: dict) -> Any:
         import_jsonformer()
         return values
 

libs/experimental/langchain_experimental/llms/lmformatenforcer_decoder.py

@@ -7,8 +7,7 @@ from typing import TYPE_CHECKING, Any, List, Optional
 from langchain.schema import LLMResult
 from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain_core.callbacks.manager import CallbackManagerForLLMRun
-
-from langchain_experimental.pydantic_v1 import Field
+from pydantic import Field
 
 if TYPE_CHECKING:
     import lmformatenforcer

libs/experimental/langchain_experimental/llms/ollama_functions.py

@@ -31,14 +31,14 @@ from langchain_core.output_parsers.json import JsonOutputParser
 from langchain_core.output_parsers.pydantic import PydanticOutputParser
 from langchain_core.outputs import ChatGeneration, ChatResult
 from langchain_core.prompts import SystemMessagePromptTemplate
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-)
 from langchain_core.runnables import Runnable, RunnableLambda
 from langchain_core.runnables.base import RunnableMap
 from langchain_core.runnables.passthrough import RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils.pydantic import is_basemodel_instance, is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+)
 
 DEFAULT_SYSTEM_TEMPLATE = """You have access to the following tools:
 
@@ -83,14 +83,14 @@ def convert_to_ollama_tool(tool: Any) -> Dict:
     """Convert a tool to an Ollama tool."""
     description = None
     if _is_pydantic_class(tool):
-        schema = tool.construct().schema()
+        schema = tool.model_construct().model_json_schema()
         name = schema["title"]
     elif isinstance(tool, BaseTool):
-        schema = tool.tool_call_schema.schema()
+        schema = tool.tool_call_schema.model_json_schema()
         name = tool.get_name()
         description = tool.description
     elif is_basemodel_instance(tool):
-        schema = tool.get_input_schema().schema()
+        schema = tool.get_input_schema().model_json_schema()
         name = tool.get_name()
         description = tool.description
     elif isinstance(tool, dict) and "name" in tool and "parameters" in tool:
@@ -192,7 +192,7 @@ class OllamaFunctions(ChatOllama):
             .. code-block:: python
 
                 from langchain_experimental.llms import OllamaFunctions
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
 
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
@@ -213,7 +213,7 @@ class OllamaFunctions(ChatOllama):
             .. code-block:: python
 
                 from langchain_experimental.llms import OllamaFunctions
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
 
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
@@ -234,7 +234,7 @@ class OllamaFunctions(ChatOllama):
             .. code-block:: python
 
                 from langchain_experimental.llms import OllamaFunctions, convert_to_ollama_tool
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
 
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''

libs/experimental/langchain_experimental/llms/rellm_decoder.py

@@ -7,8 +7,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, cast
 from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain_community.llms.utils import enforce_stop_tokens
 from langchain_core.callbacks.manager import CallbackManagerForLLMRun
-
-from langchain_experimental.pydantic_v1 import Field, root_validator
+from pydantic import Field, model_validator
 
 if TYPE_CHECKING:
     import rellm
@@ -40,10 +39,9 @@ class RELLM(HuggingFacePipeline):
         default=200, description="Maximum number of new tokens to generate."
     )
 
-    # TODO: move away from `root_validator` since it is deprecated in pydantic v2
-    #       and causes mypy type-checking failures (hence the `type: ignore`)
-    @root_validator  # type: ignore[call-overload]
-    def check_rellm_installation(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def check_rellm_installation(cls, values: dict) -> Any:
         import_rellm()
         return values