bump core dep in langchain

To fix a test in `langchain`

libs/community/Makefile

@@ -22,7 +22,7 @@ integration_tests:
 	poetry run pytest $(TEST_FILE)
 
 test_watch:
-	poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv -x tests/unit_tests
+	poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv tests/unit_tests
 
 check_imports: $(shell find langchain_community -name '*.py')
 	poetry run python ./scripts/check_imports.py $^
@@ -45,7 +45,6 @@ lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
-	./scripts/check_pydantic.sh .
 	./scripts/lint_imports.sh .
 	./scripts/check_pickle.sh .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)

libs/community/langchain_community/adapters/openai.py

@@ -25,7 +25,7 @@ from langchain_core.messages import (
     SystemMessage,
     ToolMessage,
 )
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 from typing_extensions import Literal
 
 

libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Literal, Optional
+from typing import TYPE_CHECKING, Any, List, Literal, Optional
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, model_validator
 
 from langchain_community.tools.ainetwork.app import AINAppOps
 from langchain_community.tools.ainetwork.owner import AINOwnerOps
@@ -36,8 +36,9 @@ class AINetworkToolkit(BaseToolkit):
     network: Optional[Literal["mainnet", "testnet"]] = "testnet"
     interface: Optional[Ain] = None
 
-    @root_validator(pre=True)
-    def set_interface(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def set_interface(cls, values: dict) -> Any:
         """Set the interface if not provided.
 
         If the interface is not provided, attempt to authenticate with the
@@ -53,9 +54,10 @@ class AINetworkToolkit(BaseToolkit):
             values["interface"] = authenticate(network=values.get("network", "testnet"))
         return values
 
-    class Config:
-        arbitrary_types_allowed = True
-        validate_all = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        validate_default=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py

@@ -3,9 +3,9 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, List, Optional
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.amadeus.closest_airport import AmadeusClosestAirport
 from langchain_community.tools.amadeus.flight_search import AmadeusFlightSearch
@@ -26,8 +26,9 @@ class AmadeusToolkit(BaseToolkit):
     client: Client = Field(default_factory=authenticate)
     llm: Optional[BaseLanguageModel] = Field(default=None)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py

@@ -2,9 +2,9 @@
 
 from typing import List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.cassandra_database.tool import (
     GetSchemaCassandraDatabaseTool,
@@ -24,8 +24,9 @@ class CassandraDatabaseToolkit(BaseToolkit):
 
     db: CassandraDatabase = Field(exclude=True)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/connery/toolkit.py

@@ -1,8 +1,8 @@
-from typing import List
+from typing import Any, List
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import model_validator
 
 from langchain_community.tools.connery import ConneryService
 
@@ -23,8 +23,9 @@ class ConneryToolkit(BaseToolkit):
         """
         return self.tools
 
-    @root_validator(pre=True)
-    def validate_attributes(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_attributes(cls, values: dict) -> Any:
         """
         Validate the attributes of the ConneryToolkit class.
 

libs/community/langchain_community/agent_toolkits/file_management/toolkit.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool, BaseToolkit
 from langchain_core.utils.pydantic import get_fields
+from pydantic import model_validator
 
 from langchain_community.tools.file_management.copy import CopyFileTool
 from langchain_community.tools.file_management.delete import DeleteFileTool
@@ -63,8 +63,9 @@ class FileManagementToolkit(BaseToolkit):
     selected_tools: Optional[List[str]] = None
     """If provided, only provide the selected tools. Defaults to all."""
 
-    @root_validator(pre=True)
-    def validate_tools(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_tools(cls, values: dict) -> Any:
         selected_tools = values.get("selected_tools") or []
         for tool_name in selected_tools:
             if tool_name not in _FILE_TOOLS_MAP:

libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.financial_datasets.balance_sheets import BalanceSheets
 from langchain_community.tools.financial_datasets.cash_flow_statements import (
@@ -31,8 +31,9 @@ class FinancialDatasetsToolkit(BaseToolkit):
         super().__init__()
         self.api_wrapper = api_wrapper
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/github/toolkit.py

@@ -2,9 +2,9 @@
 
 from typing import Dict, List
 
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import BaseModel, Field
 
 from langchain_community.tools.github.prompt import (
     COMMENT_ON_ISSUE_PROMPT,

libs/community/langchain_community/agent_toolkits/gmail/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.gmail.create_draft import GmailCreateDraft
 from langchain_community.tools.gmail.get_message import GmailGetMessage
@@ -117,8 +117,9 @@ class GmailToolkit(BaseToolkit):
 
     api_resource: Resource = Field(default_factory=build_resource_service)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/multion/toolkit.py

@@ -6,6 +6,7 @@ from typing import List
 
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict
 
 from langchain_community.tools.multion.close_session import MultionCloseSession
 from langchain_community.tools.multion.create_session import MultionCreateSession
@@ -25,8 +26,9 @@ class MultionToolkit(BaseToolkit):
         See https://python.langchain.com/docs/security for more information.
     """
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/nla/toolkit.py

@@ -3,9 +3,9 @@ from __future__ import annotations
 from typing import Any, List, Optional, Sequence
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import Field
 
 from langchain_community.agent_toolkits.nla.tool import NLATool
 from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec

libs/community/langchain_community/agent_toolkits/office365/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.office365.create_draft_message import (
     O365CreateDraftMessage,
@@ -40,8 +40,9 @@ class O365Toolkit(BaseToolkit):
 
     account: Account = Field(default_factory=authenticate)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -9,8 +9,8 @@ import yaml
 from langchain_core.callbacks import BaseCallbackManager
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate, PromptTemplate
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool, Tool
+from pydantic import Field
 
 from langchain_community.agent_toolkits.openapi.planner_prompt import (
     API_CONTROLLER_PROMPT,
@@ -69,7 +69,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_get"
     """Tool name."""
-    description = REQUESTS_GET_TOOL_DESCRIPTION
+    description: str = REQUESTS_GET_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -103,7 +103,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_post"
     """Tool name."""
-    description = REQUESTS_POST_TOOL_DESCRIPTION
+    description: str = REQUESTS_POST_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -134,7 +134,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_patch"
     """Tool name."""
-    description = REQUESTS_PATCH_TOOL_DESCRIPTION
+    description: str = REQUESTS_PATCH_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -167,7 +167,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_put"
     """Tool name."""
-    description = REQUESTS_PUT_TOOL_DESCRIPTION
+    description: str = REQUESTS_PUT_TOOL_DESCRIPTION
     """Tool description."""
     response_length: int = MAX_RESPONSE_LENGTH
     """Maximum length of the response to be returned."""
@@ -198,7 +198,7 @@ class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
 
     name: str = "requests_delete"
     """The name of the tool."""
-    description = REQUESTS_DELETE_TOOL_DESCRIPTION
+    description: str = REQUESTS_DELETE_TOOL_DESCRIPTION
     """The description of the tool."""
 
     response_length: Optional[int] = MAX_RESPONSE_LENGTH

libs/community/langchain_community/agent_toolkits/playwright/toolkit.py

@@ -2,10 +2,10 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Optional, Type, cast
+from typing import TYPE_CHECKING, Any, List, Optional, Type, cast
 
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool, BaseToolkit
+from pydantic import ConfigDict, model_validator
 
 from langchain_community.tools.playwright.base import (
     BaseBrowserTool,
@@ -68,12 +68,14 @@ class PlayWrightBrowserToolkit(BaseToolkit):
     sync_browser: Optional["SyncBrowser"] = None
     async_browser: Optional["AsyncBrowser"] = None
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_imports_and_browser_provided(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_imports_and_browser_provided(cls, values: dict) -> Any:
         """Check that the arguments are valid."""
         lazy_import_playwright_browsers()
         if values.get("async_browser") is None and values.get("sync_browser") is None:

libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py

@@ -13,9 +13,9 @@ from langchain_core.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.powerbi.prompt import (
     QUESTION_TO_QUERY_BASE,
@@ -63,8 +63,9 @@ class PowerBIToolkit(BaseToolkit):
     output_token_limit: Optional[int] = None
     tiktoken_model_name: Optional[str] = None
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/slack/toolkit.py

@@ -2,9 +2,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.slack.get_channel import SlackGetChannel
 from langchain_community.tools.slack.get_message import SlackGetMessage
@@ -91,8 +91,9 @@ class SlackToolkit(BaseToolkit):
 
     client: WebClient = Field(default_factory=login)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py

@@ -3,9 +3,9 @@
 from typing import List
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.spark_sql.tool import (
     InfoSparkSQLTool,
@@ -27,8 +27,9 @@ class SparkSQLToolkit(BaseToolkit):
     db: SparkSQL = Field(exclude=True)
     llm: BaseLanguageModel = Field(exclude=True)
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/sql/toolkit.py

@@ -3,9 +3,9 @@
 from typing import List
 
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
+from pydantic import ConfigDict, Field
 
 from langchain_community.tools.sql_database.tool import (
     InfoSQLDatabaseTool,
@@ -83,8 +83,9 @@ class SQLDatabaseToolkit(BaseToolkit):
         """Return string representation of SQL dialect to use."""
         return self.db.dialect
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

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

@@ -15,10 +15,11 @@ from langchain.agents.openai_assistant.base import OpenAIAssistantRunnable, Outp
 from langchain_core._api import beta
 from langchain_core.callbacks import CallbackManager
 from langchain_core.load import dumpd
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import RunnableConfig, ensure_config
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, Field, model_validator
+from typing_extensions import Self
 
 if TYPE_CHECKING:
     import openai
@@ -209,14 +210,14 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
     as_agent: bool = False
     """Use as a LangChain agent, compatible with the AgentExecutor."""
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_async_client(cls, values: dict) -> dict:
-        if values["async_client"] is None:
+    @model_validator(mode="after")
+    def validate_async_client(self) -> Self:
+        if self.async_client is None:
             import openai
 
-            api_key = values["client"].api_key
-            values["async_client"] = openai.AsyncOpenAI(api_key=api_key)
-        return values
+            api_key = self.client.api_key
+            self.async_client = openai.AsyncOpenAI(api_key=api_key)
+        return self
 
     @classmethod
     def create_assistant(

libs/community/langchain_community/chains/ernie_functions/base.py

@@ -22,9 +22,9 @@ from langchain_core.output_parsers import (
     BaseOutputParser,
 )
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import Runnable
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import BaseModel
 
 from langchain_community.output_parsers.ernie_functions import (
     JsonOutputFunctionsParser,

libs/community/langchain_community/chains/graph_qa/arangodb.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     AQL_FIX_PROMPT,

libs/community/langchain_community/chains/graph_qa/base.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     ENTITY_EXTRACTION_PROMPT,

libs/community/langchain_community/chains/graph_qa/cypher.py

@@ -22,8 +22,8 @@ from langchain_core.prompts import (
     HumanMessagePromptTemplate,
     MessagesPlaceholder,
 )
-from langchain_core.pydantic_v1 import Field
 from langchain_core.runnables import Runnable
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.cypher_utils import (
     CypherQueryCorrector,

libs/community/langchain_community/chains/graph_qa/falkordb.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_GENERATION_PROMPT,

libs/community/langchain_community/chains/graph_qa/gremlin.py

@@ -10,7 +10,7 @@ from langchain_core.callbacks.manager import CallbackManager, CallbackManagerFor
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/hugegraph.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/kuzu.py

@@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/nebulagraph.py

@@ -9,7 +9,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/neptune_cypher.py

@@ -9,7 +9,7 @@ from langchain.chains.prompt_selector import ConditionalPromptSelector
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     CYPHER_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/neptune_sparql.py

@@ -12,7 +12,7 @@ from langchain_core.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import SPARQL_QA_PROMPT
 from langchain_community.graphs import NeptuneRdfGraph

libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py

@@ -12,7 +12,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     GRAPHDB_QA_PROMPT,

libs/community/langchain_community/chains/graph_qa/sparql.py

@@ -11,7 +11,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts.base import BasePromptTemplate
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.chains.graph_qa.prompts import (
     SPARQL_GENERATION_SELECT_PROMPT,

libs/community/langchain_community/chains/llm_requests.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
 from langchain.chains import LLMChain
 from langchain.chains.base import Chain
 from langchain_core.callbacks import CallbackManagerForChainRun
-from langchain_core.pydantic_v1 import Field, root_validator
+from pydantic import ConfigDict, Field, model_validator
 
 from langchain_community.utilities.requests import TextRequestsWrapper
 
@@ -38,9 +38,10 @@ class LLMRequestsChain(Chain):
     input_key: str = "url"  #: :meta private:
     output_key: str = "output"  #: :meta private:
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
     @property
     def input_keys(self) -> List[str]:
@@ -58,8 +59,9 @@ class LLMRequestsChain(Chain):
         """
         return [self.output_key]
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
         try:
             from bs4 import BeautifulSoup  # noqa: F401

libs/community/langchain_community/chains/openapi/chain.py

@@ -11,7 +11,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 from requests import Response
 
 from langchain_community.tools.openapi.utils.api_models import APIOperation

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -17,8 +17,8 @@ from langchain_core.callbacks import (
 )
 from langchain_core.documents import Document
 from langchain_core.language_models import BaseLanguageModel
-from langchain_core.pydantic_v1 import Field, validator
 from langchain_core.vectorstores import VectorStoreRetriever
+from pydantic import ConfigDict, Field, validator
 
 from langchain_community.chains.pebblo_retrieval.enforcement_filters import (
     SUPPORTED_VECTORSTORES,
@@ -189,10 +189,11 @@ class PebbloRetrievalQA(Chain):
         else:
             return {self.output_key: answer}
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
     @property
     def input_keys(self) -> List[str]:

libs/community/langchain_community/chains/pebblo_retrieval/models.py

@@ -2,7 +2,7 @@
 
 from typing import Any, List, Optional, Union
 
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 
 
 class AuthContext(BaseModel):

libs/community/langchain_community/chains/pebblo_retrieval/utilities.py

@@ -10,9 +10,9 @@ import aiohttp
 from aiohttp import ClientTimeout
 from langchain_core.documents import Document
 from langchain_core.env import get_runtime_environment
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.utils import get_from_dict_or_env
 from langchain_core.vectorstores import VectorStoreRetriever
+from pydantic import BaseModel
 from requests import Response, request
 from requests.exceptions import RequestException
 

libs/community/langchain_community/chat_models/anthropic.py

@@ -20,6 +20,7 @@ from langchain_core.messages import (
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.prompt_values import PromptValue
+from pydantic import ConfigDict
 
 from langchain_community.llms.anthropic import _AnthropicCommon
 
@@ -91,9 +92,10 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
             model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
     """
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     @property
     def lc_secrets(self) -> Dict[str, str]:

libs/community/langchain_community/chat_models/anyscale.py

@@ -5,12 +5,12 @@ from __future__ import annotations
 import logging
 import os
 import sys
-from typing import TYPE_CHECKING, Dict, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set
 
 import requests
 from langchain_core.messages import BaseMessage
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import Field, SecretStr, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 from langchain_community.chat_models.openai import (
@@ -102,8 +102,9 @@ class ChatAnyscale(ChatOpenAI):
 
         return {model["id"] for model in models_response.json()["data"]}
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["anyscale_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(

libs/community/langchain_community/chat_models/azure_openai.py

@@ -9,8 +9,8 @@ from typing import Any, Callable, Dict, List, Union
 
 from langchain_core._api.deprecation import deprecated
 from langchain_core.outputs import ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from pydantic import BaseModel, Field
 
 from langchain_community.chat_models.openai import ChatOpenAI
 from langchain_community.utils.openai import is_openai_v1

libs/community/langchain_community/chat_models/baichuan.py

@@ -44,7 +44,6 @@ from langchain_core.output_parsers.openai_tools import (
     parse_tool_call,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import (
@@ -53,6 +52,13 @@ from langchain_core.utils import (
     get_pydantic_field_names,
 )
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
 
 from langchain_community.chat_models.llamacpp import (
     _lc_invalid_tool_call_to_openai_tool_call,
@@ -375,11 +381,13 @@ class ChatBaichuan(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})
@@ -404,8 +412,9 @@ class ChatBaichuan(BaseChatModel):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["baichuan_api_base"] = get_from_dict_or_env(
             values,
             "baichuan_api_base",

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -41,17 +41,18 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-    root_validator,
-)
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import get_fields, is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -248,7 +249,7 @@ class QianfanChatEndpoint(BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class GetWeather(BaseModel):
@@ -287,7 +288,7 @@ class QianfanChatEndpoint(BaseChatModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class Joke(BaseModel):
@@ -380,11 +381,13 @@ class QianfanChatEndpoint(BaseChatModel):
     endpoint: Optional[str] = None
     """Endpoint of the Qianfan LLM, required if custom model used."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["qianfan_ak"] = convert_to_secret_str(
             get_from_dict_or_env(
                 values, ["qianfan_ak", "api_key"], "QIANFAN_AK", default=""
@@ -747,7 +750,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                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.'''
@@ -768,7 +771,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                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.'''
@@ -789,7 +792,7 @@ class QianfanChatEndpoint(BaseChatModel):
             .. code-block:: python
 
                 from langchain_mistralai import QianfanChatEndpoint
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
                 class AnswerWithJustification(BaseModel):

libs/community/langchain_community/chat_models/bedrock.py

@@ -16,6 +16,7 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from pydantic import ConfigDict
 
 from langchain_community.chat_models.anthropic import (
     convert_messages_to_prompt_anthropic,
@@ -231,8 +232,9 @@ class BedrockChat(BaseChatModel, BedrockBase):
 
         return attributes
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     def _stream(
         self,

libs/community/langchain_community/chat_models/cohere.py

@@ -19,6 +19,7 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from pydantic import ConfigDict
 
 from langchain_community.llms.cohere import BaseCohere
 
@@ -117,9 +118,10 @@ class ChatCohere(BaseChatModel, BaseCohere):
             chat.invoke(messages)
     """
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     @property
     def _llm_type(self) -> str:

libs/community/langchain_community/chat_models/coze.py

@@ -19,11 +19,11 @@ from langchain_core.messages import (
     HumanMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -111,11 +111,13 @@ class ChatCoze(BaseChatModel):
     "Streaming response" will provide real-time response of the model to the client, and
     the client needs to assemble the final reply based on the type of message. """
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["coze_api_base"] = get_from_dict_or_env(
             values,
             "coze_api_base",

libs/community/langchain_community/chat_models/dappier.py

@@ -13,8 +13,8 @@ from langchain_core.messages import (
     BaseMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 from langchain_community.utilities.requests import Requests
 
@@ -70,11 +70,13 @@ class ChatDappierAI(BaseChatModel):
 
     dappier_api_key: Optional[SecretStr] = Field(None, description="Dappier API Token")
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key exists in environment."""
         values["dappier_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(values, "dappier_api_key", "DAPPIER_API_KEY")

libs/community/langchain_community/chat_models/deepinfra.py

@@ -54,11 +54,12 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from typing_extensions import Self
 
 from langchain_community.utilities.requests import Requests
 
@@ -222,10 +223,9 @@ class ChatDeepInfra(BaseChatModel):
     streaming: bool = False
     max_retries: int = 1
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     @property
     def _default_params(self) -> Dict[str, Any]:
@@ -291,8 +291,9 @@ class ChatDeepInfra(BaseChatModel):
 
         return await _completion_with_retry(**kwargs)
 
-    @root_validator(pre=True)
-    def init_defaults(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def init_defaults(cls, values: Dict) -> Any:
         """Validate api key, python package exists, temperature, top_p, and top_k."""
         # For compatibility with LiteLLM
         api_key = get_from_dict_or_env(
@@ -309,18 +310,18 @@ class ChatDeepInfra(BaseChatModel):
         )
         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:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
+        if self.temperature is not None and not 0 <= self.temperature <= 1:
             raise ValueError("temperature must be in the range [0.0, 1.0]")
 
-        if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
+        if self.top_p is not None and not 0 <= self.top_p <= 1:
             raise ValueError("top_p must be in the range [0.0, 1.0]")
 
-        if values["top_k"] is not None and values["top_k"] <= 0:
+        if self.top_k is not None and self.top_k <= 0:
             raise ValueError("top_k must be positive")
 
-        return values
+        return self
 
     def _generate(
         self,

libs/community/langchain_community/chat_models/edenai.py

@@ -47,16 +47,17 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-)
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+)
 
 from langchain_community.utilities.requests import Requests
 
@@ -296,8 +297,9 @@ class ChatEdenAI(BaseChatModel):
 
     edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:

libs/community/langchain_community/chat_models/ernie.py

@@ -13,8 +13,8 @@ from langchain_core.messages import (
     HumanMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -108,8 +108,9 @@ class ErnieBotChat(BaseChatModel):
 
     _lock = threading.Lock()
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         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/everlyai.py

@@ -4,11 +4,11 @@ from __future__ import annotations
 
 import logging
 import sys
-from typing import TYPE_CHECKING, Dict, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set
 
 from langchain_core.messages import BaseMessage
-from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import Field, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 from langchain_community.chat_models.openai import (
@@ -76,8 +76,9 @@ class ChatEverlyAI(ChatOpenAI):
             ]
         )
 
-    @root_validator(pre=True)
-    def validate_environment_override(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment_override(cls, values: dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["openai_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(

libs/community/langchain_community/chat_models/fireworks.py

@@ -32,9 +32,9 @@ from langchain_core.messages import (
     SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str
 from langchain_core.utils.env import get_from_dict_or_env
+from pydantic import Field, SecretStr, model_validator
 
 from langchain_community.adapters.openai import convert_message_to_dict
 
@@ -112,8 +112,9 @@ class ChatFireworks(BaseChatModel):
         """Get the namespace of the langchain object."""
         return ["langchain", "chat_models", "fireworks"]
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key in environment."""
         try:
             import fireworks.client

libs/community/langchain_community/chat_models/google_palm.py

@@ -21,8 +21,8 @@ from langchain_core.outputs import (
     ChatGeneration,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import BaseModel, SecretStr
 from tenacity import (
     before_sleep_log,
     retry,

libs/community/langchain_community/chat_models/gpt_router.py

@@ -30,8 +30,9 @@ from langchain_core.language_models.chat_models import (
 from langchain_core.language_models.llms import create_base_retry_decorator
 from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from pydantic import BaseModel, Field, SecretStr, model_validator
+from typing_extensions import Self
 
 from langchain_community.adapters.openai import (
     convert_dict_to_message,
@@ -150,7 +151,7 @@ class GPTRouter(BaseChatModel):
     """
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
-    models_priority_list: List[GPTRouterModel] = Field(min_items=1)
+    models_priority_list: List[GPTRouterModel] = Field(min_length=1)
     gpt_router_api_base: str = Field(default=None)
     """WriteSonic GPTRouter custom endpoint"""
     gpt_router_api_key: Optional[SecretStr] = None
@@ -167,8 +168,9 @@ class GPTRouter(BaseChatModel):
     """Number of chat completions to generate for each prompt."""
     max_tokens: int = 256
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["gpt_router_api_base"] = get_from_dict_or_env(
             values,
             "gpt_router_api_base",
@@ -185,8 +187,8 @@ class GPTRouter(BaseChatModel):
         )
         return values
 
-    @root_validator(pre=True, skip_on_failure=True)
-    def post_init(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def post_init(self) -> Self:
         try:
             from gpt_router.client import GPTRouterClient
 
@@ -197,12 +199,14 @@ class GPTRouter(BaseChatModel):
             )
 
         gpt_router_client = GPTRouterClient(
-            values["gpt_router_api_base"],
-            values["gpt_router_api_key"].get_secret_value(),
+            self.gpt_router_api_base,
+            self.gpt_router_api_key.get_secret_value()
+            if self.gpt_router_api_key
+            else None,
         )
-        values["client"] = gpt_router_client
+        self.client = gpt_router_client
 
-        return values
+        return self
 
     @property
     def lc_secrets(self) -> Dict[str, str]:

libs/community/langchain_community/chat_models/huggingface.py

@@ -25,7 +25,8 @@ from langchain_core.outputs import (
     ChatResult,
     LLMResult,
 )
-from langchain_core.pydantic_v1 import root_validator
+from pydantic import model_validator
+from typing_extensions import Self
 
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 from langchain_community.llms.huggingface_hub import HuggingFaceHub
@@ -76,17 +77,17 @@ class ChatHuggingFace(BaseChatModel):
             else self.tokenizer
         )
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_llm(cls, values: dict) -> dict:
+    @model_validator(mode="after")
+    def validate_llm(self) -> Self:
         if not isinstance(
-            values["llm"],
+            self.llm,
             (HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub),
         ):
             raise TypeError(
                 "Expected llm to be one of HuggingFaceTextGenInference, "
-                f"HuggingFaceEndpoint, HuggingFaceHub, received {type(values['llm'])}"
+                f"HuggingFaceEndpoint, HuggingFaceHub, received {type(self.llm)}"
             )
-        return values
+        return self
 
     def _stream(
         self,

libs/community/langchain_community/chat_models/human.py

@@ -15,7 +15,7 @@ from langchain_core.messages import (
     messages_to_dict,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.llms.utils import enforce_stop_tokens
 

libs/community/langchain_community/chat_models/hunyuan.py

@@ -19,13 +19,13 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -138,11 +138,13 @@ class ChatHunyuan(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/javelin_ai_gateway.py

@@ -18,7 +18,7 @@ from langchain_core.outputs import (
     ChatGeneration,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
 
 logger = logging.getLogger(__name__)
 
@@ -62,14 +62,15 @@ class ChatJavelinAIGateway(BaseChatModel):
     params: Optional[ChatParams] = None
     """Parameters for the Javelin AI Gateway LLM."""
 
-    client: Any
+    client: Any = None
     """javelin client."""
 
     javelin_api_key: Optional[SecretStr] = Field(None, alias="api_key")
     """The API key for the Javelin AI Gateway."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     def __init__(self, **kwargs: Any):
         try:

libs/community/langchain_community/chat_models/jinachat.py

@@ -40,13 +40,13 @@ from langchain_core.messages import (
     SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import ConfigDict, Field, SecretStr, model_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -188,11 +188,13 @@ class JinaChat(BaseChatModel):
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/kinetica.py

@@ -26,7 +26,7 @@ from langchain_core.messages import (
 )
 from langchain_core.output_parsers.transform import BaseOutputParser
 from langchain_core.outputs import ChatGeneration, ChatResult, Generation
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 LOG = logging.getLogger(__name__)
 
@@ -543,8 +543,9 @@ class KineticaSqlResponse(BaseModel):
     dataframe: Any = Field(default=None)
     """The Pandas dataframe containing the fetched data."""
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
 
 class KineticaSqlOutputParser(BaseOutputParser[KineticaSqlResponse]):
@@ -582,8 +583,9 @@ class KineticaSqlOutputParser(BaseOutputParser[KineticaSqlResponse]):
     kdbc: Any = Field(exclude=True)
     """ Kinetica DB connection. """
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def parse(self, text: str) -> KineticaSqlResponse:
         df = self.kdbc.to_df(text)

libs/community/langchain_community/chat_models/konko.py

@@ -23,8 +23,8 @@ from langchain_core.callbacks import (
 )
 from langchain_core.messages import AIMessageChunk, BaseMessage
 from langchain_core.outputs import ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import Field, SecretStr
 
 from langchain_community.adapters.openai import (
     convert_message_to_dict,

libs/community/langchain_community/chat_models/litellm.py

@@ -52,11 +52,11 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env, pre_init
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, Field
 
 logger = logging.getLogger(__name__)
 

libs/community/langchain_community/chat_models/llama_edge.py

@@ -21,8 +21,8 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_pydantic_field_names
+from pydantic import ConfigDict, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -84,11 +84,13 @@ class LlamaEdgeChatService(BaseChatModel):
     streaming: bool = False
     """Whether to stream the results or not."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/llamacpp.py

@@ -46,15 +46,16 @@ from langchain_core.output_parsers.openai_tools import (
     parse_tool_call,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    root_validator,
-)
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    Field,
+    model_validator,
+)
+from typing_extensions import Self
 
 
 class ChatLlamaCpp(BaseChatModel):
@@ -172,8 +173,8 @@ class ChatLlamaCpp(BaseChatModel):
     verbose: bool = True
     """Print verbose output to stderr."""
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
         """Validate that llama-cpp-python library is installed."""
         try:
             from llama_cpp import Llama, LlamaGrammar
@@ -184,7 +185,7 @@ class ChatLlamaCpp(BaseChatModel):
                 "use this embedding model: pip install llama-cpp-python"
             )
 
-        model_path = values["model_path"]
+        model_path = self.model_path
         model_param_names = [
             "rope_freq_scale",
             "rope_freq_base",
@@ -203,35 +204,35 @@ class ChatLlamaCpp(BaseChatModel):
             "last_n_tokens_size",
             "verbose",
         ]
-        model_params = {k: values[k] for k in model_param_names}
+        model_params = {k: getattr(self, k) for k in model_param_names}
         # For backwards compatibility, only include if non-null.
-        if values["n_gpu_layers"] is not None:
-            model_params["n_gpu_layers"] = values["n_gpu_layers"]
+        if self.n_gpu_layers is not None:
+            model_params["n_gpu_layers"] = self.n_gpu_layers
 
-        model_params.update(values["model_kwargs"])
+        model_params.update(self.model_kwargs)
 
         try:
-            values["client"] = Llama(model_path, **model_params)
+            self.client = Llama(model_path, **model_params)
         except Exception as e:
             raise ValueError(
                 f"Could not load Llama model from path: {model_path}. "
                 f"Received error {e}"
             )
 
-        if values["grammar"] and values["grammar_path"]:
-            grammar = values["grammar"]
-            grammar_path = values["grammar_path"]
+        if self.grammar and self.grammar_path:
+            grammar = self.grammar
+            grammar_path = self.grammar_path
             raise ValueError(
                 "Can only pass in one of grammar and grammar_path. Received "
                 f"{grammar=} and {grammar_path=}."
             )
-        elif isinstance(values["grammar"], str):
-            values["grammar"] = LlamaGrammar.from_string(values["grammar"])
-        elif values["grammar_path"]:
-            values["grammar"] = LlamaGrammar.from_file(values["grammar_path"])
+        elif isinstance(self.grammar, str):
+            self.grammar = LlamaGrammar.from_string(self.grammar)
+        elif self.grammar_path:
+            self.grammar = LlamaGrammar.from_file(self.grammar_path)
         else:
             pass
-        return values
+        return self
 
     def _get_parameters(self, stop: Optional[List[str]]) -> Dict[str, Any]:
         """
@@ -433,7 +434,7 @@ class ChatLlamaCpp(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatLlamaCpp
-                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.'''
@@ -465,7 +466,7 @@ class ChatLlamaCpp(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatLlamaCpp
-                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.'''
@@ -497,7 +498,7 @@ class ChatLlamaCpp(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatLlamaCpp
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
                 class AnswerWithJustification(BaseModel):

libs/community/langchain_community/chat_models/maritalk.py

@@ -16,7 +16,7 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 from requests import Response
 from requests.exceptions import HTTPError
 

libs/community/langchain_community/chat_models/minimax.py

@@ -44,12 +44,18 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import get_fields
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -267,7 +273,7 @@ class MiniMaxChat(BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class GetWeather(BaseModel):
@@ -307,7 +313,7 @@ class MiniMaxChat(BaseChatModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class Joke(BaseModel):
@@ -384,11 +390,13 @@ class MiniMaxChat(BaseChatModel):
     streaming: bool = False
     """Whether to stream the results or not."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["minimax_api_key"] = convert_to_secret_str(
             get_from_dict_or_env(
@@ -694,7 +702,7 @@ class MiniMaxChat(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import MiniMaxChat
-                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.'''
@@ -715,7 +723,7 @@ class MiniMaxChat(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import MiniMaxChat
-                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.'''
@@ -737,7 +745,7 @@ class MiniMaxChat(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import MiniMaxChat
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
                 class AnswerWithJustification(BaseModel):

libs/community/langchain_community/chat_models/mlflow.py

@@ -42,14 +42,14 @@ from langchain_core.output_parsers.openai_tools import (
     parse_tool_call,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
+from langchain_core.runnables import Runnable, RunnableConfig
+from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import (
     BaseModel,
     Field,
     PrivateAttr,
 )
-from langchain_core.runnables import Runnable, RunnableConfig
-from langchain_core.tools import BaseTool
-from langchain_core.utils.function_calling import convert_to_openai_tool
 
 logger = logging.getLogger(__name__)
 

libs/community/langchain_community/chat_models/mlflow_ai_gateway.py

@@ -18,7 +18,7 @@ from langchain_core.outputs import (
     ChatGeneration,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 
 logger = logging.getLogger(__name__)
 

libs/community/langchain_community/chat_models/oci_generative_ai.py

@@ -38,10 +38,10 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_function
+from pydantic import BaseModel, ConfigDict
 
 from langchain_community.llms.oci_generative_ai import OCIGenAIBase
 from langchain_community.llms.utils import enforce_stop_tokens
@@ -499,8 +499,10 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
 
     """  # noqa: E501
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+        arbitrary_types_allowed=True,
+    )
 
     @property
     def _llm_type(self) -> str:
@@ -546,6 +548,9 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
 
         chat_params = {**_model_kwargs, **kwargs, **oci_params}
 
+        if not self.model_id:
+            raise ValueError("Model ID is required to chat")
+
         if self.model_id.startswith(CUSTOM_ENDPOINT_PREFIX):
             serving_mode = models.DedicatedServingMode(endpoint_id=self.model_id)
         else:

libs/community/langchain_community/chat_models/octoai.py

@@ -2,8 +2,8 @@
 
 from typing import Dict
 
-from langchain_core.pydantic_v1 import Field, SecretStr
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import Field, SecretStr
 
 from langchain_community.chat_models.openai import ChatOpenAI
 from langchain_community.utils.openai import is_openai_v1

libs/community/langchain_community/chat_models/openai.py

@@ -44,13 +44,13 @@ from langchain_core.messages import (
     ToolMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import Runnable
 from langchain_core.utils import (
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from langchain_community.adapters.openai import (
     convert_dict_to_message,
@@ -244,11 +244,13 @@ class ChatOpenAI(BaseChatModel):
     http_client: Union[Any, None] = None
     """Optional httpx.Client."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/pai_eas_endpoint.py

@@ -17,8 +17,8 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import model_validator
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -67,8 +67,9 @@ class PaiEasChatEndpoint(BaseChatModel):
 
     timeout: Optional[int] = 5000
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
         values["eas_service_url"] = get_from_dict_or_env(
             values, "eas_service_url", "EAS_SERVICE_URL"

libs/community/langchain_community/chat_models/perplexity.py

@@ -35,8 +35,12 @@ from langchain_core.messages import (
     ToolMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, root_validator
-from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
+from langchain_core.utils import (
+    from_env,
+    get_pydantic_field_names,
+)
+from pydantic import ConfigDict, Field, model_validator
+from typing_extensions import Self
 
 logger = logging.getLogger(__name__)
 
@@ -60,14 +64,16 @@ class ChatPerplexity(BaseChatModel):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: str = "llama-3.1-sonar-small-128k-online"
     """Model name."""
     temperature: float = 0.7
     """What sampling temperature to use."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `create` call not explicitly specified."""
-    pplx_api_key: Optional[str] = Field(None, alias="api_key")
+    pplx_api_key: Optional[str] = Field(
+        default_factory=from_env("PPLX_API_KEY", default=None), alias="api_key"
+    )
     """Base URL path for API requests,
     leave blank if not using a proxy or service emulator."""
     request_timeout: Optional[Union[float, Tuple[float, float]]] = Field(
@@ -81,15 +87,17 @@ class ChatPerplexity(BaseChatModel):
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     @property
     def lc_secrets(self) -> Dict[str, str]:
         return {"pplx_api_key": "PPLX_API_KEY"}
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})
@@ -114,12 +122,9 @@ class ChatPerplexity(BaseChatModel):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
         """Validate that api key and python package exists in environment."""
-        values["pplx_api_key"] = get_from_dict_or_env(
-            values, "pplx_api_key", "PPLX_API_KEY"
-        )
         try:
             import openai
         except ImportError:
@@ -128,8 +133,8 @@ class ChatPerplexity(BaseChatModel):
                 "Please install it with `pip install openai`."
             )
         try:
-            values["client"] = openai.OpenAI(
-                api_key=values["pplx_api_key"], base_url="https://api.perplexity.ai"
+            self.client = openai.OpenAI(
+                api_key=self.pplx_api_key, base_url="https://api.perplexity.ai"
             )
         except AttributeError:
             raise ValueError(
@@ -137,7 +142,7 @@ class ChatPerplexity(BaseChatModel):
                 "due to an old version of the openai package. Try upgrading it "
                 "with `pip install --upgrade openai`."
             )
-        return values
+        return self
 
     @property
     def _default_params(self) -> Dict[str, Any]:

libs/community/langchain_community/chat_models/premai.py

@@ -38,15 +38,16 @@ from langchain_core.messages import (
     ToolMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-)
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env, pre_init
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+)
 
 if TYPE_CHECKING:
     from premai.api.chat_completions.v1_chat_completions_create import (
@@ -306,10 +307,11 @@ class ChatPremAI(BaseChatModel, BaseModel):
 
     client: Any
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
     @pre_init
     def validate_environments(cls, values: Dict) -> Dict:

libs/community/langchain_community/chat_models/snowflake.py

@@ -11,7 +11,6 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
@@ -19,6 +18,7 @@ from langchain_core.utils import (
     pre_init,
 )
 from langchain_core.utils.utils import build_extra_kwargs
+from pydantic import Field, SecretStr, model_validator
 
 SUPPORTED_ROLES: List[str] = [
     "system",
@@ -126,8 +126,9 @@ class ChatSnowflakeCortex(BaseChatModel):
     snowflake_role: Optional[str] = Field(default=None, alias="role")
     """Automatically inferred from env var `SNOWFLAKE_ROLE` if not provided."""
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/solar.py

@@ -3,8 +3,8 @@
 from typing import Dict
 
 from langchain_core._api import deprecated
-from langchain_core.pydantic_v1 import Field
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from pydantic import ConfigDict, Field
 
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon
@@ -30,10 +30,11 @@ class SolarChat(SolarCommon, ChatOpenAI):
     max_tokens: int = Field(default=1024)
 
     # this is needed to match ChatOpenAI superclass
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "ignore"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="ignore",
+    )
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:

libs/community/langchain_community/chat_models/sparkllm.py

@@ -41,12 +41,12 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import (
     get_from_dict_or_env,
     get_pydantic_field_names,
 )
 from langchain_core.utils.pydantic import get_fields
+from pydantic import ConfigDict, Field, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -296,11 +296,13 @@ class ChatSparkLLM(BaseChatModel):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for API call not explicitly specified."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})
@@ -326,8 +328,9 @@ class ChatSparkLLM(BaseChatModel):
 
         return values
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         values["spark_app_id"] = get_from_dict_or_env(
             values,
             ["spark_app_id", "app_id"],

libs/community/langchain_community/chat_models/symblai_nebula.py

@@ -16,8 +16,8 @@ from langchain_core.language_models.chat_models import (
 )
 from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr
 from langchain_core.utils import convert_to_secret_str
+from pydantic import ConfigDict, Field, SecretStr
 
 
 def _convert_role(role: str) -> str:
@@ -89,11 +89,10 @@ class ChatNebula(BaseChatModel):
 
     nebula_api_key: Optional[SecretStr] = Field(None, description="Nebula API Token")
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     def __init__(self, **kwargs: Any) -> None:
         if "nebula_api_key" in kwargs:

libs/community/langchain_community/chat_models/tongyi.py

@@ -53,16 +53,17 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import (
-    BaseModel,
-    Field,
-    SecretStr,
-)
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import is_basemodel_subclass
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+)
 from requests.exceptions import HTTPError
 from tenacity import (
     before_sleep_log,
@@ -348,7 +349,7 @@ class ChatTongyi(BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class GetWeather(BaseModel):
@@ -386,7 +387,7 @@ class ChatTongyi(BaseChatModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class Joke(BaseModel):
@@ -457,8 +458,9 @@ class ChatTongyi(BaseChatModel):
     max_retries: int = 10
     """Maximum number of retries to make when generating."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     @property
     def _llm_type(self) -> str:

libs/community/langchain_community/chat_models/yi.py

@@ -25,12 +25,12 @@ from langchain_core.messages import (
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
 )
+from pydantic import ConfigDict, Field, SecretStr
 
 logger = logging.getLogger(__name__)
 
@@ -115,8 +115,9 @@ class ChatYi(BaseChatModel):
     top_p: float = 0.7
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     def __init__(self, **kwargs: Any) -> None:
         kwargs["yi_api_key"] = convert_to_secret_str(

libs/community/langchain_community/chat_models/yuan2.py

@@ -40,12 +40,12 @@ from langchain_core.messages import (
     SystemMessageChunk,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.utils import (
     get_from_dict_or_env,
     get_pydantic_field_names,
     pre_init,
 )
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -122,8 +122,9 @@ class ChatYuan2(BaseChatModel):
     repeat_penalty: Optional[float] = 1.18
     """The penalty to apply to repeated tokens."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     @property
     def lc_secrets(self) -> Dict[str, str]:
@@ -141,8 +142,9 @@ class ChatYuan2(BaseChatModel):
 
         return attributes
 
-    @root_validator(pre=True)
-    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
         extra = values.get("model_kwargs", {})

libs/community/langchain_community/chat_models/zhipuai.py

@@ -50,11 +50,11 @@ from langchain_core.output_parsers.openai_tools import (
     PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -331,7 +331,7 @@ class ChatZhipuAI(BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class GetWeather(BaseModel):
@@ -371,7 +371,7 @@ class ChatZhipuAI(BaseChatModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
 
             class Joke(BaseModel):
@@ -480,11 +480,13 @@ class ChatZhipuAI(BaseChatModel):
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict[str, Any]) -> Any:
         values["zhipuai_api_key"] = get_from_dict_or_env(
             values, ["zhipuai_api_key", "api_key"], "ZHIPUAI_API_KEY"
         )
@@ -773,7 +775,7 @@ class ChatZhipuAI(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatZhipuAI
-                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.'''
@@ -793,7 +795,7 @@ class ChatZhipuAI(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatZhipuAI
-                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.'''
@@ -814,7 +816,7 @@ class ChatZhipuAI(BaseChatModel):
             .. code-block:: python
 
                 from langchain_community.chat_models import ChatZhipuAI
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
                 class AnswerWithJustification(BaseModel):

libs/community/langchain_community/cross_encoders/fake.py

@@ -1,7 +1,7 @@
 from difflib import SequenceMatcher
 from typing import List, Tuple
 
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 
 from langchain_community.cross_encoders.base import BaseCrossEncoder
 

libs/community/langchain_community/cross_encoders/huggingface.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, List, Tuple
 
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from langchain_community.cross_encoders.base import BaseCrossEncoder
 
@@ -45,8 +45,9 @@ class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
             self.model_name, **self.model_kwargs
         )
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
         """Compute similarity scores using a HuggingFace transformer model.

libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py

@@ -1,7 +1,7 @@
 import json
 from typing import Any, Dict, List, Optional, Tuple
 
-from langchain_core.pydantic_v1 import BaseModel, root_validator
+from pydantic import BaseModel, ConfigDict, model_validator
 
 from langchain_community.cross_encoders.base import BaseCrossEncoder
 
@@ -89,12 +89,14 @@ class SagemakerEndpointCrossEncoder(BaseModel, BaseCrossEncoder):
    .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
    """
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that AWS credentials to and python package exists in environment."""
         try:
             import boto3

libs/community/langchain_community/document_compressors/dashscope_rerank.py

@@ -5,8 +5,8 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 
 from langchain_core.callbacks.base import Callbacks
 from langchain_core.documents import BaseDocumentCompressor, Document
-from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import ConfigDict, Field, model_validator
 
 
 class DashScopeRerank(BaseDocumentCompressor):
@@ -25,13 +25,15 @@ class DashScopeRerank(BaseDocumentCompressor):
     """DashScope API key. Must be specified directly or via environment variable 
         DASHSCOPE_API_KEY."""
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
 
         if not values.get("client"):

libs/community/langchain_community/document_compressors/flashrank_rerank.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Dict, Optional, Sequence
+from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence
 
 from langchain_core.callbacks.manager import Callbacks
 from langchain_core.documents import BaseDocumentCompressor, Document
-from langchain_core.pydantic_v1 import root_validator
+from pydantic import ConfigDict, model_validator
 
 if TYPE_CHECKING:
     from flashrank import Ranker, RerankRequest
@@ -33,12 +33,14 @@ class FlashrankRerank(BaseDocumentCompressor):
     prefix_metadata: str = ""
     """Prefix for flashrank_rerank metadata keys"""
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
         if "client" in values:
             return values

libs/community/langchain_community/document_compressors/jina_rerank.py

@@ -6,8 +6,8 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 import requests
 from langchain_core.callbacks import Callbacks
 from langchain_core.documents import BaseDocumentCompressor, Document
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import ConfigDict, model_validator
 
 JINA_API_URL: str = "https://api.jina.ai/v1/rerank"
 
@@ -27,12 +27,14 @@ class JinaRerank(BaseDocumentCompressor):
     user_agent: str = "langchain"
     """Identifier for the application making the request."""
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key exists in environment."""
         jina_api_key = get_from_dict_or_env(values, "jina_api_key", "JINA_API_KEY")
         user_agent = values.get("user_agent", "langchain")

libs/community/langchain_community/document_compressors/llmlingua_filter.py

@@ -8,7 +8,7 @@ from langchain_core.documents import Document
 from langchain_core.documents.compressor import (
     BaseDocumentCompressor,
 )
-from langchain_core.pydantic_v1 import root_validator
+from pydantic import ConfigDict, Field, model_validator
 
 DEFAULT_LLM_LINGUA_INSTRUCTION = (
     "Given this documents, please answer the final question"
@@ -35,9 +35,9 @@ class LLMLinguaCompressor(BaseDocumentCompressor):
     """The target number of compressed tokens"""
     rank_method: str = "longllmlingua"
     """The ranking method to use"""
-    model_config: dict = {}
+    model_configuration: dict = Field(default_factory=dict, alias="model_config")
     """Custom configuration for the model"""
-    open_api_config: dict = {}
+    open_api_config: dict = Field(default_factory=dict)
     """open_api configuration"""
     instruction: str = DEFAULT_LLM_LINGUA_INSTRUCTION
     """The instruction for the LLM"""
@@ -52,8 +52,9 @@ class LLMLinguaCompressor(BaseDocumentCompressor):
     lingua: Any
     """The instance of the llm linqua"""
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that the python package exists in environment."""
         try:
             from llmlingua import PromptCompressor
@@ -71,9 +72,11 @@ class LLMLinguaCompressor(BaseDocumentCompressor):
             )
         return values
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+        populate_by_name=True,
+    )
 
     @staticmethod
     def _format_context(docs: Sequence[Document]) -> List[str]:

libs/community/langchain_community/document_compressors/openvino_rerank.py

@@ -5,7 +5,7 @@ import numpy as np
 from langchain_core.callbacks import Callbacks
 from langchain_core.documents import Document
 from langchain_core.documents.compressor import BaseDocumentCompressor
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 
 class RerankRequest:

libs/community/langchain_community/document_compressors/rankllm_rerank.py

@@ -7,8 +7,8 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence
 from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
 from langchain_core.callbacks.manager import Callbacks
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import Field, PrivateAttr, root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import ConfigDict, Field, PrivateAttr, model_validator
 
 if TYPE_CHECKING:
     from rank_llm.data import Candidate, Query, Request
@@ -36,12 +36,14 @@ class RankLLMRerank(BaseDocumentCompressor):
     """OpenAI model name."""
     _retriever: Any = PrivateAttr()
 
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate python package exists in environment."""
 
         if not values.get("client"):

libs/community/langchain_community/document_compressors/volcengine_rerank.py

@@ -5,8 +5,8 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 
 from langchain_core.callbacks.base import Callbacks
 from langchain_core.documents import BaseDocumentCompressor, Document
-from langchain_core.pydantic_v1 import root_validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import ConfigDict, model_validator
 
 
 class VolcengineRerank(BaseDocumentCompressor):
@@ -32,13 +32,15 @@ class VolcengineRerank(BaseDocumentCompressor):
     top_n: Optional[int] = 3
     """Number of documents to return."""
 
-    class Config:
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-        extra = "forbid"
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that api key and python package exists in environment."""
 
         if not values.get("client"):

libs/community/langchain_community/document_loaders/apify_dataset.py

@@ -1,7 +1,7 @@
 from typing import Any, Callable, Dict, List
 
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, root_validator
+from pydantic import BaseModel, model_validator
 
 from langchain_community.document_loaders.base import BaseLoader
 
@@ -49,8 +49,9 @@ class ApifyDatasetLoader(BaseLoader, BaseModel):
             dataset_id=dataset_id, dataset_mapping_function=dataset_mapping_function
         )
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate environment.
 
         Args:

libs/community/langchain_community/document_loaders/base_o365.py

@@ -10,13 +10,13 @@ from enum import Enum
 from pathlib import Path, PurePath
 from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence, Union
 
-from langchain_core.pydantic_v1 import (
+from pydantic import (
     BaseModel,
-    BaseSettings,
     Field,
     FilePath,
     SecretStr,
 )
+from pydantic_settings import BaseSettings, SettingsConfigDict
 
 from langchain_community.document_loaders.base import BaseLoader
 from langchain_community.document_loaders.blob_loaders.file_system import (
@@ -34,13 +34,12 @@ CHUNK_SIZE = 1024 * 1024 * 5
 
 
 class _O365Settings(BaseSettings):
-    client_id: str = Field(..., env="O365_CLIENT_ID")
-    client_secret: SecretStr = Field(..., env="O365_CLIENT_SECRET")
+    client_id: str = Field(..., alias="O365_CLIENT_ID")
+    client_secret: SecretStr = Field(..., alias="O365_CLIENT_SECRET")
 
-    class Config:
-        case_sentive = False
-        env_file = ".env"
-        env_prefix = ""
+    model_config = SettingsConfigDict(
+        case_sensitive=False, env_file=".env", env_prefix=""
+    )
 
 
 class _O365TokenStorage(BaseSettings):

libs/community/langchain_community/document_loaders/docugami.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
 import requests
 from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, root_validator
+from pydantic import BaseModel, model_validator
 
 from langchain_community.document_loaders.base import BaseLoader
 
@@ -69,10 +69,10 @@ class DocugamiLoader(BaseLoader, BaseModel):
     """Set to False if you want to full whitespace formatting in the original
     XML doc, including indentation."""
 
-    docset_id: Optional[str]
+    docset_id: Optional[str] = None
     """The Docugami API docset ID to use."""
 
-    document_ids: Optional[Sequence[str]]
+    document_ids: Optional[Sequence[str]] = None
     """The Docugami API document IDs to use."""
 
     file_paths: Optional[Sequence[Union[Path, str]]]
@@ -81,8 +81,9 @@ class DocugamiLoader(BaseLoader, BaseModel):
     include_project_metadata_in_doc_metadata: bool = True
     """Set to True if you want to include the project metadata in the doc metadata."""
 
-    @root_validator(pre=True)
-    def validate_local_or_remote(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_local_or_remote(cls, values: Dict[str, Any]) -> Any:
         """Validate that either local file paths are given, or remote API docset ID.
 
         Args:

libs/community/langchain_community/document_loaders/dropbox.py

@@ -12,7 +12,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, root_validator
+from pydantic import BaseModel, model_validator
 
 from langchain_community.document_loaders.base import BaseLoader
 
@@ -33,8 +33,9 @@ class DropboxLoader(BaseLoader, BaseModel):
     recursive: bool = False
     """Flag to indicate whether to load files recursively from subfolders."""
 
-    @root_validator(pre=True)
-    def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_inputs(cls, values: Dict[str, Any]) -> Any:
         """Validate that either folder_path or file_paths is set, but not both."""
         if (
             values.get("dropbox_folder_path") is not None

libs/community/langchain_community/document_loaders/github.py

@@ -1,12 +1,12 @@
 import base64
 from abc import ABC
 from datetime import datetime
-from typing import Callable, Dict, Iterator, List, Literal, Optional, Union
+from typing import Any, Callable, Dict, Iterator, List, Literal, Optional, Union
 
 import requests
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, root_validator, validator
 from langchain_core.utils import get_from_dict_or_env
+from pydantic import BaseModel, model_validator, validator
 
 from langchain_community.document_loaders.base import BaseLoader
 
@@ -21,8 +21,9 @@ class BaseGitHubLoader(BaseLoader, BaseModel, ABC):
     github_api_url: str = "https://api.github.com"
     """URL of GitHub API"""
 
-    @root_validator(pre=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
         """Validate that access token exists in environment."""
         values["access_token"] = get_from_dict_or_env(
             values, "access_token", "GITHUB_PERSONAL_ACCESS_TOKEN"

libs/community/langchain_community/document_loaders/googledrive.py

@@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 
 from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, root_validator, validator
+from pydantic import BaseModel, model_validator, validator
 
 from langchain_community.document_loaders.base import BaseLoader
 
@@ -52,8 +52,9 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
     file_loader_kwargs: Dict["str", Any] = {}
     """The file loader kwargs to use."""
 
-    @root_validator(pre=True)
-    def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_inputs(cls, values: Dict[str, Any]) -> Any:
         """Validate that either folder_id or document_ids is set, but not both."""
         if values.get("folder_id") and (
             values.get("document_ids") or values.get("file_ids")

libs/community/langchain_community/document_loaders/onedrive.py

@@ -6,7 +6,7 @@ import logging
 from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence, Union
 
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import Field
+from pydantic import Field
 
 from langchain_community.document_loaders.base_o365 import (
     O365BaseLoader,

libs/community/langchain_community/document_loaders/onedrive_file.py

@@ -4,7 +4,7 @@ import tempfile
 from typing import TYPE_CHECKING, List
 
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from langchain_community.document_loaders.base import BaseLoader
 from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
@@ -21,8 +21,9 @@ class OneDriveFileLoader(BaseLoader, BaseModel):
     file: File = Field(...)
     """The file to load."""
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def load(self) -> List[Document]:
         """Load Documents"""