How to use LangChain with different Pydantic versions

• Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/).
• v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/).
• Pydantic 1 End of Life was in June 2024. LangChain will be dropping support for Pydantic 1 in the near future, and likely migrating internally to Pydantic 2. The timeline is tentatively September. This change will be accompanied by a minor version bump in the main langchain packages to version 0.3.x.

As of langchain>=0.0.267, LangChain allows users to install either Pydantic V1 or V2.

Internally, LangChain continues to use the Pydantic V1 via the v1 namespace of Pydantic 2.

Because Pydantic does not support mixing .v1 and .v2 objects, users should be aware of a number of issues when using LangChain with Pydantic.

1. Passing Pydantic objects to LangChain APIs

Most LangChain APIs that accept Pydantic objects have been updated to accept both Pydantic v1 and v2 objects.

• Pydantic v1 objects correspond to subclasses of pydantic.BaseModel if pydantic 1 is installed or subclasses of pydantic.v1.BaseModel if pydantic 2 is installed.
• Pydantic v2 objects correspond to subclasses of pydantic.BaseModel if pydantic 2 is installed.

API	Pydantic 1	Pydantic 2
BaseChatModel.bind_tools	Yes	langchain-core>=0.2.23, appropriate version of partner package
BaseChatModel.with_structured_output	Yes	langchain-core>=0.2.23, appropriate version of partner package
Tool.from_function	Yes	langchain-core>=0.2.23
StructuredTool.from_function	Yes	langchain-core>=0.2.23

Partner packages that accept pydantic v2 objects via bind_tools or with_structured_output APIs:

Package Name	pydantic v1	pydantic v2
langchain-mistralai	Yes	>=0.1.11
langchain-anthropic	Yes	>=0.1.21
langchain-robocorp	Yes	>=0.0.10
langchain-openai	Yes	>=0.1.19
langchain-fireworks	Yes	>=0.1.5

Additional partner packages will be updated to accept Pydantic v2 objects in the future.

If you are still seeing issues with these APIs or other APIs that accept Pydantic objects, please open an issue, and we'll address it.

Example:

Prior to langchain-core<0.2.23, use Pydantic v1 objects when passing to LangChain APIs.

from langchain_openai import ChatOpenAI
from pydantic.v1 import BaseModel # <-- Note v1 namespace

class Person(BaseModel):
    """Personal information"""
    name: str
    
model = ChatOpenAI()
model = model.with_structured_output(Person)

model.invoke('Bob is a person.')

API Reference:ChatOpenAI

After langchain-core>=0.2.23, use either Pydantic v1 or v2 objects when passing to LangChain APIs.

from langchain_openai import ChatOpenAI
from pydantic import BaseModel

class Person(BaseModel):
    """Personal information"""
    name: str
    
    
model = ChatOpenAI()
model = model.with_structured_output(Person)

model.invoke('Bob is a person.')

API Reference:ChatOpenAI

2. Sub-classing LangChain models

Because LangChain internally uses Pydantic v1, if you are sub-classing LangChain models, you should use Pydantic v1 primitives.

Example 1: Extending via inheritance

YES

from pydantic.v1 import validator
from langchain_core.tools import BaseTool

class CustomTool(BaseTool): # BaseTool is v1 code
    x: int = Field(default=1)

    def _run(*args, **kwargs):
        return "hello"

    @validator('x') # v1 code
    @classmethod
    def validate_x(cls, x: int) -> int:
        return 1
    

CustomTool(
    name='custom_tool',
    description="hello",
    x=1,
)

API Reference:BaseTool

Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors

NO

from pydantic import Field, field_validator # pydantic v2
from langchain_core.tools import BaseTool

class CustomTool(BaseTool): # BaseTool is v1 code
    x: int = Field(default=1)

    def _run(*args, **kwargs):
        return "hello"

    @field_validator('x') # v2 code
    @classmethod
    def validate_x(cls, x: int) -> int:
        return 1
    

CustomTool( 
    name='custom_tool',
    description="hello",
    x=1,
)

API Reference:BaseTool

3. Disable run-time validation for LangChain objects used inside Pydantic v2 models

e.g.,

from typing import Annotated

from langchain_openai import ChatOpenAI # <-- ChatOpenAI uses pydantic v1
from pydantic import BaseModel, SkipValidation


class Foo(BaseModel): # <-- BaseModel is from Pydantic v2
    model: Annotated[ChatOpenAI, SkipValidation()]

Foo(model=ChatOpenAI(api_key="hello"))

API Reference:ChatOpenAI

4: LangServe cannot generate OpenAPI docs if running Pydantic 2

If you are using Pydantic 2, you will not be able to generate OpenAPI docs using LangServe.

If you need OpenAPI docs, your options are to either install Pydantic 1:

pip install pydantic==1.10.17

or else to use the APIHandler object in LangChain to manually create the routes for your API.

See: https://python.langchain.com/v0.2/docs/langserve/#pydantic