mirror of
https://github.com/hwchase17/langchain
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105 lines
2.9 KiB
Markdown
105 lines
2.9 KiB
Markdown
# Pydantic compatibility
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- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
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- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
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- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
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## LangChain Pydantic migration plan
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As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2.
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* Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
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* During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
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migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
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User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
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Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
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the case of inheritance and in the case of passing objects to LangChain.
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**Example 1: Extending via inheritance**
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**YES**
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```python
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from pydantic.v1 import root_validator, validator
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class CustomTool(BaseTool): # BaseTool is v1 code
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x: int = Field(default=1)
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def _run(*args, **kwargs):
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return "hello"
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@validator('x') # v1 code
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@classmethod
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def validate_x(cls, x: int) -> int:
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return 1
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CustomTool(
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name='custom_tool',
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description="hello",
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x=1,
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)
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```
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Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors
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**NO**
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```python
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from pydantic import Field, field_validator # pydantic v2
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class CustomTool(BaseTool): # BaseTool is v1 code
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x: int = Field(default=1)
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def _run(*args, **kwargs):
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return "hello"
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@field_validator('x') # v2 code
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@classmethod
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def validate_x(cls, x: int) -> int:
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return 1
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CustomTool(
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name='custom_tool',
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description="hello",
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x=1,
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)
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```
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**Example 2: Passing objects to LangChain**
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**YES**
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```python
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from langchain.tools.base import Tool
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from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
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class CalculatorInput(BaseModel):
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question: str = Field()
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Tool.from_function( # <-- tool uses v1 namespace
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func=lambda question: 'hello',
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name="Calculator",
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description="useful for when you need to answer questions about math",
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args_schema=CalculatorInput
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)
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```
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**NO**
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```python
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from langchain.tools.base import Tool
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from pydantic import BaseModel, Field # <-- Uses v2 namespace
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class CalculatorInput(BaseModel):
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question: str = Field()
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Tool.from_function( # <-- tool uses v1 namespace
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func=lambda question: 'hello',
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name="Calculator",
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description="useful for when you need to answer questions about math",
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args_schema=CalculatorInput
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)
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``` |