community[patch]: Use get_fields adapter for pydantic (#25191)

Change all usages of __fields__ with get_fields adapter merged into
langchain_core.

Code mod generated using the following grit pattern:

```
engine marzano(0.1)
language python


`$X.__fields__` => `get_fields($X)` where {
    add_import(source="langchain_core.utils.pydantic", name="get_fields")
}
```

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

@@ -4,6 +4,7 @@ from typing import Dict, List, Optional, Type
 
 from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseToolkit
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.tools import BaseTool
 from langchain_community.tools.file_management.copy import CopyFileTool
@@ -24,7 +25,7 @@ _FILE_TOOLS: List[Type[BaseTool]] = [
     ListDirectoryTool,
 ]
 _FILE_TOOLS_MAP: Dict[str, Type[BaseTool]] = {
-    tool_cls.__fields__["name"].default: tool_cls for tool_cls in _FILE_TOOLS
+    get_fields(tool_cls)["name"].default: tool_cls for tool_cls in _FILE_TOOLS
 }
 
 

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -51,7 +51,7 @@ 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 is_basemodel_subclass
+from langchain_core.utils.pydantic import get_fields, is_basemodel_subclass
 
 logger = logging.getLogger(__name__)
 
@@ -397,7 +397,7 @@ class QianfanChatEndpoint(BaseChatModel):
 
         default_values = {
             name: field.default
-            for name, field in cls.__fields__.items()
+            for name, field in get_fields(cls).items()
             if field.default is not None
         }
         default_values.update(values)

libs/community/langchain_community/chat_models/minimax.py

@@ -49,6 +49,7 @@ 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
 
 logger = logging.getLogger(__name__)
 
@@ -399,7 +400,7 @@ class MiniMaxChat(BaseChatModel):
 
         default_values = {
             name: field.default
-            for name, field in cls.__fields__.items()
+            for name, field in get_fields(cls).items()
             if field.default is not None
         }
         default_values.update(values)

libs/community/langchain_community/chat_models/sparkllm.py

@@ -40,6 +40,7 @@ from langchain_core.utils import (
     get_from_dict_or_env,
     get_pydantic_field_names,
 )
+from langchain_core.utils.pydantic import get_fields
 
 logger = logging.getLogger(__name__)
 
@@ -308,7 +309,7 @@ class ChatSparkLLM(BaseChatModel):
         # put extra params into model_kwargs
         default_values = {
             name: field.default
-            for name, field in cls.__fields__.items()
+            for name, field in get_fields(cls).items()
             if field.default is not None
         }
         values["model_kwargs"]["temperature"] = default_values.get("temperature")

libs/community/langchain_community/embeddings/gigachat.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.utils import pre_init
+from langchain_core.utils.pydantic import get_fields
 
 logger = logging.getLogger(__name__)
 
@@ -88,7 +89,7 @@ class GigaChatEmbeddings(BaseModel, Embeddings):
                 "Could not import gigachat python package. "
                 "Please install it with `pip install gigachat`."
             )
-        fields = set(cls.__fields__.keys())
+        fields = set(get_fields(cls).keys())
         diff = set(values.keys()) - fields
         if diff:
             logger.warning(f"Extra fields {diff} in GigaChat class")

libs/community/langchain_community/llms/bananadev.py

@@ -5,6 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -46,7 +47,7 @@ class Banana(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/beam.py

@@ -11,6 +11,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 logger = logging.getLogger(__name__)
 
@@ -78,7 +79,7 @@ class Beam(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/cerebriumai.py

@@ -6,6 +6,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -45,7 +46,7 @@ class CerebriumAI(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/edenai.py

@@ -11,6 +11,7 @@ from langchain_core.callbacks import (
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 from langchain_community.utilities.requests import Requests
@@ -82,7 +83,7 @@ class EdenAI(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/gigachat.py

@@ -12,6 +12,7 @@ from langchain_core.language_models.llms import BaseLLM
 from langchain_core.load.serializable import Serializable
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
 from langchain_core.utils import pre_init
+from langchain_core.utils.pydantic import get_fields
 
 if TYPE_CHECKING:
     import gigachat
@@ -123,7 +124,7 @@ class _BaseGigaChat(Serializable):
                 "Could not import gigachat python package. "
                 "Please install it with `pip install gigachat`."
             )
-        fields = set(cls.__fields__.keys())
+        fields = set(get_fields(cls).keys())
         diff = set(values.keys()) - fields
         if diff:
             logger.warning(f"Extra fields {diff} in GigaChat class")

libs/community/langchain_community/llms/gooseai.py

@@ -5,6 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 logger = logging.getLogger(__name__)
 
@@ -68,7 +69,7 @@ class GooseAI(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/loading.py

@@ -6,6 +6,7 @@ from typing import Any, Union
 
 import yaml
 from langchain_core.language_models.llms import BaseLLM
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms import get_type_to_cls_dict
 
@@ -26,7 +27,7 @@ def load_llm_from_config(config: dict, **kwargs: Any) -> BaseLLM:
     llm_cls = type_to_cls_dict[config_type]()
 
     load_kwargs = {}
-    if _ALLOW_DANGEROUS_DESERIALIZATION_ARG in llm_cls.__fields__:
+    if _ALLOW_DANGEROUS_DESERIALIZATION_ARG in get_fields(llm_cls):
         load_kwargs[_ALLOW_DANGEROUS_DESERIALIZATION_ARG] = kwargs.get(
             _ALLOW_DANGEROUS_DESERIALIZATION_ARG, False
         )

libs/community/langchain_community/llms/modal.py

@@ -5,6 +5,7 @@ import requests
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -40,7 +41,7 @@ class Modal(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/openai.py

@@ -34,6 +34,7 @@ from langchain_core.utils import (
     get_pydantic_field_names,
     pre_init,
 )
+from langchain_core.utils.pydantic import get_fields
 from langchain_core.utils.utils import build_extra_kwargs
 
 from langchain_community.utils.openai import is_openai_v1
@@ -1016,7 +1017,7 @@ class OpenAIChat(BaseLLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/petals.py

@@ -5,6 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -68,7 +69,7 @@ class Petals(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/pipelineai.py

@@ -10,6 +10,7 @@ from langchain_core.pydantic_v1 import (
     root_validator,
 )
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -47,7 +48,7 @@ class PipelineAI(LLM, BaseModel):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("pipeline_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/llms/replicate.py

@@ -8,6 +8,7 @@ from langchain_core.language_models.llms import LLM
 from langchain_core.outputs import GenerationChunk
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 if TYPE_CHECKING:
     from replicate.prediction import Prediction
@@ -75,7 +76,7 @@ class Replicate(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         input = values.pop("input", {})
         if input:

libs/community/langchain_community/llms/stochasticai.py

@@ -7,6 +7,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import LLM
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.llms.utils import enforce_stop_tokens
 
@@ -41,7 +42,7 @@ class StochasticAI(LLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {field.alias for field in get_fields(cls).values()}
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):

libs/community/langchain_community/retrievers/docarray.py

@@ -6,6 +6,7 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.retrievers import BaseRetriever
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.vectorstores.utils import maximal_marginal_relevance
 
@@ -182,7 +183,7 @@ class DocArrayRetriever(BaseRetriever):
             ValueError: If the document doesn't contain the content field
         """
 
-        fields = doc.keys() if isinstance(doc, dict) else doc.__fields__
+        fields = doc.keys() if isinstance(doc, dict) else get_fields(doc)
 
         if self.content_field not in fields:
             raise ValueError(

libs/community/langchain_community/vectorstores/redis/schema.py

@@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional, Union
 import numpy as np
 import yaml
 from langchain_core.pydantic_v1 import BaseModel, Field, validator
+from langchain_core.utils.pydantic import get_fields
 from typing_extensions import TYPE_CHECKING, Literal
 
 from langchain_community.vectorstores.redis.constants import REDIS_VECTOR_DTYPE_MAP
@@ -255,7 +256,7 @@ class RedisModel(BaseModel):
         if self.is_empty:
             return redis_fields
 
-        for field_name in self.__fields__.keys():
+        for field_name in get_fields(self).keys():
             if field_name not in ["content_key", "content_vector_key", "extra"]:
                 field_group = getattr(self, field_name)
                 if field_group is not None:
@@ -269,7 +270,7 @@ class RedisModel(BaseModel):
         if self.is_empty:
             return keys
 
-        for field_name in self.__fields__.keys():
+        for field_name in get_fields(self).keys():
             field_group = getattr(self, field_name)
             if field_group is not None:
                 for field in field_group:

libs/community/tests/integration_tests/llms/utils.py

@@ -1,6 +1,7 @@
 """Utils for LLM Tests."""
 
 from langchain_core.language_models.llms import BaseLLM
+from langchain_core.utils.pydantic import get_fields
 
 
 def assert_llm_equality(llm: BaseLLM, loaded_llm: BaseLLM) -> None:
@@ -9,7 +10,7 @@ def assert_llm_equality(llm: BaseLLM, loaded_llm: BaseLLM) -> None:
     assert type(llm) is type(loaded_llm)
     # Client field can be session based, so hash is different despite
     # all other values being the same, so just assess all other fields
-    for field in llm.__fields__.keys():
+    for field in get_fields(llm).keys():
         if field != "client" and field != "pipeline":
             val = getattr(llm, field)
             new_val = getattr(loaded_llm, field)

libs/community/tests/unit_tests/callbacks/test_callback_manager.py

@@ -6,6 +6,7 @@ import pytest
 from langchain_core.callbacks.manager import CallbackManager, trace_as_chain_group
 from langchain_core.outputs import LLMResult
 from langchain_core.tracers.langchain import LangChainTracer, wait_for_all_tracers
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.callbacks import get_openai_callback
 from langchain_community.callbacks.manager import get_bedrock_anthropic_callback
@@ -44,7 +45,7 @@ def test_callback_manager_configure_context_vars(
                                 "completion_tokens": 1,
                                 "total_tokens": 3,
                             },
-                            "model_name": BaseOpenAI.__fields__["model_name"].default,
+                            "model_name": get_fields(BaseOpenAI)["model_name"].default,
                         },
                     )
                     mngr.on_llm_start({}, ["prompt"])[0].on_llm_end(response)
@@ -74,7 +75,7 @@ def test_callback_manager_configure_context_vars(
                                 "completion_tokens": 1,
                                 "total_tokens": 3,
                             },
-                            "model_name": BaseOpenAI.__fields__["model_name"].default,
+                            "model_name": get_fields(BaseOpenAI)["model_name"].default,
                         },
                     )
                     mngr.on_llm_start({}, ["prompt"])[0].on_llm_end(response)

libs/community/tests/unit_tests/callbacks/test_openai_info.py

@@ -4,6 +4,7 @@ from uuid import uuid4
 import numpy as np
 import pytest
 from langchain_core.outputs import LLMResult
+from langchain_core.utils.pydantic import get_fields
 
 from langchain_community.callbacks import OpenAICallbackHandler
 from langchain_community.llms.openai import BaseOpenAI
@@ -23,7 +24,7 @@ def test_on_llm_end(handler: OpenAICallbackHandler) -> None:
                 "completion_tokens": 1,
                 "total_tokens": 3,
             },
-            "model_name": BaseOpenAI.__fields__["model_name"].default,
+            "model_name": get_fields(BaseOpenAI)["model_name"].default,
         },
     )
     handler.on_llm_end(response)

libs/community/tests/unit_tests/tools/test_exported.py

@@ -1,6 +1,7 @@
 from typing import List, Type
 
 from langchain_core.tools import BaseTool, StructuredTool
+from langchain_core.utils.pydantic import get_fields
 
 import langchain_community.tools
 from langchain_community.tools import _DEPRECATED_TOOLS
@@ -22,7 +23,7 @@ def _get_tool_classes(skip_tools_without_default_names: bool) -> List[Type[BaseT
         if isinstance(tool_class, type) and issubclass(tool_class, BaseTool):
             if tool_class in _EXCLUDE:
                 continue
-            if skip_tools_without_default_names and tool_class.__fields__[
+            if skip_tools_without_default_names and get_fields(tool_class)[
                 "name"
             ].default in [  # type: ignore
                 None,
@@ -36,6 +37,6 @@ def _get_tool_classes(skip_tools_without_default_names: bool) -> List[Type[BaseT
 def test_tool_names_unique() -> None:
     """Test that the default names for our core tools are unique."""
     tool_classes = _get_tool_classes(skip_tools_without_default_names=True)
-    names = sorted([tool_cls.__fields__["name"].default for tool_cls in tool_classes])
+    names = sorted([get_fields(tool_cls)["name"].default for tool_cls in tool_classes])
     duplicated_names = [name for name in names if names.count(name) > 1]
     assert not duplicated_names