chore: delete deprecated items (#33192)

Removed:
- `libs/core/langchain_core/chat_history.py`: `add_user_message` and
`add_ai_message` in favor of `add_messages` and `aadd_messages`
- `libs/core/langchain_core/language_models/base.py`: `predict`,
`predict_messages`, and async versions in favor of `invoke`. removed
`_all_required_field_names` since it was a wrapper on
`get_pydantic_field_names`
- `libs/core/langchain_core/language_models/chat_models.py`:
`callback_manager` param in favor of `callbacks`. `__call__` and
`call_as_llm` method in favor of `invoke`
- `libs/core/langchain_core/language_models/llms.py`: `callback_manager`
param in favor of `callbacks`. `__call__`, `predict`, `apredict`, and
`apredict_messages` methods in favor of `invoke`
- `libs/core/langchain_core/prompts/chat.py`: `from_role_strings` and
`from_strings` in favor of `from_messages`
- `libs/core/langchain_core/prompts/pipeline.py`: removed
`PipelinePromptTemplate`
- `libs/core/langchain_core/prompts/prompt.py`: `input_variables` param
on `from_file` as it wasn't used
- `libs/core/langchain_core/tools/base.py`: `callback_manager` param in
favor of `callbacks`
- `libs/core/langchain_core/tracers/context.py`: `tracing_enabled` in
favor of `tracing_enabled_v2`
- `libs/core/langchain_core/tracers/langchain_v1.py`: entire module
- `libs/core/langchain_core/utils/loading.py`: entire module,
`try_load_from_hub`
- `libs/core/langchain_core/vectorstores/in_memory.py`: `upsert` in
favor of `add_documents`
- `libs/standard-tests/langchain_tests/integration_tests/chat_models.py`
and `libs/standard-tests/langchain_tests/unit_tests/chat_models.py`:
`tool_choice_value` as models should accept `tool_choice="any"`
- `langchain` will consequently no longer expose these items if it was
previously

---------

Co-authored-by: Mohammad Mohtashim <[email protected]>
Co-authored-by: Caspar Broekhuizen <[email protected]>
Co-authored-by: ccurme <[email protected]>
Co-authored-by: Christophe Bornet <[email protected]>
Co-authored-by: Eugene Yurtsev <[email protected]>
Co-authored-by: Sadra Barikbin <[email protected]>
Co-authored-by: Vadym Barda <[email protected]>

Author: 8 people

libs/cli/langchain_cli/namespaces/migrate/.grit/patterns/langchain_to_core.json

@@ -17,14 +17,12 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING
 
 from pydantic import BaseModel, Field
 
 from langchain_core.messages import (
-    AIMessage,
     BaseMessage,
-    HumanMessage,
     get_buffer_string,
 )
 from langchain_core.runnables.config import run_in_executor
@@ -126,40 +124,6 @@ async def aget_messages(self) -> list[BaseMessage]:
         """
         return await run_in_executor(None, lambda: self.messages)
 
-    def add_user_message(self, message: Union[HumanMessage, str]) -> None:
-        """Convenience method for adding a human message string to the store.
-
-        !!! note
-            This is a convenience method. Code should favor the bulk ``add_messages``
-            interface instead to save on round-trips to the persistence layer.
-
-        This method may be deprecated in a future release.
-
-        Args:
-            message: The human message to add to the store.
-        """
-        if isinstance(message, HumanMessage):
-            self.add_message(message)
-        else:
-            self.add_message(HumanMessage(content=message))
-
-    def add_ai_message(self, message: Union[AIMessage, str]) -> None:
-        """Convenience method for adding an AI message string to the store.
-
-        !!! note
-            This is a convenience method. Code should favor the bulk ``add_messages``
-            interface instead to save on round-trips to the persistence layer.
-
-        This method may be deprecated in a future release.
-
-        Args:
-            message: The AI message to add.
-        """
-        if isinstance(message, AIMessage):
-            self.add_message(message)
-        else:
-            self.add_message(AIMessage(content=message))
-
     def add_message(self, message: BaseMessage) -> None:
         """Add a Message object to the store.
 

libs/core/langchain_core/chat_history.py

@@ -20,7 +20,6 @@
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from typing_extensions import TypedDict, override
 
-from langchain_core._api import deprecated
 from langchain_core.caches import BaseCache
 from langchain_core.callbacks import Callbacks
 from langchain_core.globals import get_verbose
@@ -37,7 +36,6 @@
     StringPromptValue,
 )
 from langchain_core.runnables import Runnable, RunnableSerializable
-from langchain_core.utils import get_pydantic_field_names
 
 if TYPE_CHECKING:
     from langchain_core.outputs import LLMResult
@@ -259,102 +257,6 @@ def with_structured_output(
         # generate responses that match a given schema.
         raise NotImplementedError
 
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    @abstractmethod
-    def predict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        """Pass a single string input to the model and return a string.
-
-        Use this method when passing in raw text. If you want to pass in specific types
-        of chat messages, use predict_messages.
-
-        Args:
-            text: String input to pass to the model.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Returns:
-            Top model prediction as a string.
-
-        """
-
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    @abstractmethod
-    def predict_messages(
-        self,
-        messages: list[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        """Pass a message sequence to the model and return a message.
-
-        Use this method when passing in chat messages. If you want to pass in raw text,
-        use predict.
-
-        Args:
-            messages: A sequence of chat messages corresponding to a single model input.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Returns:
-            Top model prediction as a message.
-
-        """
-
-    @deprecated("0.1.7", alternative="ainvoke", removal="1.0")
-    @abstractmethod
-    async def apredict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        """Asynchronously pass a string to the model and return a string.
-
-        Use this method when calling pure text generation models and only the top
-        candidate generation is needed.
-
-        Args:
-            text: String input to pass to the model.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Returns:
-            Top model prediction as a string.
-
-        """
-
-    @deprecated("0.1.7", alternative="ainvoke", removal="1.0")
-    @abstractmethod
-    async def apredict_messages(
-        self,
-        messages: list[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        """Asynchronously pass messages to the model and return a message.
-
-        Use this method when calling chat models and only the top candidate generation
-        is needed.
-
-        Args:
-            messages: A sequence of chat messages corresponding to a single model input.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Returns:
-            Top model prediction as a message.
-
-        """
-
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""
@@ -417,12 +319,3 @@ def get_num_tokens_from_messages(
                 stacklevel=2,
             )
         return sum(self.get_num_tokens(get_buffer_string([m])) for m in messages)
-
-    @classmethod
-    def _all_required_field_names(cls) -> set:
-        """DEPRECATED: Kept for backwards compatibility.
-
-        Use ``get_pydantic_field_names``.
-
-        """
-        return get_pydantic_field_names(cls)

libs/core/langchain_core/language_models/base.py

@@ -6,22 +6,19 @@
 import inspect
 import json
 import typing
-import warnings
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator, Iterator, Sequence
 from functools import cached_property
 from operator import itemgetter
 from typing import TYPE_CHECKING, Any, Callable, Literal, Optional, Union, cast
 
-from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import override
 
-from langchain_core._api import deprecated
 from langchain_core.caches import BaseCache
 from langchain_core.callbacks import (
     AsyncCallbackManager,
     AsyncCallbackManagerForLLMRun,
-    BaseCallbackManager,
     CallbackManager,
     CallbackManagerForLLMRun,
     Callbacks,
@@ -42,7 +39,6 @@
     AIMessageChunk,
     AnyMessage,
     BaseMessage,
-    HumanMessage,
     convert_to_messages,
     is_data_content_block,
     message_chunk_to_message,
@@ -319,16 +315,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     """  # noqa: E501
 
-    callback_manager: Optional[BaseCallbackManager] = deprecated(
-        name="callback_manager", since="0.1.7", removal="1.0", alternative="callbacks"
-    )(
-        Field(
-            default=None,
-            exclude=True,
-            description="Callback manager to add to the run trace.",
-        )
-    )
-
     rate_limiter: Optional[BaseRateLimiter] = Field(default=None, exclude=True)
     "An optional rate limiter to use for limiting the number of requests."
 
@@ -373,27 +359,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     """
 
-    @model_validator(mode="before")
-    @classmethod
-    def raise_deprecation(cls, values: dict) -> Any:
-        """Emit deprecation warning if ``callback_manager`` is used.
-
-        Args:
-            values (Dict): Values to validate.
-
-        Returns:
-            Dict: Validated values.
-
-        """
-        if values.get("callback_manager") is not None:
-            warnings.warn(
-                "callback_manager is deprecated. Please use callbacks instead.",
-                DeprecationWarning,
-                stacklevel=5,
-            )
-            values["callbacks"] = values.pop("callback_manager", None)
-        return values
-
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
     )
@@ -1455,40 +1420,6 @@ async def _astream(
                 break
             yield item  # type: ignore[misc]
 
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    def __call__(
-        self,
-        messages: list[BaseMessage],
-        stop: Optional[list[str]] = None,
-        callbacks: Callbacks = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        """Call the model.
-
-        Args:
-            messages: List of messages.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Raises:
-            ValueError: If the generation is not a chat generation.
-
-        Returns:
-            The model output message.
-
-        """
-        generation = self.generate(
-            [messages], stop=stop, callbacks=callbacks, **kwargs
-        ).generations[0][0]
-        if isinstance(generation, ChatGeneration):
-            return generation.message
-        msg = "Unexpected generation type"
-        raise ValueError(msg)
-
     async def _call_async(
         self,
         messages: list[BaseMessage],
@@ -1505,91 +1436,6 @@ async def _call_async(
         msg = "Unexpected generation type"
         raise ValueError(msg)
 
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    def call_as_llm(
-        self, message: str, stop: Optional[list[str]] = None, **kwargs: Any
-    ) -> str:
-        """Call the model.
-
-        Args:
-            message: The input message.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Returns:
-            The model output string.
-
-        """
-        return self.predict(message, stop=stop, **kwargs)
-
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    @override
-    def predict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        """Predict the next message.
-
-        Args:
-            text: The input message.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
-
-        Raises:
-            ValueError: If the output is not a string.
-
-        Returns:
-            The predicted output string.
-
-        """
-        stop_ = None if stop is None else list(stop)
-        result = self([HumanMessage(content=text)], stop=stop_, **kwargs)
-        if isinstance(result.content, str):
-            return result.content
-        msg = "Cannot use predict when output is not a string."
-        raise ValueError(msg)
-
-    @deprecated("0.1.7", alternative="invoke", removal="1.0")
-    @override
-    def predict_messages(
-        self,
-        messages: list[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        stop_ = None if stop is None else list(stop)
-        return self(messages, stop=stop_, **kwargs)
-
-    @deprecated("0.1.7", alternative="ainvoke", removal="1.0")
-    @override
-    async def apredict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        stop_ = None if stop is None else list(stop)
-        result = await self._call_async(
-            [HumanMessage(content=text)], stop=stop_, **kwargs
-        )
-        if isinstance(result.content, str):
-            return result.content
-        msg = "Cannot use predict when output is not a string."
-        raise ValueError(msg)
-
-    @deprecated("0.1.7", alternative="ainvoke", removal="1.0")
-    @override
-    async def apredict_messages(
-        self,
-        messages: list[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        stop_ = None if stop is None else list(stop)
-        return await self._call_async(messages, stop=stop_, **kwargs)
-
     @property
     @abstractmethod
     def _llm_type(self) -> str: