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core[minor], langchain[minor]: deprecate old Chain and LLM methods (#15499)

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Bagatur
2024-01-05 11:58:35 -05:00
committed by GitHub
parent fd5fbb507d
commit 00dfbd2a99
4 changed files with 265 additions and 135 deletions
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24
libs/core/langchain_core/language_models/base.py
View File

@@ -15,6 +15,7 @@ from typing import (
from typing_extensions import TypeAlias
from langchain_core._api import deprecated
from langchain_core.messages import AnyMessage, BaseMessage, get_buffer_string
from langchain_core.prompt_values import PromptValue
from langchain_core.runnables import Runnable, RunnableSerializable
@@ -60,17 +61,6 @@ class BaseLanguageModel(
"""Abstract base class for interfacing with language models.
All language model wrappers inherit from BaseLanguageModel.
Exposes three main methods:
- generate_prompt: generate language model outputs for a sequence of prompt
values. A prompt value is a model input that can be converted to any language
model input format (string or messages).
- predict: pass in a single string to a language model and return a string
prediction.
- predict_messages: pass in a sequence of BaseMessages (corresponding to a single
model call) to a language model and return a BaseMessage prediction.
Each of these has an equivalent asynchronous method.
"""
@property
@@ -160,11 +150,12 @@ class BaseLanguageModel(
prompt and additional model provider-specific output.
"""
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
@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 prediction.
"""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.
@@ -180,6 +171,7 @@ class BaseLanguageModel(
Top model prediction as a string.
"""
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
@abstractmethod
def predict_messages(
self,
@@ -188,7 +180,7 @@ class BaseLanguageModel(
stop: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> BaseMessage:
"""Pass a message sequence to the model and return a message prediction.
"""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.
@@ -204,11 +196,12 @@ class BaseLanguageModel(
Top model prediction as a message.
"""
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
@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 prediction.
"""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.
@@ -224,6 +217,7 @@ class BaseLanguageModel(
Top model prediction as a string.
"""
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
@abstractmethod
async def apredict_messages(
self,
@@ -232,7 +226,7 @@ class BaseLanguageModel(
stop: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> BaseMessage:
"""Asynchronously pass messages to the model and return a message prediction.
"""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.

59
libs/core/langchain_core/language_models/chat_models.py
View File

@@ -16,6 +16,7 @@ from typing import (
cast,
)
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManager,
AsyncCallbackManagerForLLMRun,
@@ -108,7 +109,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
callbacks: Callbacks = Field(default=None, exclude=True)
"""Callbacks to add to the run trace."""
callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""Callback manager to add to the run trace."""
"""[DEPRECATED] Callback manager to add to the run trace."""
tags: Optional[List[str]] = Field(default=None, exclude=True)
"""Tags to add to the run trace."""
metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
@@ -345,7 +346,30 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
run_name: Optional[str] = None,
**kwargs: Any,
) -> LLMResult:
"""Top Level call"""
"""Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of 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.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
@@ -407,7 +431,30 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
run_name: Optional[str] = None,
**kwargs: Any,
) -> LLMResult:
"""Top Level call"""
"""Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of 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.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
@@ -632,6 +679,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
) -> AsyncIterator[ChatGenerationChunk]:
raise NotImplementedError()
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def __call__(
self,
messages: List[BaseMessage],
@@ -663,11 +711,13 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
else:
raise ValueError("Unexpected generation type")
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def call_as_llm(
self, message: str, stop: Optional[List[str]] = None, **kwargs: Any
) -> str:
return self.predict(message, stop=stop, **kwargs)
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def predict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
@@ -681,6 +731,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
else:
raise ValueError("Cannot use predict when output is not a string.")
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def predict_messages(
self,
messages: List[BaseMessage],
@@ -694,6 +745,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
_stop = list(stop)
return self(messages, stop=_stop, **kwargs)
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
async def apredict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
@@ -709,6 +761,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
else:
raise ValueError("Cannot use predict when output is not a string.")
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
async def apredict_messages(
self,
messages: List[BaseMessage],

60
libs/core/langchain_core/language_models/llms.py
View File

@@ -36,6 +36,7 @@ from tenacity import (
wait_exponential,
)
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManager,
AsyncCallbackManagerForLLMRun,
@@ -157,14 +158,17 @@ class BaseLLM(BaseLanguageModel[str], ABC):
It should take in a prompt and return a string."""
cache: Optional[bool] = None
"""Whether to cache the response."""
verbose: bool = Field(default_factory=_get_verbosity)
"""Whether to print out response text."""
callbacks: Callbacks = Field(default=None, exclude=True)
callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""Callbacks to add to the run trace."""
tags: Optional[List[str]] = Field(default=None, exclude=True)
"""Tags to add to the run trace."""
metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
"""Metadata to add to the run trace."""
callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""[DEPRECATED]"""
class Config:
"""Configuration for this pydantic object."""
@@ -576,7 +580,30 @@ class BaseLLM(BaseLanguageModel[str], ABC):
run_name: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
"""Pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of string prompts.
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.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
if not isinstance(prompts, list):
raise ValueError(
"Argument 'prompts' is expected to be of type List[str], received"
@@ -754,7 +781,30 @@ class BaseLLM(BaseLanguageModel[str], ABC):
run_name: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
"""Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of string prompts.
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.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
# Create callback managers
if isinstance(callbacks, list) and (
isinstance(callbacks[0], (list, BaseCallbackManager))
@@ -927,6 +977,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
)
return result.generations[0][0].text
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def predict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
@@ -936,6 +987,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
_stop = list(stop)
return self(text, stop=_stop, **kwargs)
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
def predict_messages(
self,
messages: List[BaseMessage],
@@ -951,6 +1003,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
content = self(text, stop=_stop, **kwargs)
return AIMessage(content=content)
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
async def apredict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
@@ -960,6 +1013,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
_stop = list(stop)
return await self._call_async(text, stop=_stop, **kwargs)
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
async def apredict_messages(
self,
messages: List[BaseMessage],