forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
146 lines
5.3 KiB
Python
146 lines
5.3 KiB
Python
from abc import ABC, abstractmethod
|
|
from typing import List, Optional
|
|
|
|
from pydantic import BaseModel, Extra, Field, validator
|
|
|
|
import langchain
|
|
from langchain.callbacks import get_callback_manager
|
|
from langchain.callbacks.base import BaseCallbackManager
|
|
from langchain.schema import (
|
|
AIMessage,
|
|
BaseLanguageModel,
|
|
BaseMessage,
|
|
ChatGeneration,
|
|
ChatResult,
|
|
HumanMessage,
|
|
LLMResult,
|
|
PromptValue,
|
|
)
|
|
|
|
|
|
def _get_verbosity() -> bool:
|
|
return langchain.verbose
|
|
|
|
|
|
class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
|
|
verbose: bool = Field(default_factory=_get_verbosity)
|
|
"""Whether to print out response text."""
|
|
callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
arbitrary_types_allowed = True
|
|
|
|
@validator("callback_manager", pre=True, always=True)
|
|
def set_callback_manager(
|
|
cls, callback_manager: Optional[BaseCallbackManager]
|
|
) -> BaseCallbackManager:
|
|
"""If callback manager is None, set it.
|
|
|
|
This allows users to pass in None as callback manager, which is a nice UX.
|
|
"""
|
|
return callback_manager or get_callback_manager()
|
|
|
|
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
|
return {}
|
|
|
|
def generate(
|
|
self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
"""Top Level call"""
|
|
results = [self._generate(m, stop=stop) for m in messages]
|
|
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
|
|
generations = [res.generations for res in results]
|
|
return LLMResult(generations=generations, llm_output=llm_output)
|
|
|
|
async def agenerate(
|
|
self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
"""Top Level call"""
|
|
results = [await self._agenerate(m, stop=stop) for m in messages]
|
|
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
|
|
generations = [res.generations for res in results]
|
|
return LLMResult(generations=generations, llm_output=llm_output)
|
|
|
|
def generate_prompt(
|
|
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
prompt_messages = [p.to_messages() for p in prompts]
|
|
prompt_strings = [p.to_string() for p in prompts]
|
|
self.callback_manager.on_llm_start(
|
|
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
|
|
)
|
|
try:
|
|
output = self.generate(prompt_messages, stop=stop)
|
|
except (KeyboardInterrupt, Exception) as e:
|
|
self.callback_manager.on_llm_error(e, verbose=self.verbose)
|
|
raise e
|
|
self.callback_manager.on_llm_end(output, verbose=self.verbose)
|
|
return output
|
|
|
|
async def agenerate_prompt(
|
|
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
|
|
) -> LLMResult:
|
|
prompt_messages = [p.to_messages() for p in prompts]
|
|
prompt_strings = [p.to_string() for p in prompts]
|
|
if self.callback_manager.is_async:
|
|
await self.callback_manager.on_llm_start(
|
|
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
|
|
)
|
|
else:
|
|
self.callback_manager.on_llm_start(
|
|
{"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
|
|
)
|
|
try:
|
|
output = await self.agenerate(prompt_messages, stop=stop)
|
|
except (KeyboardInterrupt, Exception) as e:
|
|
if self.callback_manager.is_async:
|
|
await self.callback_manager.on_llm_error(e, verbose=self.verbose)
|
|
else:
|
|
self.callback_manager.on_llm_error(e, verbose=self.verbose)
|
|
raise e
|
|
if self.callback_manager.is_async:
|
|
await self.callback_manager.on_llm_end(output, verbose=self.verbose)
|
|
else:
|
|
self.callback_manager.on_llm_end(output, verbose=self.verbose)
|
|
return output
|
|
|
|
@abstractmethod
|
|
def _generate(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
|
|
) -> ChatResult:
|
|
"""Top Level call"""
|
|
|
|
@abstractmethod
|
|
async def _agenerate(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
|
|
) -> ChatResult:
|
|
"""Top Level call"""
|
|
|
|
def __call__(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
|
|
) -> BaseMessage:
|
|
return self._generate(messages, stop=stop).generations[0].message
|
|
|
|
def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:
|
|
result = self([HumanMessage(content=message)], stop=stop)
|
|
return result.content
|
|
|
|
|
|
class SimpleChatModel(BaseChatModel):
|
|
def _generate(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
|
|
) -> ChatResult:
|
|
output_str = self._call(messages, stop=stop)
|
|
message = AIMessage(content=output_str)
|
|
generation = ChatGeneration(message=message)
|
|
return ChatResult(generations=[generation])
|
|
|
|
@abstractmethod
|
|
def _call(
|
|
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
|
|
) -> str:
|
|
"""Simpler interface."""
|