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836 lines
28 KiB
Python
836 lines
28 KiB
Python
from __future__ import annotations
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import asyncio
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import inspect
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import warnings
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from abc import ABC, abstractmethod
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from typing import (
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TYPE_CHECKING,
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Optional,
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Sequence,
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cast,
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)
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from langchain_core._api import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManager,
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AsyncCallbackManagerForLLMRun,
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BaseCallbackManager,
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CallbackManager,
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CallbackManagerForLLMRun,
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Callbacks,
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)
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from langchain_core.globals import get_llm_cache
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from langchain_core.language_models.base import BaseLanguageModel, LanguageModelInput
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from langchain_core.load import dumpd, dumps
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from langchain_core.messages import (
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AIMessage,
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AnyMessage,
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BaseMessage,
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BaseMessageChunk,
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HumanMessage,
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convert_to_messages,
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message_chunk_to_message,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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LLMResult,
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RunInfo,
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)
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from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.runnables.config import ensure_config, run_in_executor
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if TYPE_CHECKING:
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from langchain_core.runnables import RunnableConfig
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def _get_verbosity() -> bool:
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from langchain_core.globals import get_verbose
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return get_verbose()
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def generate_from_stream(stream: Iterator[ChatGenerationChunk]) -> ChatResult:
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"""Generate from a stream."""
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generation: Optional[ChatGenerationChunk] = None
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for chunk in stream:
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return ChatResult(
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generations=[
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ChatGeneration(
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message=message_chunk_to_message(generation.message),
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generation_info=generation.generation_info,
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)
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]
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)
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async def agenerate_from_stream(
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stream: AsyncIterator[ChatGenerationChunk],
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) -> ChatResult:
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"""Async generate from a stream."""
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generation: Optional[ChatGenerationChunk] = None
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async for chunk in stream:
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return ChatResult(
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generations=[
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ChatGeneration(
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message=message_chunk_to_message(generation.message),
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generation_info=generation.generation_info,
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)
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]
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)
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class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
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"""Base class for Chat models."""
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cache: Optional[bool] = None
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"""Whether to cache the response."""
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verbose: bool = Field(default_factory=_get_verbosity)
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"""Whether to print out response text."""
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callbacks: Callbacks = Field(default=None, exclude=True)
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"""Callbacks to add to the run trace."""
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callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
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"""[DEPRECATED] Callback manager to add to the run trace."""
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tags: Optional[List[str]] = Field(default=None, exclude=True)
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"""Tags to add to the run trace."""
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metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
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"""Metadata to add to the run trace."""
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@root_validator()
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def raise_deprecation(cls, values: Dict) -> Dict:
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"""Raise deprecation warning if callback_manager is used."""
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if values.get("callback_manager") is not None:
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warnings.warn(
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"callback_manager is deprecated. Please use callbacks instead.",
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DeprecationWarning,
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)
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values["callbacks"] = values.pop("callback_manager", None)
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return values
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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# --- Runnable methods ---
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@property
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def OutputType(self) -> Any:
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"""Get the output type for this runnable."""
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return AnyMessage
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def _convert_input(self, input: LanguageModelInput) -> PromptValue:
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if isinstance(input, PromptValue):
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return input
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elif isinstance(input, str):
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return StringPromptValue(text=input)
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elif isinstance(input, Sequence):
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return ChatPromptValue(messages=convert_to_messages(input))
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else:
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raise ValueError(
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f"Invalid input type {type(input)}. "
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"Must be a PromptValue, str, or list of BaseMessages."
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)
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def invoke(
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self,
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input: LanguageModelInput,
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config: Optional[RunnableConfig] = None,
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*,
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stop: Optional[List[str]] = None,
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**kwargs: Any,
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) -> BaseMessage:
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config = ensure_config(config)
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return cast(
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ChatGeneration,
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self.generate_prompt(
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[self._convert_input(input)],
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stop=stop,
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callbacks=config.get("callbacks"),
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tags=config.get("tags"),
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metadata=config.get("metadata"),
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run_name=config.get("run_name"),
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**kwargs,
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).generations[0][0],
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).message
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async def ainvoke(
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self,
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input: LanguageModelInput,
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config: Optional[RunnableConfig] = None,
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*,
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stop: Optional[List[str]] = None,
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**kwargs: Any,
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) -> BaseMessage:
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config = ensure_config(config)
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llm_result = await self.agenerate_prompt(
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[self._convert_input(input)],
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stop=stop,
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callbacks=config.get("callbacks"),
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tags=config.get("tags"),
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metadata=config.get("metadata"),
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run_name=config.get("run_name"),
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**kwargs,
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)
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return cast(ChatGeneration, llm_result.generations[0][0]).message
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def stream(
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self,
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input: LanguageModelInput,
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config: Optional[RunnableConfig] = None,
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*,
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stop: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Iterator[BaseMessageChunk]:
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if type(self)._stream == BaseChatModel._stream:
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# model doesn't implement streaming, so use default implementation
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yield cast(
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BaseMessageChunk, self.invoke(input, config=config, stop=stop, **kwargs)
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)
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else:
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config = ensure_config(config)
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messages = self._convert_input(input).to_messages()
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params = self._get_invocation_params(stop=stop, **kwargs)
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options = {"stop": stop, **kwargs}
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callback_manager = CallbackManager.configure(
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config.get("callbacks"),
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self.callbacks,
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self.verbose,
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config.get("tags"),
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self.tags,
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config.get("metadata"),
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self.metadata,
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)
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(run_manager,) = callback_manager.on_chat_model_start(
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dumpd(self),
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[messages],
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invocation_params=params,
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options=options,
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name=config.get("run_name"),
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batch_size=1,
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)
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generation: Optional[ChatGenerationChunk] = None
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try:
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for chunk in self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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):
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yield chunk.message
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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except BaseException as e:
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run_manager.on_llm_error(
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e,
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response=LLMResult(
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generations=[[generation]] if generation else []
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),
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)
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raise e
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else:
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run_manager.on_llm_end(LLMResult(generations=[[generation]]))
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async def astream(
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self,
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input: LanguageModelInput,
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config: Optional[RunnableConfig] = None,
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*,
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stop: Optional[List[str]] = None,
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**kwargs: Any,
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) -> AsyncIterator[BaseMessageChunk]:
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if type(self)._astream == BaseChatModel._astream:
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# model doesn't implement streaming, so use default implementation
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yield cast(
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BaseMessageChunk,
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await self.ainvoke(input, config=config, stop=stop, **kwargs),
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)
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else:
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config = ensure_config(config)
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messages = self._convert_input(input).to_messages()
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params = self._get_invocation_params(stop=stop, **kwargs)
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options = {"stop": stop, **kwargs}
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callback_manager = AsyncCallbackManager.configure(
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config.get("callbacks"),
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self.callbacks,
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self.verbose,
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config.get("tags"),
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self.tags,
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config.get("metadata"),
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self.metadata,
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)
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(run_manager,) = await callback_manager.on_chat_model_start(
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dumpd(self),
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[messages],
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invocation_params=params,
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options=options,
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name=config.get("run_name"),
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batch_size=1,
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)
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generation: Optional[ChatGenerationChunk] = None
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try:
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async for chunk in self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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):
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yield chunk.message
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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except BaseException as e:
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await run_manager.on_llm_error(
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e,
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response=LLMResult(
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generations=[[generation]] if generation else []
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),
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)
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raise e
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else:
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await run_manager.on_llm_end(
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LLMResult(generations=[[generation]]),
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)
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# --- Custom methods ---
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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return {}
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def _get_invocation_params(
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self,
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stop: Optional[List[str]] = None,
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|
**kwargs: Any,
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) -> dict:
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params = self.dict()
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params["stop"] = stop
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return {**params, **kwargs}
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def _get_llm_string(self, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
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if self.is_lc_serializable():
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params = {**kwargs, **{"stop": stop}}
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param_string = str(sorted([(k, v) for k, v in params.items()]))
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llm_string = dumps(self)
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return llm_string + "---" + param_string
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else:
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params = self._get_invocation_params(stop=stop, **kwargs)
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params = {**params, **kwargs}
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return str(sorted([(k, v) for k, v in params.items()]))
|
|
|
|
def generate(
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self,
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messages: List[List[BaseMessage]],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
*,
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|
tags: Optional[List[str]] = None,
|
|
metadata: Optional[Dict[str, Any]] = None,
|
|
run_name: Optional[str] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
"""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
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|
type (e.g., pure text completion models vs chat models).
|
|
|
|
Args:
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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
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|
prompt and additional model provider-specific output.
|
|
"""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
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|
options = {"stop": stop}
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|
|
|
callback_manager = CallbackManager.configure(
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|
callbacks,
|
|
self.callbacks,
|
|
self.verbose,
|
|
tags,
|
|
self.tags,
|
|
metadata,
|
|
self.metadata,
|
|
)
|
|
run_managers = callback_manager.on_chat_model_start(
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dumpd(self),
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messages,
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invocation_params=params,
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|
options=options,
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|
name=run_name,
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|
batch_size=len(messages),
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)
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results = []
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for i, m in enumerate(messages):
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try:
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results.append(
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self._generate_with_cache(
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m,
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stop=stop,
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run_manager=run_managers[i] if run_managers else None,
|
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**kwargs,
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)
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)
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|
except BaseException as e:
|
|
if run_managers:
|
|
run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
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|
raise e
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|
flattened_outputs = [
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LLMResult(generations=[res.generations], llm_output=res.llm_output)
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|
for res in results
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|
]
|
|
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
|
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generations = [res.generations for res in results]
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output = LLMResult(generations=generations, llm_output=llm_output)
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|
if run_managers:
|
|
run_infos = []
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|
for manager, flattened_output in zip(run_managers, flattened_outputs):
|
|
manager.on_llm_end(flattened_output)
|
|
run_infos.append(RunInfo(run_id=manager.run_id))
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|
output.run = run_infos
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|
return output
|
|
|
|
async def agenerate(
|
|
self,
|
|
messages: List[List[BaseMessage]],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
*,
|
|
tags: Optional[List[str]] = None,
|
|
metadata: Optional[Dict[str, Any]] = None,
|
|
run_name: Optional[str] = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
"""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}
|
|
|
|
callback_manager = AsyncCallbackManager.configure(
|
|
callbacks,
|
|
self.callbacks,
|
|
self.verbose,
|
|
tags,
|
|
self.tags,
|
|
metadata,
|
|
self.metadata,
|
|
)
|
|
|
|
run_managers = await callback_manager.on_chat_model_start(
|
|
dumpd(self),
|
|
messages,
|
|
invocation_params=params,
|
|
options=options,
|
|
name=run_name,
|
|
batch_size=len(messages),
|
|
)
|
|
|
|
results = await asyncio.gather(
|
|
*[
|
|
self._agenerate_with_cache(
|
|
m,
|
|
stop=stop,
|
|
run_manager=run_managers[i] if run_managers else None,
|
|
**kwargs,
|
|
)
|
|
for i, m in enumerate(messages)
|
|
],
|
|
return_exceptions=True,
|
|
)
|
|
exceptions = []
|
|
for i, res in enumerate(results):
|
|
if isinstance(res, BaseException):
|
|
if run_managers:
|
|
await run_managers[i].on_llm_error(
|
|
res, response=LLMResult(generations=[])
|
|
)
|
|
exceptions.append(res)
|
|
if exceptions:
|
|
if run_managers:
|
|
await asyncio.gather(
|
|
*[
|
|
run_manager.on_llm_end(
|
|
LLMResult(
|
|
generations=[res.generations], llm_output=res.llm_output
|
|
)
|
|
)
|
|
for run_manager, res in zip(run_managers, results)
|
|
if not isinstance(res, Exception)
|
|
]
|
|
)
|
|
raise exceptions[0]
|
|
flattened_outputs = [
|
|
LLMResult(generations=[res.generations], llm_output=res.llm_output)
|
|
for res in results
|
|
]
|
|
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
|
|
generations = [res.generations for res in results]
|
|
output = LLMResult(generations=generations, llm_output=llm_output)
|
|
await asyncio.gather(
|
|
*[
|
|
run_manager.on_llm_end(flattened_output)
|
|
for run_manager, flattened_output in zip(
|
|
run_managers, flattened_outputs
|
|
)
|
|
]
|
|
)
|
|
if run_managers:
|
|
output.run = [
|
|
RunInfo(run_id=run_manager.run_id) for run_manager in run_managers
|
|
]
|
|
return output
|
|
|
|
def generate_prompt(
|
|
self,
|
|
prompts: List[PromptValue],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
prompt_messages = [p.to_messages() for p in prompts]
|
|
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
|
|
|
|
async def agenerate_prompt(
|
|
self,
|
|
prompts: List[PromptValue],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
**kwargs: Any,
|
|
) -> LLMResult:
|
|
prompt_messages = [p.to_messages() for p in prompts]
|
|
return await self.agenerate(
|
|
prompt_messages, stop=stop, callbacks=callbacks, **kwargs
|
|
)
|
|
|
|
def _generate_with_cache(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
new_arg_supported = inspect.signature(self._generate).parameters.get(
|
|
"run_manager"
|
|
)
|
|
disregard_cache = self.cache is not None and not self.cache
|
|
llm_cache = get_llm_cache()
|
|
if llm_cache is None or disregard_cache:
|
|
# This happens when langchain.cache is None, but self.cache is True
|
|
if self.cache is not None and self.cache:
|
|
raise ValueError(
|
|
"Asked to cache, but no cache found at `langchain.cache`."
|
|
)
|
|
if new_arg_supported:
|
|
return self._generate(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
else:
|
|
return self._generate(messages, stop=stop, **kwargs)
|
|
else:
|
|
llm_string = self._get_llm_string(stop=stop, **kwargs)
|
|
prompt = dumps(messages)
|
|
cache_val = llm_cache.lookup(prompt, llm_string)
|
|
if isinstance(cache_val, list):
|
|
return ChatResult(generations=cache_val)
|
|
else:
|
|
if new_arg_supported:
|
|
result = self._generate(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
else:
|
|
result = self._generate(messages, stop=stop, **kwargs)
|
|
llm_cache.update(prompt, llm_string, result.generations)
|
|
return result
|
|
|
|
async def _agenerate_with_cache(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
new_arg_supported = inspect.signature(self._agenerate).parameters.get(
|
|
"run_manager"
|
|
)
|
|
disregard_cache = self.cache is not None and not self.cache
|
|
llm_cache = get_llm_cache()
|
|
if llm_cache is None or disregard_cache:
|
|
# This happens when langchain.cache is None, but self.cache is True
|
|
if self.cache is not None and self.cache:
|
|
raise ValueError(
|
|
"Asked to cache, but no cache found at `langchain.cache`."
|
|
)
|
|
if new_arg_supported:
|
|
return await self._agenerate(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
else:
|
|
return await self._agenerate(messages, stop=stop, **kwargs)
|
|
else:
|
|
llm_string = self._get_llm_string(stop=stop, **kwargs)
|
|
prompt = dumps(messages)
|
|
cache_val = await llm_cache.alookup(prompt, llm_string)
|
|
if isinstance(cache_val, list):
|
|
return ChatResult(generations=cache_val)
|
|
else:
|
|
if new_arg_supported:
|
|
result = await self._agenerate(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
else:
|
|
result = await self._agenerate(messages, stop=stop, **kwargs)
|
|
await llm_cache.aupdate(prompt, llm_string, result.generations)
|
|
return result
|
|
|
|
@abstractmethod
|
|
def _generate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
"""Top Level call"""
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
"""Top Level call"""
|
|
return await run_in_executor(
|
|
None,
|
|
self._generate,
|
|
messages,
|
|
stop,
|
|
run_manager.get_sync() if run_manager else None,
|
|
**kwargs,
|
|
)
|
|
|
|
def _stream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
raise NotImplementedError()
|
|
|
|
def _astream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
raise NotImplementedError()
|
|
|
|
@deprecated("0.1.7", alternative="invoke", removal="0.2.0")
|
|
def __call__(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
**kwargs: Any,
|
|
) -> BaseMessage:
|
|
generation = self.generate(
|
|
[messages], stop=stop, callbacks=callbacks, **kwargs
|
|
).generations[0][0]
|
|
if isinstance(generation, ChatGeneration):
|
|
return generation.message
|
|
else:
|
|
raise ValueError("Unexpected generation type")
|
|
|
|
async def _call_async(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
callbacks: Callbacks = None,
|
|
**kwargs: Any,
|
|
) -> BaseMessage:
|
|
result = await self.agenerate(
|
|
[messages], stop=stop, callbacks=callbacks, **kwargs
|
|
)
|
|
generation = result.generations[0][0]
|
|
if isinstance(generation, ChatGeneration):
|
|
return generation.message
|
|
else:
|
|
raise ValueError("Unexpected generation type")
|
|
|
|
@deprecated("0.1.7", 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("0.1.7", alternative="invoke", removal="0.2.0")
|
|
def predict(
|
|
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
|
|
) -> str:
|
|
if stop is None:
|
|
_stop = None
|
|
else:
|
|
_stop = list(stop)
|
|
result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
|
|
if isinstance(result.content, str):
|
|
return result.content
|
|
else:
|
|
raise ValueError("Cannot use predict when output is not a string.")
|
|
|
|
@deprecated("0.1.7", alternative="invoke", removal="0.2.0")
|
|
def predict_messages(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
*,
|
|
stop: Optional[Sequence[str]] = None,
|
|
**kwargs: Any,
|
|
) -> BaseMessage:
|
|
if stop is None:
|
|
_stop = None
|
|
else:
|
|
_stop = list(stop)
|
|
return self(messages, stop=_stop, **kwargs)
|
|
|
|
@deprecated("0.1.7", alternative="ainvoke", removal="0.2.0")
|
|
async def apredict(
|
|
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
|
|
) -> str:
|
|
if stop is None:
|
|
_stop = None
|
|
else:
|
|
_stop = list(stop)
|
|
result = await self._call_async(
|
|
[HumanMessage(content=text)], stop=_stop, **kwargs
|
|
)
|
|
if isinstance(result.content, str):
|
|
return result.content
|
|
else:
|
|
raise ValueError("Cannot use predict when output is not a string.")
|
|
|
|
@deprecated("0.1.7", alternative="ainvoke", removal="0.2.0")
|
|
async def apredict_messages(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
*,
|
|
stop: Optional[Sequence[str]] = None,
|
|
**kwargs: Any,
|
|
) -> BaseMessage:
|
|
if stop is None:
|
|
_stop = None
|
|
else:
|
|
_stop = list(stop)
|
|
return await self._call_async(messages, stop=_stop, **kwargs)
|
|
|
|
@property
|
|
def _identifying_params(self) -> Dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {}
|
|
|
|
@property
|
|
@abstractmethod
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
|
|
def dict(self, **kwargs: Any) -> Dict:
|
|
"""Return a dictionary of the LLM."""
|
|
starter_dict = dict(self._identifying_params)
|
|
starter_dict["_type"] = self._llm_type
|
|
return starter_dict
|
|
|
|
|
|
class SimpleChatModel(BaseChatModel):
|
|
"""Simple Chat Model."""
|
|
|
|
def _generate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
|
|
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,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Simpler interface."""
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
return await run_in_executor(
|
|
None,
|
|
self._generate,
|
|
messages,
|
|
stop=stop,
|
|
run_manager=run_manager.get_sync() if run_manager else None,
|
|
**kwargs,
|
|
)
|