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https://github.com/hwchase17/langchain
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a79345f199
#16396 Fixed 1. golden_query 2. google_lens 3. memorize 4. merriam_webster 5. open_weather_map 6. pub_med 7. stack_exchange 8. generate_image 9. wikipedia
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from abc import abstractmethod
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from typing import Any, Optional, Protocol, Sequence, runtime_checkable
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from langchain_core.callbacks import (
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AsyncCallbackManagerForToolRun,
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CallbackManagerForToolRun,
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)
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from langchain_core.pydantic_v1 import Field
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from langchain_core.tools import BaseTool
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from langchain_community.llms.gradient_ai import TrainResult
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@runtime_checkable
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class TrainableLLM(Protocol):
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"""Protocol for trainable language models."""
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@abstractmethod
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def train_unsupervised(
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self,
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inputs: Sequence[str],
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**kwargs: Any,
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) -> TrainResult:
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...
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@abstractmethod
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async def atrain_unsupervised(
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self,
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inputs: Sequence[str],
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**kwargs: Any,
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) -> TrainResult:
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...
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class Memorize(BaseTool):
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"""Tool that trains a language model."""
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name: str = "memorize"
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description: str = (
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"Useful whenever you observed novel information "
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"from previous conversation history, "
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"i.e., another tool's action outputs or human comments. "
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"The action input should include observed information in detail, "
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"then the tool will fine-tune yourself to remember it."
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)
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llm: TrainableLLM = Field()
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def _run(
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self,
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information_to_learn: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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train_result = self.llm.train_unsupervised((information_to_learn,))
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return f"Train complete. Loss: {train_result['loss']}"
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async def _arun(
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self,
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information_to_learn: str,
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run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
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) -> str:
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train_result = await self.llm.atrain_unsupervised((information_to_learn,))
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return f"Train complete. Loss: {train_result['loss']}"
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