forked from Archives/langchain
129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
"""Implement an LLM driven browser."""
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from __future__ import annotations
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import warnings
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from typing import Any, Dict, List, Optional
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from pydantic import Extra, root_validator
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.chains.natbot.prompt import PROMPT
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from langchain.llms.openai import OpenAI
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class NatBotChain(Chain):
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"""Implement an LLM driven browser.
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Example:
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.. code-block:: python
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from langchain import NatBotChain
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natbot = NatBotChain.from_default("Buy me a new hat.")
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"""
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llm_chain: LLMChain
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objective: str
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"""Objective that NatBot is tasked with completing."""
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated] LLM wrapper to use."""
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input_url_key: str = "url" #: :meta private:
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input_browser_content_key: str = "browser_content" #: :meta private:
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previous_command: str = "" #: :meta private:
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output_key: str = "command" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def raise_deprecation(cls, values: Dict) -> Dict:
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if "llm" in values:
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warnings.warn(
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"Directly instantiating an NatBotChain with an llm is deprecated. "
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"Please instantiate with llm_chain argument or using the from_llm "
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"class method."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=PROMPT)
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return values
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@classmethod
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def from_default(cls, objective: str, **kwargs: Any) -> NatBotChain:
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"""Load with default LLMChain."""
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llm = OpenAI(temperature=0.5, best_of=10, n=3, max_tokens=50)
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return cls.from_llm(llm, objective, **kwargs)
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@classmethod
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def from_llm(
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cls, llm: BaseLanguageModel, objective: str, **kwargs: Any
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) -> NatBotChain:
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"""Load from LLM."""
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llm_chain = LLMChain(llm=llm, prompt=PROMPT)
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return cls(llm_chain=llm_chain, objective=objective, **kwargs)
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@property
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def input_keys(self) -> List[str]:
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"""Expect url and browser content.
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:meta private:
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"""
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return [self.input_url_key, self.input_browser_content_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return command.
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:meta private:
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"""
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return [self.output_key]
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def _call(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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url = inputs[self.input_url_key]
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browser_content = inputs[self.input_browser_content_key]
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llm_cmd = self.llm_chain.predict(
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objective=self.objective,
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url=url[:100],
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previous_command=self.previous_command,
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browser_content=browser_content[:4500],
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callbacks=_run_manager.get_child(),
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)
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llm_cmd = llm_cmd.strip()
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self.previous_command = llm_cmd
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return {self.output_key: llm_cmd}
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def execute(self, url: str, browser_content: str) -> str:
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"""Figure out next browser command to run.
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Args:
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url: URL of the site currently on.
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browser_content: Content of the page as currently displayed by the browser.
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Returns:
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Next browser command to run.
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Example:
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.. code-block:: python
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browser_content = "...."
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llm_command = natbot.run("www.google.com", browser_content)
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"""
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_inputs = {
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self.input_url_key: url,
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self.input_browser_content_key: browser_content,
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}
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return self(_inputs)[self.output_key]
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@property
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def _chain_type(self) -> str:
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return "nat_bot_chain"
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