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85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
"""Chain that does self ask with search."""
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from typing import Any, ClassVar, List, Tuple
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from langchain.agents.agent import Agent
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from langchain.agents.self_ask_with_search.prompt import PROMPT
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from langchain.agents.tools import Tool
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from langchain.chains.llm import LLMChain
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from langchain.chains.serpapi import SerpAPIChain
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from langchain.llms.base import LLM
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from langchain.prompts.base import BasePromptTemplate
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class SelfAskWithSearchAgent(Agent):
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"""Agent for the self-ask-with-search paper."""
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prompt: ClassVar[BasePromptTemplate] = PROMPT
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@classmethod
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def _validate_tools(cls, tools: List[Tool]) -> None:
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if len(tools) != 1:
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raise ValueError(f"Exactly one tool must be specified, but got {tools}")
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tool_names = {tool.name for tool in tools}
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if tool_names != {"Intermediate Answer"}:
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raise ValueError(
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f"Tool name should be Intermediate Answer, got {tool_names}"
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)
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def _extract_tool_and_input(self, text: str) -> Tuple[str, str]:
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followup = "Follow up:"
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if "\n" not in text:
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last_line = text
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else:
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last_line = text.split("\n")[-1]
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if followup not in last_line:
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finish_string = "So the final answer is: "
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if finish_string not in last_line:
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raise ValueError("We should probably never get here")
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return "Final Answer", text[len(finish_string) :]
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if ":" not in last_line:
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after_colon = last_line
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else:
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after_colon = text.split(":")[-1]
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if " " == after_colon[0]:
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after_colon = after_colon[1:]
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if "?" != after_colon[-1]:
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print("we probably should never get here..." + text)
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return "Intermediate Answer", after_colon
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@property
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def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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return "Intermediate answer: "
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@property
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def llm_prefix(self) -> str:
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"""Prefix to append the LLM call with."""
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return ""
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@property
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def starter_string(self) -> str:
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"""Put this string after user input but before first LLM call."""
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return "\nAre follow up questions needed here:"
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class SelfAskWithSearchChain(SelfAskWithSearchAgent):
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"""Chain that does self ask with search.
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Example:
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.. code-block:: python
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from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain
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search_chain = SerpAPIChain()
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self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
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"""
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def __init__(self, llm: LLM, search_chain: SerpAPIChain, **kwargs: Any):
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"""Initialize with just an LLM and a search chain."""
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search_tool = Tool(name="Intermediate Answer", func=search_chain.run)
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llm_chain = LLMChain(llm=llm, prompt=PROMPT)
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super().__init__(llm_chain=llm_chain, tools=[search_tool], **kwargs)
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