"""Chain that does self-ask with search."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Sequence, Union
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool, Tool
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.self_ask_with_search.output_parser import SelfAskOutputParser
from langchain.agents.self_ask_with_search.prompt import PROMPT
from langchain.agents.utils import validate_tools_single_input
if TYPE_CHECKING:
from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
from langchain_community.utilities.serpapi import SerpAPIWrapper
[docs]
@deprecated("0.1.0", alternative="create_self_ask_with_search", removal="1.0")
class SelfAskWithSearchAgent(Agent):
"""Agent for the self-ask-with-search paper."""
output_parser: AgentOutputParser = Field(default_factory=SelfAskOutputParser)
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return SelfAskOutputParser()
@property
def _agent_type(self) -> str:
"""Return Identifier of an agent type."""
return AgentType.SELF_ASK_WITH_SEARCH
[docs]
@classmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Prompt does not depend on tools."""
return PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
validate_tools_single_input(cls.__name__, tools)
super()._validate_tools(tools)
if len(tools) != 1:
raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Intermediate answer: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the LLM call with."""
return ""
[docs]
@deprecated("0.1.0", removal="1.0")
class SelfAskWithSearchChain(AgentExecutor):
"""[Deprecated] Chain that does self-ask with search."""
def __init__(
self,
llm: BaseLanguageModel,
search_chain: Union[
GoogleSerperAPIWrapper, SearchApiAPIWrapper, SerpAPIWrapper
],
**kwargs: Any,
):
"""Initialize only with an LLM and a search chain."""
search_tool = Tool(
name="Intermediate Answer",
func=search_chain.run,
coroutine=search_chain.arun,
description="Search",
)
agent = SelfAskWithSearchAgent.from_llm_and_tools(llm, [search_tool])
super().__init__(agent=agent, tools=[search_tool], **kwargs)
[docs]
def create_self_ask_with_search_agent(
llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate
) -> Runnable:
"""Create an agent that uses self-ask with search prompting.
Args:
llm: LLM to use as the agent.
tools: List of tools. Should just be of length 1, with that tool having
name `Intermediate Answer`
prompt: The prompt to use, must have input key `agent_scratchpad` which will
contain agent actions and tool outputs.
Returns:
A Runnable sequence representing an agent. It takes as input all the same input
variables as the prompt passed in does. It returns as output either an
AgentAction or AgentFinish.
Examples:
.. code-block:: python
from langchain import hub
from langchain_community.chat_models import ChatAnthropic
from langchain.agents import (
AgentExecutor, create_self_ask_with_search_agent
)
prompt = hub.pull("hwchase17/self-ask-with-search")
model = ChatAnthropic(model="claude-3-haiku-20240307")
tools = [...] # Should just be one tool with name `Intermediate Answer`
agent = create_self_ask_with_search_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"input": "hi"})
Prompt:
The prompt must have input key `agent_scratchpad` which will
contain agent actions and tool outputs as a string.
Here's an example:
.. code-block:: python
from langchain_core.prompts import PromptTemplate
template = '''Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate answer: Alan Turing was 41 years old when he died.
So the final answer is: Muhammad Ali
Question: When was the founder of craigslist born?
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
Question: Who was the maternal grandfather of George Washington?
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer is: Joseph Ball
Question: Are both the directors of Jaws and Casino Royale from the same country?
Are follow up questions needed here: Yes.
Follow up: Who is the director of Jaws?
Intermediate answer: The director of Jaws is Steven Spielberg.
Follow up: Where is Steven Spielberg from?
Intermediate answer: The United States.
Follow up: Who is the director of Casino Royale?
Intermediate answer: The director of Casino Royale is Martin Campbell.
Follow up: Where is Martin Campbell from?
Intermediate answer: New Zealand.
So the final answer is: No
Question: {input}
Are followup questions needed here:{agent_scratchpad}'''
prompt = PromptTemplate.from_template(template)
""" # noqa: E501
missing_vars = {"agent_scratchpad"}.difference(
prompt.input_variables + list(prompt.partial_variables)
)
if missing_vars:
raise ValueError(f"Prompt missing required variables: {missing_vars}")
if len(tools) != 1:
raise ValueError("This agent expects exactly one tool")
tool = list(tools)[0]
if tool.name != "Intermediate Answer":
raise ValueError(
"This agent expects the tool to be named `Intermediate Answer`"
)
llm_with_stop = llm.bind(stop=["\nIntermediate answer:"])
agent = (
RunnablePassthrough.assign(
agent_scratchpad=lambda x: format_log_to_str(
x["intermediate_steps"],
observation_prefix="\nIntermediate answer: ",
llm_prefix="",
),
# Give it a default
chat_history=lambda x: x.get("chat_history", ""),
)
| prompt
| llm_with_stop
| SelfAskOutputParser()
)
return agent
