forked from Archives/langchain
docs for restructure
Harrison Chase
2 years ago
parent
8c8eb47765
commit
71a0940435
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:mod:`langchain.routing_chains`
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===============================
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.. automodule:: langchain.routing_chains
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:members:
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:undoc-members:
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"""Routing chains."""
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from langchain.routing_chains.mrkl.base import MRKLChain
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from langchain.routing_chains.react.base import ReActChain
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from langchain.routing_chains.router import LLMRouter
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from langchain.routing_chains.routing_chain import RoutingChain
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from langchain.routing_chains.self_ask_with_search.base import SelfAskWithSearchChain
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__all__ = [
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"MRKLChain",
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"SelfAskWithSearchChain",
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"ReActChain",
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"LLMRouter",
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"RoutingChain",
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]
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"""Chain that takes in an input and produces an action and action input."""
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from abc import ABC, abstractmethod
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from typing import NamedTuple, Optional, Tuple
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from pydantic import BaseModel
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from langchain.chains.llm import LLMChain
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class RouterOutput(NamedTuple):
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"""Output of a router."""
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tool: str
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tool_input: str
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log: str
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class Router(ABC):
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"""Chain responsible for deciding the action to take."""
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@abstractmethod
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def route(self, text: str) -> RouterOutput:
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"""Given input, decided how to route it.
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Args:
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text: input string
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Returns:
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RouterOutput specifying what tool to use.
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"""
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@property
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@abstractmethod
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def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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@property
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@abstractmethod
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def router_prefix(self) -> str:
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"""Prefix to append the router call with."""
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@property
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def finish_tool_name(self) -> str:
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"""Name of the tool to use to finish the chain."""
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return "Final Answer"
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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 router call."""
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return "\n"
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class LLMRouter(Router, BaseModel, ABC):
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"""Router that uses an LLM."""
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llm_chain: LLMChain
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@abstractmethod
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def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
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"""Extract tool and tool input from llm output."""
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def _fix_text(self, text: str) -> str:
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"""Fix the text."""
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raise ValueError("fix_text not implemented for this router.")
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def route(self, text: str) -> RouterOutput:
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"""Given input, decided how to route it.
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Args:
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text: input string
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Returns:
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RouterOutput specifying what tool to use.
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"""
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input_key = self.llm_chain.input_keys[0]
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inputs = {input_key: text, "stop": [self.observation_prefix]}
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full_output = self.llm_chain.predict(**inputs)
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parsed_output = self._extract_tool_and_input(full_output)
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while parsed_output is None:
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full_output = self._fix_text(full_output)
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inputs = {input_key: text + full_output, "stop": [self.observation_prefix]}
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output = self.llm_chain.predict(**inputs)
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full_output += output
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parsed_output = self._extract_tool_and_input(full_output)
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tool, tool_input = parsed_output
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return RouterOutput(tool, tool_input, full_output)
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"""Router-Expert framework."""
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from typing import Callable, Dict, List, NamedTuple
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from pydantic import BaseModel, Extra
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from langchain.chains.base import Chain
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from langchain.input import ChainedInput, get_color_mapping
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from langchain.routing_chains.router import Router
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class ToolConfig(NamedTuple):
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"""Configuration for tools."""
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tool_name: str
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tool: Callable[[str], str]
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class RoutingChain(Chain, BaseModel):
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"""Chain that uses a router to use tools."""
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router: Router
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"""Router to use."""
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tool_configs: List[ToolConfig]
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"""Tool configs this chain has access to."""
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :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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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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name_to_tool_map = {tc.tool_name: tc.tool for tc in self.tool_configs}
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starter_string = (
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inputs[self.input_key]
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+ self.router.starter_string
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+ self.router.router_prefix
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)
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chained_input = ChainedInput(starter_string, verbose=self.verbose)
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color_mapping = get_color_mapping(
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[c.tool_name for c in self.tool_configs], excluded_colors=["green"]
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)
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while True:
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output = self.router.route(chained_input.input)
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chained_input.add(output.log, color="green")
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if output.tool == self.router.finish_tool_name:
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return {self.output_key: output.tool_input}
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chain = name_to_tool_map[output.tool]
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observation = chain(output.tool_input)
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chained_input.add(f"\n{self.router.observation_prefix}")
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chained_input.add(observation, color=color_mapping[output.tool])
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chained_input.add(f"\n{self.router.router_prefix}")
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"""Smart chains."""
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from langchain.smart_chains.mrkl.base import MRKLChain
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from langchain.smart_chains.react.base import ReActChain
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from langchain.smart_chains.self_ask_with_search.base import SelfAskWithSearchChain
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__all__ = ["MRKLChain", "SelfAskWithSearchChain", "ReActChain"]
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"""Chain that takes in an input and produces an action and action input."""
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional, Tuple
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from pydantic import BaseModel
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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class RouterChain(Chain, BaseModel, ABC):
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"""Chain responsible for deciding the action to take."""
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input_key: str = "input_text" #: :meta private:
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action_key: str = "action" #: :meta private:
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action_input_key: str = "action_input" #: :meta private:
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log_key: str = "log" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Will be the input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return three keys: the action, the action input, and the log.
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:meta private:
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"""
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return [self.action_key, self.action_input_key, self.log_key]
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@abstractmethod
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def get_action_and_input(self, text: str) -> Tuple[str, str, str]:
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"""Return action, action input, and log (in that order)."""
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@property
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@abstractmethod
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def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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@property
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@abstractmethod
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def router_prefix(self) -> str:
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"""Prefix to append the router call with."""
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@property
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def finish_action_name(self) -> str:
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"""Name of the action of when to finish the chain."""
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return "Final Answer"
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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action, action_input, log = self.get_action_and_input(inputs[self.input_key])
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return {
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self.action_key: action,
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self.action_input_key: action_input,
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self.log_key: log,
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}
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class LLMRouterChain(RouterChain, BaseModel, ABC):
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"""RouterChain that uses an LLM."""
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llm_chain: LLMChain
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stops: Optional[List[str]]
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@abstractmethod
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def _extract_action_and_input(self, text: str) -> Optional[Tuple[str, str]]:
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"""Extract action and action input from llm output."""
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def _fix_text(self, text: str) -> str:
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"""Fix the text."""
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raise ValueError("fix_text not implemented for this router.")
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def get_action_and_input(self, text: str) -> Tuple[str, str, str]:
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"""Return action, action input, and log (in that order)."""
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input_key = self.llm_chain.input_keys[0]
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inputs = {input_key: text, "stop": self.stops}
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full_output = self.llm_chain.predict(**inputs)
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parsed_output = self._extract_action_and_input(full_output)
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while parsed_output is None:
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full_output = self._fix_text(full_output)
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inputs = {input_key: text + full_output, "stop": self.stops}
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output = self.llm_chain.predict(**inputs)
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full_output += output
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parsed_output = self._extract_action_and_input(full_output)
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action, action_input = parsed_output
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return action, action_input, full_output
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"""Router-Expert framework."""
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from typing import Callable, Dict, List, NamedTuple
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from pydantic import BaseModel, Extra
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from langchain.chains.base import Chain
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from langchain.input import ChainedInput, get_color_mapping
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from langchain.smart_chains.router import RouterChain
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class ExpertConfig(NamedTuple):
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"""Configuration for experts."""
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expert_name: str
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expert: Callable[[str], str]
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class RouterExpertChain(Chain, BaseModel):
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"""Chain that implements the Router/Expert system."""
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router_chain: RouterChain
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"""Router chain."""
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expert_configs: List[ExpertConfig]
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"""Expert configs this chain has access to."""
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starter_string: str = "\n"
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"""String to put after user input but before first router."""
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :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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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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action_to_chain_map = {e.expert_name: e.expert for e in self.expert_configs}
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starter_string = (
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inputs[self.input_key]
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+ self.starter_string
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+ self.router_chain.router_prefix
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)
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chained_input = ChainedInput(
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starter_string,
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verbose=self.verbose,
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)
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color_mapping = get_color_mapping(
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[c.expert_name for c in self.expert_configs], excluded_colors=["green"]
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)
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while True:
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action, action_input, log = self.router_chain.get_action_and_input(
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chained_input.input
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)
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chained_input.add(log, color="green")
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if action == self.router_chain.finish_action_name:
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return {self.output_key: action_input}
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chain = action_to_chain_map[action]
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ca = chain(action_input)
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chained_input.add(f"\n{self.router_chain.observation_prefix}")
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chained_input.add(ca, color=color_mapping[action])
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chained_input.add(f"\n{self.router_chain.router_prefix}")
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"""Test routing chain functionality."""
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@ -1 +0,0 @@
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"""Test smart chain functionality."""
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