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105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
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import re
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from typing import Any, Optional, Tuple
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from pydantic import BaseModel
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from langchain.chains.llm import LLMChain
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from langchain.docstore.base import Docstore
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from langchain.docstore.document import Document
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from langchain.llms.base import LLM
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from langchain.routing_chains.react.prompt import PROMPT
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from langchain.routing_chains.router import LLMRouter
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from langchain.routing_chains.routing_chain import RoutingChain, ToolConfig
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class ReActRouterChain(LLMRouter, BaseModel):
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"""Router for the ReAct chin."""
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i: int = 1
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def __init__(self, llm: LLM, **kwargs: Any):
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"""Initialize with the language model."""
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llm_chain = LLMChain(llm=llm, prompt=PROMPT)
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stops = ["\nObservation 1:"]
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super().__init__(llm_chain=llm_chain, stops=stops, **kwargs)
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def _fix_text(self, text: str) -> str:
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return text + f"\nAction {self.i}:"
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def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
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action_prefix = f"Action {self.i}: "
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if not text.split("\n")[-1].startswith(action_prefix):
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return None
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self.i += 1
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self.stops = [f"\nObservation {self.i}:"]
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action_block = text.split("\n")[-1]
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action_str = action_block[len(action_prefix) :]
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# Parse out the action and the directive.
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re_matches = re.search(r"(.*?)\[(.*?)\]", action_str)
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if re_matches is None:
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raise ValueError(f"Could not parse action directive: {action_str}")
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return re_matches.group(1), re_matches.group(2)
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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 "Finish"
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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 f"Observation {self.i - 1}: "
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@property
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def router_prefix(self) -> str:
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"""Prefix to append the router call with."""
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return f"Thought {self.i}:"
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class DocstoreExplorer:
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"""Class to assist with exploration of a document store."""
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def __init__(self, docstore: Docstore):
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"""Initialize with a docstore, and set initial document to None."""
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self.docstore = docstore
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self.document: Optional[Document] = None
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def search(self, term: str) -> str:
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"""Search for a term in the docstore, and if found save."""
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result = self.docstore.search(term)
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if isinstance(result, Document):
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self.document = result
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return self.document.summary
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else:
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self.document = None
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return result
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def lookup(self, term: str) -> str:
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"""Lookup a term in document (if saved)."""
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if self.document is None:
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raise ValueError("Cannot lookup without a successful search first")
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return self.document.lookup(term)
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class ReActChain(RoutingChain):
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"""Chain that implements the ReAct paper.
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Example:
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.. code-block:: python
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from langchain import ReActChain, OpenAI
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react = ReAct(llm=OpenAI())
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"""
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def __init__(self, llm: LLM, docstore: Docstore, **kwargs: Any):
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"""Initialize with the LLM and a docstore."""
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router = ReActRouterChain(llm)
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docstore_explorer = DocstoreExplorer(docstore)
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tool_configs = [
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ToolConfig(tool_name="Search", tool=docstore_explorer.search),
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ToolConfig(tool_name="Lookup", tool=docstore_explorer.lookup),
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]
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super().__init__(router=router, expert_configs=tool_configs, **kwargs)
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