forked from Archives/langchain
Add Structure Chat Agent (#3912)
Create a new chat agent that is compatible with the Multi-input toolsfix_agent_callbacks
Zander Chase
1 year ago
committed by
GitHub
parent
ec21b7126c
commit
c582f2e9e3
@ -0,0 +1,130 @@
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import re
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from typing import Any, List, Optional, Sequence, Tuple
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from pydantic import Field
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from langchain.agents.agent import Agent, AgentOutputParser
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from langchain.agents.structured_chat.output_parser import (
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StructuredChatOutputParser,
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StructuredChatOutputParserWithRetries,
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)
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from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains.llm import LLMChain
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import AgentAction
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from langchain.tools import BaseTool
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class StructuredChatAgent(Agent):
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output_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
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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 "Observation: "
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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 "Thought:"
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def _construct_scratchpad(
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self, intermediate_steps: List[Tuple[AgentAction, str]]
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) -> str:
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agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
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if not isinstance(agent_scratchpad, str):
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raise ValueError("agent_scratchpad should be of type string.")
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if agent_scratchpad:
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return (
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f"This was your previous work "
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f"(but I haven't seen any of it! I only see what "
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f"you return as final answer):\n{agent_scratchpad}"
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)
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else:
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return agent_scratchpad
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@classmethod
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def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
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pass
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@classmethod
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def _get_default_output_parser(
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cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
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) -> AgentOutputParser:
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return StructuredChatOutputParserWithRetries.from_llm(llm=llm)
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@property
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def _stop(self) -> List[str]:
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return ["Observation:"]
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@classmethod
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def create_prompt(
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cls,
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tools: Sequence[BaseTool],
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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format_instructions: str = FORMAT_INSTRUCTIONS,
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input_variables: Optional[List[str]] = None,
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) -> BasePromptTemplate:
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tool_strings = []
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for tool in tools:
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args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
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tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
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formatted_tools = "\n".join(tool_strings)
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tool_names = ", ".join([tool.name for tool in tools])
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format_instructions = format_instructions.format(tool_names=tool_names)
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template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
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messages = [
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SystemMessagePromptTemplate.from_template(template),
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HumanMessagePromptTemplate.from_template("{input}\n\n{agent_scratchpad}"),
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]
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if input_variables is None:
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input_variables = ["input", "agent_scratchpad"]
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return ChatPromptTemplate(input_variables=input_variables, messages=messages)
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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callback_manager: Optional[BaseCallbackManager] = None,
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output_parser: Optional[AgentOutputParser] = None,
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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format_instructions: str = FORMAT_INSTRUCTIONS,
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input_variables: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Agent:
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"""Construct an agent from an LLM and tools."""
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cls._validate_tools(tools)
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prompt = cls.create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix,
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format_instructions=format_instructions,
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input_variables=input_variables,
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)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
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return cls(
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llm_chain=llm_chain,
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allowed_tools=tool_names,
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output_parser=_output_parser,
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**kwargs,
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)
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@property
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def _agent_type(self) -> str:
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raise ValueError
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@ -0,0 +1,81 @@
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from __future__ import annotations
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import json
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import logging
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import re
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from typing import Optional, Union
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from pydantic import Field
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from langchain.agents.agent import AgentOutputParser
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from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS
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from langchain.base_language import BaseLanguageModel
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from langchain.output_parsers import OutputFixingParser
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from langchain.output_parsers.pydantic import PydanticOutputParser
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from langchain.schema import AgentAction, AgentFinish, OutputParserException
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logger = logging.getLogger(__name__)
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class StructuredChatOutputParser(AgentOutputParser):
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def get_format_instructions(self) -> str:
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return FORMAT_INSTRUCTIONS
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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try:
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action_match = re.search(r"```(.*?)```?", text, re.DOTALL)
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if action_match is not None:
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response = json.loads(action_match.group(1).strip(), strict=False)
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if isinstance(response, list):
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# gpt turbo frequently ignores the directive to emit a single action
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logger.warning("Got multiple action responses: %s", response)
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response = response[0]
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if response["action"] == "Final Answer":
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return AgentFinish({"output": response["action_input"]}, text)
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else:
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return AgentAction(
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response["action"], response.get("action_input", {}), text
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)
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else:
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return AgentFinish({"output": text}, text)
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except Exception as e:
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raise OutputParserException(f"Could not parse LLM output: {text}") from e
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class StructuredChatOutputParserWithRetries(AgentOutputParser):
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base_parser: PydanticOutputParser = Field(
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default_factory=StructuredChatOutputParser
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)
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output_fixing_parser: Optional[OutputFixingParser] = None
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def get_format_instructions(self) -> str:
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return FORMAT_INSTRUCTIONS
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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try:
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if self.output_fixing_parser is not None:
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parsed_obj: Union[
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AgentAction, AgentFinish
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] = self.output_fixing_parser.parse(text)
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else:
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parsed_obj = self.base_parser.parse(text)
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return parsed_obj
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except Exception as e:
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raise OutputParserException(f"Could not parse LLM output: {text}") from e
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@classmethod
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def from_llm(
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cls,
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llm: Optional[BaseLanguageModel] = None,
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base_parser: Optional[StructuredChatOutputParser] = None,
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) -> StructuredChatOutputParserWithRetries:
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if llm is not None:
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base_parser = base_parser or StructuredChatOutputParser()
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output_fixing_parser = OutputFixingParser.from_llm(
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llm=llm, parser=base_parser
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)
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return cls(output_fixing_parser=output_fixing_parser)
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elif base_parser is not None:
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return cls(base_parser=base_parser)
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else:
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return cls()
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@ -0,0 +1,35 @@
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# flake8: noqa
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PREFIX = """Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
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FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
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Valid "action" values: "Final Answer" or {tool_names}
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Provide only ONE action per $JSON_BLOB, as shown:
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```
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{{{{
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"action": $TOOL_NAME,
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"action_input": $INPUT
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}}}}
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```
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Follow this format:
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Question: input question to answer
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Thought: consider previous and subsequent steps
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Action:
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```
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$JSON_BLOB
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```
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Observation: action result
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... (repeat Thought/Action/Observation N times)
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Thought: I know what to respond
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Action:
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```
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{{{{
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"action": "Final Answer",
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"action_input": "Final response to human"
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}}}}
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```"""
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SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
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Thought:"""
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