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Add Structure Chat Agent (langchain-ai#3912)
Create a new chat agent that is compatible with the Multi-input tools
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docs/modules/agents/agents/examples/structured_chat.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| import re | ||
| from typing import Any, List, Optional, Sequence, Tuple | ||
|
|
||
| from pydantic import Field | ||
|
|
||
| from langchain.agents.agent import Agent, AgentOutputParser | ||
| from langchain.agents.structured_chat.output_parser import ( | ||
| StructuredChatOutputParser, | ||
| StructuredChatOutputParserWithRetries, | ||
| ) | ||
| from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX | ||
| from langchain.base_language import BaseLanguageModel | ||
| from langchain.callbacks.base import BaseCallbackManager | ||
| from langchain.chains.llm import LLMChain | ||
| from langchain.prompts.base import BasePromptTemplate | ||
| from langchain.prompts.chat import ( | ||
| ChatPromptTemplate, | ||
| HumanMessagePromptTemplate, | ||
| SystemMessagePromptTemplate, | ||
| ) | ||
| from langchain.schema import AgentAction | ||
| from langchain.tools import BaseTool | ||
|
|
||
|
|
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| class StructuredChatAgent(Agent): | ||
| output_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser) | ||
|
|
||
| @property | ||
| def observation_prefix(self) -> str: | ||
| """Prefix to append the observation with.""" | ||
| return "Observation: " | ||
|
|
||
| @property | ||
| def llm_prefix(self) -> str: | ||
| """Prefix to append the llm call with.""" | ||
| return "Thought:" | ||
|
|
||
| def _construct_scratchpad( | ||
| self, intermediate_steps: List[Tuple[AgentAction, str]] | ||
| ) -> str: | ||
| agent_scratchpad = super()._construct_scratchpad(intermediate_steps) | ||
| if not isinstance(agent_scratchpad, str): | ||
| raise ValueError("agent_scratchpad should be of type string.") | ||
| if agent_scratchpad: | ||
| return ( | ||
| f"This was your previous work " | ||
| f"(but I haven't seen any of it! I only see what " | ||
| f"you return as final answer):\n{agent_scratchpad}" | ||
| ) | ||
| else: | ||
| return agent_scratchpad | ||
|
|
||
| @classmethod | ||
| def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: | ||
| pass | ||
|
|
||
| @classmethod | ||
| def _get_default_output_parser( | ||
| cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any | ||
| ) -> AgentOutputParser: | ||
| return StructuredChatOutputParserWithRetries.from_llm(llm=llm) | ||
|
|
||
| @property | ||
| def _stop(self) -> List[str]: | ||
| return ["Observation:"] | ||
|
|
||
| @classmethod | ||
| def create_prompt( | ||
| cls, | ||
| tools: Sequence[BaseTool], | ||
| prefix: str = PREFIX, | ||
| suffix: str = SUFFIX, | ||
| format_instructions: str = FORMAT_INSTRUCTIONS, | ||
| input_variables: Optional[List[str]] = None, | ||
| ) -> BasePromptTemplate: | ||
| tool_strings = [] | ||
| for tool in tools: | ||
| args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args))) | ||
| tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}") | ||
| formatted_tools = "\n".join(tool_strings) | ||
| tool_names = ", ".join([tool.name for tool in tools]) | ||
| format_instructions = format_instructions.format(tool_names=tool_names) | ||
| template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix]) | ||
| messages = [ | ||
| SystemMessagePromptTemplate.from_template(template), | ||
| HumanMessagePromptTemplate.from_template("{input}\n\n{agent_scratchpad}"), | ||
| ] | ||
| if input_variables is None: | ||
| input_variables = ["input", "agent_scratchpad"] | ||
| return ChatPromptTemplate(input_variables=input_variables, messages=messages) | ||
|
|
||
| @classmethod | ||
| def from_llm_and_tools( | ||
| cls, | ||
| llm: BaseLanguageModel, | ||
| tools: Sequence[BaseTool], | ||
| callback_manager: Optional[BaseCallbackManager] = None, | ||
| output_parser: Optional[AgentOutputParser] = None, | ||
| prefix: str = PREFIX, | ||
| suffix: str = SUFFIX, | ||
| format_instructions: str = FORMAT_INSTRUCTIONS, | ||
| input_variables: Optional[List[str]] = None, | ||
| **kwargs: Any, | ||
| ) -> Agent: | ||
| """Construct an agent from an LLM and tools.""" | ||
| cls._validate_tools(tools) | ||
| prompt = cls.create_prompt( | ||
| tools, | ||
| prefix=prefix, | ||
| suffix=suffix, | ||
| format_instructions=format_instructions, | ||
| input_variables=input_variables, | ||
| ) | ||
| llm_chain = LLMChain( | ||
| llm=llm, | ||
| prompt=prompt, | ||
| callback_manager=callback_manager, | ||
| ) | ||
| tool_names = [tool.name for tool in tools] | ||
| _output_parser = output_parser or cls._get_default_output_parser(llm=llm) | ||
| return cls( | ||
| llm_chain=llm_chain, | ||
| allowed_tools=tool_names, | ||
| output_parser=_output_parser, | ||
| **kwargs, | ||
| ) | ||
|
|
||
| @property | ||
| def _agent_type(self) -> str: | ||
| raise ValueError |
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