Source code for langchain.agents.structured_chat.base

import re
from typing import Any, List, Optional, Sequence, Tuple

from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain_core.pydantic_v1 import Field
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool

from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import JSONAgentOutputParser
from langchain.agents.structured_chat.output_parser import (
    StructuredChatOutputParserWithRetries,
)
from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.chains.llm import LLMChain
from langchain.tools.render import ToolsRenderer, render_text_description_and_args

HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"


[docs]@deprecated("0.1.0", alternative="create_structured_chat_agent", removal="0.2.0") class StructuredChatAgent(Agent): """Structured Chat Agent.""" output_parser: AgentOutputParser = Field( default_factory=StructuredChatOutputParserWithRetries ) """Output parser for the agent.""" @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:"]
[docs] @classmethod def create_prompt( cls, tools: Sequence[BaseTool], prefix: str = PREFIX, suffix: str = SUFFIX, human_message_template: str = HUMAN_MESSAGE_TEMPLATE, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, memory_prompts: Optional[List[BasePromptTemplate]] = 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]) if input_variables is None: input_variables = ["input", "agent_scratchpad"] _memory_prompts = memory_prompts or [] messages = [ SystemMessagePromptTemplate.from_template(template), *_memory_prompts, HumanMessagePromptTemplate.from_template(human_message_template), ] return ChatPromptTemplate(input_variables=input_variables, messages=messages)
[docs] @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, human_message_template: str = HUMAN_MESSAGE_TEMPLATE, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, memory_prompts: Optional[List[BasePromptTemplate]] = 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, human_message_template=human_message_template, format_instructions=format_instructions, input_variables=input_variables, memory_prompts=memory_prompts, ) 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
[docs]def create_structured_chat_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate, tools_renderer: ToolsRenderer = render_text_description_and_args, ) -> Runnable: """Create an agent aimed at supporting tools with multiple inputs. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. 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 ChatOpenAI from langchain.agents import AgentExecutor, create_structured_chat_agent prompt = hub.pull("hwchase17/structured-chat-agent") model = ChatOpenAI() tools = ... agent = create_structured_chat_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Using with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", "chat_history": [ HumanMessage(content="hi! my name is bob"), AIMessage(content="Hello Bob! How can I assist you today?"), ], } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions and arguments for each tool. * `tool_names`: contains all tool names. * `agent_scratchpad`: contains previous agent actions and tool outputs as a string. Here's an example: .. code-block:: python from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools: {tools} Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). Valid "action" values: "Final Answer" or {tool_names} Provide only ONE action per $JSON_BLOB, as shown: ``` {{ "action": $TOOL_NAME, "action_input": $INPUT }} ``` Follow this format: Question: input question to answer Thought: consider previous and subsequent steps Action: ``` $JSON_BLOB ``` Observation: action result ... (repeat Thought/Action/Observation N times) Thought: I know what to respond Action: ``` {{ "action": "Final Answer", "action_input": "Final response to human" }} Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation''' human = '''{input} {agent_scratchpad} (reminder to respond in a JSON blob no matter what)''' prompt = ChatPromptTemplate.from_messages( [ ("system", system), MessagesPlaceholder("chat_history", optional=True), ("human", human), ] ) """ # noqa: E501 missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference( prompt.input_variables ) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") prompt = prompt.partial( tools=tools_renderer(list(tools)), tool_names=", ".join([t.name for t in tools]), ) llm_with_stop = llm.bind(stop=["Observation"]) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]), ) | prompt | llm_with_stop | JSONAgentOutputParser() ) return agent