tjddlmZddlmZddlmZddlmZddlm Z m Z ddl m Z ddl mZmZddlmZdd lmZdd lmZd efd d ddZd S)) annotations)Sequence)BaseLanguageModel)BasePromptTemplate)RunnableRunnablePassthrough)BaseTool) ToolsRendererrender_text_description)AgentOutputParserformat_log_to_str)ReActSingleInputOutputParserNT) stop_sequencellmrtoolsSequence[BaseTool]promptr output_parserAgentOutputParser | Nonetools_rendererr rbool | list[str]returnrchd|jt|jz}|rd|}t |||t|dd|D}|r |durdgn|}||} n|} |p t}tj d |z| z|zS) a Create an agent that uses ReAct prompting. Based on paper "ReAct: Synergizing Reasoning and Acting in Language Models" (https://arxiv.org/abs/2210.03629) !!! warning This implementation is based on the foundational ReAct paper but is older and not well-suited for production applications. For a more robust and feature-rich implementation, we recommend using the `create_agent` function from the `langchain` library. See the [reference doc](https://reference.langchain.com/python/langchain/agents/) for more information. 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. output_parser: AgentOutputParser for parse the LLM output. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. stop_sequence: bool or list of str. If `True`, adds a stop token of "Observation:" to avoid hallucinates. If `False`, does not add a stop token. If a list of str, uses the provided list as the stop tokens. You may to set this to False if the LLM you are using does not support stop sequences. 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: ```python from langchain_classic import hub from langchain_openai import OpenAI from langchain_classic.agents import AgentExecutor, create_react_agent prompt = hub.pull("hwchase17/react") model = OpenAI() tools = ... agent = create_react_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Use with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", # Notice that chat_history is a string # since this prompt is aimed at LLMs, not chat models "chat_history": "Human: My name is Bob\nAI: Hello Bob!", } ) ``` 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: ```python from langchain_core.prompts import PromptTemplate template = '''Answer the following questions as best you can. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question Begin! Question: {input} Thought:{agent_scratchpad}''' prompt = PromptTemplate.from_template(template) ``` >r tool_namesagent_scratchpadz#Prompt missing required variables: z, cg|] }|j S)name).0ts b/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/agents/react/agent.py z&create_react_agent..s444af444)rrTz Observation)stopc,t|dS)Nintermediate_stepsr )xs r"z$create_react_agent..s'8;O9P'Q'Qr$)r) differenceinput_variableslistpartial_variables ValueErrorpartialjoinbindrrassign) rrrrrr missing_varsmsgr% llm_with_stops r"create_react_agentr6s,\?>>IIf&>!?!??LBLBBoo ^^nT%[[))9944e44455F$1T$9$9  }d++  !C%A%C%CM"QQ           r$)rrrrrrrrrr rrrr) __future__rcollections.abcrlangchain_core.language_modelsrlangchain_core.promptsrlangchain_core.runnablesrrlangchain_core.toolsr langchain_core.tools.renderr r langchain_classic.agentsr *langchain_classic.agents.format_scratchpadr'langchain_classic.agents.output_parsersrr6rr$r"rAs""""""$$$$$$<<<<<<555555BBBBBBBB))))))NNNNNNNN666666HHHHHHPPPPPP/3$; F'+FFFFFFFFr$