from typing import Any, List, Sequence, Union from langchain_core.prompts import PromptTemplate from langchain.agents import AgentExecutor, create_tool_calling_agent,create_structured_chat_agent from langchain.tools import BaseTool # from langgraph.prebuilt import create_react_agent from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.chat import ChatPromptTemplate from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.output_parsers.json import parse_json_markdown from langchain_core.agents import AgentAction, AgentFinish from langchain_core.exceptions import OutputParserException from langchain.tools.render import ToolsRenderer, render_text_description_and_args from langchain.agents.format_scratchpad import format_log_to_str from langchain.agents.agent import AgentOutputParser class ChartAgentOutputParser(AgentOutputParser): """Parses tool invocations and final answers in JSON format. Expects output to be in one of two formats. If the output signals that an action should be taken, should be in the below format. This will result in an AgentAction being returned. ``` { "action": "search", "action_input": "2+2", "action_cache": "" } ``` If the output signals that a final answer should be given, should be in the below format. This will result in an AgentFinish being returned. ``` { "action": "Final Answer", "action_input": "4", "action_cache": "" } ``` """ def parse(self, text: str) -> Union[AgentAction, AgentFinish]: try: response = parse_json_markdown(text) if isinstance(response, list): # gpt turbo frequently ignores the directive to emit a single action # logger.warning("Got multiple action responses: %s", response) response = response[0] if response["action"] == "Final Answer": if "action_cache" in response: return AgentFinish({"output": response["action_input"],"cache":response["action_cache"]}, text) else: return AgentFinish({"output": response["action_input"]}, text) else: return AgentAction( response["action"], response.get("action_input", {}), text ) except Exception as e: raise OutputParserException(f"Could not parse LLM output: {text}") from e @property def _type(self) -> str: return "chart-agent" def create_chart_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate, chart_tool: str, tools_renderer: ToolsRenderer = render_text_description_and_args, *, stop_sequence: Union[bool, List[str]] = True, ) -> Runnable: """Create an agent aimed at supporting chart tools with multiple inputs. """ missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference( prompt.input_variables + list(prompt.partial_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]), chart_tool=chart_tool, ) if stop_sequence: stop = ["\nObservation"] if stop_sequence is True else stop_sequence llm_with_stop = llm.bind(stop=stop) else: llm_with_stop = llm agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]), ) | prompt | llm_with_stop | ChartAgentOutputParser() ) return agent class Agent: def __init__(self, llm, tools: List[BaseTool],prompt: PromptTemplate = None, verbose: bool = False,**args): self.llm = llm self.tools = tools self.prompt = prompt if not prompt: raise ValueError("PromptTemplate is required") agent = create_chart_agent(llm, tools, prompt, **args) self.agent_executor = AgentExecutor(agent=agent,tools=tools,verbose=verbose) def exec(self, prompt_args: dict = {}, stream: bool = False): # if stream: # for step in self.agent_executor.stream(prompt_args): # yield step return self.agent_executor.invoke(input=prompt_args) def stream(self, prompt_args: dict = {}): for step in self.agent_executor.stream(prompt_args): yield step