173 lines
5.4 KiB
Python
173 lines
5.4 KiB
Python
from dataclasses import dataclass, field, is_dataclass
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from typing import Type, TypedDict, Literal, Dict, List, Tuple
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import tyro
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from pydantic import BaseModel, Field
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from loguru import logger
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from PIL import Image
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from io import BytesIO
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import matplotlib.pyplot as plt
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from lang_agent.config import KeyConfig
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from lang_agent.tool_manager import ToolManager, ToolManagerConfig
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from lang_agent.base import GraphBase
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from langchain.chat_models import init_chat_model
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain.agents import create_agent
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from langgraph.graph import StateGraph, START, END
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from langgraph.checkpoint.memory import MemorySaver
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@tyro.conf.configure(tyro.conf.SuppressFixed)
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@dataclass
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class RoutingConfig(KeyConfig):
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_target: Type = field(default_factory=lambda: RoutingGraph)
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llm_name: str = "qwen-turbo"
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"""name of llm"""
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llm_provider:str = "openai"
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"""provider of the llm"""
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base_url:str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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"""base url; could be used to overwrite the baseurl in llm provider"""
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tool_manager_config: ToolManagerConfig = field(default_factory=ToolManagerConfig)
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class Route(BaseModel):
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step: Literal["chat", "order"] = Field(
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None, description="The next step in the routing process"
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)
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class State(TypedDict):
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inp: Tuple[Dict[str, List[SystemMessage | HumanMessage]],
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Dict[str, Dict[str, str|int]]]
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messages: List[SystemMessage | HumanMessage]
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decision:str
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class RoutingGraph(GraphBase):
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def __init__(self, config: RoutingConfig):
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self.config = config
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self.chat_sys_msg = None
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self._build_modules()
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self.workflow = self._build_graph()
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def invoke(self, *nargs, as_stream:bool=False, **kwargs):
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assert len(kwargs) == 0, "due to inp assumptions"
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if as_stream:
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# TODO： this doesn't stream the entire process, we are blind
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for step in self.workflow.stream({"inp": nargs}, stream_mode="values", **kwargs):
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if "messages" in step:
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step["messages"]["messages"][-1].pretty_print()
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state = step
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else:
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state = self.workflow.invoke({"inp": nargs})
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return state["messages"]
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def _build_modules(self):
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self.llm = init_chat_model(model=self.config.llm_name,
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model_provider=self.config.llm_provider,
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api_key=self.config.api_key,
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base_url=self.config.base_url)
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self.memory = MemorySaver()
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self.router = self.llm.with_structured_output(Route)
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tool_manager:ToolManager = self.config.tool_manager_config.setup()
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self.chat_model = create_agent(self.llm, [], checkpointer=self.memory)
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self.tool_model = create_agent(self.llm, tool_manager.get_langchain_tools(), checkpointer=self.memory)
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def _router_call(self, state:State):
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decision:Route = self.router.invoke(
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[
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SystemMessage(
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content="Return a JSON object with 'step'.the value should be one of 'chat' or 'order' based on the user input"
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),
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self._get_human_msg(state)
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]
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)
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return {"decision": decision.step}
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def _get_human_msg(self, state: State)->HumanMessage:
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"""
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get user message of current invocation
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"""
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msgs = state["inp"][0]["messages"]
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candidate_hum_msg = msgs[1]
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assert isinstance(candidate_hum_msg, HumanMessage), "not a human message"
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return candidate_hum_msg
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def _route_decision(self, state:State):
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logger.info(f"decision:{state["decision"]}")
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if state["decision"] == "chat":
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return "chat"
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else:
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return "tool"
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def _chat_model_call(self, state:State):
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if state.get("messages") is not None:
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inp = state["messages"], state["inp"][1]
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else:
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inp = state["inp"]
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out = self.chat_model.invoke(*inp)
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return {"messages": out}
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def _tool_model_call(self, state:State):
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inp = {"messages":[
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SystemMessage(
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"You must use tool to complete the possible task"
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),
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# self._get_human_msg(state)
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*state["inp"][0][1:]
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]}, state["inp"][1]
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out = self.tool_model.invoke(*inp)
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return {"messages": out}
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def _build_graph(self):
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builder = StateGraph(State)
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# add nodes
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builder.add_node("chat_model_call", self._chat_model_call)
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builder.add_node("tool_model_call", self._tool_model_call)
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builder.add_node("router_call", self._router_call)
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# add edge connections
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builder.add_edge(START, "router_call")
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builder.add_conditional_edges(
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"router_call",
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self._route_decision,
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{
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"chat": "chat_model_call",
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"tool": "tool_model_call"
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}
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)
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builder.add_edge("tool_model_call", END)
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# builder.add_edge("tool_model_call", "chat_model_call")
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builder.add_edge("chat_model_call", END)
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workflow = builder.compile()
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return workflow
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def show_graph(self):
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img = Image.open(BytesIO(self.workflow.get_graph().draw_mermaid_png()))
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plt.imshow(img)
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plt.show() |