237 lines
8.5 KiB
Python
237 lines
8.5 KiB
Python
from dataclasses import dataclass, field, is_dataclass
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from typing import Type, TypedDict, Literal, Dict, List, Tuple, Any, AsyncIterator, Optional
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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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import os.path as osp
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import commentjson
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import glob
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import time
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from lang_agent.config import LLMKeyConfig
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from lang_agent.components.tool_manager import ToolManager, ToolManagerConfig
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from lang_agent.components.prompt_store import build_prompt_store
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from lang_agent.base import GraphBase, ToolNodeBase
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from lang_agent.graphs.graph_states import State
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from lang_agent.graphs.tool_nodes import AnnotatedToolNode, ToolNodeConfig
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from lang_agent.components.text_releaser import TextReleaser, AsyncTextReleaser
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from langchain.chat_models import init_chat_model
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from langchain_core.messages import SystemMessage, HumanMessage, BaseMessage
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from langchain_core.messages.base import BaseMessageChunk
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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(LLMKeyConfig):
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_target: Type = field(default_factory=lambda: RoutingGraph)
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llm_name: str = "qwen-plus"
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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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sys_promp_dir: str = osp.join(osp.dirname(osp.dirname(osp.dirname(__file__))), "configs", "route_sys_prompts")
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"""path to directory or json contantaining system prompt for graphs; Will overwrite systemprompt from xiaozhi if 'chat_prompt' is provided"""
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pipeline_id: Optional[str] = None
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"""If set, load prompts from database (with file fallback)"""
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prompt_set_id: Optional[str] = None
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"""If set, load from this specific prompt set instead of the active one"""
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tool_manager_config: ToolManagerConfig = field(default_factory=ToolManagerConfig)
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tool_node_config: AnnotatedToolNode = field(default_factory=ToolNodeConfig)
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class Route(BaseModel):
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step: Literal["chat", "tool"] = Field(
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None, description="The next step in the routing process"
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)
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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._build_modules()
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self.workflow = self._build_graph()
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self.streamable_tags:List[List[str]] = self.tool_node.get_streamable_tags() + [["route_chat_llm"]]
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self.textreleaser_delay_keys = self.tool_node.get_delay_keys()
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def _build_modules(self):
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self.chat_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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temperature=0,
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tags=["route_chat_llm"])
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self.fast_llm = init_chat_model(model='qwen-flash',
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model_provider='openai',
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["route_fast"])
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self.memory = MemorySaver() # shared memory between the two branch
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self.router = self.fast_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.chat_llm, [], checkpointer=self.memory)
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# Propagate pipeline_id and prompt_set_id to tool node config
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if self.config.pipeline_id and hasattr(self.config.tool_node_config, 'pipeline_id'):
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self.config.tool_node_config.pipeline_id = self.config.pipeline_id
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if self.config.prompt_set_id and hasattr(self.config.tool_node_config, 'prompt_set_id'):
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self.config.tool_node_config.prompt_set_id = self.config.prompt_set_id
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self.tool_node:ToolNodeBase = self.config.tool_node_config.setup(tool_manager=tool_manager,
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memory=self.memory)
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self._load_sys_prompts()
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def _load_sys_prompts(self):
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self.prompt_store = build_prompt_store(
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pipeline_id=self.config.pipeline_id,
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prompt_set_id=self.config.prompt_set_id,
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file_path=self.config.sys_promp_dir,
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)
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self.prompt_dict: Dict[str, str] = self.prompt_store.get_all()
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for k in self.prompt_dict:
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logger.info(f"loaded '{k}' system prompt")
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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=self.prompt_dict["route_prompt"]
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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 = None
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for msg in msgs:
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if isinstance(msg, HumanMessage):
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candidate_hum_msg = msg
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break
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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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if self.prompt_dict.get("chat_prompt") is not None:
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inp = {"messages":[
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SystemMessage(
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self.prompt_dict["chat_prompt"]
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),
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*self._get_inp_msgs(state)
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]}, state["inp"][1]
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out = self.chat_model.invoke(*inp)
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return {"messages": out["messages"]}
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def _tool_model_call(self, state:State):
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out = self.tool_node.invoke(state)
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return {"messages": out["messages"]}
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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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if __name__ == "__main__":
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from dotenv import load_dotenv
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from langchain.messages import SystemMessage, HumanMessage
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from langchain_core.messages.base import BaseMessageChunk
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from lang_agent.graphs.tool_nodes import AnnotatedToolNode, ToolNodeConfig, ChattyToolNodeConfig
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load_dotenv()
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route:RoutingGraph = RoutingConfig().setup()
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# route:RoutingGraph = RoutingConfig(tool_node_config=ChattyToolNodeConfig()).setup()
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graph = route.workflow
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nargs = {
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"messages": [SystemMessage("you are a helpful bot named jarvis"),
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HumanMessage("use calculator to calculate 926*84")]
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},{"configurable": {"thread_id": "3"}}
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# for chunk in route.invoke(*nargs, as_stream=True):
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# # print(f"\033[92m{chunk}\033[0m", end="", flush=True)
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# continue
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for _, mode, out in graph.stream({"inp": nargs},
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subgraphs=True,
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stream_mode=["messages", "values"]):
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# print(mode)
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if mode == "values":
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msgs = out.get("messages")
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l = len(msgs) if msgs is not None else -1
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print(type(out), out.keys(), l)
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else:
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print(type(out), mode, "==================================================")
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print(out[0].content)
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