220 lines
7.7 KiB
Python
220 lines
7.7 KiB
Python
from dataclasses import dataclass, field, is_dataclass
|
|
from typing import Type, TypedDict, Literal, Dict, List, Tuple
|
|
import tyro
|
|
import os.path as osp
|
|
|
|
|
|
from lang_agent.config import InstantiateConfig, KeyConfig
|
|
from lang_agent.tool_manager import ToolManager
|
|
from lang_agent.base import ToolNodeBase
|
|
from lang_agent.graphs.graph_states import State, ChattyToolState
|
|
from lang_agent.utils import make_llm
|
|
|
|
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
|
|
from langchain.agents import create_agent
|
|
from langchain.chat_models import init_chat_model
|
|
|
|
from langgraph.checkpoint.memory import MemorySaver
|
|
from langgraph.graph import StateGraph, START, END
|
|
|
|
|
|
@dataclass
|
|
class ToolNodeConfig(InstantiateConfig):
|
|
_target: Type = field(default_factory=lambda: ToolNode)
|
|
|
|
tool_prompt_f:str = osp.join(osp.dirname(osp.dirname(osp.dirname(__file__))), "configs", "route_sys_prompts", "tool_prompt.txt")
|
|
|
|
|
|
class ToolNode(ToolNodeBase):
|
|
def __init__(self, config: ToolNodeConfig,
|
|
tool_manager:ToolManager,
|
|
memory:MemorySaver):
|
|
self.config = config
|
|
self.tool_manager = tool_manager
|
|
self.mem = memory
|
|
|
|
self.populate_modules()
|
|
|
|
def populate_modules(self):
|
|
self.llm = make_llm(tags=["tool_llm"])
|
|
|
|
self.tool_agent = create_agent(self.llm, self.tool_manager.get_list_langchain_tools(), checkpointer=self.mem)
|
|
with open(self.config.tool_prompt_f, "r") as f:
|
|
self.sys_prompt = f.read()
|
|
|
|
def invoke(self, state:State):
|
|
inp = {"messages":[
|
|
SystemMessage(
|
|
self.sys_prompt
|
|
),
|
|
*state["inp"][0]["messages"][1:]
|
|
]}, state["inp"][1]
|
|
|
|
out = self.tool_agent.invoke(*inp)
|
|
return {"messages": out}
|
|
|
|
def get_streamable_tags(self):
|
|
return super().get_streamable_tags()
|
|
|
|
|
|
@dataclass
|
|
class ChattyToolNodeConfig(KeyConfig, ToolNodeConfig):
|
|
_target: Type = field(default_factory=lambda: ChattyToolNode)
|
|
|
|
llm_name: str = "qwen-plus"
|
|
"""name of llm"""
|
|
|
|
llm_provider:str = "openai"
|
|
"""provider of the llm"""
|
|
|
|
base_url:str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|
"""base url; could be used to overwrite the baseurl in llm provider"""
|
|
|
|
chatty_sys_prompt_f:str = osp.join(osp.dirname(osp.dirname(osp.dirname(__file__))), "configs", "route_sys_prompts", "chatty_prompt.txt")
|
|
|
|
|
|
class ChattyToolNode(ToolNodeBase):
|
|
def __init__(self, config:ChattyToolNodeConfig,
|
|
tool_manager:ToolManager,
|
|
memory:MemorySaver):
|
|
self.config = config
|
|
self.tool_manager = tool_manager
|
|
self.mem = memory
|
|
self.tool_done = False
|
|
|
|
self.populate_modules()
|
|
self.build_graph()
|
|
|
|
|
|
def populate_modules(self):
|
|
self.chatty_llm = init_chat_model(model=self.config.llm_name,
|
|
model_provider=self.config.llm_provider,
|
|
api_key=self.config.api_key,
|
|
base_url=self.config.base_url,
|
|
temperature=0,
|
|
tags=["chatty_llm"])
|
|
self.tool_llm = init_chat_model(model=self.config.llm_name,
|
|
model_provider=self.config.llm_provider,
|
|
api_key=self.config.api_key,
|
|
base_url=self.config.base_url,
|
|
temperature=0,
|
|
tags=["tool_llm"])
|
|
self.reit_llm = make_llm(model="qwen-flash", tags=["reit_llm"])
|
|
|
|
self.chatty_agent = create_agent(self.chatty_llm, [], checkpointer=self.mem)
|
|
self.tool_agent = create_agent(self.tool_llm, self.tool_manager.get_list_langchain_tools(), checkpointer=self.mem)
|
|
|
|
with open(self.config.chatty_sys_prompt_f, "r") as f:
|
|
self.chatty_sys_prompt = f.read()
|
|
|
|
with open(self.config.tool_prompt_f, "r") as f:
|
|
self.tool_sys_prompt = f.read()
|
|
|
|
def get_streamable_tags(self):
|
|
return [["chatty_llm"], ["reit_llm"]]
|
|
|
|
def invoke(self, state:State):
|
|
self.tool_done = False
|
|
|
|
inp = {"inp": state["inp"]}
|
|
out = self.workflow.invoke(inp)
|
|
chat_msgs = out.get("chatty_messages")["messages"]
|
|
tool_msgs = out.get("tool_messages")["messages"]
|
|
|
|
state_msgs = [] if state.get("messages") is None else state.get("messages")
|
|
return {"messages": state_msgs + chat_msgs + tool_msgs}
|
|
|
|
def _tool_node_call(self, state:ChattyToolState):
|
|
inp = {"messages":[
|
|
SystemMessage(
|
|
self.tool_sys_prompt
|
|
),
|
|
*state["inp"][0]["messages"][1:]
|
|
]}, state["inp"][1]
|
|
|
|
out = self.tool_agent.invoke(*inp)
|
|
|
|
self.tool_done = True
|
|
return {"tool_messages": out}
|
|
|
|
|
|
def _chat_node_call(self, state:ChattyToolState):
|
|
outs = []
|
|
|
|
while not self.tool_done:
|
|
inp = {"messages":[
|
|
SystemMessage(
|
|
self.chatty_sys_prompt
|
|
),
|
|
*state["inp"][0]["messages"][1:]
|
|
]}, state["inp"][1]
|
|
outs.extend(self.chatty_agent.invoke(*inp)["messages"])
|
|
|
|
return {"chatty_messages": {"messages":outs}}
|
|
|
|
|
|
def _handoff_node(self, state:ChattyToolState):
|
|
# NOTE: This exists just to stream the thing correctly
|
|
tool_msgs = state.get("tool_messages")["messages"]
|
|
inp = [
|
|
SystemMessage(
|
|
"do nothing and repeat the last message"
|
|
),
|
|
tool_msgs[-1].content
|
|
]
|
|
self.reit_llm.invoke(inp)
|
|
return {}
|
|
|
|
|
|
def build_graph(self):
|
|
builder = StateGraph(ChattyToolState)
|
|
builder.add_node("chatty_tool_call", self._tool_node_call)
|
|
builder.add_node("chatty_chat_call", self._chat_node_call)
|
|
builder.add_node("chatty_handoff_node", self._handoff_node)
|
|
|
|
builder.add_edge(START, "chatty_tool_call")
|
|
builder.add_edge(START, "chatty_chat_call")
|
|
builder.add_edge("chatty_chat_call", "chatty_handoff_node")
|
|
builder.add_edge("chatty_tool_call", "chatty_handoff_node")
|
|
builder.add_edge("chatty_handoff_node", END)
|
|
|
|
self.workflow = builder.compile()
|
|
|
|
|
|
|
|
tool_node_dict = {
|
|
"tool_node" : ToolNodeConfig(),
|
|
"chatty_tool_node" : ChattyToolNodeConfig()
|
|
}
|
|
|
|
tool_node_union = tyro.extras.subcommand_type_from_defaults(tool_node_dict, prefix_names=False)
|
|
AnnotatedToolNode = tyro.conf.OmitSubcommandPrefixes[tyro.conf.SuppressFixed[tool_node_union]]
|
|
|
|
if __name__ == "__main__":
|
|
from langchain_core.messages.base import BaseMessageChunk
|
|
from langchain_core.messages import BaseMessage
|
|
|
|
from lang_agent.tool_manager import ToolManagerConfig
|
|
|
|
from dotenv import load_dotenv
|
|
load_dotenv()
|
|
|
|
mem = MemorySaver()
|
|
tool_manager = ToolManagerConfig().setup()
|
|
chatty_node:ChattyToolNode = ChattyToolNodeConfig().setup(tool_manager=tool_manager,
|
|
memory=mem)
|
|
|
|
query = "use calculator to calculate 33*42"
|
|
input = {"inp" : ({"messages":[SystemMessage("you are a kind helper"), HumanMessage(query)]},
|
|
{"configurable": {"thread_id": '3'}})}
|
|
inp = input
|
|
graph = chatty_node.workflow
|
|
|
|
for chunk, metadata in graph.stream(inp, stream_mode="messages"):
|
|
tags = metadata.get("tags")
|
|
if not (tags in [["chatty_llm"], ["reit_llm"]]):
|
|
continue
|
|
|
|
if isinstance(chunk, (BaseMessageChunk, BaseMessage)) and getattr(chunk, "content", None):
|
|
print(chunk.content, end="", flush=True)
|