从输入输入的字典,任意
从 langchain openai 导入 ChatOpenAI
从 langchain core. message 导入任意信件
从langraph.graph 导入状态 Graph,START, END,信件状态
从 langgraph. 预建导入工具节点
从 langgraph. checkpoint.memory 导入内存
从 langmem. 短期导入汇总点,运行摘要
类别状态( Messages State):
上下文:dict [str,运行摘要]
类 LLM 输入国( TypedDict ):
摘要( M):列表[任何消息]
型号 = ChatOpenAI(型号 ="gpt-4o")
summarization model = model.bind(max tokens=128) 互联网档案馆的存檔,存档日期2014-09-02.
总和节点=总和节点(
符号 counter=model.get num tokens from messages
模型=摘要模型
最大( T) = 256
最大值 tokens before 摘要=1024
最大缩略语 tokens=128
)
def call model( 状态:LLMInput State):
响应=model.bind tools(tools).invoke(状态["摘要消息"]).
返回 {“消息”: [应。
建构器 = StateGraph( 状态)
buildinger.add node("摘要",summarization node) 互联网档案馆的存檔,存档日期2013-03-02.
builder.add node (“ call model”,“ call model”) 创建器。
构建器.add node("工具","工具")
构建器。 set entry point ("摘要")
builder.add dedge(“摘要”、“ call model”)
图 = builder.compile(检查点 = 记忆保护())
本文由 AI图鉴自动抓取并翻译,版权归原作者所有;如需查看原文请点下方链接。
from typing import TypedDict, Any
from langchain_openai import ChatOpenAI
from langchain_core.messages import AnyMessage
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import InMemorySaver
from langmem.short_term import SummarizationNode, RunningSummary
class State(MessagesState):
context: dict[str, RunningSummary]
class LLMInputState(TypedDict):
summarized_messages: list[AnyMessage]
model = ChatOpenAI(model="gpt-4o")
summarization_model = model.bind(max_tokens=128)
summarization_node = SummarizationNode(
token_counter=model.get_num_tokens_from_messages,
model=summarization_model,
max_tokens=256,
max_tokens_before_summary=1024,
max_summary_tokens=128,
)
def call_model(state: LLMInputState):
response = model.bind_tools(tools).invoke(state["summarized_messages"])
return {"messages": [response]}
builder = StateGraph(State)
builder.add_node("summarize", summarization_node)
builder.add_node("call_model", call_model)
builder.add_node("tools", ToolNode(tools))
builder.set_entry_point("summarize")
builder.add_edge("summarize", "call_model")
graph = builder.compile(checkpointer=InMemorySaver())
Auto-fetched and translated by AI Atlas. All rights belong to the original author.