gqt
135 lines
3.4 KiB
Plaintext
135 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"import os\n",
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"from langchain.chat_models import init_chat_model\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"llm = init_chat_model(\n",
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" model=\"openai:qwen3.6-flash\",\n",
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" base_url=os.getenv(\"BASE_URL\"),\n",
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" api_key=os.getenv(\"API_KEY\")\n",
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")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"from langgraph.graph.message import MessagesState\n",
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"\n",
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"\n",
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"class OverallState(MessagesState):\n",
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" output: str\n",
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"\n",
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"\n",
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"def llm_node(state: OverallState) -> OverallState:\n",
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" messages = state[\"messages\"]\n",
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" res = llm.invoke(messages)\n",
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" return {\n",
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" \"messages\": [res]\n",
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" }\n",
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"\n",
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"\n",
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"def output_node(state: OverallState) -> OverallState:\n",
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" return {\n",
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" \"output\": state[\"messages\"][-1].content\n",
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" }"
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],
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"id": "b443de7fa7929ba2",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"from langgraph.graph import StateGraph, START, END\n",
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"\n",
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"builder = StateGraph(state_schema=OverallState)\n",
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"builder.add_node(\"llm_node\", llm_node)\n",
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"builder.add_node(\"output_node\", output_node)\n",
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"\n",
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"builder.add_edge(START, \"llm_node\")\n",
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"builder.add_edge(\"llm_node\", \"output_node\")\n",
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"builder.add_edge(\"output_node\", END)"
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],
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"id": "887432b11f020144",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"from langgraph.checkpoint.postgres import PostgresSaver\n",
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"from langchain_core.runnables import RunnableConfig\n",
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"from rich import print as rp\n",
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"\n",
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"with PostgresSaver.from_conn_string(os.getenv(\"LANGGRAPH_DATABASE_URL\")) as checkpointer:\n",
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" checkpointer.setup()\n",
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" graph = builder.compile(checkpointer=checkpointer)\n",
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"\n",
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" config: RunnableConfig = {\n",
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" \"configurable\": {\n",
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" \"thread_id\": \"chapter_0804\"\n",
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" }\n",
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" }\n",
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"\n",
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" messages1 = {\n",
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" \"messages\": [\n",
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" {\"role\": \"user\", \"content\": \"你好，我是gqt\"}\n",
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" ]\n",
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" }\n",
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" messages2 = {\n",
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" \"messages\": [\n",
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" {\"role\": \"user\", \"content\": \"你好，我是谁？\"}\n",
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" ]\n",
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" }\n",
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"\n",
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" res1 = graph.invoke(messages2, config=config)\n",
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"\n",
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"\n",
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" rp(res1)\n",
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" history_checkpoint = list(graph.get_state_history(config=config))\n",
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"\n",
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" # rp(history_checkpoint)"
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],
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"id": "e0f6b05e0dfeb578",
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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