petya
1fd7486a85
Each of the 122 code slides now has a standalone .py script in examples/slide-NNN/script.py with the LLM provider pattern from bro-js/agents/teacher/assistant/src/angry_teacher/llm.py: - load_dotenv() + ChatOpenAI with base_url=https://llm.brojs.ru/v1 - OPENAI_API_KEY from .env (with safe (or '' or None) pattern) - Same comment block with alternative model/base_url choices Layout: - sec1/sec5: code extracted from slide-NN.js (addCodeBlock) - sec2/sec3/sec4: code extracted from sectionN.pptx via python-pptx (JetBrains Mono font shapes) 100 scripts contain runnable code, 22 are visual-only (dividers, intro, recap) and just print the slide title. Setup: cp .env.example .env # fill OPENAI_API_KEY (or symlink to your # teacher/assistant/.env) pip install -r requirements.txt python examples/slide-005/script.py
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
"""
|
|
Slide 58: Slide 31
|
|
Section 2: LangGraph 1.0
|
|
Source: slides/section2-langgraph/section2.pptx
|
|
|
|
Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
|
|
Паттерн провайдера скопирован из
|
|
bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.
|
|
|
|
Запуск:
|
|
cd langchain-evolution-deck
|
|
cp .env.example .env # заполни OPENAI_API_KEY
|
|
python examples/slide-058/script.py
|
|
"""
|
|
import os
|
|
from dotenv import load_dotenv
|
|
from langchain_openai import ChatOpenAI
|
|
|
|
load_dotenv()
|
|
|
|
llm = ChatOpenAI(
|
|
# model="openrouter/free",
|
|
# model="qwen/qwen3.5-35b-a3b",
|
|
model="openai/gpt-oss-20b",
|
|
# model="nvidia/nemotron-3-nano",
|
|
# model="qwen/qwen3.5-9b",
|
|
base_url="https://llm.brojs.ru/v1",
|
|
# base_url="https://api.minimax.io/v1",
|
|
# base_url="http://0.0.0.0:8090/v1",
|
|
# base_url="https://openrouter.ai/api/v1",
|
|
# api_key=os.getenv("MINIMAX_API_KEY"),
|
|
api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
|
|
temperature=0.7,
|
|
stream_usage=True,
|
|
)
|
|
|
|
|
|
# === Code from slide 58 ===
|
|
> import { StateGraph, START, END, Annotation } from "@langchain/langgraph";
|
|
> import { MemorySaver } from "@langchain/langgraph-checkpoint";
|
|
|
|
const State = Annotation.Root({
|
|
messages: Annotation({
|
|
reducer: (a, b) => a.concat(b),
|
|
default: () => [],
|
|
}),
|
|
});
|
|
|
|
const g = new StateGraph(State)
|
|
.addNode("echo", (s) => ({
|
|
messages: [{ role: "assistant", content: "echo: " + s.messages.at(-1).content }],
|
|
}))
|
|
.addEdge(START, "echo")
|
|
.addEdge("echo", END);
|
|
|
|
const app = g.compile({ checkpointer: new MemorySaver() });
|
|
> const cfg = { configurable: { thread_id: "t1" } };
|
|
const result = await app.invoke({ messages: [{ role: "user", content: "hi" }] }, cfg);
|
|
|
|
// note: snippet has 20 lines, card fits ~18
|