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
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
"""
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Slide 114: @traceable -- декоратор для любой функции
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Section 5: Экосистема
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Source: slides/section5-ecosystem/slide-04.js
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Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
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Паттерн провайдера скопирован из
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bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.
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Запуск:
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cd langchain-evolution-deck
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cp .env.example .env # заполни OPENAI_API_KEY
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python examples/slide-114/script.py
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"""
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import os
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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load_dotenv()
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llm = ChatOpenAI(
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# model="openrouter/free",
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# model="qwen/qwen3.5-35b-a3b",
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model="openai/gpt-oss-20b",
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# model="nvidia/nemotron-3-nano",
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# model="qwen/qwen3.5-9b",
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base_url="https://llm.brojs.ru/v1",
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# base_url="https://api.minimax.io/v1",
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# base_url="http://0.0.0.0:8090/v1",
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# base_url="https://openrouter.ai/api/v1",
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# api_key=os.getenv("MINIMAX_API_KEY"),
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api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
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temperature=0.7,
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stream_usage=True,
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)
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# === Code from slide 114 ===
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from langsmith import traceable
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# ÐекоÑаÑÐ¾Ñ Ð¿ÑевÑаÑÐ°ÐµÑ ÑÑнкÑÐ¸Ñ Ð² traced Run.
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# ÐÑе аÑгÑменÑÑ Ð¸ return попадаÑÑ Ð² trace авÑомаÑиÑеÑки.
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@traceable(name="retrieve_docs", run_type="retriever")
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def retrieve(query: str, k: int = 4) -> list[str]:
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return vector_store.similarity_search(query, k=k)
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@traceable(name="generate_answer", run_type="chain")
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def generate_answer(query: str) -> str:
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docs = retrieve(query) # nested Run
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context = "\n".join(docs)
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return llm.invoke(f"Q: {query}\nCtx: {context}")
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# ÐеÑево: generate_answer -> retrieve_docs -> ChatModel
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print(generate_answer("What is LCEL?"))
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