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
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""
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Slide 18: Vector store -> retriever -> RAG-цепочка
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Section 1: LangChain 1.0
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Source: slides/section1-chains/slide-18.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-018/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 18 ===
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain_core.runnables import RunnablePassthrough
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# 1. Ðмбеддинги и индекÑ
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embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
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docs = ["Cats are mammals.", "Python is a programming language.",
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"LangChain helps build LLM apps."]
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vectorstore = FAISS.from_texts(docs, embedding=embeddings)
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# 2. .as_retriever() пÑевÑаÑÐ°ÐµÑ store в Runnable
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retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
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# 3. RAG-ÑепоÑка ÑеÑез LCEL: context + question -> answer
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chat_models import init_chat_model
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prompt = ChatPromptTemplate.from_template(
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"Answer based on context.\nContext: {context}\nQ: {question}"
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)
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model = init_chat_model("openai:gpt-4.1-mini")
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rag = (
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{"context": retriever, "question": RunnablePassthrough()}
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| prompt | model | StrOutputParser()
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)
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