"""
Slide 18: Vector store -> retriever -> RAG-цепочка
Section 1: LangChain 1.0
Source: slides/section1-chains/slide-18.js

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-018/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 18 ===
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables import RunnablePassthrough

# 1. Эмбеддинги и индекс
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
docs = ["Cats are mammals.", "Python is a programming language.",
        "LangChain helps build LLM apps."]
vectorstore = FAISS.from_texts(docs, embedding=embeddings)

# 2. .as_retriever() превращает store в Runnable
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

# 3. RAG-цепочка через LCEL: context + question -> answer
from langchain_core.prompts import ChatPromptTemplate
from langchain.chat_models import init_chat_model

prompt = ChatPromptTemplate.from_template(
    "Answer based on context.\nContext: {context}\nQ: {question}"
)
model = init_chat_model("openai:gpt-4.1-mini")
rag = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt | model | StrOutputParser()
)