Удалить solution.py

solution.py

@@ -1,71 +0,0 @@
-import os
-from typing import List
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.qdrant import Qdrant
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms.openai import ChatOpenAI
-from rich.console import Console
-
-# Конфигурация
-QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
-QDRANT_COLLECTION = os.getenv("QDRANT_COLLECTION", "rag_memory")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-if not OPENAI_API_KEY:
-    raise RuntimeError("Не задана переменная окружения OPENAI_API_KEY")
-
-console = Console()
-
-# Инициализация компонентов
-embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
-llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
-
-# Создание или подключение к коллекции Qdrant
-vectorstore = Qdrant(
-    client=None,
-    collection_name=QDRANT_COLLECTION,
-    embeddings=embeddings,
-    url=QDRANT_URL,
-)
-
-app = FastAPI(title="RAG Agent")
-
-class Document(BaseModel):
-    content: str
-
-class QueryRequest(BaseModel):
-    question: str
-    top_k: int = 5
-
-@app.post("/add_document")
-def add_document(doc: Document):
-    """
-    Добавляет документ в память агента.
-    """
-    try:
-        vectorstore.add_texts([doc.content])
-        console.log(f"[green]Документ добавлен:[/green] {doc.content[:50]}...")
-        return {"status": "ok"}
-    except Exception as e:
-        console.print_exception()
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/ask")
-def ask(request: QueryRequest):
-    """
-    Отвечает на вопрос, используя RAG.
-    """
-    try:
-        # Получаем похожие документы
-        docs = vectorstore.similarity_search_with_score(request.question, k=request.top_k)
-        contexts = [doc.page_content for doc, _ in docs]
-        console.log(f"[blue]Найдено контекстов:[/blue] {len(contexts)}")
-
-        chain = load_qa_chain(llm=llm, chain_type="stuff")
-        answer = chain.run(input_documents=[{"content": c} for c in contexts], question=request.question)
-        return {"answer": answer}
-    except Exception as e:
-        console.print_exception()
-        raise HTTPException(status_code=500, detail=str(e))