diff --git a/solution.py b/solution.py
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+++ b/solution.py
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+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))
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