import os
import asyncio
from pathlib import Path
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from langchain_tavily import TavilySearchResults
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# --------------------- 1. Загрузка переменных окружения ---------------------
load_dotenv()

# --------------------- 2. LLM и Embeddings ---------------------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
)

# --------------------- 3. Векторное хранилище Chroma ---------------------
CHROMA_DIR = Path("./chroma_db")
CHROMA_DIR.mkdir(parents=True, exist_ok=True)
vector_store = Chroma(
    collection_name="knowledge",
    embedding_function=embeddings,
    persist_directory=str(CHROMA_DIR),
)

# --------------------- 4. Загрузка документов ---------------------
DOCS_DIR = Path("./documents")
if DOCS_DIR.exists():
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    for file_path in DOCS_DIR.rglob("*.txt"):
        text = file_path.read_text(encoding="utf-8")
        docs = splitter.split_text(text)
        documents = [Document(page_content=chunk, metadata={"source": str(file_path)}) for chunk in docs]
        vector_store.add_documents(documents)
    vector_store.persist()

# --------------------- 5. Инструменты ---------------------
@tool
def search_local_kb(query: str, top_k: int = 3) -> str:
    """Semantic search in the local knowledge base."""
    docs = vector_store.similarity_search(query, k=top_k)
    if not docs:
        return "No relevant local knowledge found."
    return "\n---\n".join(f"{i+1}. {doc.page_content[:200]}..." for i, doc in enumerate(docs))

@tool
def web_search(query: str) -> str:
    """Web search using Tavily."""
    tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
    results = tavily.run(query)
    if not results:
        return "No web results found."
    return "\n---\n".join(f"{i+1}. {res['title']}\n{res['url']}\n{res['content'][:200]}..." for i, res in enumerate(results))

# --------------------- 6. Backend ---------------------
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# --------------------- 7. Создание агента ---------------------
agent = create_deep_agent(
    model=llm,
    tools=[search_local_kb, web_search],
    backend=backend,
    system_prompt=(
        "You are an assistant that answers user questions.\n"
        "If the question is about information that should be in the local knowledge base, use the tool `search_local_kb`.\n"
        "If the question requires up‑to‑date information from the web, use the tool `web_search`.\n"
        "Always indicate the source of the answer in the format: `Источник: chromadb` or `Источник: tavily`."
    ),
)

# --------------------- 8. CLI ---------------------
async def chat_loop():
    print("Добро пожаловать в RAG‑агент. Введите 'exit' для выхода.")
    while True:
        user_input = input("\nЗапрос: ")
        if user_input.lower() in {"exit", "quit", "q"}:
            print("До свидания!")
            break
        result = await agent.ainvoke(
            {"messages": [{"role": "user", "content": user_input}]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # Последнее сообщение агента
        content = result["messages"][-1]["content"]
        print(content)

if __name__ == "__main__":
    asyncio.run(chat_loop())