import os
import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.messages import HumanMessage
from langchain_core.documents import Document
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_qdrant import QdrantVectorStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
from qdrant_client import QdrantClient

# Загрузка переменных окружения
load_dotenv()

# Инициализация LLM
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,
)

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

# Инициализация Qdrant
client = QdrantClient(url="http://localhost:6333")
collection_name = "knowledge_base"
vector_store = QdrantVectorStore(
    client=client,
    collection_name=collection_name,
    embeddings=embeddings,
)

# Чанкинг
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# Инструмент поиска
@tool
def search_knowledge_base(query: str, max_results: int) -> str:
    """Semantic search in the knowledge base."""
    docs = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No results found."
    return "\n".join(doc.page_content for doc in docs)

# Инструмент добавления
@tool
def add_to_knowledge_base(content: str, title: str) -> str:
    """Add content to the knowledge base."""
    chunks = splitter.split_text(content)
    docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
    vector_store.add_documents(docs)
    return f"Added {len(docs)} chunks for {title}."

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

# Создание агента
agent = create_deep_agent(
    model=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    backend=backend,
    system_prompt="You are a helpful agent with access to a knowledge base. Use the tools to search and add information.",
)

async def main():
    print("Interactive agent. Commands: /add, /search, /quit")
    while True:
        try:
            user_input = input("> ").strip()
        except EOFError:
            break
        if not user_input:
            continue
        if user_input.startswith("/add"):
            title = input("Title: ").strip()
            content = input("Content: ").strip()
            result = add_to_knowledge_base(content, title)
            print(result)
        elif user_input.startswith("/search"):
            query = input("Query: ").strip()
            max_str = input("Max results (int): ").strip()
            try:
                max_results = int(max_str)
            except ValueError:
                max_results = 3
            result = search_knowledge_base(query, max_results)
            print(result)
        elif user_input.startswith("/quit"):
            print("Goodbye!")
            break
        else:
            # обычный диалог с агентом
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=user_input)]},
                {"configurable": {"thread_id": "session-1"}},
            )
            print(response["messages"][-1].content)

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