# main.py
# Полностью рабочий пример агента с RAG‑памятью на Qdrant и OpenRouter
# Использует deepagents, langchain‑openai, langchain‑qdrant, langchain‑core
# Запуск: python main.py

import os
import asyncio
from pathlib import Path
from typing import List

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

# ---------------------------------------------------------------------------
# Конфигурация 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"),
)

# ---------------------------------------------------------------------------
# Qdrant клиент и коллекция
# ---------------------------------------------------------------------------
# Предполагается, что Qdrant запущен локально на порту 6333
qdrant_url = "http://localhost:6333"
collection_name = "knowledge_base"

vector_store = QdrantVectorStore(
    embeddings=embeddings,
    url=qdrant_url,
    collection_name=collection_name,
    # Если коллекция не существует, она будет создана автоматически
)

# ---------------------------------------------------------------------------
# Чанкинг
# ---------------------------------------------------------------------------
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)

# ---------------------------------------------------------------------------
# Инструменты для агента
# ---------------------------------------------------------------------------
@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Semantic search in the knowledge base.
    Returns a formatted string with the top results.
    """
    results = vector_store.similarity_search_with_score(query, k=max_results)
    if not results:
        return "No relevant documents found."
    formatted = []
    for doc, score in results:
        formatted.append(f"Title: {doc.metadata.get('title', 'Untitled')}\nScore: {score:.4f}\nContent: {doc.page_content[:200]}...\n")
    return "\n".join(formatted)

@tool
def add_to_knowledge_base(content: str, title: str) -> str:
    """Add a new document to the knowledge base.
    The content is split into chunks, embedded and stored.
    """
    # Split into chunks
    chunks = text_splitter.split_text(content)
    docs: List[Document] = []
    for i, chunk in enumerate(chunks):
        docs.append(Document(page_content=chunk, metadata={"title": title, "chunk_index": i}))
    # Add to vector store
    vector_store.add_documents(docs)
    return f"Added {len(docs)} chunks for document '{title}'."

# ---------------------------------------------------------------------------
# Backend для deepagents
# ---------------------------------------------------------------------------
backend = CompositeBackend(
    default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
    routes={},
)

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

# ---------------------------------------------------------------------------
# Инициализация: загрузка документов из директории
# ---------------------------------------------------------------------------
DOCS_DIR = Path("./docs")

async def load_documents_from_dir(directory: Path):
    if not directory.exists():
        return
    for file_path in directory.rglob("*.txt"):
        content = file_path.read_text(encoding="utf-8")
        title = file_path.stem
        await agent.ainvoke(
            {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
            {"configurable": {"thread_id": "init-session"}},
        )

# ---------------------------------------------------------------------------
# Интерактивный клиент
# ---------------------------------------------------------------------------
async def interactive_loop():
    print("Welcome to the RAG agent. Commands: /add <title>, /search <query>, /quit")
    thread_id = "interactive-session"
    while True:
        user_input = input("> ")
        if user_input.strip() == "/quit":
            print("Goodbye!")
            break
        if user_input.startswith("/add "):
            title = user_input[5:].strip()
            print("Enter content (end with a single line containing only 'END'): ")
            lines = []
            while True:
                line = input()
                if line.strip() == "END":
                    break
                lines.append(line)
            content = "\n".join(lines)
            await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
                {"configurable": {"thread_id": thread_id}},
            )
            print(f"Document '{title}' added.")
        elif user_input.startswith("/search "):
            query = user_input[8:].strip()
            result = await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/search {query}")]},
                {"configurable": {"thread_id": thread_id}},
            )
            print(result["messages"][-1].content)
        else:
            # обычный запрос к LLM
            result = await agent.ainvoke(
                {"messages": [HumanMessage(content=user_input)]},
                {"configurable": {"thread_id": thread_id}},
            )
            print(result["messages"][-1].content)

# ---------------------------------------------------------------------------
# Основной запуск
# ---------------------------------------------------------------------------
async def main():
    # Загрузим документы из каталога docs при старте
    await load_documents_from_dir(DOCS_DIR)
    await interactive_loop()

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