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

from langchain_ollama import Ollama, OllamaEmbeddings
from langchain_core.documents import Document
from langchain_qdrant import QdrantVectorStore
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.tools import tool
from langchain_core.messages import HumanMessage
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from qdrant_client import QdrantClient

# Инициализация эмбеддингов и LLM через Ollama
embeddings = OllamaEmbeddings(model="nomic-embed-text")
llm = Ollama(model="llama3")

# Инициализация Qdrant
client = QdrantClient(host="localhost", port=6333)
collection_name = "knowledge"
vector_store = QdrantVectorStore(
    client=client,
    collection_name=collection_name,
    embedding=embeddings,
)

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

# Инструмент: поиск в базе знаний
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Search the knowledge base for relevant information."""
    docs: List[Document] = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No results."
    return "\n".join(doc.page_content for doc in docs)

# Инструмент: добавление документа в базу знаний
@tool
def add_to_knowledge_base(content: str, title: str = "doc") -> 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: {title} ({len(chunks)} chunks)."

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

system_prompt = (
    "You are a helpful agent with access to a knowledge base. "
    "Use the tools to search and add information. "
    "When you need to retrieve information, call search_knowledge_base. "
    "When you need to store new information, call add_to_knowledge_base."
)

agent = create_deep_agent(
    model=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    backend=backend,
    system_prompt=system_prompt,
)

# Загрузка документов из директории
def load_documents(dir_path: str) -> None:
    """Load all .txt files from dir_path into the knowledge base."""
    path = Path(dir_path)
    if not path.is_dir():
        print(f"Directory not found: {dir_path}")
        return
    for file_path in path.glob("*.txt"):
        try:
            content = file_path.read_text(encoding="utf-8")
            title = file_path.stem
            result = add_to_knowledge_base(content, title)
            print(f"Loaded {file_path.name}: {result}")
        except Exception as e:
            print(f"Error loading {file_path.name}: {e}")

# Интерактивный клиент
async def run_cli() -> None:
    thread_id = "session-1"
    print("Welcome to the RAG agent CLI.")
    print("Commands:")
    print("  /add    - add a new document")
    print("  /search - search the knowledge base")
    print("  /quit   - exit")
    while True:
        try:
            user_input = input("\n> ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\nExiting.")
            break

        if not user_input:
            continue

        if user_input.lower() == "/quit":
            print("Goodbye.")
            break

        if user_input.lower() == "/add":
            title = input("Title: ").strip()
            print("Enter content (end with a single line containing only 'END'):")
            lines: List[str] = []
            while True:
                line = input()
                if line.strip() == "END":
                    break
                lines.append(line)
            content = "\n".join(lines)
            result = add_to_knowledge_base(content, title)
            print(result)
            continue

        if user_input.lower() == "/search":
            query = input("Enter search query: ").strip()
            if not query:
                print("Empty query.")
                continue
            result = search_knowledge_base(query)
            print("\nSearch results:")
            print(result)
            continue

        # Any other message is sent to the agent
        response = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_input)]},
            {"configurable": {"thread_id": thread_id}},
        )
        agent_reply = response["messages"][-1].content
        print(f"\nAgent: {agent_reply}")

def main() -> None:
    # Optional: load documents from a directory passed as first argument
    if len(sys.argv) > 1:
        dir_path = sys.argv[1]
        print(f"Loading documents from {dir_path}...")
        load_documents(dir_path)
    asyncio.run(run_cli())

if __name__ == "__main__":
    main()