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_core.messages import HumanMessage
from langchain.tools import tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_qdrant import QdrantVectorStore
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
QDRANT_COLLECTION = "knowledge_base"
EMBEDDING_MODEL = "text-embedding-3-small"
LLM_MODEL = "openai/gpt-oss-20b:free"
BASE_URL = "https://openrouter.ai/api/v1"
API_KEY = os.getenv("OPENAI_API_KEY")

# ---------------------------------------------------------------------------
# Embeddings and Vector Store
# ---------------------------------------------------------------------------
embeddings = OpenAIEmbeddings(
    model=EMBEDDING_MODEL,
    base_url=BASE_URL,
    api_key=API_KEY,
)

vector_store = QdrantVectorStore(
    url=QDRANT_URL,
    collection_name=QDRANT_COLLECTION,
    embedding_function=embeddings,
)

# ---------------------------------------------------------------------------
# Text splitter
# ---------------------------------------------------------------------------
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# ---------------------------------------------------------------------------
# Tools
# ---------------------------------------------------------------------------
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Semantic search in the knowledge base."""
    docs: List[Document] = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No relevant documents found."
    return "\n\n---\n\n".join(doc.page_content for doc in docs)

@tool
def add_to_knowledge_base(content: str, title: str = "untitled") -> str:
    """Add a new document to the knowledge base."""
    # Split content into chunks
    chunks = text_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 '{title}'."

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

# ---------------------------------------------------------------------------
# LLM
# ---------------------------------------------------------------------------
llm = ChatOpenAI(
    model=LLM_MODEL,
    base_url=BASE_URL,
    api_key=API_KEY,
    temperature=0.0,
)

# ---------------------------------------------------------------------------
# Agent
# ---------------------------------------------------------------------------
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.",
)

# ---------------------------------------------------------------------------
# Document loader for initialization
# ---------------------------------------------------------------------------
async def load_documents_from_dir(directory: str):
    """Load all text files from a directory into the vector store."""
    dir_path = Path(directory)
    if not dir_path.is_dir():
        print(f"Directory {directory} does not exist.")
        return
    for file_path in dir_path.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": f"init-{file_path.name}"}},
        )
    print("Initialization complete.")

# ---------------------------------------------------------------------------
# Interactive CLI
# ---------------------------------------------------------------------------
async def interactive_loop():
    print("Welcome to the RAG agent. Commands: /add <title>, /search <query>, /quit")
    while True:
        user_input = input("> ")
        if user_input.strip() == "/quit":
            print("Goodbye!")
            break
        if user_input.startswith("/add "):
            parts = user_input.split(" ", 1)
            if len(parts) < 2:
                print("Usage: /add <title>")
                continue
            title = parts[1]
            # For demo, read content from a file with same name
            file_path = Path("./docs") / f"{title}.txt"
            if not file_path.exists():
                print(f"File {file_path} not found.")
                continue
            content = file_path.read_text(encoding="utf-8")
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
                {"configurable": {"thread_id": f"add-{title}"}},
            )
            print(response["messages"][-1].content)
        elif user_input.startswith("/search "):
            query = user_input[len("/search "):]
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/search {query}")]},
                {"configurable": {"thread_id": f"search-{query}"}},
            )
            print(response["messages"][-1].content)
        else:
            # Regular message to agent
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=user_input)]},
                {"configurable": {"thread_id": "interactive"}},
            )
            print(response["messages"][-1].content)

# ---------------------------------------------------------------------------
# Main entry point
# ---------------------------------------------------------------------------
async def main():
    # Optional: load initial documents
    # await load_documents_from_dir("./initial_docs")
    await interactive_loop()

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