import os
import asyncio
from pathlib import Path

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

# Load environment variables (OPENAI_API_KEY)
load_dotenv()

# ---------------------------------------------------------------------------
# LLM and embeddings configuration (OpenRouter)
# ---------------------------------------------------------------------------
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 vector store setup
# ---------------------------------------------------------------------------
from qdrant_client import QdrantClient

qdrant_client = QdrantClient(url="http://localhost:6333")  # Qdrant must be running locally
vector_store = QdrantVectorStore(
    embedding_function=embeddings,
    client=qdrant_client,
    collection_name="knowledge",
)

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

# ---------------------------------------------------------------------------
# Tools for the agent
# ---------------------------------------------------------------------------
@tool
async def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Perform a semantic search in the knowledge base."""
    docs = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No relevant documents found."
    return "\n\n".join([f"Title: {doc.metadata.get('title', 'N/A')}\n{doc.page_content}" for doc in docs])

@tool
async def add_to_knowledge_base(content: str, title: str = "Unnamed Document") -> str:
    """Add a document to the knowledge base after chunking."""
    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"Document '{title}' added with {len(chunks)} chunks."

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

# ---------------------------------------------------------------------------
# DeepAgent definition
# ---------------------------------------------------------------------------
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 documents. Respond concisely.",
)

# ---------------------------------------------------------------------------
# Helper function to load all .txt files from a directory into the vector store
# ---------------------------------------------------------------------------
async def load_documents_from_directory(directory: str):
    dir_path = Path(directory)
    for txt_file in dir_path.rglob("*.txt"):
        content = txt_file.read_text(encoding="utf-8")
        title = txt_file.stem
        await add_to_knowledge_base(content, title)
    print(f"Loaded {len(list(dir_path.rglob('*.txt')))} documents from {directory}.")

# ---------------------------------------------------------------------------
# Interactive CLI client
# ---------------------------------------------------------------------------
async def interactive_loop():
    print("Welcome to the RAG Agent CLI. Commands: /add title content | /search query | /quit")
    while True:
        user_input = input("> ")
        if user_input.lower() == "/quit":
            print("Goodbye!")
            break
        if user_input.startswith("/add "):
            try:
                _, rest = user_input.split("/add ", 1)
                title, content = rest.split(" ", 1)
            except ValueError:
                print("Usage: /add title content")
                continue
            human_msg = f"Please add a document titled '{title}' with content: {content}"
        elif user_input.startswith("/search "):
            query = user_input[len("/search "):]
            human_msg = f"Please search for: {query}"
        else:
            print("Unknown command. Use /add, /search, or /quit.")
            continue

        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=human_msg)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        print(result["messages"][-1].content)

# ---------------------------------------------------------------------------
# Main entry point
# ---------------------------------------------------------------------------
async def main():
    # Optionally load documents from a directory on startup
    # await load_documents_from_directory("./data")
    await interactive_loop()

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