import os
import asyncio
from pathlib import Path
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 FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_core.messages import HumanMessage

# Configuration
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise RuntimeError("OPENAI_API_KEY not set in environment")

# LLM via OpenRouter
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
    temperature=0.0,
)

# Embeddings via OpenRouter
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
)

# Qdrant client and vector store
from qdrant_client import QdrantClient
qdrant_client = QdrantClient(url="http://localhost:6333")
collection_name = "knowledge_base"
vector_store = QdrantVectorStore(
    client=qdrant_client,
    collection_name=collection_name,
    embedding_function=embeddings,
)

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

# Tool: search knowledge base
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Search the knowledge base for relevant information."""
    docs = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No results found."
    return "\n\n".join(f"Title: {doc.metadata.get('title', 'unknown')}\n{doc.page_content}" for doc in docs)

# Tool: add to knowledge base
@tool
def add_to_knowledge_base(content: str, title: str = "document") -> 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 from '{title}'."

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

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

# Helper: load documents from a directory
def load_documents_from_dir(directory: str):
    dir_path = Path(directory)
    if not dir_path.is_dir():
        raise ValueError(f"Directory {directory} does not exist.")
    for file_path in dir_path.rglob("*"):
        if file_path.is_file() and file_path.suffix.lower() in {".txt", ".md", ".py", ".json"}:
            content = file_path.read_text(encoding="utf-8")
            title = file_path.stem
            add_to_knowledge_base(content, title)

# Interactive CLI
async def interactive_loop():
    print("RAG Agent CLI. Commands: /add <file_path>, /search <query>, /quit")
    while True:
        user_input = input(">> ").strip()
        if not user_input:
            continue
        if user_input.lower() == "/quit":
            print("Exiting.")
            break
        if user_input.startswith("/add "):
            _, file_path = user_input.split(maxsplit=1)
            try:
                content = Path(file_path).read_text(encoding="utf-8")
                title = Path(file_path).stem
                result = add_to_knowledge_base(content, title)
                print(result)
            except Exception as e:
                print(f"Error adding file: {e}")
            continue
        if user_input.startswith("/search "):
            _, query = user_input.split(maxsplit=1)
            result = search_knowledge_base(query, max_results=3)
            print(result)
            continue
        # Treat as normal message to agent
        try:
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=user_input)]},
                {"configurable": {"thread_id": "session-1"}},
            )
            print(response["messages"][-1].content)
        except Exception as e:
            print(f"Agent error: {e}")

def main():
    # Optional: load initial docs from a folder
    init_dir = os.getenv("INIT_DOCS_DIR")
    if init_dir:
        try:
            load_documents_from_dir(init_dir)
            print(f"Loaded documents from {init_dir}")
        except Exception as e:
            print(f"Failed to load initial docs: {e}")
    asyncio.run(interactive_loop())

if __name__ == "__main__":
    main()