import os
import asyncio
from pathlib import Path
from langchain_openai import ChatOpenAI
from langchain_ollama import OllamaEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_core.documents import Document
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_core.messages import HumanMessage

# ---------- LLM ----------
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 ----------
# Using Ollama embeddings as per assignment correction
embeddings = OllamaEmbeddings(model="nomic-embed-text")

# ---------- Vector Store (Qdrant) ----------
# Ensure Qdrant is running locally (default port 6333)
vector_store = QdrantVectorStore(
    url="http://localhost:6333",
    collection_name="knowledge",
    embedding_function=embeddings,
)

# ---------- Tools ----------
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Semantic search in the knowledge base."""
    docs = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No results found."
    return "\n---\n".join(f"{i+1}. {doc.page_content[:200]}..." for i, doc in enumerate(docs))

@tool
def add_to_knowledge_base(content: str, title: str = "untitled") -> str:
    """Add a document to the knowledge base."""
    doc = Document(page_content=content, metadata={"title": title})
    vector_store.add_documents([doc])
    return f"Document '{title}' added to the knowledge base."

# ---------- Backend ----------
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 an assistant with access to a knowledge base. Use the provided tools to search and add information."
)

# ---------- Document Loader ----------
async def load_documents_from_dir(directory: str):
    """Load all text files from a directory into the vector store."""
    for file_path in Path(directory).rglob("*.txt"):
        text = file_path.read_text(encoding="utf-8")
        title = file_path.stem
        await agent.ainvoke(
            {"messages": [HumanMessage(content=f"/add {title}")], "content": text},
            {"configurable": {"thread_id": "init"}},
        )

# ---------- 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)
            title = parts[1] if len(parts) > 1 else "untitled"
            content = input("Enter content: ")
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
                {"configurable": {"thread_id": "session"}},
            )
            print(response["messages"][-1].content)
        elif user_input.startswith("/search "):
            query = user_input.split(" ", 1)[1]
            response = await agent.ainvoke(
                {"messages": [HumanMessage(content=f"/search {query}")]},
                {"configurable": {"thread_id": "session"}},
            )
            print(response["messages"][-1].content)
        else:
            print("Unknown command. Use /add, /search, or /quit.")

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

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