"""
# main.py – RAG‑agent with Qdrant, OpenRouter, and deepagents
# ----------------------------------------------------------
# 1.  Imports and configuration
# 2.  Qdrant vector store wrapper (embedding, add, search)
# 3.  Text splitter (RecursiveCharacterTextSplitter)
# 4.  LangChain tools: search_knowledge_base, add_to_knowledge_base
# 5.  DeepAgent creation (create_deep_agent)
# 6.  CLI client for /add, /search, /quit
# ----------------------------------------------------------
"""
import os
import asyncio
import json
from pathlib import Path
from typing import List

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

# ------------------------------------------------------------------
# 1. Configuration
# ------------------------------------------------------------------
# Load environment variables (e.g. OPENAI_API_KEY)
from dotenv import load_dotenv
load_dotenv()

# LLM – OpenRouter (free tier)
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 – OpenAI via OpenRouter
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
)

# Qdrant client – assumes Qdrant is running locally on default port
qdrant_url = os.getenv("QDRANT_URL", "http://localhost:6333")
collection_name = "knowledge_base"
vector_store = QdrantVectorStore(
    url=qdrant_url,
    collection_name=collection_name,
    embeddings=embeddings,
)

# Text splitter – 1000 chars max, 200 overlap
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# ------------------------------------------------------------------
# 2. Tools
# ------------------------------------------------------------------
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Semantic search in the Qdrant 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([f"{doc.metadata.get('title', 'Untitled')}:\n{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.
    The content is split into chunks before being stored.
    """
    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 document '{title}'."

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

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.",
)

# ------------------------------------------------------------------
# 4. CLI client
# ------------------------------------------------------------------
async def run_cli():
    print("Welcome to the RAG Agent CLI. Commands: /add <title> <file>, /search <query>, /quit")
    thread_id = "cli-session"
    while True:
        try:
            user_input = input("> ")
        except EOFError:
            break
        if not user_input:
            continue
        if user_input.startswith("/quit"):
            print("Goodbye!")
            break
        if user_input.startswith("/add"):
            parts = user_input.split(maxsplit=2)
            if len(parts) < 3:
                print("Usage: /add <title> <file_path>")
                continue
            title, file_path = parts[1], parts[2]
            try:
                content = Path(file_path).read_text(encoding="utf-8")
            except Exception as e:
                print(f"Error reading file: {e}")
                continue
            # Invoke tool directly
            result = add_to_knowledge_base(content, title)
            print(result)
            continue
        if user_input.startswith("/search"):
            query = user_input[len("/search"):].strip()
            if not query:
                print("Usage: /search <query>")
                continue
            # Use agent to perform search via tool
            response = await agent.ainvoke(
                {"messages": [{"role": "user", "content": f"search {query}"}]},
                {"configurable": {"thread_id": thread_id}},
            )
            print(response["messages"][-1].content)
            continue
        # Default: treat as normal user message
        response = await agent.ainvoke(
            {"messages": [{"role": "user", "content": user_input}]},
            {"configurable": {"thread_id": thread_id}},
        )
        print(response["messages"][-1].content)

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