import os
import asyncio
from pathlib import Path
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings
from langchain_tavily import TavilySearchResults
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# Load env vars
load_dotenv()

# ---------- 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,
)

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

# ---------- Vector Store ----------
PERSIST_DIR = Path("./chroma_db")
PERSIST_DIR.mkdir(exist_ok=True)

embeddings = OllamaEmbeddings(model="nomic-embed-text")
vectorstore = Chroma(persist_directory=str(PERSIST_DIR), embedding_function=embeddings)

# Load documents from ./documents if not already loaded
if not vectorstore.get_collection().count():
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    docs = []
    for file in Path("./documents").glob("*.txt"):
        text = file.read_text(encoding="utf-8")
        docs.extend(splitter.split_text(text))
    vectorstore.add_texts(docs)

# ---------- Tools ----------
@tool
def search_local_kb(query: str, top_k: int = 3) -> str:
    """Semantic search in local ChromaDB knowledge base."""
    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
    docs = retriever.invoke(query)
    return "\n".join(doc.page_content for doc in docs) if docs else "No local results found."

@tool
def web_search(query: str) -> str:
    """Web search via Tavily."""
    tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
    results = tavily.invoke(query)
    return "\n".join(f"{r['title']}: {r['url']}" for r in results) if results else "No web results found."

# ---------- Agent ----------
agent = create_deep_agent(
    model=llm,
    tools=[search_local_kb, web_search],
    backend=backend,
    system_prompt="You are a helpful RAG agent. For questions about local documents use search_local_kb, for up‑to‑date facts use web_search. Always state the source (chromadb or tavily) in the answer.",
)

# ---------- CLI ----------
async def main():
    print("RAG Agent ready. Type 'exit' to quit.")
    while True:
        user_input = input("\nЗапрос: ")
        if user_input.lower() in {"exit", "quit"}:
            break
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_input)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # The agent returns a list of messages; last is the assistant reply
        reply = result["messages"][-1].content
        print(f"\nОтвет: {reply}")

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