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

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_tavily import TavilySearchResults
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# Load environment variables
load_dotenv()

# ---------- LLM and Embeddings ----------
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"),
)

# ---------- Vector Store ----------
CHROMA_DIR = Path("./chroma_db")
vector_store = Chroma(
    collection_name="knowledge",
    embedding_function=embeddings,
    persist_directory=str(CHROMA_DIR),
)

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

@tool
def web_search(query: str) -> str:
    """Web search using Tavily."""
    tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
    results = tavily.run(query)
    if not results:
        return "No web results found."
    return "\n---\n".join(f"{i+1}. {res['title']}\n{res['content'][:200]}..." for i, res in enumerate(results))

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

# ---------- Agent ----------
agent = create_deep_agent(
    model=llm,
    tools=[search_local_kb, web_search],
    backend=backend,
    system_prompt=(
        "You are a helpful assistant. For any user query, decide whether to use the local knowledge base or perform a web search. "
        "If the query is about recent events, news, or requires up‑to‑date information, use the web_search tool. "
        "Otherwise, use search_local_kb. "
        "Always return the source used in the response (either 'chromadb' or 'tavily')."
    ),
)

# ---------- Document Loader ----------
def load_documents(directory: str, vectorstore: Chroma):
    """Load .txt and .md files from a directory, chunk them, and add to the vector store."""
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    docs = []
    for file_path in Path(directory).glob("**/*"):
        if file_path.suffix.lower() in {".txt", ".md"}:
            text = file_path.read_text(encoding="utf-8")
            chunks = splitter.split_text(text)
            docs.extend([Document(page_content=chunk, metadata={"source": str(file_path)}) for chunk in chunks])
    if docs:
        vectorstore.add_documents(docs)
        vectorstore.persist()

# ---------- CLI ----------
async def main():
    # Ensure vector store is loaded
    if not CHROMA_DIR.exists() or not any(CHROMA_DIR.iterdir()):
        print("Loading documents into ChromaDB…")
        load_documents("./documents", vector_store)
    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": [{"role": "user", "content": user_input}]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # Extract last message content
        content = result["messages"][-1].content
        print(f"\nОтвет:\n{content}")

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