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

# ---------- 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(),
])

# ---------- Vectorstore utilities ----------
PERSIST_DIR = Path("./chroma_db")
PERSIST_DIR.mkdir(parents=True, exist_ok=True)

# Create or load Chroma vectorstore
vectorstore = Chroma(
    persist_directory=str(PERSIST_DIR),
    embedding_function=OllamaEmbeddings(model="nomic-embed-text"),
)

# Load documents from a directory into the vectorstore

def load_documents(directory: str, vectorstore):
    docs = []
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    for file_path in Path(directory).glob("**/*"):
        if file_path.suffix.lower() in {".txt", ".md"}:
            text = file_path.read_text(encoding="utf-8")
            docs.extend(splitter.split_text(text))
    # Convert to LangChain Documents
    from langchain_core.documents import Document
    documents = [Document(page_content=chunk) for chunk in docs]
    vectorstore.add_documents(documents)
    vectorstore.persist()

# Load documents once at startup (if not already loaded)
if not any(PERSIST_DIR.iterdir()):
    load_documents("./documents", vectorstore)

# ---------- Tools ----------
@tool
def search_local_kb(query: str, top_k: int = 3) -> str:
    """Semantic search in the local ChromaDB knowledge base."""
    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
    docs = retriever.get_relevant_documents(query)
    if not docs:
        return "No relevant documents found in local KB."
    return "\n---\n".join(doc.page_content for doc in 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"{r['title']}\n{r['url']}\n{r.get('content', '')}" for r in results)

# ---------- Agent ----------
agent = create_deep_agent(
    model=llm,
    tools=[search_local_kb, web_search],
    backend=backend,
    system_prompt=(
        "You are an AI assistant that answers user questions.\n"
        "If the question is about information that should be in the local knowledge base,\n"
        "use the search_local_kb tool.\n"
        "If the question requires up‑to‑date information from the web,\n"
        "use the web_search tool.\n"
        "Always indicate the source of the answer in the format:\n"
        "[Source: chromadb] or [Source: tavily] before the answer."
    ),
)

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

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