import os
import functools
from dotenv import load_dotenv
from supabase.client import create_client, Client
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_core.tools import tool
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain.tools.retriever import create_retriever_tool

load_dotenv()

def _format_search_results(docs, label: str, truncate: int = None) -> dict:
    """Helper to format document search results."""
    entries = []
    for d in docs:
        content = d.page_content if truncate is None else d.page_content[:truncate]
        entries.append(
            f'<Document source="{d.metadata.get("source","")}" '
            f'page="{d.metadata.get("page","")}"/>\n{content}\n</Document>'
        )
    return {label: "\n\n---\n\n".join(entries)}

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.
    Args:
        a: first int
        b: second int
    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a - b

@tool
def divide(a: int, b: int) -> int:
    """Divide two numbers.
    
    Args:
        a: first int
        b: second int
    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Get the modulus of two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    
    Args:
        query: The search query."""
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    return _format_search_results(docs, "wiki_results")

@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    docs = TavilySearchResults(max_results=3).invoke(query=query)
    return _format_search_results(docs, "web_results")

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    return _format_search_results(docs, "arvix_results", truncate=1000)

# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# System message
sys_msg = SystemMessage(content=system_prompt)

# build a retriever once
_embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
_supabase: Client = create_client(
    os.environ["SUPABASE_URL"], os.environ["SUPABASE_SERVICE_KEY"]
)
_vector_store = SupabaseVectorStore(
    client=_supabase,
    embedding=_embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)
_retriever = _vector_store.as_retriever()
_question_search_tool = create_retriever_tool(
    retriever=_retriever,
    name="Question Search",
    description="A tool to retrieve similar questions from a vector store.",
)

tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
    _question_search_tool,
]

_LLM_PROVIDERS = {
    "google": lambda: ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0),
    "groq": lambda: ChatGroq(model="qwen-qwq-32b", temperature=0),
    "huggingface": lambda: ChatHuggingFace(
        llm=HuggingFaceEndpoint(
            url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
            temperature=0,
        )
    ),
}

@functools.lru_cache(maxsize=None)
def get_llm(provider: str):
    """
    Retrieve and cache the LLM client for the given provider.
    """
    try:
        return _LLM_PROVIDERS[provider]()
    except KeyError:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")

def build_graph(provider: str = "google"):
    """Build the graph"""
    llm = get_llm(provider).bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm.invoke(state["messages"])]}

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        doc = _retriever.similarity_search(query, k=1)[0]
        content = doc.page_content
        if "Final answer :" in content:
            answer = content.split("Final answer :")[-1].strip()
        else:
            answer = content.strip()
        return {"messages": [AIMessage(content=answer)]}

    graph = StateGraph(MessagesState)
    graph.add_node("retriever", retriever)
    graph.set_entry_point("retriever")
    graph.set_finish_point("retriever")
    return graph.compile()