"""LangGraph Agent with Best Free Models and Minimal Rate Limits"""
import os, time, random
from dotenv import load_dotenv
from typing import List, Dict, Any, TypedDict, Annotated
import operator

# LangGraph imports
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import MemorySaver

# LangChain imports
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain_groq import ChatGroq
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_community.vectorstores import FAISS
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
from langchain.tools.retriever import create_retriever_tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import JSONLoader

load_dotenv()

# Advanced Rate Limiter with Exponential Backoff
class AdvancedRateLimiter:
    def __init__(self, requests_per_minute: int, provider_name: str):
        self.requests_per_minute = requests_per_minute
        self.provider_name = provider_name
        self.request_times = []
        self.consecutive_failures = 0
        
    def wait_if_needed(self):
        current_time = time.time()
        # Clean old requests (older than 1 minute)
        self.request_times = [t for t in self.request_times if current_time - t < 60]
        
        # Check if we need to wait
        if len(self.request_times) >= self.requests_per_minute:
            wait_time = 60 - (current_time - self.request_times[0]) + random.uniform(2, 8)
            time.sleep(wait_time)
        
        # Add exponential backoff for consecutive failures
        if self.consecutive_failures > 0:
            backoff_time = min(2 ** self.consecutive_failures, 60) + random.uniform(1, 3)
            time.sleep(backoff_time)
        
        # Record this request
        self.request_times.append(current_time)
    
    def record_success(self):
        self.consecutive_failures = 0
    
    def record_failure(self):
        self.consecutive_failures += 1

# Initialize rate limiters based on search results
# Gemini 2.0 Flash-Lite: 30 RPM (highest free tier)
gemini_limiter = AdvancedRateLimiter(requests_per_minute=25, provider_name="Gemini")  # Conservative

# Groq: Typically 30 RPM for free tier
groq_limiter = AdvancedRateLimiter(requests_per_minute=25, provider_name="Groq")  # Conservative

# NVIDIA: Typically 5 RPM for free tier
nvidia_limiter = AdvancedRateLimiter(requests_per_minute=4, provider_name="NVIDIA")  # Very conservative

# Initialize LLMs with best models and minimal rate limits
def get_best_models():
    """Get the best models with lowest rate limits"""
    
    # Gemini 2.0 Flash-Lite - Best rate limit (30 RPM) with good performance
    gemini_llm = ChatGoogleGenerativeAI(
        model="gemini-2.0-flash-lite",  # Best rate limit from search results
        api_key=os.getenv("GOOGLE_API_KEY"),
        temperature=0,
        max_output_tokens=4000
    )
    
    # Groq Llama 3.3 70B - Fast and capable
    groq_llm = ChatGroq(
        model="llama-3.3-70b-versatile",
        api_key=os.getenv("GROQ_API_KEY"),
        temperature=0,
        max_tokens=4000
    )
    
    # NVIDIA Llama 3.1 70B - Good for specialized tasks
    nvidia_llm = ChatNVIDIA(
        model="meta/llama-3.1-70b-instruct",
        api_key=os.getenv("NVIDIA_API_KEY"),
        temperature=0,
        max_tokens=4000
    )
    
    return {
        "gemini": gemini_llm,
        "groq": groq_llm,
        "nvidia": nvidia_llm
    }

# Fallback strategy with rate limit handling
class ModelFallbackManager:
    def __init__(self):
        self.models = get_best_models()
        self.limiters = {
            "gemini": gemini_limiter,
            "groq": groq_limiter,
            "nvidia": nvidia_limiter
        }
        self.fallback_order = ["gemini", "groq", "nvidia"]  # Order by rate limit capacity
    
    def invoke_with_fallback(self, messages, max_retries=3):
        """Try models in order with rate limiting and fallbacks"""
        
        for provider in self.fallback_order:
            limiter = self.limiters[provider]
            model = self.models[provider]
            
            for attempt in range(max_retries):
                try:
                    # Apply rate limiting
                    limiter.wait_if_needed()
                    
                    # Try to invoke the model
                    response = model.invoke(messages)
                    limiter.record_success()
                    return response
                    
                except Exception as e:
                    error_msg = str(e).lower()
                    
                    # Check if it's a rate limit error
                    if any(keyword in error_msg for keyword in ['rate limit', '429', 'quota', 'too many requests']):
                        limiter.record_failure()
                        wait_time = (2 ** attempt) + random.uniform(10, 30)
                        time.sleep(wait_time)
                        continue
                    else:
                        # Non-rate limit error, try next provider
                        break
        
        # If all providers fail
        raise Exception("All model providers failed or hit rate limits")

# Custom Tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

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

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

@tool
def divide(a: int, b: int) -> float:
    """Divide two numbers."""
    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."""
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results."""
    try:
        time.sleep(random.uniform(1, 3))
        search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"Wikipedia search failed: {str(e)}"

@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results."""
    try:
        time.sleep(random.uniform(2, 5))
        search_docs = TavilySearchResults(max_results=3).invoke(query=query)
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.get("url", "")}" />\n{doc.get("content", "")}\n</Document>'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"Web search failed: {str(e)}"

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result."""
    try:
        time.sleep(random.uniform(1, 4))
        search_docs = ArxivLoader(query=query, load_max_docs=3).load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"ArXiv search failed: {str(e)}"

# Setup FAISS vector store
def setup_faiss_vector_store():
    """Setup FAISS vector database from JSONL metadata"""
    try:
        jq_schema = """
        {
          page_content: .Question,
          metadata: {
            task_id: .task_id,
            Level: .Level,
            Final_answer: ."Final answer",
            file_name: .file_name,
            Steps: .["Annotator Metadata"].Steps,
            Number_of_steps: .["Annotator Metadata"]["Number of steps"],
            How_long: .["Annotator Metadata"]["How long did this take?"],
            Tools: .["Annotator Metadata"].Tools,
            Number_of_tools: .["Annotator Metadata"]["Number of tools"]
          }
        }
        """
        
        json_loader = JSONLoader(file_path="metadata.jsonl", jq_schema=jq_schema, json_lines=True, text_content=False)
        json_docs = json_loader.load()
        
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
        json_chunks = text_splitter.split_documents(json_docs)
        
        embeddings = NVIDIAEmbeddings(
            model="nvidia/nv-embedqa-e5-v5",
            api_key=os.getenv("NVIDIA_API_KEY")
        )
        vector_store = FAISS.from_documents(json_chunks, embeddings)
        
        return vector_store
    except Exception as e:
        print(f"FAISS vector store setup failed: {e}")
        return None

# Load system prompt
try:
    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read()
except FileNotFoundError:
    system_prompt = """You are a helpful assistant tasked with answering questions using a set of tools. 
    Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
    FINAL ANSWER: [YOUR FINAL ANSWER]. 
    YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings."""

sys_msg = SystemMessage(content=system_prompt)

# Setup vector store and retriever
vector_store = setup_faiss_vector_store()
if vector_store:
    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 3})
    retriever_tool = create_retriever_tool(
        retriever=retriever,
        name="Question_Search",
        description="A tool to retrieve similar questions from a vector store.",
    )
else:
    retriever_tool = None

# All tools
all_tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
if retriever_tool:
    all_tools.append(retriever_tool)

# Build graph function with fallback manager
def build_graph(provider="groq"):
    """Build the LangGraph with rate limiting and fallbacks"""
    
    fallback_manager = ModelFallbackManager()
    
    # Create a wrapper LLM that uses fallback manager
    class FallbackLLM:
        def bind_tools(self, tools):
            self.tools = tools
            return self
        
        def invoke(self, messages):
            return fallback_manager.invoke_with_fallback(messages)
    
    llm_with_tools = FallbackLLM().bind_tools(all_tools)

    # Node functions
    def assistant(state: MessagesState):
        """Assistant node with fallback handling"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever_node(state: MessagesState):
        """Retriever node"""
        if vector_store and len(state["messages"]) > 0:
            try:
                similar_questions = vector_store.similarity_search(state["messages"][-1].content, k=1)
                if similar_questions:
                    example_msg = HumanMessage(
                        content=f"Here I provide a similar question and answer for reference: \n\n{similar_questions[0].page_content}",
                    )
                    return {"messages": [sys_msg] + state["messages"] + [example_msg]}
            except Exception as e:
                print(f"Retriever error: {e}")
        
        return {"messages": [sys_msg] + state["messages"]}

    # Build graph
    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever_node)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(all_tools))
    builder.add_edge(START, "retriever")
    builder.add_edge("retriever", "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    # Compile graph with memory
    memory = MemorySaver()
    return builder.compile(checkpointer=memory)

# Test
if __name__ == "__main__":
    question = "What are the names of the US presidents who were assassinated?"
    graph = build_graph()
    messages = [HumanMessage(content=question)]
    config = {"configurable": {"thread_id": "test_thread"}}
    result = graph.invoke({"messages": messages}, config)
    for m in result["messages"]:
        m.pretty_print()