"""LangGraph Agent"""
import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
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
from langchain_community.document_loaders import ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.vectorstores import Chroma # Ny import för Chroma
from langchain_core.documents import Document # Ny import för att skapa dokument
import shutil # För att hantera kataloger

load_dotenv()

@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."""
    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 {"wiki_results": formatted_search_docs}

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

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    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 {"arvix_results": formatted_search_docs}

# 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)

# --- Start ChromaDB Setup ---
# Define the directory for ChromaDB persistence
CHROMA_DB_DIR = "./chroma_db"

# Build embeddings (this remains the same)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768

# Initialize ChromaDB
# If the directory exists, load the existing vector store.
# Otherwise, create a new one and add some dummy documents.
if os.path.exists(CHROMA_DB_DIR) and os.listdir(CHROMA_DB_DIR):
    print(f"Loading existing ChromaDB from {CHROMA_DB_DIR}")
    vector_store = Chroma(
        persist_directory=CHROMA_DB_DIR,
        embedding_function=embeddings
    )
else:
    print(f"Creating new ChromaDB at {CHROMA_DB_DIR} and adding dummy documents.")
    # Ensure the directory is clean before creating new
    if os.path.exists(CHROMA_DB_DIR):
        shutil.rmtree(CHROMA_DB_DIR)
    os.makedirs(CHROMA_DB_DIR)

    # Example dummy documents to populate the vector store
    # In a real application, you would load your actual documents here
    documents = [
        Document(page_content="What is the capital of France?", metadata={"source": "internal", "answer": "Paris"}),
        Document(page_content="Who wrote Hamlet?", metadata={"source": "internal", "answer": "William Shakespeare"}),
        Document(page_content="What is the highest mountain in the world?", metadata={"source": "internal", "answer": "Mount Everest"}),
        Document(page_content="When was the internet invented?", metadata={"source": "internal", "answer": "The internet, as we know it, evolved from ARPANET in the late 1960s and early 1970s. The TCP/IP protocol, which forms the basis of the internet, was standardized in 1978."}),
        Document(page_content="What is the square root of 64?", metadata={"source": "internal", "answer": "8"}),
        Document(page_content="Who is the current president of the United States?", metadata={"source": "internal", "answer": "Joe Biden"}),
        Document(page_content="What is the chemical symbol for water?", metadata={"source": "internal", "answer": "H2O"}),
        Document(page_content="What is the largest ocean on Earth?", metadata={"source": "internal", "answer": "Pacific Ocean"}),
        Document(page_content="What is the speed of light?", metadata={"source": "internal", "answer": "Approximately 299,792,458 meters per second in a vacuum."}),
        Document(page_content="What is the capital of Sweden?", metadata={"source": "internal", "answer": "Stockholm"}),
    ]

    vector_store = Chroma.from_documents(
        documents=documents,
        embedding=embeddings,
        persist_directory=CHROMA_DB_DIR
    )
    vector_store.persist() # Save the new vector store to disk
    print("ChromaDB initialized and persisted with dummy documents.")

# Create retriever tool using the Chroma vector store
retriever_tool = create_retriever_tool( # Changed variable name to avoid conflict with function name
    retriever=vector_store.as_retriever(),
    name="Question_Search", # Changed name to be more descriptive and valid for tool use
    description="A tool to retrieve similar questions from a vector store and their answers.",
)

# Add the new retriever tool to your list of tools
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
    retriever_tool, # Add the new retriever tool here
]

# Build graph function
def build_graph(provider: str = "google"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        # Groq https://console.groq.com/docs/models
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
    elif provider == "huggingface":
        # TODO: Add huggingface endpoint
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            ),
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    from langchain_core.messages import AIMessage

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        # Use the retriever tool to get similar documents
        similar_docs = retriever_tool.invoke(query) # Call the tool directly
        
        # The tool returns a list of Documents, so we need to process it
        # Assuming the tool returns a list of documents, we take the first one
        if similar_docs:
            # The tool output is a string representation of the documents.
            # We need to parse it or adjust the tool to return structured data.
            # For simplicity, let's assume the tool returns a list of Document objects
            # or a string that can be directly used.
            # Given the original `retriever` node, it expected `similar_question[0].page_content`.
            # If `retriever_tool.invoke(query)` returns a list of Document objects,
            # then `similar_docs[0].page_content` is correct.
            # If it returns a string, we need to adapt.
            # For now, let's assume it returns a list of Documents or a string that contains the answer.

            # If retriever_tool returns a string directly (as per your tool definition):
            # content = similar_docs # This would be the string output from the tool
            
            # If retriever_tool returns a list of Document objects from its internal retriever:
            # Let's assume the `retriever_tool` internally uses `vector_store.as_retriever().invoke(query)`
            # which returns a list of `Document` objects.
            # The `create_retriever_tool` wraps this, so `retriever_tool.invoke` will return a string
            # that is the `page_content` of the retrieved documents.
            
            # The original `retriever` node was using `vector_store.similarity_search` directly.
            # Now `retriever_tool` is a LangChain tool.
            # When `retriever_tool.invoke(query)` is called, it will return the formatted string
            # from the `create_retriever_tool` definition.
            # So, `similar_docs` will be a string.

            # We need to parse the `similar_docs` string to extract the answer.
            # The `Question_Search` tool description is "A tool to retrieve similar questions from a vector store and their answers."
            # The `create_retriever_tool` automatically formats the output of the retriever.
            # Let's assume the output string from `retriever_tool.invoke(query)` will look something like:
            # "content='What is the capital of Sweden?' metadata={'source': 'internal', 'answer': 'Stockholm'}"
            # We need to extract the 'answer' part.

            # A more robust way would be to make the retriever node *call* the tool,
            # and then the LLM decides if it wants to use the tool.
            # However, your current graph structure has a dedicated "retriever" node
            # that directly fetches and returns an AIMessage.
            
            # Let's refine the retriever node to parse the output of the tool more robustly.
            # The `create_retriever_tool` returns a string where documents are joined.
            # We need to extract the content that would be the "answer".
            
            # The dummy documents have `metadata={"source": "internal", "answer": "..."}`.
            # The `create_retriever_tool` will return `doc.page_content` by default.
            # So, `similar_docs` will contain the question itself.
            # We need to ensure the retriever provides the *answer* not just the question.

            # Let's adjust the `retriever` node to directly access the `vector_store`
            # for `similarity_search` and then extract the answer from metadata,
            # similar to your original implementation. This bypasses the tool wrapper
            # for this specific node, ensuring we get the full Document object.
            
            similar_doc = vector_store.similarity_search(query, k=1)[0]
            
            # Check if an 'answer' is directly available in metadata
            if "answer" in similar_doc.metadata:
                answer = similar_doc.metadata["answer"]
            elif "Final answer :" in similar_doc.page_content:
                answer = similar_doc.page_content.split("Final answer :")[-1].strip()
            else:
                answer = similar_doc.page_content.strip() # Fallback to page_content if no explicit answer

            return {"messages": [AIMessage(content=answer)]}
        else:
            # If no similar documents found, return an empty AIMessage or a message indicating no answer
            return {"messages": [AIMessage(content="No similar questions found in the knowledge base.")]}


    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)

    # Retriever ist Start und Endpunkt
    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")

    # Compile graph
    return builder.compile()