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

# LangChain imports
from langchain_ollama import ChatOllama, OllamaEmbeddings
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_qdrant import QdrantVectorStore
from langchain.agents import create_agent
from langchain.tools import tool
from langchain_core.messages import HumanMessage

# Load environment variables (e.g., Ollama host)
load_dotenv()

# LLM and embeddings configuration
llm = ChatOllama(model="llama3")
embeddings = OllamaEmbeddings(model="nomic-embed-text")

# Qdrant client and vector store initialization
from qdrant_client import QdrantClient

qdrant_client = QdrantClient(host="localhost", port=6333)
vector_store = QdrantVectorStore(
    client=qdrant_client,
    collection_name="knowledge",
    embeddings=embeddings,
)

# Text splitter for chunking documents
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# Tool: Search knowledge base
@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Search the knowledge base for relevant information."""
    docs = vector_store.similarity_search(query, k=max_results)
    if not docs:
        return "No relevant documents found."
    return "\n\n---\n\n".join(f"<{doc.metadata.get('title', 'Untitled')}>
{doc.page_content}" for doc in docs)

# Tool: Add document to knowledge base
@tool
def add_to_knowledge_base(content: str, title: str = "Document") -> str:
    """Add content to the knowledge base."""
    # Split content into chunks
    chunks = splitter.split_text(content)
    documents = [Document(page_content=chunk, metadata={"title": title, "chunk_index": idx})
                 for idx, chunk in enumerate(chunks)]
    vector_store.add_documents(documents)
    return f"Added {len(chunks)} chunks from '{title}'."

# Agent creation
agent = create_agent(
    llm=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    system_prompt="You are an AI assistant with access to a knowledge base. Use the provided tools to search and add information."
)

# Helper to invoke agent asynchronously
async def invoke_agent(user_input: str) -> str:
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_input)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    # The last message is the agent's reply
    return result["messages"][-1].content

# CLI loop
async def main():
    print("Welcome to the RAG agent CLI. Commands: /add <file_path> | /search <query> | /quit")
    while True:
        user_input = input(">>> ")
        if not user_input:
            continue
        if user_input.lower() == "/quit":
            print("Goodbye!")
            break
        if user_input.lower().startswith("/add "):
            path = user_input[5:].strip()
            if Path(path).is_file():
                content = Path(path).read_text(encoding="utf-8")
                title = Path(path).stem
                # Directly invoke tool via agent
                response = await invoke_agent(f"Add document: {title}\n{content}")
            else:
                print("File not found.")
                continue
        elif user_input.lower().startswith("/search "):
            query = user_input[8:].strip()
            response = await invoke_agent(f"Search for: {query}")
        else:
            print("Unknown command. Use /add, /search, or /quit.")
            continue
        print("\n--- Agent Response ---\n")
        print(response)
        print("\n-----------------------\n")

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