from pathlib import Path
import os
from typing import List

# LLM and embeddings via Ollama
from langchain_ollama import ChatOllama, OllamaEmbeddings
# Tools
from langchain.tools import tool
# Vector store
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
# Text splitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Agent
from langchain.agents import create_agent
# Documents
from langchain_core.documents import Document

# -------------------- 1. RAG tools --------------------
@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Search the knowledge base for relevant documents."""
    results = vector_store.similarity_search_with_score(query, k=max_results)
    if not results:
        return "No relevant documents found."
    response_lines = []
    for doc, score in results:
        title = doc.metadata.get("title", "N/A")
        snippet = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
        response_lines.append(f"Score: {score:.4f}\nTitle: {title}\nContent: {snippet}")
    return "\n\n".join(response_lines)

@tool
def add_to_knowledge_base(content: str, title: str) -> str:
    """Add a new document to the knowledge base."""
    doc = Document(page_content=content, metadata={"title": title})
    vector_store.add_documents([doc])
    return f"Document '{title}' added successfully."

# -------------------- 2. Qdrant setup --------------------
qdrant_client = QdrantClient(":memory:")
qdrant_client.create_collection(
    collection_name="knowledge_base",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)

embeddings = OllamaEmbeddings(model="nomic-embed-text")
vector_store = QdrantVectorStore(
    client=qdrant_client,
    collection_name="knowledge_base",
    embedding=embeddings,
)

# -------------------- 3. Text splitter --------------------
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)

def load_and_index(directory: str):
    """Load all .txt files from directory and index them."""
    docs: List[Document] = []
    for file_path in Path(directory).glob("*.txt"):
        text = file_path.read_text(encoding="utf-8")
        chunks = splitter.split_text(text)
        for i, chunk in enumerate(chunks):
            docs.append(
                Document(
                    page_content=chunk,
                    metadata={
                        "title": f"{file_path.stem} #{i+1}",
                        "source": str(file_path),
                    },
                )
            )
    vector_store.add_documents(docs)

# -------------------- 4. Agent --------------------
system_prompt = """
You are an assistant that can search and add documents to a knowledge base.
Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
"""

agent = create_agent(
    model=ChatOllama(model="llama3", temperature=0.2),
    tools=[search_knowledge_base, add_to_knowledge_base],
    system_prompt=system_prompt,
)

# -------------------- 5. CLI client --------------------
def main():
    # Load initial documents
    load_and_index("docs")  # ensure a 'docs' folder with .txt files

    print(
        "RAG Agent ready. Commands: /add <title> <content>, /search <query>, /quit"
    )
    while True:
        user_input = input("> ").strip()
        if not user_input:
            continue
        if user_input.lower() in ("exit", "quit", "/quit"):
            break

        if user_input.startswith("/add"):
            try:
                _, title, content = user_input.split(" ", 2)
                result_msg = add_to_knowledge_base(content=content, title=title)
                print(result_msg)
            except ValueError:
                print("Usage: /add <title> <content>")
        elif user_input.startswith("/search"):
            query = user_input[len("/search") :].strip()
            if not query:
                print("Provide a search query.")
                continue
            response = agent.invoke({"messages": [{"role": "human", "content": query}]})
            for msg in response["messages"]:
                if hasattr(msg, "content"):
                    print(msg.content)
        else:
            # Regular chat with the agent
            response = agent.invoke(
                {"messages": [{"role": "human", "content": user_input}]}
            )
            for msg in response["messages"]:
                if hasattr(msg, "content"):
                    print(msg.content)

if __name__ == "__main__":
    main()