from pathlib import Path

# LLM and embeddings via Ollama
from langchain_ollama import ChatOllama, OllamaEmbeddings
from langchain.tools import tool
from langchain.agents import create_agent
from langchain_core.documents import Document
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_core.messages import HumanMessage

# ---------- Qdrant setup ----------
client = QdrantClient(":memory:")
client.create_collection(
    collection_name="knowledge",
    vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
)
embeddings = OllamaEmbeddings(model="nomic-embed-text")
vector_store = QdrantVectorStore(
    client=client,
    collection_name="knowledge",
    embedding=embeddings,
)

# ---------- Tools ----------
@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Search the knowledge base for relevant documents."""
    docs_with_score = vector_store.similarity_search_with_score(query, k=max_results)
    if not docs_with_score:
        return "No results found."
    return "\n".join(
        f"{i+1}. {doc.page_content[:200]}..."
        for i, (doc, _) in enumerate(docs_with_score)
    )

@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."

# ---------- Agent ----------
llm = ChatOllama(model="llama3")
agent = create_agent(
    model=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    system_prompt="You are a helpful assistant that can search and store knowledge.",
)

# ---------- CLI ----------
def main():
    print("RAG Agent CLI. Commands: /add <title> | <content>, /search <query>, /quit")
    while True:
        try:
            inp = input("> ").strip()
        except EOFError:
            break
        if not inp:
            continue
        if inp.lower() in ("quit", "/quit"):
            print("Bye!")
            break

        # Add document
        if inp.startswith("/add "):
            _, rest = inp.split(maxsplit=1)
            try:
                title, content = rest.split("|", 1)
            except ValueError:
                print("Usage: /add <title> | <content>")
                continue
            res = agent.invoke({"messages": [HumanMessage(content=f"Add document {title}")]})
            print(res.messages[-1].content)

        # Search documents
        elif inp.startswith("/search "):
            query = inp[len("/search "):]
            res = agent.invoke({"messages": [HumanMessage(content=f"Search for {query}")]})
            print(res.messages[-1].content)

        # General chat
        else:
            res = agent.invoke({"messages": [HumanMessage(content=inp)]})
            print(res.messages[-1].content)

if __name__ == "__main__":
    main()