import json
import os
from pathlib import Path
from typing import List, Dict, Any

import httpx
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.tools import tool

# Path to the data directory
DATA_DIR = Path(__file__).parent.parent / "data"
CHROMA_DIR = Path(__file__).parent.parent / "chroma_faq"

def load_faq_to_chroma() -> Chroma:
    """
    Load all .md files from the data directory, chunk them, embed with Ollama,
    and persist into a Chroma vector store.
    """
    # Check if the Chroma collection already exists
    if CHROMA_DIR.exists():
        # Load existing collection
        return Chroma(persist_directory=str(CHROMA_DIR), embedding_function=OllamaEmbeddings(model="nomic-embed-text"))

    # Gather all markdown files
    md_files = list(DATA_DIR.glob("*.md"))
    documents: List[Document] = []

    for md_file in md_files:
        loader = TextLoader(str(md_file), encoding="utf-8")
        docs = loader.load()
        documents.extend(docs)

    # Create embeddings
    embeddings = OllamaEmbeddings(model="nomic-embed-text")

    # Create Chroma vector store
    chroma = Chroma.from_documents(
        documents=documents,
        embedding=embeddings,
        persist_directory=str(CHROMA_DIR),
    )
    return chroma

@tool
def search_course_docs(query: str, k: int = 3) -> List[Dict[str, Any]]:
    """
    Search the local FAQ Chroma vector store for relevant documents.

    Returns a list of dictionaries containing the content and metadata.
    """
    chroma = load_faq_to_chroma()
    results = chroma.similarity_search(query, k=k)
    output = []
    for doc in results:
        output.append(
            {
                "content": doc.page_content,
                "metadata": doc.metadata,
            }
        )
    return output

@tool
def fetch_course_meta(query: str) -> Dict[str, Any]:
    """
    Simulate an MCP-style HTTP tool that returns course metadata
    matching the query. The metadata is read from a local JSON file.
    """
    meta_path = DATA_DIR / "course_meta.json"
    with open(meta_path, "r", encoding="utf-8") as f:
        data = json.load(f)

    # Simple keyword matching in schedule and instructor fields
    results = {}
    if "schedule" in query.lower():
        results["schedule"] = data.get("schedule", [])
    if "instructor" in query.lower() or "professor" in query.lower():
        results["instructor"] = data.get("instructor", {})
    if not results:
        # Default to returning the whole metadata if no keyword matched
        results = data
    return results