"""
# main.py
# FAQ bot using deepagents, ChromaDB and a single MCP‑style HTTP tool.
# The agent decides whether to query the local knowledge base or the
# external metadata service and annotates the answer with a `source` field.
"""
import os
import asyncio
import json
from pathlib import Path
from typing import List, Dict

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage, SystemMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
# Load API key from .env or environment variable
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise RuntimeError("OPENAI_API_KEY not set")

# LLM configuration – OpenRouter
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
    temperature=0.0,
)

# Embeddings – OpenRouter
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
)

# ---------------------------------------------------------------------------
# ChromaDB setup
# ---------------------------------------------------------------------------
CHROMA_PATH = Path("./chroma_faq")
CHROMA_COLLECTION = "faq_collection"

vector_store = Chroma(
    collection_name=CHROMA_COLLECTION,
    embedding_function=embeddings,
    persist_directory=str(CHROMA_PATH),
)

# Load markdown files into Chroma if not already persisted
if not CHROMA_PATH.exists() or not list(CHROMA_PATH.iterdir()):
    def load_faq_to_chroma(md_dir: str = "data"):
        md_path = Path(md_dir)
        docs: List[Document] = []
        for file in md_path.glob("*.md"):
            text = file.read_text(encoding="utf-8")
            docs.append(Document(page_content=text, metadata={"source": file.name}))
        vector_store.add_documents(docs)
        vector_store.persist()

    load_faq_to_chroma()

# ---------------------------------------------------------------------------
# Tools
# ---------------------------------------------------------------------------
@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search the local FAQ collection for relevant passages."""
    docs = vector_store.similarity_search(query, k=k)
    if not docs:
        return "No relevant information found in the course materials."
    return "\n\n---\n\n".join(d.page_content for d in docs)

# MCP‑style tool – simple HTTP GET to a local JSON file
# For the purpose of this assignment we use a static JSON file
# located at ./meta/course_meta.json
@tool
def fetch_course_meta(query: str) -> str:
    """Return metadata that matches the query from a local JSON file."""
    meta_path = Path("./meta/course_meta.json")
    if not meta_path.exists():
        return "Metadata file not found."
    data = json.loads(meta_path.read_text(encoding="utf-8"))
    # Very naive matching: return any entry where the query is a substring
    matches = [item for item in data if query.lower() in item.get("title", "").lower()]
    if not matches:
        return "No matching metadata found."
    return json.dumps(matches, indent=2)

# ---------------------------------------------------------------------------
# Backend for deepagents
# ---------------------------------------------------------------------------
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# ---------------------------------------------------------------------------
# Agent definition
# ---------------------------------------------------------------------------
# System prompt instructs the agent to choose the appropriate tool and
# to annotate the answer with a `source` field.
SYSTEM_PROMPT = (
    "You are an FAQ assistant for a course.\n"
    "If the user asks about course materials, use the `search_course_docs` tool.\n"
    "If the user asks about schedule or metadata, use the `fetch_course_meta` tool.\n"
    "Respond in JSON format with two fields: `answer` (string) and `source` (either `chroma` or `mcp_meta`).\n"
    "Do not call both tools unless absolutely necessary."
)

agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt=SYSTEM_PROMPT,
)

# ---------------------------------------------------------------------------
# CLI helpers
# ---------------------------------------------------------------------------
PRESET_QUESTIONS = [
    "What topics are covered in the first lecture?",  # chroma
    "How can I access the lecture slides?",          # chroma
    "What is the schedule for the next week?",        # mcp_meta
]

async def run_interactive():
    print("FAQ Bot – type your question (or 'exit' to quit).\n")
    while True:
        user_input = input("> ")
        if user_input.lower() in {"exit", "quit"}:
            break
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_input)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # The agent returns a list of messages; the last is the assistant
        assistant_msg = result["messages"][-1].content
        try:
            data = json.loads(assistant_msg)
            print(f"\nAnswer: {data['answer']}\nSource: {data['source']}\n")
        except Exception:
            print("\nUnexpected response format:\n", assistant_msg)

async def run_presets():
    for q in PRESET_QUESTIONS:
        print(f"\nQuestion: {q}")
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=q)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        assistant_msg = result["messages"][-1].content
        try:
            data = json.loads(assistant_msg)
            print(f"Answer: {data['answer']}\nSource: {data['source']}")
        except Exception:
            print("Unexpected response format:\n", assistant_msg)

# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="FAQ Bot CLI")
    parser.add_argument("--presets", action="store_true", help="Run preset questions")
    args = parser.parse_args()

    if args.presets:
        asyncio.run(run_presets())
    else:
        asyncio.run(run_interactive())