"""
LangGraph Research Brief Agent
=============================

This repository implements a LangGraph agent that generates a short research brief.
The agent follows the specification from the BroJS assignment:

*   Build an outline of 4‑5 points for a given topic.
*   For each point perform one web search via Tavily and collect a concise note.
*   Synthesize all notes into a coherent brief (≈½–1 page).
*
The implementation uses the official `langgraph` library, `langchain-openai`
for LLM calls and `langchain-tavily` for web searching.  The agent is
exposed through a simple CLI that accepts a topic as an argument.
"""

from __future__ import annotations

import os
import sys
from typing import TypedDict, List

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from langchain_openai import ChatOpenAI
from langchain_tavily import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage

# ---------------------------------------------------------------------------
# 1. State definition
# ---------------------------------------------------------------------------
class BriefState(TypedDict):
    topic: str
    outline: List[str] | None
    step_index: int
    notes: List[str]
    final_brief: str | None

# ---------------------------------------------------------------------------
# 2. LLM and tools
# ---------------------------------------------------------------------------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
    api_key=os.getenv("JOURNAL_MCP_PAT"),
    temperature=0.2,
)

search_tool = TavilySearchResults(max_results=3, tavily_api_key=os.getenv("TAVILY_API_KEY"))

# ---------------------------------------------------------------------------
# 3. Nodes
# ---------------------------------------------------------------------------
async def outline_node(state: BriefState) -> dict:
    """Generate a short outline of 4‑5 research points."""
    system = SystemMessage(
        content="You are an assistant that creates a concise outline for a research brief.")
    user = HumanMessage(content=f"Create 4–5 bullet points outlining the main aspects to cover when researching: {state['topic']}")
    response = await llm.ainvoke([system, user])
    # Parse bullets
    bullets = [line.strip("- ").strip() for line in response.content.splitlines() if line.strip().startswith("-")]
    return {"outline": bullets, "step_index": 0, "notes": []}

async def research_step_node(state: BriefState) -> dict:
    """For the current outline point perform a web search and collect a short note."""
    point = state["outline"][state["step_index"]]
    # Search via Tavily
    results = await search_tool.ainvoke({"query": point})
    notes_text = "\n".join([f"{i+1}. {r['title']}: {r['content']}" for i, r in enumerate(results)])
    new_notes = state["notes"] + [f"**{point}**:\n{notes_text}"]
    next_index = state["step_index"] + 1
    return {"notes": new_notes, "step_index": next_index}

async def synthesize_node(state: BriefState) -> dict:
    """Combine all notes into a single brief."""
    system = SystemMessage(content="You are an assistant that writes a concise research brief.")
    user = HumanMessage(
        content=f"Using the following notes, write a ½–1 page brief on {state['topic']}:\n\n{chr(10).join(state['notes'])}")
    response = await llm.ainvoke([system, user])
    return {"final_brief": response.content.strip()}

# ---------------------------------------------------------------------------
# 4. Graph definition
# ---------------------------------------------------------------------------
builder = StateGraph(BriefState)
builder.add_node("outline", outline_node)
builder.add_node("research_step", research_step_node)
builder.add_node("synthesize", synthesize_node)

builder.set_entry_point("outline")
builder.add_edge("outline", "research_step")
# Loop until all points processed
builder.add_conditional_edges(
    "research_step",
    lambda state: "synthesize" if state["step_index"] >= len(state["outline"]) else "research_step",
)
builder.add_edge("synthesize", END)

graph = builder.compile(checkpointer=MemorySaver())

# ---------------------------------------------------------------------------
# 5. CLI helper
# ---------------------------------------------------------------------------
async def run_brief(topic: str) -> None:
    state: BriefState = {"topic": topic, "outline": None, "step_index": 0, "notes": [], "final_brief": None}
    result = await graph.ainvoke(state)
    print("\n=== Outline ===")
    for i, point in enumerate(result["outline"]):
        print(f"{i+1}. {point}")
    print("\n=== Notes ===")
    for note in result["notes"]:
        print(note)
    print("\n=== Final Brief ===")
    print(result["final_brief"])

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python main.py '<topic>'")
        sys.exit(1)
    topic = sys.argv[1]
    import asyncio
    asyncio.run(run_brief(topic))

# ---------------------------------------------------------------------------
# 6. Example usage (for documentation only, not executed by the script)
# ---------------------------------------------------------------------------
# Example 1: "How to integrate LangGraph with Tavily"
# Example 2: "Best practices for building research briefs in AI"
# Example 3: "Using LangChain and LangGraph for educational projects"
"""