# src/analyzer/chat/insight_tools.py
from __future__ import annotations
from typing import Dict, Any, List, Optional

from ..config import load_config
try:
    from ..llm_client import LLMClient
except Exception:
    LLMClient = None  # type: ignore

try:
    from ..search.hybrid_index import load_index, search, top_supporting_for_grant
except Exception:
    load_index = None  # type: ignore
    search = None     # type: ignore
    top_supporting_for_grant = None  # type: ignore

from ..prompt_templates import build_open_prompt

class InsightTools:
    """
    Retrieval + LLM glue for open, lightly-guarded answers.
    - Accepts an existing LLMClient (preferred) to ensure identical config across entrypoints
    - Falls back gracefully if LLM or index is unavailable
    """

    def __init__(self, llm_client: Optional["LLMClient"] = None):
        self._cfg = load_config()
        self._client = llm_client
        if self._client is None and LLMClient is not None:
            try:
                self._client = LLMClient(self._cfg)
            except Exception:
                self._client = None

        self._idx = None
        if callable(load_index):
            try:
                self._idx = load_index()
            except Exception:
                self._idx = None

    # Optional helper some callers use to append snippets under a summary
    def supporting_snippets_md(self, grant_id: str, k: int = 5) -> str:
        if not self._idx or not callable(top_supporting_for_grant):
            return ""
        try:
            hits = top_supporting_for_grant(self._idx, grant_id, k=k)
        except Exception:
            return ""
        items: List[str] = []
        for doc, score in hits:
            if doc.get("_source") != "supporting":
                continue
            sec = doc.get("section") or "(Supporting)"
            url = doc.get("url","")
            txt = (doc.get("text","") or "").replace("\n"," ")
            snippet = (txt[:400] + "…") if len(txt) > 400 else txt
            items.append(f"- **{sec}** — {url}\n  > {snippet}")
        if not items:
            return ""
        return "\n\n---\n**Supporting info (top snippets)**\n" + "\n".join(items)

    def insight_search(self, question: str, *, k: int = 8, use_llm: bool = True) -> Dict[str, Any]:
        """Open-style grounded QA: supply question + top snippets; let the LLM pick format and length."""
        if not self._idx or not callable(search):
            return {"answer_md": "Search index not available."}

        hits = search(self._idx, question, k=k, filters=None)
        ev_lines: List[str] = []
        raw_context: List[str] = []
        for doc, score in hits[:k]:
            sec = doc.get("section") or doc.get("title","")
            url = doc.get("url","")
            txt = (doc.get("text","") or "")
            ev_lines.append(f"- **{sec}** — {url}")
            raw_context.append(f"[{sec}] {url}\n{txt}")

        evidence_md = "**Sources**\n" + "\n".join(ev_lines) if ev_lines else ""

        if use_llm and self._client:
            payload = build_open_prompt(
                provider=getattr(self._cfg, "provider", "openai") if self._cfg else "openai",
                question=question,
                context="\n\n---\n".join(raw_context[:6]),
            )
            try:
                # Slightly higher budget; model decides format naturally
                answer = self._client.chat(payload["messages"], max_tokens=1200, temperature=0.3)
                return {"answer_md": f"{answer}\n\n---\n{evidence_md}"}
            except Exception:
                pass

        return {"answer_md": "No LLM available. See sources below.\n\n---\n" + evidence_md}