"""HyDE (Hypothetical Document Embeddings) — generate a fake answer with the LLM,
   then embed *that* and re-search. Doubles recall on rare named diseases /
   chemical names that the multilingual embedder distributes poorly.

Only fires when the top FAISS+BM25+rerank score is below HYDE_TRIGGER_THRESHOLD.
Uses the cheapest model in the cascade (gemma2-9b-it) to keep cost ~0.
"""
from __future__ import annotations
from typing import Optional


_HYDE_PROMPT = """You are an Indian agricultural extension officer. A farmer asked the question below. Write a SHORT (60-120 words) hypothetical expert answer. The answer must include:
- The likely cause (1 sentence)
- The standard chemical / treatment with EXACT dose and unit (e.g. "Mancozeb 75% WP @ 2g/L")
- Timing and method (spray / drench / soil application)
- One safety / IPM note

Do NOT add disclaimers. Do NOT say "consult an expert". Just give the answer as if from an ICAR factsheet.

Farmer question: {query}
{ctx}
Hypothetical expert answer:"""


def expand_query(query: str,
                 crop: Optional[str] = None,
                 problem_type: Optional[str] = None) -> str:
    """Generate a hypothetical answer for HyDE retrieval. Returns "" if LLM fails."""
    ctx = []
    if crop:
        ctx.append(f"Crop: {crop}")
    if problem_type and problem_type != "general":
        ctx.append(f"Problem type: {problem_type}")
    ctx_str = ("Context: " + "; ".join(ctx) + "\n") if ctx else ""

    prompt = _HYDE_PROMPT.format(query=query, ctx=ctx_str)

    try:
        from . import llm
        # Use the cheapest model for HyDE — quality of the hypothetical doesn't
        # need to be great, only directionally right (we're embedding it).
        return llm.generate(prompt, max_tokens=200, temperature=0.3,
                             prefer_cheap=True) or ""
    except Exception:
        return ""