# # llm_engine.py

import google.generativeai as genai
from app.core.config import GEMINI_API_KEY
from langchain_google_genai import ChatGoogleGenerativeAI

# ✅ Configure Gemini client
genai.configure(api_key=GEMINI_API_KEY)


llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    google_api_key=GEMINI_API_KEY,
    temperature=0.2,
    max_output_tokens=800,
)


# # ✅ Separate LLM for evaluator — needs near-deterministic JSON output
# eval_llm = ChatGoogleGenerativeAI(
#     model="gemini-2.5-flash",
#     google_api_key=GEMINI_API_KEY,
#     temperature=0.0,              # ✅ deterministic — evaluator must return valid JSON
#     max_output_tokens=200,        # ✅ evaluator only returns a small JSON blob
#     thinking_level="none" # to disable chain-of-thought
# )


eval_llm = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",  # no thinking, faster
    google_api_key=GEMINI_API_KEY,
    temperature=0.0,
    max_output_tokens=200,
    # model_kwargs={
    #     "generation_config": {
    #         "thinking_config": {
    #             "thinking_budget": 0  # ✅ 0 = disabled, bypasses langchain validation entirely
    #         }
    #     }
    # }
)