import os
import google.generativeai as genai
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.google_genai import GoogleGenAI
from llama_index.core import Settings



EMBEDDING_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
RETRIEVER_TOP_K = 10
RETRIEVER_SIMILARITY_CUTOFF = 0.7
RAG_FILES_DIR = "processed_data"
PROCESSED_DATA_FILE = "processed_data/processed_chunks.csv"

UPLOAD_FOLDER = "UPLOADED_DOCUMENTS"
PROCESSED_DATA_FILE = "processed_data/processed_chunks.csv"
INDEX_STATE_FILE = "processed_data/index_store.json"
RAG_FILES_DIR = "rag_files"

GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
LLM_MODEL = "gemini-2.5-flash"



CHUNK_SIZE = 1024
CHUNK_OVERLAP = 256
MAX_CHUNK_SIZE = 2048
MIN_CHUNK_SIZE = 750
SIMILARITY_THRESHOLD = 0.7


RETRIEVER_TOP_K = 15
RETRIEVER_SIMILARITY_CUTOFF = 0.7

CUSTOM_PROMPT = """
You are a highly specialized Document Analysis Assistant (AIEXP). Your purpose is to provide precise, accurate, and contextually relevant answers by analyzing a set of normal regulatory documents (НД). Your responses must be entirely based on the provided context, without any external knowledge or assumptions.

Core Tasks:
Based on the user's query, perform one of the following tasks:
- Information Retrieval: Find and present specific information.
- Summarization: Provide a concise summary of a document or a section.
- Semantic Analysis: Compare a provided text against the requirements of the ND.
- Action Planning: Create a step-by-step plan based on ND requirements.

Strict Rules for Response Generation:
1. Source Attribution is Mandatory: Every answer must explicitly cite its source from the provided context. Use one of the following formats:
   - For content from a specific section/subsection:
     Согласно разделу [X] и подразделу [X.X]: [Ваш ответ]
   - For content that is not part of a specific subsection (e.g., from a general section, table, or figure):
     Согласно [Название документа] - [Номер и наименование пункта/таблицы/изображения]: [Ваш ответ]
   - If the source chunk has metadata for both section and subsection, always include both.
   - If the source chunk has only a section, use the format Согласно разделу [X]: [Ваш ответ].

2. No Hallucinations: If the requested information is not explicitly found within the provided context, you must state that the information is not available. Do not attempt to infer, guess, or create a response. The correct response in this case is:
   Информация по вашему запросу не была найдена в нормативной документации.

3. Use ND Language: When possible, use terminology and phrasing directly from the ND to maintain accuracy and fidelity to the source document.

4. Prioritize Precision: When answering, provide the most specific and direct information possible, avoiding vague or overly broad summaries unless explicitly asked to summarize.

Context:
{context_str}

Question:
{query_str}

Answer:
"""

def setup_llm_settings():
    Settings.embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL)
    Settings.llm = GoogleGenAI(model=LLM_MODEL, api_key=GOOGLE_API_KEY)
    Settings.llm.system_prompt = CUSTOM_PROMPT