from google import genai
from google.genai import types
import numpy as np
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from dotenv import load_dotenv
load_dotenv()

client = genai.Client(api_key=os.getenv("api_key"))

class RAG:
    def __init__(self):
        self.CHUNK_SIZE = 1024;
        self.CHUNK_OVERLAP = 75;
        self.MAX_BATCH_SIZE = 100;
        self.MODEL = "text-embedding-004";
        self.TASK_TYPE = "SEMANTIC_SIMILARITY";
    
    def split_text(self,text):
        try:
            return RecursiveCharacterTextSplitter(
                chunk_size=self.CHUNK_SIZE,
                chunk_overlap=self.CHUNK_OVERLAP,
                separators=["\n\n", "\n", ".", "!", "?", "。", " ", ""]
            ).split_text(text)
        except Exception as e: 
            raise ValueError(f"an error occured: {e}")
    
    def generate_embedding(self,text,task_type=None):
        try:
            if(not task_type):
                task_type = self.TASK_TYPE
            embeddings = []
            chunks = self.split_text(text)
            for i in range(0,len(chunks),self.MAX_BATCH_SIZE):
                response = client.models.embed_content(
                    model=self.MODEL,
                    contents=chunks[i:i + self.MAX_BATCH_SIZE],
                    config=types.EmbedContentConfig(task_type=task_type)
                )
                for chunk_embedding in response.embeddings:
                    embeddings.append(chunk_embedding.values)
            return {"embeddings": embeddings, "chunks": chunks}, 200
        except Exception as e: 
            return {"an error occured": f"{e}"}, 500
    
        

rag = RAG()