from google import genai
from google.genai import types
import numpy as np
from concurrent.futures import ThreadPoolExecutor, as_completed
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 _embed_batch(self, chunk_batch, task_type):
        response = client.models.embed_content(
            model=self.MODEL,
            contents=chunk_batch,
            config=types.EmbedContentConfig(task_type=task_type)
        )
        return [embedding.values for embedding in response.embeddings]
    
    def generate_embedding(self, text, task_type=None):
        try:
            if not task_type:
                task_type = self.TASK_TYPE

            chunks = self.split_text(text)
            batches = [
                chunks[i:i + self.MAX_BATCH_SIZE]
                for i in range(0, len(chunks), self.MAX_BATCH_SIZE)
            ]

            embeddings = []
            with ThreadPoolExecutor(max_workers=50) as executor:
                futures = {
                    executor.submit(self._embed_batch, batch, task_type): batch
                    for batch in batches
                }

                for future in as_completed(futures):
                    result = future.result()
                    embeddings.extend(result)

            return {"embeddings": embeddings, "chunks": chunks}, 200

        except Exception as e:
            return {"an error occurred": f"{e}"}, 500
    
        

rag = RAG()