"""
LightRAG meets Amazon Bedrock ⛰️
"""

import asyncio
import logging
import os

import nest_asyncio

from lightrag import LightRAG, QueryParam
from lightrag.llm.bedrock import bedrock_complete, bedrock_embed
from lightrag.utils import EmbeddingFunc

nest_asyncio.apply()

logging.getLogger('aiobotocore').setLevel(logging.WARNING)

WORKING_DIR = './dickens'
if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)


async def initialize_rag():
    rag = LightRAG(
        working_dir=WORKING_DIR,
        llm_model_func=bedrock_complete,
        llm_model_name='Anthropic Claude 3 Haiku // Amazon Bedrock',
        embedding_func=EmbeddingFunc(embedding_dim=1024, max_token_size=8192, func=bedrock_embed),
    )

    await rag.initialize_storages()  # Auto-initializes pipeline_status
    return rag


def main():
    rag = asyncio.run(initialize_rag())

    with open('./book.txt', encoding='utf-8') as f:
        rag.insert(f.read())

    for mode in ['naive', 'local', 'global', 'hybrid']:
        print('\n+-' + '-' * len(mode) + '-+')
        print(f'| {mode.capitalize()} |')
        print('+-' + '-' * len(mode) + '-+\n')
        print(rag.query('What are the top themes in this story?', param=QueryParam(mode=mode)))


if __name__ == '__main__':
    main()