"""
Jina Embedding Interface Module
==========================

This module provides interfaces for interacting with jina system,
including embedding capabilities.

Author: Lightrag team
Created: 2024-01-24
License: MIT License

Copyright (c) 2024 Lightrag

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

Version: 1.0.0

Change Log:
- 1.0.0 (2024-01-24): Initial release
    * Added embedding generation

Dependencies:
    - tenacity
    - numpy
    - pipmaster
    - Python >= 3.10

Usage:
    from llm_interfaces.jina import jina_embed
"""

__version__ = "1.0.0"
__author__ = "lightrag Team"
__status__ = "Production"

import os
import pipmaster as pm  # Pipmaster for dynamic library install

# install specific modules
if not pm.is_installed("lmdeploy"):
    pm.install("lmdeploy")
if not pm.is_installed("tenacity"):
    pm.install("tenacity")


import numpy as np
import aiohttp


async def fetch_data(url, headers, data):
    async with aiohttp.ClientSession() as session:
        async with session.post(url, headers=headers, json=data) as response:
            response_json = await response.json()
            data_list = response_json.get("data", [])
            return data_list


async def jina_embed(
    texts: list[str],
    dimensions: int = 1024,
    late_chunking: bool = False,
    base_url: str = None,
    api_key: str = None,
) -> np.ndarray:
    if api_key:
        os.environ["JINA_API_KEY"] = api_key
    url = "https://api.jina.ai/v1/embeddings" if not base_url else base_url
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ['JINA_API_KEY']}",
    }
    data = {
        "model": "jina-embeddings-v3",
        "normalized": True,
        "embedding_type": "float",
        "dimensions": f"{dimensions}",
        "late_chunking": late_chunking,
        "input": texts,
    }
    data_list = await fetch_data(url, headers, data)
    return np.array([dp["embedding"] for dp in data_list])