|
|
""" |
|
|
SiliconCloud Embedding Interface Module |
|
|
========================== |
|
|
|
|
|
This module provides interfaces for interacting with SiliconCloud 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.siliconcloud import siliconcloud_model_complete, siliconcloud_embed |
|
|
""" |
|
|
|
|
|
__version__ = "1.0.0" |
|
|
__author__ = "lightrag Team" |
|
|
__status__ = "Production" |
|
|
|
|
|
import sys |
|
|
|
|
|
if sys.version_info < (3, 9): |
|
|
pass |
|
|
else: |
|
|
pass |
|
|
import pipmaster as pm |
|
|
|
|
|
|
|
|
if not pm.is_installed("lmdeploy"): |
|
|
pm.install("lmdeploy") |
|
|
|
|
|
from openai import ( |
|
|
APIConnectionError, |
|
|
RateLimitError, |
|
|
APITimeoutError, |
|
|
) |
|
|
from tenacity import ( |
|
|
retry, |
|
|
stop_after_attempt, |
|
|
wait_exponential, |
|
|
retry_if_exception_type, |
|
|
) |
|
|
|
|
|
|
|
|
import numpy as np |
|
|
import aiohttp |
|
|
import base64 |
|
|
import struct |
|
|
|
|
|
|
|
|
@retry( |
|
|
stop=stop_after_attempt(3), |
|
|
wait=wait_exponential(multiplier=1, min=4, max=60), |
|
|
retry=retry_if_exception_type( |
|
|
(RateLimitError, APIConnectionError, APITimeoutError) |
|
|
), |
|
|
) |
|
|
async def siliconcloud_embedding( |
|
|
texts: list[str], |
|
|
model: str = "netease-youdao/bce-embedding-base_v1", |
|
|
base_url: str = "https://api.siliconflow.cn/v1/embeddings", |
|
|
max_token_size: int = 512, |
|
|
api_key: str = None, |
|
|
) -> np.ndarray: |
|
|
if api_key and not api_key.startswith("Bearer "): |
|
|
api_key = "Bearer " + api_key |
|
|
|
|
|
headers = {"Authorization": api_key, "Content-Type": "application/json"} |
|
|
|
|
|
truncate_texts = [text[0:max_token_size] for text in texts] |
|
|
|
|
|
payload = {"model": model, "input": truncate_texts, "encoding_format": "base64"} |
|
|
|
|
|
base64_strings = [] |
|
|
async with aiohttp.ClientSession() as session: |
|
|
async with session.post(base_url, headers=headers, json=payload) as response: |
|
|
content = await response.json() |
|
|
if "code" in content: |
|
|
raise ValueError(content) |
|
|
base64_strings = [item["embedding"] for item in content["data"]] |
|
|
|
|
|
embeddings = [] |
|
|
for string in base64_strings: |
|
|
decode_bytes = base64.b64decode(string) |
|
|
n = len(decode_bytes) // 4 |
|
|
float_array = struct.unpack("<" + "f" * n, decode_bytes) |
|
|
embeddings.append(float_array) |
|
|
return np.array(embeddings) |
|
|
|
