一、实践背景介绍
1、业务背景
京东健康内容中台H2有一个目标就是需要替换两家CP内容（总体内容体量百万级），我们现在的逻辑是想按照PV热度优先高热去新生产和替换。替换后可以极大的节省cp内容引入的成本。
第一步：这么多内容，我们的生产逻辑需要按照学科和索引归类和分配，进而批量生产，靠人工一篇篇补索引，效率会很低。希望借助算法的能力，如果现在还不是非常准确，也可以算法+人工修正，
第二步：按索引归类好之后，我们和库内非CP但主题相似内容进行比对，已经有的就不做重复生产。最后剩下来的进行批量生产和替换。
2、技术背景
M3E（M3E（Multimodal Multitask Meta-Embedding）是一个开源的中文嵌入模型
Vearch 是对大规模深度学习向量进行高性能相似搜索的弹性分布式系统。也是京东自研开源的项目，具有强大的相似搜索的弹性分布式能力。
OpenAI的迅速发展对算法成本产生了重大影响。随着技术的进步和研究的不断推进，OpenAI已经取得了许多突破，使得算法的开发和部署成本大大降低。OpenAI的Chat模式和Embedding模式是OpenAI API中的两种不同的使用方式。
1、Chat模式： Chat模式是OpenAI API的一种使用方式，旨在支持对话式的人机交互。在Chat模式下，您可以通过向API发送一系列的用户消息来与模型进行交互，模型将逐条回复每个消息。这种交互式的方式使得您可以与模型进行对话，提出问题、请求解释、寻求建议等。
ini
import openai
response = openai.Completion.create(
engine="davinci",
prompt="What is the capital of France?",
max_tokens=100,
n=1,
stop=None,
temperature=0.7
)
print(response.choices[0].text.strip())2、Embedding模式： Embedding模式是OpenAI API的另一种使用方式，旨在获取文本的嵌入表示。在Embedding模式下，您可以将一段文本传递给API，并获取该文本的高维向量表示，也称为嵌入向量。这些嵌入向量可以用于计算文本之间的相似度、聚类、分类等任务。
ini
import openai
response = openai.Embed.create(
model="text-embedding-ada-002",
documents=["Once upon a time", "In a land far, far away"],
)
embedding1 = response.embeddings[0]
embedding2 = response.embeddings[1]
# 进行嵌入向量的相似度计算等其它操作本次实践主要使用了Embedding，具体实践如下文。
二、实践流程
1、总体流程
(1)、总体流程图
(2)、OpenAi/M3E向量生成部分代码实践
python
async def embed_and_store_with_limit_and_check(
self, semaphore, id, vector_store, text_future_func = None, text: Union[str, list[str]] = "", **additional_properties
):
async with semaphore:
retry_count = (
3 # Task failed with exception Response payload is not completed
)
retry_count_doubled = False
retry = 1
last_error = None
while retry <= retry_count: # Retry up to 3 times.
try:
try:
data = await vector_store.get(vector_id=id)
id = data.id
embedding = data.result.embedding.feature
return (id, embedding)
except VearchRouterGetNotFoundError:
try:
return await self.embed_and_store(
text=text,
id=id,
vector_store=vector_store,
text_future_func=text_future_func,
**additional_properties,
)
except asyncio.TimeoutError:
logger.error(
f"embed_and_store_with_limit_and_check - id {id} #[{vector_store.space_name} {vector_store.db_name}] - Timeout during embed_and_store()"
)
raise
except Exception as error:
error_message = f"{error}" or f"{error.__class__} {error.__doc__}"
logger.error(
f"embed_and_store_with_limit_and_check - id {id} #[{vector_store.space_name} {vector_store.db_name}] - failed with exception {error_message}, retry {retry}"
)
if isinstance(error, VearchRouterStatusError):
if error.reason == "partition_not_leader":
logger.info(
f"embed_and_store_with_limit_and_check - id {id} #[{vector_store.space_name} {vector_store.db_name}] - {error_message}, retry {retry} asyncio.sleep(10) doubled"
)
await asyncio.sleep(10) # Response payload is not completed
if not retry_count_doubled:
retry_count = retry_count * 2
retry_count_doubled = True
if isinstance(error, aiohttp.client_exceptions.ClientPayloadError):
await asyncio.sleep(5) # Response payload is not completed
if not retry_count_doubled:
retry_count = retry_count * 2
retry_count_doubled = True
else:
await asyncio.sleep(1) # Wait for 1 second before retrying
retry = retry + 1
last_error = error
raise VearchRouterClientRetryError(
retry_count,
f"embed_and_store_with_limit_and_check - id {id} #[{vector_store.space_name} {vector_store.db_name}] - completely failed with exception {last_error} - retried {retry_count} times",
error=last_error,
)(3)、vearch向量存储及相似度搜索部分代码实
python
async def score_similarity(
self, vector_store, embedding=None, id=None, **search_properties
):
"""Find the most similar word and the similarity score for a given word in the document"""
if not isinstance(embedding, list):
try:
results_with_scores = await vector_store.search_by_ids(ids=[id])
# embedding = response.result.embedding.feature
return results_with_scores.results[0].hits.hits
except VearchRouterStatusError as error:
raise error
# if error.found == False:
# query_result = await embeddings.embed_query(word)
results_with_scores = await vector_store.search(
feature=embedding, **search_properties
)
return results_with_scores.hits.hits2、OpenAi实现查重的局限性
(1)、成本
以目前100万数据量为例，如果使用目前OpenAi的开放接口实现，每篇内容由于token等限制进出一次需要0.007美元，100万篇内容需要7000美元才可以完成数据特征提取和向量生成，依照目前的内容体量和运用，这个成本还是高于预期，在成本方面没有比其他方案有优势。
(2)、效率
同样以100万数据为例，一篇内容特征提取和向量生成的时间由于国内各种限制，时间最快也在6-9s，即便是在并发以及多token的情况下，那100万内容执行完成最少也大于30天，这在实效性方面相比于其他方案也不占优势。
3、M3E模型引入
(1)、模型调研介绍
M3E（Moka Massive Mixed Embedding）是一个开源的中文嵌入模型，具有以下优势：
多模态支持：M3E模型能够同时处理多种模态的数据，如文本、图像、语音等。这种多模态的支持使得模型能够更好地处理复杂的现实场景，提供更全面的语义理解。
多任务学习：M3E模型支持同时学习多个任务，而不需要针对每个任务单独训练一个模型。通过共享模型的参数和特征表示，M3E能够将不同任务之间的知识相互传递和共享，提高学习效率和泛化能力。
元嵌入学习：M3E模型采用元学习的思想，通过在训练过程中模拟快速学习新任务的过程，使模型能够更好地适应新任务。这种元学习的能力使得M3E模型在面对新任务时能够从少量样本中快速学习并取得良好的性能。
中文语义理解：M3E模型专注于中文语义理解任务，具有针对中文语言特点的优化。这使得M3E模型在处理中文文本时能够更好地捕捉语义信息，提供更准确的嵌入表示。
开源和可定制性：M3E模型是开源的，可以根据具体需求进行定制和扩展。开放源代码使得用户可以自由地修改和优化模型，以适应不同的应用场景。
模型对比：
| 参数数量 | 维度 | 中文 | 英文 | s2s | s2p | s2c | 开源 | 兼容性 | s2s Acc | s2p ndcg@10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| m3e-small | 24M | 512 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5834 | 0.7262 |
| m3e-base | 110M | 768 | 是 | 是 | 是 | 是 | 否 | 是 | 优 | 0.6157 | 0.8004 |
| text2vec | 110M | 768 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5755 | 0.6346 |
| openai-ada-002 | 未知 | 1536 | 是 | 是 | 是 | 是 | 是 | 否 | 优 | 0.5956 | 0.7786 |
(2)、M3E选择的必要
a、实践过程中在不牺牲准确度的情况下向量维度长度短，节省存储空间和带宽，且在和vearch向量库结合使用的过程中发现768维度的向量生在查询和存储时表现的更优越。
b、模型非商业开源并且可以本地微调模型，有效结合业务场景进行
c、可以有针对性的根据数据规模和场景优化和分配资源，定时高效的达到业务预期效果目标。
d、兼容性，代表了模型在开源社区中各种项目被支持的程度，由于 m3e 和 text2vec 都可以直接通过 sentence-transformers 直接使用，所以和 openai 在社区的支持度上相当
e、使用场景主要是中文，少量英文的情况，建议使用 m3e 系列的模型，M3E 在大规模句对数据集上的训练，包含中文百科，金融，医疗，法律，新闻，学术等多个领域共计 2200W 句对样本，数据集详见 M3E 数据集
f、模型持续优化中，开发过程中可以持续提高数据质量，后续可期待更加优秀的模型。
(3)、运用
less
pip3 install -i https://mirrors.jd.com/pypi/simple sentence-transformers==2.2.2
#### Download m3e-base
python3 -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('moka-ai/m3e-base'); print(model.encode(['Hello World!', '你好,世界!']))"
#### Save m3e-base to local path
python3 -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('moka-ai/m3e-base'); model.save('m3e-base-model/')"
python
代码示例：
async def embed (self, text_or_documents):
if isinstance(text_or_documents, list):
documents = text_or_documents
else:
"""Split the text_or_documents, embed the documents and insert the embedding into the online vector storage"""
text_splitter = LocalTextSplitter().get_instance() # self._tokenizer = spacy.load(pipeline)
documents = text_splitter.split_text(text_or_documents)
embedding_return = await self._async_***_with_****(documents=documents)
if len(embedding_return) > 1:
# Compute the mean vector
**************
# Normalizing the mean vector
*************
return embedding
向量生成示例：
{"_index":"content_gpt_db","_type":"content_space_m3e","_id":867602,"found":true,"_source":{"content_type":1,"embedding":{"feature":[0.04050827,0.021327972,-0.0051002502,0.017009735,-0.016672134,-0.01061821,0.026807785,-0.018224716,-0.03107071,-0.0053977966,0.043376923,0.028705597,0.004207611,-0.020687103,-0.0447731,-0.009578705,0.05571747,0.06632233,-0.051948547,-0.013450623,-0.032985687,-0.008350372,-0.043361664,-0.02400589,-0.019294739,-0.023269653,0.005455017,0.0059661865,0.008682251,-0.023887634,0.046310425,-0.036338806,-0.0020313263,0.0062503815,0.05295372,0.026079178,0.011068344,-0.028791428,0.029096603,0.030740738,0.026367188,0.052009583,-0.009216309,-0.004173279,0.0009822845,0.018190384,0.033262253,0.05126381,0.012481689,0.005584717,-0.011810303,0.35385132,-0.043067932,0.0099105835,-0.014457703,0.038978577,0.022174835,-0.039844513,-0.012966156,-0.011081696,0.009370804,-0.024477005,-0.01061058,0.0028133392,-0.009471893,-0.027820587,-0.041484833,0.011547089,0.009700775,-0.05132675,0.06669235,-0.06849289,-0.0129470825,0.004447937,0.074913025,0.008506775,-0.033031464,0.017101288,0.045627594,-0.009830475,0.02917099,0.030750275,-0.017490387,-0.016429901,-0.042669296,-0.014154434,0.0004749298,0.049741745,0.07151413,-0.012218475,-0.013538361,-0.016918182,0.016963959,-0.015842438,-0.03572464,-0.034015656,0.046806335,-0.001625061,-0.006690979,0.040275574,-0.035312653,0.008182526,-0.024295807,-0.047908783,0.023643494,0.054634094,-0.07056427,0.04160309,-0.014863968,0.00399971,0.025701523,-0.0082912445,-0.022632599,0.0016212463,-0.059513092,-0.022808075,-0.008533478,-0.052440643,0.037700653,-0.045360565,0.0012359619,0.06803894,-0.04005432,-0.02885437,-0.032421112,0.010250092,-0.0092430115,0.055828094,-0.05140686,-0.0019073486,0.012435913,-0.04206848,-0.08063507,-0.016105652,-0.00031280518,-0.005180359,0.002243042,-0.009155273,-0.044174194,-0.007598877,0.015665054,0.015577316,0.006883621,-0.031778336,-0.017795563,0.016918182,0.019405365,0.0077323914,-0.012916565,-0.007698059,0.031211853,-0.048286438,0.017166138,0.0033416748,-0.02381897,0.03614807,-0.014591217,0.06523514,-0.04491043,-0.05462265,0.029396057,0.03844452,0.011238098,-0.051124573,-0.024749756,0.0068511963,0.0137786865,-0.033081055,-0.0028033257,0.0011496544,-0.012090206,-0.013271809,-0.018554688,-0.019104004,-0.004699707,-0.11206055,0.007501602,0.0144023895,-0.019788742,0.028829575,-0.03552246,0.028182983,-0.027923584,0.014785767,-0.032590866,-0.0011997223,0.003458023,0.036985397,-0.012435913,-0.040542603,-0.034469604,-0.0028839111,-0.014625549,0.014442444,0.06880951,0.01688385,-0.044792175,-0.014442444,-0.01712799,0.024909973,0.036842346,-0.015365601,0.032600403,-0.023117065,-0.017802238,-0.011162758,0.021027565,-0.0071382523,0.0023880005,0.016410828,-0.07878876,-0.033210754,0.029317856,0.037729263,-0.013490677,0.01420784,-0.076553345,0.03074646,0.020904541,-0.016113281,-0.008716583,-0.058559418,-0.03612137,-0.029781342,-0.03557396,-0.026613235,-0.0034923553,0.033971786,0.01530838,0.019039154,0.05249405,-0.06877518,-0.05325699,-0.054332733,0.022380829,0.0017127991,-0.00060653687,0.003200531,-0.05033493,0.031169891,-0.027420044,0.07209778,0.03919983,0.023788452,-0.03340912,0.038368225,-0.011619568,-0.049583435,0.023187637,-0.031404495,0.001543045,0.011007309,0.03263092,0.0027999878,-0.029151917,-0.03868866,-0.01224041,-0.006829262,-0.014925957,-0.008881569,-0.0025873184,0.012497902,0.018328667,0.0066041946,-0.03035736,-0.0110321045,-0.03830719,-0.026245117,-0.03142929,-0.007991791,0.019321442,-0.021755219,-0.008829117,-0.050519943,0.010892868,-0.015569687,0.0134391785,0.02917862,0.00075912476,-0.09794235,0.011421204,0.04624176,0.066841125,-0.0044174194,-0.019325256,-0.0010528564,-0.03643036,-0.025726318,-0.014377594,-0.024211884,-0.03343582,0.020572662,0.027690887,0.0475502,0.03835678,-0.043956757,-0.00034713745,0.048107147,0.025608063,-0.014255524,0.028633118,-0.07511139,-0.048667908,0.0210495,0.06496048,0.013729095,-0.0051841736,0.016643524,-0.022533417,0.0012626648,0.034671783,-0.029605865,-0.011131287,0.0044937134,-0.065330505,0.019874573,-0.05259323,0.00045394897,-0.008098602,0.01354599,0.05250168,0.07034683,-0.0058631897,0.07423782,0.011419296,-0.037618637,0.01867485,0.000062942505,0.004085541,0.038211823,0.019878387,-0.0754509,0.0065402985,0.0045223236,0.030115128,0.0017757416,-0.014886856,-0.011007309,0.026533127,0.033769608,-0.051013947,0.035007477,0.05788803,-0.049877167,-0.037107468,0.0016613007,0.015481949,-0.02353859,-0.039718628,-0.04598999,-0.044052124,0.010528564,-0.028961182,-0.016166687,0.0015945435,-0.013336182,0.032533646,0.018568039,0.03763771,0.025045395,-0.052635193,-0.051948547,-0.062217712,0.08403778,0.0012397766,-0.0012321472,0.056552887,-0.027065277,0.04188156,-0.03208542,0.06875229,0.0647316,-0.013954163,-0.022972107,0.11660004,0.032203674,-0.031936646,0.0020599365,-0.020370483,-0.06651306,0.0062942505,-0.049430847,0.04660797,0.020118713,-0.031578064,-0.005180359,-0.053260803,-0.027565002,-0.031951904,-0.041366577,-0.002593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(1)、vearch详细介绍
Vearch 是对大规模深度学习向量进行高性能相似搜索的弹性分布式系统。具有以下功能：
1、支持CPU与GPU两种版本。
2、支持实时添加数据到索引。
3、支持单个文档定义多个向量字段, 添加、搜索批量操作。
4、支持数值字段范围过滤与string字段标签过滤。
5、支持IVFPQ、HNSW、二进制等索引方式(HNSW、二进制方式4月下旬发布)。
6、支持Python SDK本地快速开发验证。
7、支持机器学习算法插件方便系统部署使用。
Vearch京东自研开源的项目，具有强大的相似搜索的弹性分布式能力。
(2)、向量存储
python
vearch_instance = VearchInstance(vearch_llm_instance=vearch_llm_instance)
import random
async def embed_content (
content_generator,
concurrent_task_limit,
vearch_instance,
pbar
):
semaphore = asyncio.Semaphore(concurrent_task_limit)
@handle_error_and_log
@handle_client_response_type_check
async def insert(
self, db_name, space_name, vector_id, **vector_properties
) -> VearchRouterOperationResponse:
if "feature" in vector_properties:
properties = {**vector_properties}
del properties["feature"]
return await self.router.insert(
db_name,
space_name,
vector_id,
embedding={ # NOTE/FUTURE hard coded
"feature": vector_properties["feature"]
},
**properties,
)
return await self.router.insert(
db_name, space_name, vector_id, **vector_properties
)(3)、相似度查询
bash
查询语句：
http://jdh-content-gpt-vector-router.vectorbase.svc.ht09.n.jd.local/content_gpt_db/content_space_m3e/_search
{
"query":{
"ids":[
580670
],
"sum":[
{
"field":"embedding",
"feature":[
]
}
]
},
"retrieval_param":{
"parallel_on_queries":1,
"recall_num":100,
"nprobe":80,
"metric_type":"InnerProduct"
},
"is_brute_search":0,
"online_log_level":"debug",
"quick":false,
"vector_value":false,
"client_type":"leader",
"l2_sqrt":true,
"size":10
}三、查重结果及M3E、OpenAi查重相似度效果比较
1、查重相似度验证结果展示
python
import asyncio
import aiofiles
import os
import openpyxl
import json
import sys
import re
# from langchain.document_loaders import TextLoader
# from langchain.schema import Document
import numpy as np
import aiohttp
import logging
import asyncio
# Get the directory containing the current file
current_dir = os.path.dirname(os.path.abspath(__file__))
# Get the parent directory (project root directory)
project_root_dir = os.path.dirname(current_dir)
# Add it to sys.path
sys.path.append(project_root_dir)
from shared.VearchInstance import VearchInstance
logger = logging.getLogger(__name__)
async def async_os_walk(root_dir):
"""A simple, async version of os.walk."""
for root, dirs, files in os.walk(root_dir):
for filename in files:
yield root, filename
"""Main execution function"""
from shared.TerminalColor import bcolors
async def main():
# from shared.VearchOpenAI import VearchOpenAI
from shared.VearchM3e import VearchM3e
vearch_instance = VearchInstance(VearchM3e)
content_vector_store = vearch_instance.content_vector_store
root_logger = logging.getLogger("")
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
force_recreate_spaces = True
await vearch_instance.client.prewarm()
if force_recreate_spaces:
await vearch_instance.ensure_empty()
else:
await vearch_instance.ensure()
# Limit to 10 concurrent tasks.
concurrent_task_limit = 16
semaphore = asyncio.Semaphore(concurrent_task_limit)
vearch_instance.log_configurations(
"===Concurrency===",
f"max concurrent requests: {concurrent_task_limit}",
f"semaphore: {semaphore}",
root_logger=root_logger,
)
# Define paths to the index and content data, and the label file
new_content_to_analyze_dir = os.path.join(current_dir, "./data/content/")
# Load the label data from the Excel file
wb = openpyxl.load_workbook(os.path.join(current_dir, "./data/content.xlsx"))
sheet = wb.active
content_id_dict = {}
async def process_row(row):
preowned_article_body = row[0].value
preowned_article_id = vearch_instance.generate_id(preowned_article_body)
root_logger.info(
"preowned_article_body: {}, preowned_article_id: {}".format(
preowned_article_body[:20], preowned_article_id
)
)
content_id_dict[preowned_article_id] = preowned_article_body[:50]
return await vearch_instance.llm.embed_and_store_with_limit_and_check(
semaphore=semaphore,
text=preowned_article_body,
id=preowned_article_id,
vector_store=content_vector_store,
content_type=vearch_instance.content_type_look_up("preowned_article"),
)
await asyncio.gather(*[process_row(row) for row in sheet.iter_rows()])
# Asynchronously walk through every file in the root directory
async for dirpath, filename in async_os_walk(new_content_to_analyze_dir):
# Asynchronously build the search index for the document with filename in the dirpath
content_file_path = os.path.join(dirpath, filename)
match = re.search(r"(\d+)", filename)
if match:
content_id = int(match.group(1))
else:
content_id = vearch_instance.generate_id(content_file_path)
root_logger.info("filename: {}, content_id: {}".format(filename, content_id))
text = await vearch_instance.llm.load_file(file_path=content_file_path)
embedding = await vearch_instance.llm.embed(text)
# Asynchronously get the most similar texts and their similarity score for the label
search_result = await vearch_instance.llm.score_similarity(
embedding=embedding, vector_store=content_vector_store, min_score=-0.1
)
sorted_search_result = sorted(
search_result, key=lambda hit: hit.score, reverse=True
)
for preowned_article in sorted_search_result:
if preowned_article.id in content_id_dict:
text = content_id_dict[preowned_article.id]
root_logger.info(
f"{filename}: {bcolors.OKBLUE} score {preowned_article.score}{bcolors.ENDC}: ({bcolors.UNDERLINE}{text[0:100]}{bcolors.ENDC})"
)
from shared.AsyncThread import start_asyncio_in_new_thread
# Running the main function using asyncio
if __name__ == "__main__":
async_thread = start_asyncio_in_new_thread()
async_thread.run(main())2、M3E、OpenAi查重相似度效果比较
利用M3E和OpenAi不同模型提取的特征生成向量后计算的相似度基本上一致，且M3E提取的特征对中文的支持更好，更细化，导致最终计算分值以后也更加直观，能够快速的验证定位出相似度界限，对于内容查重业务更加友好，且在成本和效率上更具有优势。
四、总结
经过实践，本次处理47万篇内容，经过多轮优化，最终达到向量生成、验证及插入在使用规格配置32c50g的机器同时启用三个线程派发任务，32个进程共享内存的情况下，可在5小时内完成的。相似度搜索及存储到mysql可在20分钟内完成30万数据的处理。
OpenAI在算法研究方面的创新推动了成本的降低。通过引入更高效的算法和模型架构，OpenAI能够在相同的计算资源下取得更好的性能。这意味着开发者可以更快地训练和部署模型，减少了算法开发的时间和成本。但介于目前的技术环境及规则限制，选择一些开源的像M3E之类的模型才是更贴近我们目前的业务需求和日常使用。
利用M3E模型提取的特征对中文的支持也挺好，也更加细化，尤其除了基本的服务器和开发成本外在不需要额外的支出，效率也可以通过并发和增加资源的手段优化，成本和效率方面具有明显优势。768纬度的向量和vearch结合的也更优越。
作者：京东健康 刘继帅
来源：京东云开发者社区 转载请注明来源