From 11c670addc492bd92765d817fb0005cb751699f6 Mon Sep 17 00:00:00 2001 From: zhaozhiming Date: Tue, 23 Jan 2024 09:16:14 +0800 Subject: [PATCH] Site updated: 2024-01-23 09:16:13 --- .../index.html | 4 +- .../rerank-model-deploy-and-usage/index.html | 300 ++++++++++++++++++ archives/2024/01/index.html | 16 +- archives/2024/index.html | 16 +- archives/index.html | 30 +- archives/page/10/index.html | 28 +- archives/page/11/index.html | 28 +- archives/page/12/index.html | 28 +- archives/page/13/index.html | 28 +- archives/page/14/index.html | 28 +- archives/page/15/index.html | 28 +- archives/page/16/index.html | 28 +- archives/page/17/index.html | 28 +- archives/page/18/index.html | 28 +- archives/page/19/index.html | 28 +- archives/page/2/index.html | 28 +- archives/page/20/index.html | 14 + archives/page/3/index.html | 28 +- archives/page/4/index.html | 28 +- archives/page/5/index.html | 28 +- archives/page/6/index.html | 28 +- archives/page/7/index.html | 28 +- archives/page/8/index.html | 28 +- archives/page/9/index.html | 28 +- atom.xml | 56 ++-- categories/ai/index.html | 30 +- categories/ai/page/2/index.html | 28 +- categories/ai/page/3/index.html | 14 + images/post/2024/01/rag-rerank.png | Bin 0 -> 587827 bytes images/post/2024/01/rerank-flow.png | Bin 0 -> 137785 bytes images/post/2024/01/tei-api.png | Bin 0 -> 246063 bytes index.html | 64 ++-- page/10/index.html | 60 ++-- page/11/index.html | 60 ++-- page/12/index.html | 60 ++-- page/13/index.html | 60 ++-- page/14/index.html | 61 ++-- page/15/index.html | 60 ++-- page/16/index.html | 58 ++-- page/17/index.html | 60 ++-- page/18/index.html | 61 ++-- page/19/index.html | 59 ++-- page/2/index.html | 62 ++-- page/20/index.html | 33 +- page/3/index.html | 62 ++-- page/4/index.html | 62 ++-- page/5/index.html | 63 ++-- page/6/index.html | 63 ++-- page/7/index.html | 62 ++-- page/8/index.html | 61 ++-- page/9/index.html | 60 ++-- tags/elasticsearch/index.html | 2 +- tags/{embdding => embedding}/index.html | 4 +- tags/llamaindex/index.html | 16 +- tags/rag/index.html | 2 +- tags/rerank/index.html | 185 +++++++++++ tags/text-embedding-inherence/index.html | 185 +++++++++++ 57 files changed, 1683 insertions(+), 914 deletions(-) create mode 100644 2024/01/18/rerank-model-deploy-and-usage/index.html create mode 100644 images/post/2024/01/rag-rerank.png create mode 100644 images/post/2024/01/rerank-flow.png create mode 100644 images/post/2024/01/tei-api.png rename tags/{embdding => embedding}/index.html (97%) create mode 100644 tags/rerank/index.html create mode 100644 tags/text-embedding-inherence/index.html diff --git a/2024/01/13/llamaindex-eleasticsearch-rga-practice/index.html b/2024/01/13/llamaindex-eleasticsearch-rga-practice/index.html index 42a33195c..81d7326d4 100644 --- a/2024/01/13/llamaindex-eleasticsearch-rga-practice/index.html +++ b/2024/01/13/llamaindex-eleasticsearch-rga-practice/index.html @@ -7,7 +7,7 @@ - + @@ -124,7 +124,7 @@

使用 LlamaIndex 结合 Eleasticsearch 进行 RAG 检索增
diff --git a/2024/01/18/rerank-model-deploy-and-usage/index.html b/2024/01/18/rerank-model-deploy-and-usage/index.html new file mode 100644 index 000000000..58f053b32 --- /dev/null +++ b/2024/01/18/rerank-model-deploy-and-usage/index.html @@ -0,0 +1,300 @@ + + + + + + Rerank 模型的部署及使用 - Hacker and Geeker's Way + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Rerank 模型的部署及使用

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Rerank 在 RAG(Retrieval-Augmented Generation)过程中扮演了一个非常重要的角色,普通的 RAG 可能会检索到大量的文档,但这些文档可能并不是所有的都跟问题相关,而 Rerank 可以对文档进行重新排序和筛选,让相关的文档排在前面,从而提高 RAG 的效果。本文将介绍使用 HuggingFace 的 Text Embedding Inherence 工具部署 Rerank 模型,以及演示如何在 LlamaIndex 的 RAG 中加入 Rerank 功能。

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Rerank 介绍

RAG 是一种结合了信息检索和文本生成的语言模型技术。简单来说,当你向大语言模型(LLM)提出一个问题时,RAG 首先会在一个大型的文档集合中寻找相关信息,然后再基于这些信息生成回答。

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Rerank 的工作就像是一个智能的筛选器,当 RAG 从文档集合中检索到多个文档时,这些文档可能与你的问题相关度各不相同。有些文档可能非常贴切,而有些则可能只是稍微相关或者甚至是不相关的。这时,Rerank 的任务就是评估这些文档的相关性,然后对它们进行重新排序。它会把那些最有可能提供准确、相关回答的文档排在前面。这样,当 LLM 开始生成回答时,它会优先考虑这些排名靠前的、更加相关的文档,从而提高生成回答的准确性和质量。通俗来说,Rerank 就像是在图书馆里帮你从一堆书中挑出最相关的那几本,让你在寻找答案时更加高效和精准。

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Rerank 模型部署

目前可用的 Rerank 模型并不多,有 Cohere 的线上模型,通过 API 的形式进行调用。开源的模型有智源的bge-reranker-basebge-reranker-large。今天我们将使用 bge-reranker-large 模型来进行部署演示。

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Text Embedding Inherence

我们将使用 HuggingFace 推出的 Text Embedding Inherence(以下简称 TEI)工具来部署 Rerank 模型,TEI 是一个用于部署和提供开源文本嵌入和序列分类模型的工具,该工具主要是以部署 Embedding 模型为主,但是也支持 Rerank 和其他类型的模型的部署,同时它还支持部署兼容 OpenAI API 的 API 服务。

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我们先进行 TEI 的安装,安装方式有 2 种,一种是通过 Docker 方式,另外一种是通过源码安装的方式,可以同时支持 GPU 和 CPU 的机器部署。

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因为 Docker 安装需要有 GPU 的服务器,而一些云 GPU 服务器不方便使用 Docker,因此我们在 Mac M1 电脑上通过源码的方式来进行安装。

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首先需要在电脑上安装 Rust,建议安装 Rust 的最新版本 1.75.0,安装命令如下:

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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
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然后下载 TEI 的 github 仓库,并安装相关依赖,命令如下:

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git clone https://github.com/huggingface/text-embeddings-inference.git
cd text--embeddings-inference
cargo install --path router -F candle -F metal
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  • 其中 router 是 TEI 仓库里面的一个目录
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  • 安装成功后,可以使用text-embeddings-router --help命令来查看工具的相关参数
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TEI 安装完成后,我们使用它来部署 Rerank 模型,命令如下:

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text-embeddings-router --model-id BAAI/bge-reranker-large --revision refs/pr/5 --port 8080
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  • --model-id是指模型在 Huggingface 上的 ID,revision是相关的版本号
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  • --port是指服务的端口号
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  • 执行命令后,TEI 会从 Huggingface 上下载模型,下载到本地路径~/.cache/huggingface/hub/models--BAAI--bge-reranker-large
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服务启动后,我们可以在浏览器访问地址http://localhost:8080/docs来查看服务的 API 文档:

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在图中可以看到有 Rerank 的接口,我们尝试用 Curl 工具来调用该接口进行验证:

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curl -X 'POST' \
'http://localhost:8080/rerank' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"query": "What is Deep Learning?",
"texts": [
"Deep Learning is ...",
"hello"
]
}'

# 显示结果
[
{
"index":0,
"score":0.99729556
},
{
"index":1,
"score":0.00009387641
}
]
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Rerank 的接口比较简单,只需要传问题query和相关的文档texts这 2 个参数即可,返回结果表示每个文档和问题的相似度分数,然后按照分数大小来进行排序,可以看到第一个文档与问题语义相近所以得分比较高,第二个文档和问题不太相关所以得分低。

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需要注意的是,因为该模型是 Rerank 模型,所以如果是调用其中的embedding接口会报模型不支持的错误。如果你想同时拥有 Rerank 和 Embedding 的功能,可以再使用 TEI 部署一个 Embedding 模型,只要端口号不冲突就可以了。

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TEI 也支持 Embedding 模型和序列分类模型的部署,其它模型的部署可以参考 TEI 的官方仓库,这里就不再赘述。

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LlamaIndex 使用 Rerank 功能

我们先来看下 LlamaIndex 普通的 RAG 功能,示例代码如下:

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from llama_index import ServiceContext, VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
service_context = ServiceContext.from_defaults(llm=None)
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
query_engine = index.as_query_engine()

response = query_engine.query("健康饮食的好处是什么?")
print(f"response: {response}")
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  • data是放我们测试文档的目录,测试文档内容待会会介绍
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  • LlamaIndex 默认使用 OpenAI 的 LLM,这样的话response是 LLM 生成的答案,这里我们将llm设置为Noneresponse就只会显示传递给 LLM 的提示词模板
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  • LlamaIndex 默认使用 OpenAI 的 Embedding 来向量化文档,因此需要设置环境变量OPENAI_API_KEY为你的 OpenAI API Key
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  • 其他部分就是 LlamaIndex 的一个普通 RAG 代码,加载目录文档,解析分块索引保存,最后进行查询
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我们再来看下测试文档内容:

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$ tree data
data/
├── rerank-A.txt
├── rerank-B.txt
└── rerank-C.txt

$ cat rerank-A.txt
### 快餐的负面影响:健康与生活方式的隐忧
快餐,一种在现代快节奏生活中极为普遍的饮食选择......

$ cat rerank-B.txt
### 选择有机,选择健康:探索有机食品的无限好处
在今天这个注重健康和可持续生活的时代......

$ cat rerank-C.txt
### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键......
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这些测试文档都是和饮食相关的文档,我们执行下代码看下结果:

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# response 显示结果
LLM is explicitly disabled. Using MockLLM.
response: Context information is below.
---------------------
file_path: data/rerank-C.txt

### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键.....

file_path: data/rerank-A.txt

### 快餐的负面影响:健康与生活方式的隐忧
快餐,一种在现代快节奏生活中极为普遍的饮食选择......
---------------------
Given the context information and not prior knowledge, answer the query.
Query: 健康饮食的好处是什么?
Answer:
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可以看到程序会检索出和问题相似度最高的 2 个文档rerank-C.txtrerank-A.txt,但 A 文档似乎和问题关联性不大,我们可以使用 Rerank 来改进这一点。

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我们需要使用 LlamaIndex 的Node PostProcessor组件来调用 Rerank 功能,Node Postprocessor 的作用是在查询结果传递到查询流程的下一个阶段之前,修改或增强这些结果。因此我们先来定一个自定义的Node PostProcessor来调用我们刚才部署的 Rerank 接口,代码如下:

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import requests
from typing import List, Optional
from llama_index.bridge.pydantic import Field, PrivateAttr
from llama_index.postprocessor.types import BaseNodePostprocessor
from llama_index.schema import NodeWithScore, QueryBundle

class CustomRerank(BaseNodePostprocessor):
url: str = Field(description="Rerank server url.")
top_n: int = Field(description="Top N nodes to return.")

def __init__(
self,
top_n: int,
url: str,
):
super().__init__(url=url, top_n=top_n)

def rerank(self, query, texts):
url = f"{self.url}/rerank"
request_body = {"query": query, "texts": texts, "truncate": False}
response = requests.post(url, json=request_body)
if response.status_code != 200:
raise RuntimeError(f"Failed to rerank documents, detail: {response}")
return response.json()

@classmethod
def class_name(cls) -> str:
return "CustomerRerank"

def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
if query_bundle is None:
raise ValueError("Missing query bundle in extra info.")
if len(nodes) == 0:
return []

texts = [node.text for node in nodes]
results = self.rerank(
query=query_bundle.query_str,
texts=texts,
)

new_nodes = []
for result in results[0 : self.top_n]:
new_node_with_score = NodeWithScore(
node=nodes[int(result["index"])].node,
score=result["score"],
)
new_nodes.append(new_node_with_score)
return new_nodes
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  • 我们定义了一个CustomRerank类,继承自BaseNodePostprocessor,并实现了_postprocess_nodes方法
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  • CustomRerank类有 2 个参数,url是我们刚才部署的 Rerank 服务地址,top_n是指返回的文档数量
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  • _postprocess_nodes方法中,我们先将原始检索到的文档转化为文本列表,再和问题一起传递给 rerank方法
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  • rerank方法会调用 Rerank 接口,这里要注意的是,TEI 中的 texts参数每个文档的长度不能超过 512 个字符,如果超过了会报 413 请求参数超过限制大小的错误,这时可以将truncate参数设置为True,接口会自动将过长的文档进行截断
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  • 得到 Rerank 的结果后,我们根据top_n参数来截取前 N 个文档,然后返回重新排序后的文档列表
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我们再来看如何在 LlamaIndex 中使用CustomRerank,代码如下:

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from custom_rerank import CustomRerank

......
query_engine = index.as_query_engine(
node_postprocessors=[CustomRerank(url="http://localhost:8080", top_n=1)],
)

response = query_engine.query("健康饮食的好处是什么?")
print(f"response: {response}")
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  • 我们在as_query_engine方法中传递了node_postprocessors参数,这里我们将CustomRerank类传递进去
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  • CustomRerank类中,我们设置 Rerank 的 API 地址和 top_n 参数,这里我们设置为 1,表示只返回一个文档
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修改完代码后,我们再次运行程序,可以看到结果如下:

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# response 显示结果
LLM is explicitly disabled. Using MockLLM.
response: Context information is below.
---------------------
file_path: data/rerank-C.txt

### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键.....

---------------------
Given the context information and not prior knowledge, answer the query.
Query: 健康饮食的好处是什么?
Answer:
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可以看到这次传递给 LLM 的文档只有rerank-C.txt,Rerank 只获取了最接近问题的一个文档,这样 LLM 生成的答案就更加准确了。我们可以在CustomRerank类打印原始检索的得分和经过 Rerank 后的得分,结果如下所示:

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source node score: 0.8659382811170047
source node score: 0.8324490144594573
----------------------
rerank node score: 0.9941347
rerank node score: 0.072374016
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可以看到两者的得分是有差距的,这是因为原始检索和 Rerank 使用的模型不同,所以得到的分数也不同。

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总结

今天我们介绍了 Rerank 模型的部署和使用,Rerank 模型可以帮助我们对检索到的文档进行重新排序,让相关的文档排在前面,并且过滤掉不相关的文档,从而提高 RAG 的效果。我们使用 HuggingFace 的 Text Embedding Inherence 工具来部署 Rerank 模型,同时演示了如何在 LlamaIndex 的 RAG 加入 Rerank 功能。希望本文对你有所帮助,如果有什么问题欢迎在评论区留言。

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关注我,一起学习各种人工智能和 AIGC 新技术,欢迎交流,如果你有什么想问想说的,欢迎在评论区留言。

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2024

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使用 LlamaIndex 结合 Eleasticsearch 进行 RAG 检索增强生成

1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/archives/2024/index.html b/archives/2024/index.html index 200a41deb..ef803cbf7 100644 --- a/archives/2024/index.html +++ b/archives/2024/index.html @@ -128,13 +128,27 @@

2024

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使用 LlamaIndex 结合 Eleasticsearch 进行 RAG 检索增强生成

1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/archives/index.html b/archives/index.html index 904e10af9..5e2773fa0 100644 --- a/archives/index.html +++ b/archives/index.html @@ -128,13 +128,27 @@

2024

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2015

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2015

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2014

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2014

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2014

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2013

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2012

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2012

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2012

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diff --git a/archives/page/2/index.html b/archives/page/2/index.html index a98cc36fc..5717b1e20 100644 --- a/archives/page/2/index.html +++ b/archives/page/2/index.html @@ -128,6 +128,20 @@

2023

+ + - -
diff --git a/archives/page/20/index.html b/archives/page/20/index.html index 3c566fbce..d48c78a2f 100644 --- a/archives/page/20/index.html +++ b/archives/page/20/index.html @@ -128,6 +128,20 @@

2012

+ +

在junit中添加fail--有test失败即build failed

diff --git a/archives/page/3/index.html b/archives/page/3/index.html index ce7b93467..928016a65 100644 --- a/archives/page/3/index.html +++ b/archives/page/3/index.html @@ -128,6 +128,20 @@

2023

+ + - -
diff --git a/archives/page/4/index.html b/archives/page/4/index.html index 2957505db..abdc2e6a4 100644 --- a/archives/page/4/index.html +++ b/archives/page/4/index.html @@ -128,6 +128,20 @@

2023

+ + -
diff --git a/archives/page/5/index.html b/archives/page/5/index.html index 3ab6413fc..5fdc9f40b 100644 --- a/archives/page/5/index.html +++ b/archives/page/5/index.html @@ -128,6 +128,20 @@

2019

+ + -
diff --git a/archives/page/6/index.html b/archives/page/6/index.html index 2cec4aa8c..029be52fc 100644 --- a/archives/page/6/index.html +++ b/archives/page/6/index.html @@ -128,6 +128,20 @@

2018

+ + -
diff --git a/archives/page/7/index.html b/archives/page/7/index.html index 7152787da..e96e47945 100644 --- a/archives/page/7/index.html +++ b/archives/page/7/index.html @@ -128,6 +128,20 @@

2017

+ + - -
diff --git a/archives/page/8/index.html b/archives/page/8/index.html index 6b2369d87..9d239acd3 100644 --- a/archives/page/8/index.html +++ b/archives/page/8/index.html @@ -128,6 +128,20 @@

2017

+ + - -
diff --git a/archives/page/9/index.html b/archives/page/9/index.html index a656bdfc2..039ad0f01 100644 --- a/archives/page/9/index.html +++ b/archives/page/9/index.html @@ -128,6 +128,20 @@

2016

+ + - -
diff --git a/atom.xml b/atom.xml index 82dc60602..28c54877c 100644 --- a/atom.xml +++ b/atom.xml @@ -6,7 +6,7 @@ - 2024-01-15T10:28:36.151Z + 2024-01-19T06:13:16.464Z https://example.com/ @@ -16,12 +16,37 @@ Hexo + + Rerank 模型的部署及使用 + + https://example.com/2024/01/18/rerank-model-deploy-and-usage/ + 2024-01-18T03:55:56.000Z + 2024-01-19T06:13:16.464Z + +

Rerank 在 RAG(Retrieval-Augmented Generation)过程中扮演了一个非常重要的角色,普通的 RAG 可能会检索到大量的文档,但这些文档可能并不是所有的都跟问题相关,而 Rerank 可以对文档进行重新排序和筛选,让相关的文档排在前面,从而提高 RAG 的效果。本文将介绍使用 HuggingFace 的 Text Embedding Inherence 工具部署 Rerank 模型,以及演示如何在 LlamaIndex 的 RAG 中加入 Rerank 功能。

Rerank 介绍

RAG 是一种结合了信息检索和文本生成的语言模型技术。简单来说,当你向大语言模型(LLM)提出一个问题时,RAG 首先会在一个大型的文档集合中寻找相关信息,然后再基于这些信息生成回答。

Rerank 的工作就像是一个智能的筛选器,当 RAG 从文档集合中检索到多个文档时,这些文档可能与你的问题相关度各不相同。有些文档可能非常贴切,而有些则可能只是稍微相关或者甚至是不相关的。这时,Rerank 的任务就是评估这些文档的相关性,然后对它们进行重新排序。它会把那些最有可能提供准确、相关回答的文档排在前面。这样,当 LLM 开始生成回答时,它会优先考虑这些排名靠前的、更加相关的文档,从而提高生成回答的准确性和质量。通俗来说,Rerank 就像是在图书馆里帮你从一堆书中挑出最相关的那几本,让你在寻找答案时更加高效和精准。

Rerank 模型部署

目前可用的 Rerank 模型并不多,有 Cohere 的线上模型,通过 API 的形式进行调用。开源的模型有智源的bge-reranker-basebge-reranker-large。今天我们将使用 bge-reranker-large 模型来进行部署演示。

Text Embedding Inherence

我们将使用 HuggingFace 推出的 Text Embedding Inherence(以下简称 TEI)工具来部署 Rerank 模型,TEI 是一个用于部署和提供开源文本嵌入和序列分类模型的工具,该工具主要是以部署 Embedding 模型为主,但是也支持 Rerank 和其他类型的模型的部署,同时它还支持部署兼容 OpenAI API 的 API 服务。

我们先进行 TEI 的安装,安装方式有 2 种,一种是通过 Docker 方式,另外一种是通过源码安装的方式,可以同时支持 GPU 和 CPU 的机器部署。

因为 Docker 安装需要有 GPU 的服务器,而一些云 GPU 服务器不方便使用 Docker,因此我们在 Mac M1 电脑上通过源码的方式来进行安装。

首先需要在电脑上安装 Rust,建议安装 Rust 的最新版本 1.75.0,安装命令如下:

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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

然后下载 TEI 的 github 仓库,并安装相关依赖,命令如下:

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git clone https://github.com/huggingface/text-embeddings-inference.git
cd text--embeddings-inference
cargo install --path router -F candle -F metal
  • 其中 router 是 TEI 仓库里面的一个目录
  • 安装成功后,可以使用text-embeddings-router --help命令来查看工具的相关参数

TEI 安装完成后,我们使用它来部署 Rerank 模型,命令如下:

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text-embeddings-router --model-id BAAI/bge-reranker-large --revision refs/pr/5 --port 8080
  • --model-id是指模型在 Huggingface 上的 ID,revision是相关的版本号
  • --port是指服务的端口号
  • 执行命令后,TEI 会从 Huggingface 上下载模型,下载到本地路径~/.cache/huggingface/hub/models--BAAI--bge-reranker-large

服务启动后,我们可以在浏览器访问地址http://localhost:8080/docs来查看服务的 API 文档:

在图中可以看到有 Rerank 的接口,我们尝试用 Curl 工具来调用该接口进行验证:

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curl -X 'POST' \
'http://localhost:8080/rerank' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"query": "What is Deep Learning?",
"texts": [
"Deep Learning is ...",
"hello"
]
}'

# 显示结果
[
{
"index":0,
"score":0.99729556
},
{
"index":1,
"score":0.00009387641
}
]

Rerank 的接口比较简单,只需要传问题query和相关的文档texts这 2 个参数即可,返回结果表示每个文档和问题的相似度分数,然后按照分数大小来进行排序,可以看到第一个文档与问题语义相近所以得分比较高,第二个文档和问题不太相关所以得分低。

需要注意的是,因为该模型是 Rerank 模型,所以如果是调用其中的embedding接口会报模型不支持的错误。如果你想同时拥有 Rerank 和 Embedding 的功能,可以再使用 TEI 部署一个 Embedding 模型,只要端口号不冲突就可以了。

TEI 也支持 Embedding 模型和序列分类模型的部署,其它模型的部署可以参考 TEI 的官方仓库,这里就不再赘述。

LlamaIndex 使用 Rerank 功能

我们先来看下 LlamaIndex 普通的 RAG 功能,示例代码如下:

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from llama_index import ServiceContext, VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
service_context = ServiceContext.from_defaults(llm=None)
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
query_engine = index.as_query_engine()

response = query_engine.query("健康饮食的好处是什么?")
print(f"response: {response}")
  • data是放我们测试文档的目录,测试文档内容待会会介绍
  • LlamaIndex 默认使用 OpenAI 的 LLM,这样的话response是 LLM 生成的答案,这里我们将llm设置为Noneresponse就只会显示传递给 LLM 的提示词模板
  • LlamaIndex 默认使用 OpenAI 的 Embedding 来向量化文档,因此需要设置环境变量OPENAI_API_KEY为你的 OpenAI API Key
  • 其他部分就是 LlamaIndex 的一个普通 RAG 代码,加载目录文档,解析分块索引保存,最后进行查询

我们再来看下测试文档内容:

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$ tree data
data/
├── rerank-A.txt
├── rerank-B.txt
└── rerank-C.txt

$ cat rerank-A.txt
### 快餐的负面影响:健康与生活方式的隐忧
快餐,一种在现代快节奏生活中极为普遍的饮食选择......

$ cat rerank-B.txt
### 选择有机,选择健康:探索有机食品的无限好处
在今天这个注重健康和可持续生活的时代......

$ cat rerank-C.txt
### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键......

这些测试文档都是和饮食相关的文档,我们执行下代码看下结果:

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# response 显示结果
LLM is explicitly disabled. Using MockLLM.
response: Context information is below.
---------------------
file_path: data/rerank-C.txt

### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键.....

file_path: data/rerank-A.txt

### 快餐的负面影响:健康与生活方式的隐忧
快餐,一种在现代快节奏生活中极为普遍的饮食选择......
---------------------
Given the context information and not prior knowledge, answer the query.
Query: 健康饮食的好处是什么?
Answer:

可以看到程序会检索出和问题相似度最高的 2 个文档rerank-C.txtrerank-A.txt,但 A 文档似乎和问题关联性不大,我们可以使用 Rerank 来改进这一点。

我们需要使用 LlamaIndex 的Node PostProcessor组件来调用 Rerank 功能,Node Postprocessor 的作用是在查询结果传递到查询流程的下一个阶段之前,修改或增强这些结果。因此我们先来定一个自定义的Node PostProcessor来调用我们刚才部署的 Rerank 接口,代码如下:

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import requests
from typing import List, Optional
from llama_index.bridge.pydantic import Field, PrivateAttr
from llama_index.postprocessor.types import BaseNodePostprocessor
from llama_index.schema import NodeWithScore, QueryBundle

class CustomRerank(BaseNodePostprocessor):
url: str = Field(description="Rerank server url.")
top_n: int = Field(description="Top N nodes to return.")

def __init__(
self,
top_n: int,
url: str,
):
super().__init__(url=url, top_n=top_n)

def rerank(self, query, texts):
url = f"{self.url}/rerank"
request_body = {"query": query, "texts": texts, "truncate": False}
response = requests.post(url, json=request_body)
if response.status_code != 200:
raise RuntimeError(f"Failed to rerank documents, detail: {response}")
return response.json()

@classmethod
def class_name(cls) -> str:
return "CustomerRerank"

def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
if query_bundle is None:
raise ValueError("Missing query bundle in extra info.")
if len(nodes) == 0:
return []

texts = [node.text for node in nodes]
results = self.rerank(
query=query_bundle.query_str,
texts=texts,
)

new_nodes = []
for result in results[0 : self.top_n]:
new_node_with_score = NodeWithScore(
node=nodes[int(result["index"])].node,
score=result["score"],
)
new_nodes.append(new_node_with_score)
return new_nodes
  • 我们定义了一个CustomRerank类,继承自BaseNodePostprocessor,并实现了_postprocess_nodes方法
  • CustomRerank类有 2 个参数,url是我们刚才部署的 Rerank 服务地址,top_n是指返回的文档数量
  • _postprocess_nodes方法中,我们先将原始检索到的文档转化为文本列表,再和问题一起传递给 rerank方法
  • rerank方法会调用 Rerank 接口,这里要注意的是,TEI 中的 texts参数每个文档的长度不能超过 512 个字符,如果超过了会报 413 请求参数超过限制大小的错误,这时可以将truncate参数设置为True,接口会自动将过长的文档进行截断
  • 得到 Rerank 的结果后,我们根据top_n参数来截取前 N 个文档,然后返回重新排序后的文档列表

我们再来看如何在 LlamaIndex 中使用CustomRerank,代码如下:

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from custom_rerank import CustomRerank

......
query_engine = index.as_query_engine(
node_postprocessors=[CustomRerank(url="http://localhost:8080", top_n=1)],
)

response = query_engine.query("健康饮食的好处是什么?")
print(f"response: {response}")
  • 我们在as_query_engine方法中传递了node_postprocessors参数,这里我们将CustomRerank类传递进去
  • CustomRerank类中,我们设置 Rerank 的 API 地址和 top_n 参数,这里我们设置为 1,表示只返回一个文档

修改完代码后,我们再次运行程序,可以看到结果如下:

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# response 显示结果
LLM is explicitly disabled. Using MockLLM.
response: Context information is below.
---------------------
file_path: data/rerank-C.txt

### 健康饮食的益处:营养学视角的探讨
摘要:健康饮食是维持和提升整体健康的关键.....

---------------------
Given the context information and not prior knowledge, answer the query.
Query: 健康饮食的好处是什么?
Answer:

可以看到这次传递给 LLM 的文档只有rerank-C.txt,Rerank 只获取了最接近问题的一个文档,这样 LLM 生成的答案就更加准确了。我们可以在CustomRerank类打印原始检索的得分和经过 Rerank 后的得分,结果如下所示:

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source node score: 0.8659382811170047
source node score: 0.8324490144594573
----------------------
rerank node score: 0.9941347
rerank node score: 0.072374016

可以看到两者的得分是有差距的,这是因为原始检索和 Rerank 使用的模型不同,所以得到的分数也不同。

总结

今天我们介绍了 Rerank 模型的部署和使用,Rerank 模型可以帮助我们对检索到的文档进行重新排序,让相关的文档排在前面,并且过滤掉不相关的文档,从而提高 RAG 的效果。我们使用 HuggingFace 的 Text Embedding Inherence 工具来部署 Rerank 模型,同时演示了如何在 LlamaIndex 的 RAG 加入 Rerank 功能。希望本文对你有所帮助,如果有什么问题欢迎在评论区留言。

关注我,一起学习各种人工智能和 AIGC 新技术,欢迎交流,如果你有什么想问想说的,欢迎在评论区留言。

]]>
+ + + 介绍使用 TEI 部署 Rerank 模型以及使用LlamaIndex 进行 Rerank 检索 + + + + + + + + + + + + +
+ 使用 LlamaIndex 结合 Eleasticsearch 进行 RAG 检索增强生成 https://example.com/2024/01/13/llamaindex-eleasticsearch-rga-practice/ 2024-01-13T07:12:46.000Z - 2024-01-15T10:28:36.151Z + 2024-01-18T03:59:05.315Z

检索增强生成(Retrieval-Augmented Generation,RAG)是一种结合了检索(Retrieval)和生成(Generation)的技术,它有效地解决了大语言模型(LLM)的一些问题,比如幻觉、知识限制等。随着 RAG 技术的发展,RAG 涉及到的向量技术受到了大家的关注,向量数据库也慢慢被大家所了解,一些老牌的数据库厂商也纷纷表示支持向量检索,比如 Elasticsearch 也在最近的版本增加了向量检索的支持。本文将介绍 Elasticsearch 和 RAG 中相关的 Embedding 模型的部署,以及在 LLM 框架 LLamaIndex 中如何使用 Elasticsearch 进行文档索引入库和检索。

RAG 介绍

在使用 LLM 时我们经常会遇到这样一些情况,比如当我们的问题超出 LLM 的知识范围时,它要么解释说这个问题超出它的知识范围(这是 LLM 的知识限制),要么它会很自信地瞎编一些答案(这是我们所说的 LLM 幻觉)。

为了应对 LLM 的这些问题,RAG(检索增强生成)技术应运而生,RAG 的主要原理是将文档向量化后进行存储,在提出问题时将问题进行向量检索,检索出相关的文档,然后再将文档作为问题的上下文,一起发送给 LLM,让 LLM 来生成问题的答案,有了相关文档的支持,LLM 在内容的生成上就会参考这些文档,这样就可以有效地解决 LLM 的幻觉问题。同时,RAG 可以让 LLM 更快地了解到最新的信息,通常要让 LLM 了解到更新的信息,需要对 LLM 进行重新训练,训练方式不管是预训练还是微调,成本都是比较高的,而 RAG 只需要将最新的文档加入到数据库中即可,这样 LLM 就可以通过向量检索的方式来获取最新的信息。

关键字检索和语义检索

RAG 的相关技术包括向量检索,也称为语义检索,它不同于传统的关键字检索,关键字检索依赖于在文档中查找与查询中使用的确切词汇匹配的单词或短语,它通常只关注字面上的匹配,而不考虑查询的上下文或语义含义,而语义检索旨在理解查询的意图和上下文含义,不仅仅是文字匹配,它通过分析词语的语义关系(如同义词、词义消歧)来提高检索的相关性。

举一个简单的例子,比如我们输入苹果 2024 新品发布,关键字检索可能返回关于苹果公司 2024 年的任何新闻发布,但也可能包括与水果苹果相关的新品种发布的信息,语义检索则会查找出关于苹果公司最新电子产品发布的新闻,而忽略与水果苹果相关的内容。

Elasitcsearch(以下简称 ES) 虽然一开始只是全文搜索引擎,也就是关键字检索,但是随着向量检索技术的发展,ES 也开始支持向量检索,这让 ES 成为了一个既可以做关键字检索,又可以做语义检索的数据库。下面我们就来介绍 ES 数据库的部署。

Elasticsearch 部署

部署 ES 最简单的方式是通过 Docker,首先需要安装 Docker,可以参考 Docker 的官方安装文档

Docker 安装完成后开始安装 ES,我们需要使用 ES 的最新版本,因为最新的版本包括了向量检索的功能,目前最新的版本是8.11.3,安装启动命令如下:

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docker run -d --name es -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" elasticsearch:8.11.3

使用docker run命令启动 ES 服务,-d参数表示以后台方式运行,--name参数表示容器的名称,-p参数表示端口映射,-e参数表示环境变量,elasticsearch:8.11.3表示使用elasticsearch相关版本的镜像。如果你是单机部署的话,可以不需要映射9300端口,这个端口主要用于 ES 集群内部节点之间的通信。

修改 ES 用户密码

ES 默认的配置会开启安全认证,这意味着在访问 ES 时需要通过用户名和密码认证,因此我们需要先获取到 ES 的用户名和密码,ES 的默认用户是elastic,如果不清楚该用户的密码,可以通过以下命令来重置用户密码:

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# 进入 ES 容器
$ docker exec -it es bash
# 重置密码
$ bin/elasticsearch-reset-password -u elastic -i
This tool will reset the password of the [elastic] user.
You will be prompted to enter the password.
Please confirm that you would like to continue [y/N]y


Enter password for [elastic]:
Re-enter password for [elastic]:
Password for the [elastic] user successfully reset.

在 ES 容器中我们通过elasticsearch-reset-password命令来重置elastic用户的密码,重置完成后,我们可以通过在浏览器中输入https://localhost:9200来访问 ES(注意 url 地址是 https,不是 http,后面会讲如何关闭 https),首次访问时会提示你输入用户名和密码:

输入用户名和密码后,我们就可以看到 ES 相关的 JSON 信息。

关闭 ES SSL 认证

ES 为了加强系统的安全性,会默认开启 SSL 认证,在访问 ES 时需要使用 HTTPS 协议,但如果我们只是本地使用的话不太需要这种级别的安全认证,因此我们可以关闭 ES 的 SSL 认证,关闭 SSL 认证的需要修改 ES 的配置文件elascitsearch.yml。修改该文件我们先要将 ES 默认的配置文件拷贝到本地磁盘,然后修改配置文件,最后在 ES 容器启动时挂载修改后的配置文件。

首先我们将刚才启动的 ES 容器中的配置目录拷贝到本地磁盘,然后原来的 ES 容器就可以关闭了,命令如下:

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# 拷贝配置文件
docker cp es:/usr/share/elasticsearch/config ./config
# 关闭 ES 容器
docker rm -f es

config文件夹包含了elascitsearch.yml和其他配置文件,然后我们修改elascitsearch.yml文件来关闭 SSL 认证,修改内容如下:

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# Enable encryption for HTTP API client connections, such as Kibana, Logstash, and Agents
xpack.security.http.ssl:
- enabled: true
+ enabled: false
keystore.path: certs/http.p12

# Enable encryption and mutual authentication between cluster nodes
xpack.security.transport.ssl:
- enabled: true
+ enabled: false
verification_mode: certificate
keystore.path: certs/transport.p12
truststore.path: certs/transport.p12

修改完成后,我们需要重新运行一个新的 ES 容器,并将修改后的配置文件挂载到容器中,命令如下:

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docker run -d --name es -p 9200:9200 -p 9300:9300 -v "$PWD/config":/usr/share/elasticsearch/config -e "discovery.type=single-node" elasticsearch:8.11.3

等容器启动后,我们就可以通过http://localhost:9200来访问 ES 了。这里要注意的是因为重新部署了 ES 容器,所以刚才修改的用户密码也会失效,需要重新重置用户密码。

ES 监控工具

想要查看 ES 中的数据,如果是使用命令行工具的话可能不太方便,因此我们需要一个 GUI 工具,这里推荐elasticvue,一个基于浏览器的 ES GUI 工具,安装也非常简单,同样是使用 docker 来进行安装:

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docker run -p 9080:8080 --name elasticvue -d cars10/elasticvue

然后我们在浏览器中输入http://localhost:9080来访问 elasticvue,进到首页后点击ADD ELASTICSEARCH CLUSTER按钮,可以看到如下界面:

根据上图上半部分的Configure提示,需要修改 ES 的配置文件elascitsearch.yml以接入 elasticvue,修改内容可以参考图中的Configure部分,修改完后重启 ES 容器即可。

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docker restart es

然后在 elasticvue 中添加 ES 集群,输入 ES 的地址http://localhost:9200,选择Basic auth输入用户名和密码,这样就可以连上我们的 ES 服务了。

Embedding 模型部署

向量检索的核心是向量,而向量是由 Embedding 模型生成的,我们可以使用一些线上的 Embedding 模型,比如 OpenAI 的 Embedding 模型,也可以自己部署 Embedding 模型。这里我们选择部署自己的 Embedding 模型,我们使用 BAAI/bge-base-en-v1.5 模型,这是一个英文 Embedding 模型,可以用于英文的向量生成。

我们使用 FastChat 来部署 Embedding 模型,FastChat 是一个模型训练、部署、评估的开发平台,不仅支持 LLM 模型,还支持 Embedding 模型,下面来介绍如何使用 FastChat 部署 Embedding 模型。

首先我们要安装 FastChat,然后通过 FastChat 来部署一个兼容 OpenAI API 的 Embedding API 服务,安装命令如下:

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pip3 install "fschat[model_worker,api]"

安装完成后,先使用 FastChat 的命令行工具来启动 controller 服务,命令如下:

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$ python3 -m fastchat.serve.controller --host 0.0.0.0
2024-01-14 18:29:43 | INFO | controller | args: Namespace(dispatch_method='shortest_queue', host='0.0.0.0', port=21001, ssl=False)
2024-01-14 18:29:43 | ERROR | stderr | INFO: Started server process [1154]
2024-01-14 18:29:43 | ERROR | stderr | INFO: Waiting for application startup.
2024-01-14 18:29:43 | ERROR | stderr | INFO: Application startup complete.
2024-01-14 18:29:43 | ERROR | stderr | INFO: Uvicorn running on http://0.0.0.0:21001 (Press CTRL+C to quit)

然后重新打开一个终端,使用 FastChat 的命令行工具来启动 worker 服务,命令如下:

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$ python3 -m fastchat.serve.model_worker --model-path BAAI/bge-base-en-v1.5 --host 0.0.0.0
2024-01-14 18:32:39 | INFO | model_worker | Loading the model ['bge-base-en-v1.5'] on worker 339a9e30 ...
2024-01-14 18:32:40 | INFO | model_worker | Register to controller
2024-01-14 18:32:40 | ERROR | stderr | INFO: Started server process [1229]
2024-01-14 18:32:40 | ERROR | stderr | INFO: Waiting for application startup.
2024-01-14 18:32:40 | ERROR | stderr | INFO: Application startup complete.
2024-01-14 18:32:40 | ERROR | stderr | INFO: Uvicorn running on http://0.0.0.0:21002 (Press CTRL+C to quit)

执行命令后,FastChat 会自动从 huggingface 上下载 BAAI/bge-base-en-v1.5 模型,下载完成后就会启动 worker 服务,worker 服务会自动连接到 controller 服务。

我们再打开一个终端,使用 FastChat 的命令行工具来启动 兼容 OpenAI API 的 API 服务,命令如下:

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$ python3 -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 8000
2024-01-14 18:37:10 | ERROR | stderr | INFO: Started server process [1405]
2024-01-14 18:37:10 | ERROR | stderr | INFO: Waiting for application startup.
2024-01-14 18:37:10 | ERROR | stderr | INFO: Application startup complete.
2024-01-14 18:37:10 | ERROR | stderr | INFO: Uvicorn running on http://0.0.0.0:8000(Press CTRL+C to quit)

服务启动后,我们可以访问http://localhost:8000/docs来查看 API 服务的 swagger 文档:

可以看到图中的/v1/embeddings接口就是我们需要调用的 Embedding 接口,我们可以通过 curl 命令来测试一下该接口,命令如下:

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curl -X 'POST' \
'https://localhost:8000/v1/embeddings' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "bge-base-en-v1.5",
"input": "hello"
}'

# 显示结果
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [0.013750563375651836, …], # 向量数据
"index": 0
}
],
"model": "bge-base-en-v1.5",
"usage": {
"prompt_tokens": 3,
"total_tokens": 3
}
}

我们在请求参数中输入模型名称和需要被向量化的文本,命令执行完成后,我们可以看到返回的结果包含了 embedding 后的向量数据,并且返回格式跟 OpenAI API 的格式是一样的。

FastChat 更多的相关部署内容可以参考 FastChat 的文档

LlamaIndex 文件加载与检索

LlamaIndex 是继 LangChain 之后另外一个 LLM 应用开发框架,整体功能以 RAG 为主,现在也慢慢在开发一些 Agent 相关的功能。该框架的主要编程语言是 Python,具有广泛的社区支持和贡献,包括众多的 forks 和 stars,表明其在开发社区中的受欢迎程度和实用性。

下面我们来介绍使用 LlamaIndex 结合 ES 进行文档加载与检索,在开始编写代码之前,我们需要安装 LlamaIndex 和 ES 的 Python 包,命令如下:

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pip install llama-index elasticsearch

Embedding 自定义类

安装完依赖包后,我们开始编写相关代码,首先我们需要创建一个自定义的 Embedding 类,这个 Embedding 类会调用我们刚才部署的 Embedding API 接口来实现文本的向量化,代码如下:

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from llama_index.embeddings.base import BaseEmbedding, Embedding
from llama_index.bridge.pydantic import PrivateAttr
from typing import Any, List

class CustomEmbeddings(BaseEmbedding):
"""Custom class for embeddings.

Args:
model_name (str): Mode for embedding.
url(str): Url for embedding model.
"""

_model_name: str = PrivateAttr()
_url: str = PrivateAttr()

def __init__(self, model_name: str, url: str, **kwargs: Any) -> None:
self._model_name = model_name
self._url = url
super().__init__(**kwargs)

@classmethod
def class_name(cls) -> str:
return "custom_embedding"

def _aget_query_embedding(self, query: str) -> Embedding:
return get_embedding(text=query, model_uid=self._model_name, url=self._url)

def _aget_text_embedding(self, text: str) -> Embedding:
return get_embedding(text=text, model_uid=self._model_name, url=self._url)

def _get_query_embedding(self, query: str) -> Embedding:
return get_embedding(text=query, model_uid=self._model_name, url=self._url)

def _get_text_embedding(self, text: str) -> Embedding:
return get_embedding(text=text, model_uid=self._model_name, url=self._url)

def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
return get_embeddings(
list_of_text=texts, model_uid=self._model_name, url=self._url
)
  • 使用 LlamaIndex 实现自定义的 Embedding 类,需要继承 BaseEmbedding 类,并实现相关的方法
  • 这里我们实现了_aget_query_embedding_aget_text_embedding_get_query_embedding_get_text_embedding_get_text_embeddings这几个方法,这几个方法会调用其他公共方法来实现文本转向量的功能。

我们再来看一下get_embeddingget_embeddings这两个方法的实现,代码如下:

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import requests

def send_request(model_uid: str, text: str, url: str):
url = f"{url}/v1/embeddings"
request_body = {"model": model_uid, "input": text}
response = requests.post(url, json=request_body)
if response.status_code != 200:
raise RuntimeError(
f"Failed to create the embeddings, detail: {_get_error_string(response)}"
)
return response.json()

def get_embedding(text: str, model_uid: str, url: str) -> Embedding:
"""Get embedding."""
text = text.replace("\n", " ")
response_data = send_request(model_uid, text, url)
return response_data["data"][0]["embedding"]

def get_embeddings(
list_of_text: List[str], model_uid: str, url: str
) -> List[Embedding]:
"""Get embeddings."""
assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."

list_of_text = [text.replace("\n", " ") for text in list_of_text]
response_data = send_request(model_uid, list_of_text, url)
return [d["embedding"] for d in response_data["data"]]

  • get_embeddingget_embeddings都使用send_request来获取文本的向量数据,不同的地方在于一个参数是字符串,一个参数是字符串数组
  • send_request方法会发起 HTTP 请求调用 Embedding API 接口来实现文本向量化
  • 参考之前的 API 返回结果,Embedding 向量保存在一个数组中
  • get_embedding获取返回结果的第一个向量数据,get_embeddings获取所有的向量数据

向量化文档

有了自定义 Embedding 类,我们就可以使用 LlamaIndex 来实现文档的向量存储了,首先我们连接 ES 数据库,代码如下:

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from llama_index.vector_stores import ElasticsearchStore
from llama_index import StorageContext

es_url = "http://{username}:{password}@localhost:9200"
index_name = "my_index"
store = ElasticsearchStore(
es_url=es_url,
index_name=index_name,
)
storage_context = StorageContext.from_defaults(vector_store=store)
  • 新建一个 ES store 来连接 ES,需要指定 ES 的地址和索引名称
  • ES 如果开启了安全认证,需要在 ES 的地址中添加用户名和密码
  • 使用 LlamaIndex 的 StorageContext 来集成 ES 的 store

我们再定义带有 Embedding 模型的 ServiceContext,代码如下:

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from llama_index import ServiceContext
from custom_embedding import CustomEmbeddings

embedding_model_url = "http://localhost:8000"
embedding_model_name = "bge-base-en-v1.5"
service_context = ServiceContext.from_defaults(
embed_model=CustomEmbeddings(
url=embedding_model_url, model_name=embedding_model_name
),
)
  • embedding_model_url 是我们刚才部署的 Embedding API 的地址,model_name 是模型名称

接着我们来将文档转换为 LlamaIndex 的 Document 对象,我们可以使用 LlamaIndex 的示例文档paul_graham_essay来做演示,这篇文章是 Paul Graham 关于他个人生涯和工作经历的回顾,代码如下:

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from llama_index import SimpleDirectoryReader

data_path = "./data" # paul_graham_essay.txt 所在的目录
documents = SimpleDirectoryReader(data_path).load_data()
  • SimpleDirectoryReader 对象可以对文件夹中的文件进行解析,txt 文件的解析不需要额外的依赖,但如果是其他格式的文件,比如 pdf,则需要安装相关的依赖 pypdf

我们将以上的对象组装在一起,示例代码如下:

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from llama_index import VectorStoreIndex

index = VectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
service_context=service_context,
)
  • 使用 VectorStoreIndex 集成 storage_context 和 service_context,并加载 documents
  • 不需要担心 ES 的索引是否已创建,如果没有该索引,LlamaIndex 会自动创建

代码执行后,我们就可以在 ES 中看到索引的文档了,我们通过 elasticvue 来查看索引的文档,如下图所示:

除了可以对整个文件夹进行加载外,我们还可以在已有的索引中添加新的文档,代码如下:

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filepath = "./data/paul_graham_essay.txt"
index = VectorStoreIndex.from_vector_store(
vector_store=store,
storage_context=storage_context,
service_context=service_context,
)
document = SimpleDirectoryReader(input_files=[filepath]).load_data()[0]
index.insert(document)
  • 在 VectorStoreIndex 中传入 ES store 来加载已有的 ES 索引
  • SimpleDirectoryReader 也可以传入单个文件路径,这样就可以加载单个文件
  • 使用 VectorStoreIndex 的 insert 方法来添加新的文档

问题检索与生成

接下来我们再使用 LlamaIndex 来对问题进行检索,代码如下:

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query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(f"response: {response}")

# 显示结果
# response: The author took philosophy courses, but found them boring.
# As a result, the author decided to switch to AI and started teaching
# themselves Lisp, which was regarded as the language of AI at the time.
# The author also reverse-engineered SHRDLU for their undergraduate thesis.
  • LlamaIndex 默认使用 OpenAI 的 LLM 来做生成,因此在执行代码之前,需要将 OPENAI_API_KEY 环境变量设置为你的 API KEY

我们询问了一个关于作者成长经历的问题,LlamaIndex 会先使用向量检索来检索相关的文档,然后再使用 LLM 来生成答案,我们可以看到 LlamaIndex 生成的答案是正确的。

如果我们将 LlamaIndex 中的 LLM 取消,那么 response 的结果会变成结合了相关文档的提示词模板,如下所示:

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service_context = ServiceContext.from_defaults(
llm=None, # 取消LLM
embed_model=CustomEmbeddings(
url=embedding_model_url, model_name=embedding_model_name
),
)
......
response = query_engine.query("What did the author do growing up?")
print(f"response: {response}")

# 显示结果
# LLM is explicitly disabled. Using MockLLM.
# response: Context information is below.
# ---------------------
# file_path: data/paul_graham_essay.txt
#
# I don't think it was entirely blabla ......
# ---------------------
# Given the context information and not prior knowledge, answer the query.
# Query: What did the author do growing up?
# Answer:
  • 只需在 ServiceContext 中添加参数llm=none即可取消默认的 OpenAI LLM
  • 其他代码与原来的一样

可以看到同样的问题,不使用 LLM 的情况下返回的结果是一个包含了相关文档的提示词模板。

在 response 对象中,我们还可以通过response.source_nodes可以获取到检索到的文档信息,文档的 JSON 信息如下:

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{
"py/object": "llama_index.schema.NodeWithScore",
"py/state": {
"__dict__": {
"node": {
"py/object": "llama_index.schema.TextNode",
"py/state": {
"__dict__": {
"metadata": {"file_path": "data/paul_graham_essay.txt",},
"hash": "72baf405cfa89677a1a409d46d58dab2f4c183adcba5602d8b01a27a05d9a7a5",
"text": "blabla...",
"start_char_idx": 53611,
"end_char_idx": 57967
},
}
},
"score": 1.0
},
}
},
{
"py/object": "llama_index.schema.NodeWithScore",
"py/state": {"__dict__": {},}
}
  • 可以看到 LlamaIndex 根据问题检索出 2 个 Node(可以把 Node 理解成文档的分块)
  • 每个 Node 有文本内容 text,匹配分数 score 等属性

LlamaIndex 默认是使用向量检索,我们也可以将其替换为其他检索方式,代码如下:

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from llama_index.vector_stores.types import VectorStoreQueryMode

# 使用关键字检索
query_engine = index.as_query_engine(
vector_store_query_mode=VectorStoreQueryMode.TEXT_SEARCH,
)

# 使用混合检索
query_engine = index.as_query_engine(
vector_store_query_mode=VectorStoreQueryMode.HYBRID,
)

更多的 LlamaIndex 用法可以参考官方文档

总结

RAG 是 LLM 技术的一个重要方向,它不仅可以解决 LLM 中存在的一些问题,而且可以帮助我们打造更高质量的 LLM 应用。本文从 ES 和 Embedding 模型的部署一步步展开,结合 LLM 框架 LlamaIndex 来实现 RAG 的检索增强生成,并介绍了在实践过程中相关的原理和注意事项。希望本文能够帮助大家更好地理解 RAG 技术,如果对文章内容有疑问或者建议,欢迎在评论区留言。

关注我,一起学习各种人工智能和 AIGC 新技术,欢迎交流,如果你有什么想问想说的,欢迎在评论区留言。

]]>
@@ -37,10 +62,10 @@ - - + +
@@ -503,27 +528,4 @@ - - Langchain ChatChat 本地知识库问答系统部署 - - https://example.com/2023/08/17/deploy-langchain-chatchat/ - 2023-08-17T03:10:02.000Z - 2023-08-19T01:42:27.850Z - -

之前写了一篇文章介绍Langchain-ChatGLM 项目的部署,最近该项目更新了0.2新版本,这个版本与之前的版本差别很大,底层的架构发生了很大的变化。该项目最早是基于 ChatGLM 这个 LLM(大语言模型)来搭建的,但后来集成的 LLM 越来越多, (我估计)项目团队也借此机会将项目名称改成了Langchain-Chatchat。版本更新之后,项目的部署方式也发生了变化,之前的部署方式已经不适用了,这里我将介绍一下新版本的部署方式。

机器配置

项目的部署需要一台 GPU 服务器,不管是云服务器还是本地服务器都可以,但是需要注意的是,服务器至少需要16G 的显存,太低的话项目会运行不起来。

关于云 GPU 服务器的选择可以参考我之前的文章,这里不再赘述。

我选择的是在 AutoDL 服务器上部署。

项目更新内容

Langchain-Chatchat 项目的更新内容可以参考这里,主要有以下几点:

使用 FastChat 提供开源 LLM 模型的 API

FastChat是一个用于训练和评估遵循指令 LLM 的框架,可以将 LLM 部署为带有标准应用程序编程接口(API)的软件服务。

这个改动带来的最大变化就是,原来是部署 1 个服务(WebUI),现在需要部署 3 个服务,分别是 LLM API 服务、Langchain Chatchat 本身的 API 服务和 WebUI 服务,这样的好处是可以将原先的内部服务都解耦出来,用户可以选择不同的服务来构建自己的应用。比如你的项目只需要用到 LLM,那么你只需要部署 LLM API 服务就可以了;或者是你想用自己的前端服务,那么你只需要部署 2 个 API 服务,然后自己写一个前端服务就可以。

接口参数根据 OpenAI API 接口形式接入

接口参数根据 OpenAI API 接口形式接入,让请求参数更加合理。比如以前对话 API 中的对话历史参数是一个二维数组,跟我们熟悉的 OpenAI API 参数差别很大,可以看下面的例子:

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# 原 Langchain ChatGLM 的对话历史参数
history: [["你好", "你好,有什么可以帮到你"], ["1 加 1 等于几", "2"]]

# OpenAI 的对话历史参数
messages: [{ "role": "user", "content": "你好" }, { "role": "assistant", "content": "你好,有什么可以帮到你" }]

# 新版 Langchain Chatchat 的对话历史参数
history: [{ "role": "user", "content": "你好" }, { "role": "assistant", "content": "你好,有什么可以帮到你" }]

可以看到原来的参数形式是一个二维数组,每轮对话都会放到一个子数组中,包含用户和 AI 的对话信息,而 OpenAI 的参数形式是一个对象数组,每个对象包含对话的角色和对话的内容,这样的数据结构程序会更容易处理,新版的参数形式改成了 OpenAI 的这种形式,虽然字段名有些不一样。

使用 Streamlit 提供 WebUI 服务

原先是用Gadio来编写 WebUI 页面的,现在改成了StreamlitStreamlit提供了创建更复杂应用程序的能力,而且它提供了丰富的组件库,并支持自定义组件,可以看到新版的页面更加美观。

项目中默认 LLM 模型改为 CHATGLM2-6B

原来默认用的 LLM 是 ChatGLM-6B,现在改成了 ChatGLM2-6B,ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本,在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上,增加了更强大的性能,更长的上下文,更高效的推理和更开发的协议。

默认 Embedding 模型改为 M3E-BASE

原来的 Embedding 模型是 GanymedeNil/text2vec-large-chinese,现在改成了moka-ai/m3e-base,TEXT2VEC 和 M3E-BASE 都支持中文,但 M3E-BASE 对英文的支持更好,文件加载方式与文段划分方式也有调整,后续将重新实现上下文扩充,并增加可选设置。

项目部署

下面我们开始来部署 Langchain-Chatchat 项目。

项目初始化

下载项目代码,同时安装依赖,注意新版的依赖文件有 3 个,一个是 API 服务的依赖文件 requirements_api.txt,一个是 WebUI 服务的依赖文件 requirements_webui.txt,还有一个是整个项目的依赖文件 requirements.txt,因为我们前后端都要部署,所以我们要用整个项目的依赖文件来安装依赖:

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git clone https://github.com/chatchat-space/Langchain-Chatchat.git
cd Langchain-Chatchat
pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple # 照例加上百度源提高下载速度

如果是第一次运行本项目,需要通过以下命令来初始化知识库:

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$ python init_database.py --recreate-vs

模型下载

下载 ChatGLM2-6B 和 M3E-BASE 模型,这两个模型都在 HuggingFace 上,可以直接克隆仓库:

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# 开启大文件下载
git lfs install
# 下载 ChatGLM2-6B
git clone https://huggingface.co/THUDM/chatglm2-6b
# 下载 M3E-BASE
git clone https://huggingface.co/moka-ai/m3e-base

项目配置修改

模型下载完成后接着修改配置文件,新版的项目提供了一个配置文件模板model_config.py.example文件,需要将其复制一份并重命名为model_config.py,然后修改里面的配置项:

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cd Langchain-Chatchat
cp configs/model_config.py.example configs/model_config.py

然后修改model_config.py文件中的配置项,将模型的路径改成你自己的路径:

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embedding_model_dict = {
- "m3e-base": "moka-ai/m3e-base",
+ "m3e-base": "/你的下载路径/m3e-base",

llm_model_dict = {
"chatglm2-6b": {
- "local_model_path": "THUDM/chatglm2-6b",
+ "local_model_path": "/你的下载地址/chatglm2-6b",

LLM API 服务部署

执行以下命令启动 LLM API 服务:

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python server/llm_api.py

注意: llm_api.py 中的openai_api_port端口要和configs/model_config.py中 LLM 的服务端口保持一致,否则接口调用会报错。另外如果你是在AutoDL的服务器上部署的话,服务器的8888端口会被Juypter服务占用,建议改成其他端口,代码实例如下:

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# llm_api.py
openai_api_port = 7777 # 如果你是用AutoDL服务器的话,这个端口不要用8888

# configs/model_config.py

"chatglm-6b": {
"local_model_path": "/你的下载地址/chatglm-6b",
"api_base_url": "http://localhost:7777/v1", # 这里的端口要和上面的openai_api_port保持一致
"api_key": "EMPTY"
},

服务启动后,实际会启动 3 个子服务,端口分别是8888(上面我们改成了7777)、2000120002,其中8888端口的服务是 LLM 的接口服务,里面提供了文本推理、embedding、token 检查等接口,跟 OpenAI 的接口十分相似。其他 2 个端口的服务我理解是对 LLM 服务的一些监控和管理。

如果启动服务后发现调用接口异常,可以在本地通过 curl 命令对8888端口的服务进行测试。

API 服务部署

执行以下命令启动 API 服务:

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python server/api.py

这个服务主要提供了 Langchain Chatchat 的功能接口,包括 LLM 问答、知识库问答、知识库管理等接口。同样地,我们也可以通过 curl 命令对该服务进行测试,服务的端口默认是 7861

WebUI 服务部署

最后是启动 WebUI 服务,执行以下命令启动 WebUI 服务,默认的端口号是8501,可以通过--server.port参数来修改端口:

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streamlit run webui.py --server.port 6006

我在启动 WebUI 服务时还遇到一个问题,就是启动服务时报了protobuf这个依赖包版本不对的错误,导致 Web 服务启动失败,后面我将protobuf的版本降级成了3.20.3就可以正常启动了。

启动后可以通过浏览器访问 WebUI 服务,界面如下所示:

其他未测试的部署方式

因为项目新版本才刚发布,我还没有对所有部署方式进行试用,比如对于 API 服务,除了单独启动服务外,还有一个api_allinone.py的文件,我理解是用来同时启动 LLM API 服务和 API 服务的。还有一个webui_allinone.py的文件,应该是用来同时启动所有服务的。这些功能可以让我们更加方便的部署项目,大家可以自行验证这些部署方式。

由于新版本刚发布,新版本的功能可能存在不少问题,在项目的 issue 区可以看到大量新版本的问题,包括服务启动不起来,或者是启动起来之后 Web 页面没有反应等问题,这些问题应该都是可以解决的,只是需要再给项目团队一些时间。

总结

AI 知识库问答系统是一个很有前景的方向,它是对传统知识库系统的一种升级,相信在未来会有很多垂直领域的公司会用到这个技术。从 Langchain Chatchat 项目的发展过程上来看,项目从原来的一个 demo 级别的项目,逐渐向一个完整的产品迈进,在架构、UI 界面、LLM 集成方面也在不断完善,我相信这是一个未来可期的项目。而对于那些还在用老版本的小伙伴,建议尽快升级到新版本,因为项目团队以后的主要精力都会放在新版本的开发上,老版本的功能可能不会再更新了。

关注我,一起学习各种人工智能和 AIGC 新技术,欢迎交流,如果你有什么想问想说的,欢迎在评论区留言。

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2023

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Azure OpenAI 服务开通及使用

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    标签云

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    +

    + Rerank 模型的部署及使用 +

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    Published on: +
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    Rerank 在 RAG(Retrieval-Augmented Generation)过程中扮演了一个非常重要的角色,普通的 RAG 可能会检索到大量的文档,但这些文档可能并不是所有的都跟问题相关,而 Rerank 可以对文档进行重新排序和筛选,让相关的文档排在前面,从而提高 RAG 的效果。本文将介绍使用 HuggingFace 的 Text Embedding Inherence 工具部署 Rerank 模型,以及演示如何在 LlamaIndex 的 RAG 中加入 Rerank 功能。

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    @@ -411,33 +438,6 @@

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    - 好玩到停不下来的 DALL-E 3 -

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    最近 ChatGPT 对 Plus 用户逐步开放一些多模态的功能,包括 DALL-E 3(图像生成)、 GPT-4V(图像识别),等,很多网友乐此不疲地对这些新功能进行试用,目前已经解锁了不少有趣的玩法,笔者将这些好玩的功能进行了整理并介绍给大家,希望能给大家带来一些灵感。

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    diff --git a/page/10/index.html b/page/10/index.html index b183f426c..f89119583 100644 --- a/page/10/index.html +++ b/page/10/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,32 @@

    标签云

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    +

    + TDD已死之论战 +

    +
    +
    +
    Published on: +
    +
    Tags: + +agile TDD +
    +
    + + +

    公司最近组织了一次关于TDD的培训,从而引发了同事关于TDD的一些讨论,这让我想起了一年前DHH(Ruby On Rails之父)那篇有名的文章《TDD is dead. Long live testing.》,突然想聊一下关于TDD的一些事情。

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    -

    - -
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    - 使用Openfire和Smack进行即时通讯消息开发 -

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    Openfire是由Ignite Realtime公司用Java开发的一个开源即时通讯服务器,基于XMPP协议(Jabber)进行消息交互,最新版本是3.9.3。该公司旗下有多个Java客户端可供使用,较常使用的是Smack,最新的版本是4.1.0,最新的版本与以前的版本相比有较大改动,下面我们就来介绍一下Openfire和Smack的使用。

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    diff --git a/page/11/index.html b/page/11/index.html index 5c8af82f5..6cbea9509 100644 --- a/page/11/index.html +++ b/page/11/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,32 @@

    标签云

    +
    +

    + 使用Openfire和Smack进行即时通讯消息开发 +

    +
    +
    +
    Published on: +
    +
    Tags: + +openfire smack +
    +
    + + +

    Openfire是由Ignite Realtime公司用Java开发的一个开源即时通讯服务器,基于XMPP协议(Jabber)进行消息交互,最新版本是3.9.3。该公司旗下有多个Java客户端可供使用,较常使用的是Smack,最新的版本是4.1.0,最新的版本与以前的版本相比有较大改动,下面我们就来介绍一下Openfire和Smack的使用。

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    - 如何用iso文件创建Vagrant的Box -

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    Tags: - -vagrant veewee -
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    虽然Vagrant官网上已经有了很多OS的box,比如Ubuntu,CentOS,Debian等,但像RHEL这种不免费的OS,Vagrant上面就没有它的box,如果需要用到RHEL的box,我们就需要自己来制作。下面讲下从iso文件到box文件的一个制作过程。

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    diff --git a/page/12/index.html b/page/12/index.html index 7866b3262..986a424c5 100644 --- a/page/12/index.html +++ b/page/12/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,32 @@

    标签云

    +
    +

    + 如何用iso文件创建Vagrant的Box +

    +
    +
    +
    Published on: +
    +
    Tags: + +vagrant veewee +
    +
    + + +

    虽然Vagrant官网上已经有了很多OS的box,比如Ubuntu,CentOS,Debian等,但像RHEL这种不免费的OS,Vagrant上面就没有它的box,如果需要用到RHEL的box,我们就需要自己来制作。下面讲下从iso文件到box文件的一个制作过程。

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    - Ceph认证原理 -

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    Tags: - -ceph auth -
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    为识别用户和防范攻击,ceph提供了cephx来进行用户和后台进程认证。

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    diff --git a/page/13/index.html b/page/13/index.html index 61221f889..35f2e1933 100644 --- a/page/13/index.html +++ b/page/13/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,32 @@

    标签云

    +
    +

    + Ceph认证原理 +

    +
    +
    +
    Published on: +
    +
    Tags: + +ceph auth +
    +
    + + +

    为识别用户和防范攻击,ceph提供了cephx来进行用户和后台进程认证。

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    - - - diff --git a/page/14/index.html b/page/14/index.html index ac35d53b6..fe4e9c1a0 100644 --- a/page/14/index.html +++ b/page/14/index.html @@ -132,6 +132,10 @@

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    - 番茄工作法和Bullet journal笔记法 -

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    番茄工作法是一套时间管理方法,Bullet journal是一套笔记记录的方法,最近通过学习这2个方法来管理自己的时间,让自己战胜拖延症。

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    diff --git a/page/15/index.html b/page/15/index.html index 84f95e141..a055b027a 100644 --- a/page/15/index.html +++ b/page/15/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,33 @@

    标签云

    +
    +

    + 番茄工作法和Bullet journal笔记法 +

    +
    +
    +
    Published on: +
    +
    Tags: + +time manage +
    +
    + + + +

    番茄工作法是一套时间管理方法,Bullet journal是一套笔记记录的方法,最近通过学习这2个方法来管理自己的时间,让自己战胜拖延症。

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    +
    - - -
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    - facebook的工程师文化——《打造facebook》读后感 -

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    Published on: -
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    在今年北京的QCon大会上听了facebook早期中国籍工程师王淮的演讲,受益匪浅,主题是如何打造高效能团队,主要介绍他在facebook的一些经历和管理上的经验分享。现在的他是一名天使投资人,投资的领域有移动互联,支付,云应用等。他还写了《打造facebook》一书,讲了facebook的公司文化和他在其中的开发管理等经历。

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    diff --git a/page/16/index.html b/page/16/index.html index 75a7024f1..0648d647f 100644 --- a/page/16/index.html +++ b/page/16/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,31 @@

    标签云

    +
    +

    + facebook的工程师文化——《打造facebook》读后感 +

    +
    +
    +
    Published on: +
    +
    Tags: + +facebook +
    +
    + +

    在今年北京的QCon大会上听了facebook早期中国籍工程师王淮的演讲,受益匪浅,主题是如何打造高效能团队,主要介绍他在facebook的一些经历和管理上的经验分享。现在的他是一名天使投资人,投资的领域有移动互联,支付,云应用等。他还写了《打造facebook》一书,讲了facebook的公司文化和他在其中的开发管理等经历。

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    - IDEA12使用初体验 -

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    最近将开发工具IDEA升级到了12.0版本,被它新的UI界面深深吸引,看下面,很酷哦~

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    diff --git a/page/17/index.html b/page/17/index.html index 4109428ea..f3018ba57 100644 --- a/page/17/index.html +++ b/page/17/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,31 @@

    标签云

    +
    +

    + IDEA12使用初体验 +

    +
    +
    +
    Published on: +
    +
    Tags: + +Idea +
    +
    + +

    最近将开发工具IDEA升级到了12.0版本,被它新的UI界面深深吸引,看下面,很酷哦~

    + + + + +
    + +
    +
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    - 在JavaScript中进行文件处理,第四部分:对象URLs -

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    Tags: - -javascript -
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    译注:原文是《JavaScript高级程序设计》的作者Nicholas Zakas写的,本翻译纯属为自己学习而做,仅供参考。原文链接:[这里][url1]
    [url1]: http://www.nczonline.net/blog/2012/05/31/working-with-files-in-javascript-part-4-object-urls

    -
    -

    学习到这里,你已经了解在传统方式中如何使用文件,你可以上传文件到服务端,可以从磁盘上读取文件,这些都是最常见的文件处理方式。但是,有一种全新的文件处理方式可以简化这些常见的任务,这就是使用对象URLs。

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    diff --git a/page/18/index.html b/page/18/index.html index f96ea7260..772b4921d 100644 --- a/page/18/index.html +++ b/page/18/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,33 @@

    标签云

    +
    +

    + 在JavaScript中进行文件处理,第四部分:对象URLs +

    +
    +
    +
    Published on: +
    +
    Tags: + +javascript +
    +
    + +

    译注:原文是《JavaScript高级程序设计》的作者Nicholas Zakas写的,本翻译纯属为自己学习而做,仅供参考。原文链接:[这里][url1]
    [url1]: http://www.nczonline.net/blog/2012/05/31/working-with-files-in-javascript-part-4-object-urls

    +
    +

    学习到这里,你已经了解在传统方式中如何使用文件,你可以上传文件到服务端,可以从磁盘上读取文件,这些都是最常见的文件处理方式。但是,有一种全新的文件处理方式可以简化这些常见的任务,这就是使用对象URLs。

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    - junit里面Test case的执行顺序 -

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    这里讨论的是junit在ant运行的情况,其他build工具应该也适用,但具体没试验过。

    -

    首先运行junit时是按照脚本中文件夹的顺序执行,如下脚本会先执行test1目录下的测试,其实是test2目录的,最后是test3目录的。

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    最新文章

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    标签云

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    + junit里面Test case的执行顺序 +

    +
    +
    +
    Published on: +
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    Tags: + +junit ant +
    +
    + +

    这里讨论的是junit在ant运行的情况,其他build工具应该也适用,但具体没试验过。

    +

    首先运行junit时是按照脚本中文件夹的顺序执行,如下脚本会先执行test1目录下的测试,其实是test2目录的,最后是test3目录的。

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    +

    + 好玩到停不下来的 DALL-E 3 +

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    Published on: +
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    最近 ChatGPT 对 Plus 用户逐步开放一些多模态的功能,包括 DALL-E 3(图像生成)、 GPT-4V(图像识别),等,很多网友乐此不疲地对这些新功能进行试用,目前已经解锁了不少有趣的玩法,笔者将这些好玩的功能进行了整理并介绍给大家,希望能给大家带来一些灵感。

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    - Langchain ChatChat 本地知识库问答系统部署 -

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    之前写了一篇文章介绍Langchain-ChatGLM 项目的部署,最近该项目更新了0.2新版本,这个版本与之前的版本差别很大,底层的架构发生了很大的变化。该项目最早是基于 ChatGLM 这个 LLM(大语言模型)来搭建的,但后来集成的 LLM 越来越多, (我估计)项目团队也借此机会将项目名称改成了Langchain-Chatchat。版本更新之后,项目的部署方式也发生了变化,之前的部署方式已经不适用了,这里我将介绍一下新版本的部署方式。

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    diff --git a/page/20/index.html b/page/20/index.html index 374b10daf..e04e48c3e 100644 --- a/page/20/index.html +++ b/page/20/index.html @@ -132,6 +132,10 @@

    最新文章

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    标签云

    + +

    在junit中添加fail--有test失败即build failed diff --git a/page/3/index.html b/page/3/index.html index 867e2070b..26178398d 100644 --- a/page/3/index.html +++ b/page/3/index.html @@ -132,6 +132,10 @@

    最新文章

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    标签云

    +
    +

    + Langchain ChatChat 本地知识库问答系统部署 +

    +
    +
    +
    Published on: +
    +
    Tags: + +langchain chatglm +
    +
    + + + +

    之前写了一篇文章介绍Langchain-ChatGLM 项目的部署,最近该项目更新了0.2新版本,这个版本与之前的版本差别很大,底层的架构发生了很大的变化。该项目最早是基于 ChatGLM 这个 LLM(大语言模型)来搭建的,但后来集成的 LLM 越来越多, (我估计)项目团队也借此机会将项目名称改成了Langchain-Chatchat。版本更新之后,项目的部署方式也发生了变化,之前的部署方式已经不适用了,这里我将介绍一下新版本的部署方式。

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    - Prompt 提示词在开发中的使用 -

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    OpenAI 的 ChatGPT 是一种领先的人工智能模型,它以其出色的语言理解和生成能力,为我们提供了一种全新的与机器交流的方式。但不是每个问题都可以得到令人满意的答案,如果想得到你所要的回答就要构建好你的提示词 Prompt。本文将探讨 Prompt 提示词在开发中的应用和优势,以及如何利用它来解决常见问题和加速开发过程。无论是初学者还是经验丰富的开发人员,Prompt 提示词都能为我们带来更高效的开发体验。

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    diff --git a/page/4/index.html b/page/4/index.html index 359982f44..bdf596e8c 100644 --- a/page/4/index.html +++ b/page/4/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,33 @@

    标签云

    +
    +

    + Prompt 提示词在开发中的使用 +

    +
    +
    +
    Published on: +
    +
    Tags: + +chatgpt prompt +
    +
    + + + +

    OpenAI 的 ChatGPT 是一种领先的人工智能模型,它以其出色的语言理解和生成能力,为我们提供了一种全新的与机器交流的方式。但不是每个问题都可以得到令人满意的答案,如果想得到你所要的回答就要构建好你的提示词 Prompt。本文将探讨 Prompt 提示词在开发中的应用和优势,以及如何利用它来解决常见问题和加速开发过程。无论是初学者还是经验丰富的开发人员,Prompt 提示词都能为我们带来更高效的开发体验。

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    - React Navtive App 自动打包实践指南 -

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    React Native 创建项目有 2 种方式,一种是通过 Expo 框架创建,这种项目可以通过 Expo 提供的命令进行打包;另外一种是通过 React Native 原生命令生成的项目,需要使用 GradleXcode 这些原生工具打包,虽然麻烦了一些,但是灵活性更高,今天讨论的自动打包主要是基于后者。

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    diff --git a/page/5/index.html b/page/5/index.html index 96dee255f..5fcfe5955 100644 --- a/page/5/index.html +++ b/page/5/index.html @@ -132,6 +132,10 @@

    最新文章

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    标签云

    +
    +

    + React Navtive App 自动打包实践指南 +

    +
    +
    +
    Published on: +
    + +
    + + + +

    React Native 创建项目有 2 种方式,一种是通过 Expo 框架创建,这种项目可以通过 Expo 提供的命令进行打包;另外一种是通过 React Native 原生命令生成的项目,需要使用 GradleXcode 这些原生工具打包,虽然麻烦了一些,但是灵活性更高,今天讨论的自动打包主要是基于后者。

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    - 使用 Expo 打包 iOS 企业版 APP -

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    Expo 为 React Native 开发提供了不少便利性,其中有个最方便的功能就是 APP 打包,Expo 让开发者无需使用 Xcode 和 Android Studio 就可以完成 APP 的打包工作。

    -

    下面就来介绍一下如何使用 Expo 打包 iOS 企业版的 APP。

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    diff --git a/page/6/index.html b/page/6/index.html index b0125f563..b580225fe 100644 --- a/page/6/index.html +++ b/page/6/index.html @@ -132,6 +132,10 @@

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    标签云

    +
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    + 使用 Expo 打包 iOS 企业版 APP +

    +
    +
    +
    Published on: +
    + +
    + + + +

    Expo 为 React Native 开发提供了不少便利性,其中有个最方便的功能就是 APP 打包,Expo 让开发者无需使用 Xcode 和 Android Studio 就可以完成 APP 的打包工作。

    +

    下面就来介绍一下如何使用 Expo 打包 iOS 企业版的 APP。

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    - 如何运行 react16 之后测试 -

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    React 升级到版本 16 之后,各方面都有不少改进,最重要的是完全向后兼容,功能代码几乎不用怎么修改就可以使用新版本。但是测试代码就不一定了,如果你是使用 enzyme 跑测试的话,以前的测试可能会跑不起来,那么需要如何修改呢?下面我们就来看一下吧。

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    diff --git a/page/7/index.html b/page/7/index.html index 4cab8f51b..7f235af3f 100644 --- a/page/7/index.html +++ b/page/7/index.html @@ -132,6 +132,10 @@

    最新文章

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    标签云

    +
    +

    + 如何运行 react16 之后测试 +

    +
    +
    +
    Published on: +
    +
    Tags: + +react test enzyme +
    +
    + + + +

    React 升级到版本 16 之后,各方面都有不少改进,最重要的是完全向后兼容,功能代码几乎不用怎么修改就可以使用新版本。但是测试代码就不一定了,如果你是使用 enzyme 跑测试的话,以前的测试可能会跑不起来,那么需要如何修改呢?下面我们就来看一下吧。

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    - 如何安全地使用 React 的 context -

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    在自己的 React 项目中使用了大量的 context,虽然知道这是一个不推荐使用的特性,但还是用了很多,想看看别人是怎么使用 context,所以翻译了下面这篇文章,注意文章中引用了嵌套的twitter内容,所以访问时最好是翻墙访问,以免影响阅读。

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    diff --git a/page/8/index.html b/page/8/index.html index 1042ab746..ba88e0837 100644 --- a/page/8/index.html +++ b/page/8/index.html @@ -132,6 +132,10 @@

    最新文章

    @@ -170,6 +170,33 @@

    标签云

    +
    +

    + 如何安全地使用 React 的 context +

    +
    +
    +
    Published on: +
    +
    Tags: + +react +
    +
    + + + +

    在自己的 React 项目中使用了大量的 context,虽然知道这是一个不推荐使用的特性,但还是用了很多,想看看别人是怎么使用 context,所以翻译了下面这篇文章,注意文章中引用了嵌套的twitter内容,所以访问时最好是翻墙访问,以免影响阅读。

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    - 如何写好办公室邮件 -

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    一封电子邮件可以让人看出你的职业素养和专业水平,以前我在平安科技公司工作,在那里沟通主要是通过电子邮件的方式来进行,所以我也学到了一些关于电子邮件的规范和注意事项,在这里分享给大家。

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    diff --git a/page/9/index.html b/page/9/index.html index 8d1e7d77e..2388721d3 100644 --- a/page/9/index.html +++ b/page/9/index.html @@ -132,6 +132,10 @@

    最新文章

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    标签云

    +
    +

    + 如何写好办公室邮件 +

    +
    +
    +
    Published on: +
    +
    Tags: + +email +
    +
    + + +

    一封电子邮件可以让人看出你的职业素养和专业水平,以前我在平安科技公司工作,在那里沟通主要是通过电子邮件的方式来进行,所以我也学到了一些关于电子邮件的规范和注意事项,在这里分享给大家。

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    - TDD已死之论战 -

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    公司最近组织了一次关于TDD的培训,从而引发了同事关于TDD的一些讨论,这让我想起了一年前DHH(Ruby On Rails之父)那篇有名的文章《TDD is dead. Long live testing.》,突然想聊一下关于TDD的一些事情。

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    diff --git a/tags/elasticsearch/index.html b/tags/elasticsearch/index.html index 71393d7f5..f2dab6456 100644 --- a/tags/elasticsearch/index.html +++ b/tags/elasticsearch/index.html @@ -134,7 +134,7 @@

    1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/tags/embdding/index.html b/tags/embedding/index.html similarity index 97% rename from tags/embdding/index.html rename to tags/embedding/index.html index 515088c6b..f5a9ff3d9 100644 --- a/tags/embdding/index.html +++ b/tags/embedding/index.html @@ -3,7 +3,7 @@ - Tag: embdding - Hacker and Geeker's Way + Tag: embedding - Hacker and Geeker's Way @@ -134,7 +134,7 @@

    1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/tags/llamaindex/index.html b/tags/llamaindex/index.html index 97673fe0f..bd3bc8fb5 100644 --- a/tags/llamaindex/index.html +++ b/tags/llamaindex/index.html @@ -128,13 +128,27 @@

    2024

    + +

    使用 LlamaIndex 结合 Eleasticsearch 进行 RAG 检索增强生成

    1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/tags/rag/index.html b/tags/rag/index.html index 384e6b453..d6427b400 100644 --- a/tags/rag/index.html +++ b/tags/rag/index.html @@ -134,7 +134,7 @@

    1月 13 -elasticsearch llamaindex embdding rag +elasticsearch llamaindex rag embedding Comments diff --git a/tags/rerank/index.html b/tags/rerank/index.html new file mode 100644 index 000000000..24e6ce798 --- /dev/null +++ b/tags/rerank/index.html @@ -0,0 +1,185 @@ + + + + + + Tag: rerank - Hacker and Geeker's Way + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    2024

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    2024

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