Python - 深度学习系列31 - ollama的搭建与使用

说明

做这个的主要目的是为了搭建Langchain的本地环境,使用LangChain让LLM具备调用自定义函数的功能。

内容

1 安装server

以下将ollama的安装方式,以及使用做一个简单的说明(记录)。之前对这个工具没有了解,只是从快速实践的角度上,给到一个参考。

最初是奔着LLM调用自定义函数/API这个功能去的,快速看下来,一个比较可行的方案是LangChain。

参考页面

在这里插入图片描述

后来大致明白, langchain是一个前端的使用包, langserver则是后端服务。先搭好服务,然后才能在前端使用。

教程里的几种方式,都需要一个大语言后端支持,为了避免后续的麻烦,所以我决定搭建一下ollama。
在这里插入图片描述
然后就跳到了ollama的页面

看起来ollama支持的系统还是比较全面的
在这里插入图片描述

1.1 苹果

实测:苹果的下载非常快… 秒级。展开后大约435M。
在这里插入图片描述
如要使用之前,需要在终端上先进行拉取,命令就是上面那个。
然后执行测试

import ollama
response = ollama.chat(model='llama2', messages=[
  {
    'role': 'user',
    'content': '解析出收件人地点、公司、收件人和收件人电话\n帮我寄到上海国金中心中心33F, ABC公司,Bikky收就行,电话号码13566778899。我的电话是18988998899,上海杨浦区。',
  },
])
print(response['message']['content'])

经过分析,可以知道:

* 收件人地点:上海市,特别是杨浦区
* 公司名称:ABC公司
* 收件人姓名:Bikky
* 收件人电话号码:13566778899
* 你的电话号码:18988998899,也在上海市,但不同区。

在执行效率上,我的苹果(m1 pro),配置也不算低了,但是执行效率上还是比2080TI慢了2~3倍。
在这里插入图片描述

mac m1 pro: 平均8[3.5188119411468506,
 5.56521201133728,
 7.0565900802612305,
 11.417732238769531,
 7.563968896865845,
 9.987686157226562,
 6.56359601020813,
 6.939379930496216,
 9.785239219665527,
 11.655837059020996]
In [10]: np.mean(time_list)
Out[10]: 8.005405354499818

2080Ti:平均2.9[2.193615674972534,
 2.8509912490844727,
 2.972665786743164,
 2.2655117511749268,
 3.038464069366455,
 4.976086378097534,
 3.4014697074890137,
 2.209334373474121,
 2.832230567932129,
 2.2567901611328125]
np.mean(time_list)
2.8997159719467165

在这里插入图片描述
以下是可拉取的模型,最大的70B library
在这里插入图片描述

1.2 linux安装

执行脚本

curl -fsSL https://ollama.com/install.sh | sh

启动服务

ollama serve

执行包的安装

pip3 install ollama -i https://mirrors.aliyun.com/pypi/simple/
pip3 install langchain_community -i https://mirrors.aliyun.com/pypi/simple/
pip3 install beautifulsoup4 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install faiss-cpu -i https://mirrors.aliyun.com/pypi/simple/
pip3 install langchain_text_splitters -i https://mirrors.aliyun.com/pypi/simple/
pip3 install langchain -i https://mirrors.aliyun.com/pypi/simple/

然后就可以使用了。
这种方法一般用在没有绝对控制权的情况,例如租用的算力主机。

1.3 docker安装

这种方式最为简便,也最为有用,但是要求对主机具有绝对控制权。

直接拉取最新版本

docker pull ollama/ollama

然后启动,考虑到server拉取模型会占据很大空间,所以把一个大的数据盘挂到root下。ollama下载的文件会存在root的 .ollama隐藏文件夹下面。

docker run -d \
 --name=ollama01 \
 -v /etc/localtime:/etc/localtime  \
 -v /etc/timezone:/etc/timezone\
 -v /etc/hostname:/etc/hostname \
 -v /data:/root \
 -e "LANG=C.UTF-8"\
 -p 11434:11434\
 -w /workspace \
 --gpus all \
 ollama/ollama

本地docker安装,在第一次启动时可能会碰到一些问题,

└─ $ docker run  -it --rm --gpus=all registry.cn-hangzhou.aliyuncs.com/andy08008/pytorch_jupyter:v8 bash
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

需要做一些连通显卡和docker的操作
1 添加源

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

2 安装: nvidia-container-toolkit

sudo apt-get install -y nvidia-container-toolkit

3 重启:通常也就是在这里,租用的机器要么没有systemd,要么不把这个权限给你。

systemctl restart docker

这个服务比较有趣的一点是,ollama server会自动进行显存的管理

空闲时:

在这里插入图片描述
当使用模型预测时

import ollama
response = ollama.chat(model='llama2', messages=[
  {
    'role': 'user',
    'content': '解析出收件人地点、公司、收件人和收件人电话\n帮我寄到上海国金中心中心33F, ABC公司,Bikky收就行,电话号码13566778899。我的电话是18988998899,上海杨浦区。',
  },
])
print(response['message']['content'])

 Based on the information provided, here is a breakdown of the recipient's details:

* Recipient's location: 上海国金中心 (Shanghai Gold Center) in the 33F floor.
* Company name: ABC公司 (ABC Company).
* Recipient's name: Bikky收就行 (Bikky receives).
* Recipient's phone number: 13566778899.
* Your phone number: 18988998899, located in 上海杨浦区 (Shanghai Yangpu District).

Please note that the above information is based on the text you provided and may not be accurate or up-to-date.

默认使用llama2-7b模型,调用时显存会被占用。
在这里插入图片描述
如果过一会不用,显存会自动清除。而且如果我们再使用另一个模型时,server会自动切换模型,把之前的清掉,然后载入新的,这样显存就不会爆。

import ollama
response = ollama.chat(model='llama2:13b', messages=[
  {
    'role': 'user',
    'content': '解析出收件人地点、公司、收件人和收件人电话\n帮我寄到上海国金中心中心33F, ABC公司,Bikky收就行,电话号码13566778899。我的电话是18988998899,上海杨浦区。',
  },
])
print(response['message']['content'])

OK! Here's the analysis of the information you provided:

1. Receiver's location: Shanghai, China (based on the address "上海国金中心中心33F")
2. Company name: ABC Company (based on the address "上海国金中心中心33F")
3. Receiver's name: Bikky (based on the name "Bikky收")
4. Receiver's contact information: Phone number 13566778899 (based on the phone number in the address)
5. Sender's location: Shanghai Yangpu District (based on the phone number 18988998899)
6. Sender's name: Not provided

I hope this helps! Let me know if you have any other questions.

在这里插入图片描述

2 langchain实验

先快速跟着教程走一遍

1 载入大模型,问一个简单的问题: how can langsmith help with testing?

from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
llm.invoke("how can langsmith help with testing?")

Langsmith is a tool that can be used to test and validate the correctness of language models. Here are some ways in which Langsmith can help with testing:

1. **Text generation**: Langsmith can be used to generate text samples that can be used to test the language model's ability to produce coherent and contextually relevant text. By comparing the generated text to a reference sample, Langsmith can evaluate the model's performance in terms of fluency, coherence, and relevance.
2. **Text completion**: Langsmith can be used to test the language model's ability to complete partial sentences or phrases. By providing a starting point for the model and evaluating its output, Langsmith can assess the model's ability to capture the context and produce coherent text.
3. **Sentiment analysis**: Langsmith can be used to test the language model's ability to classify text as positive, negative, or neutral. By providing a dataset of labeled text samples and evaluating the model's performance, Langsmith can assess its ability to accurately classify sentiment.
4. **Named entity recognition**: Langsmith can be used to test the language model's ability to identify named entities in text, such as people, organizations, and locations. By providing a dataset of labeled text samples and evaluating the model's performance, Langsmith can assess its ability to accurately identify named entities.
5. **Question answering**: Langsmith can be used to test the language model's ability to answer questions based on a given context or input. By providing a dataset of labeled question-answer pairs and evaluating the model's performance, Langsmith can assess its ability to accurately answer questions.
6. **Dialogue generation**: Langsmith can be used to test the language model's ability to engage in coherent and contextually relevant dialogue. By providing a dataset of labeled dialogue samples and evaluating the model's performance, Langsmith can assess its ability to produce natural-sounding dialogue.
7. **Multi-task evaluation**: Langsmith can be used to test the language model's ability to perform multiple tasks simultaneously, such as language translation, sentiment analysis, and named entity recognition. By providing a dataset of labeled text samples and evaluating the model's performance across multiple tasks, Langsmith can assess its ability to handle multi-tasking.

By using Langsmith to test these aspects of language models, developers can gain a better understanding of their strengths and weaknesses, and make informed decisions about how to improve them.

2 使用prompt模型进行修改

from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are world class technical documentation writer."),
    ("user", "{input}")
])

3 组成链,并使用链提问。感觉风格有点变,但内容好坏不确定。

chain = prompt | llm 
print(chain.invoke({"input": "how can langsmith help with testing?"}))
As a world-class technical documentation writer, I must say that Langsmith is an excellent tool for testing! Here are some ways in which Langsmith can help with testing:

1. Automated Testing: Langsmith provides automated testing capabilities, allowing you to create and run tests without any manual intervention. This saves time and effort, while also ensuring consistency and accuracy in your test results.
2. Customizable Tests: With Langsmith, you can create customizable tests tailored to your specific needs. You can define the test cases, question types, and evaluation criteria to suit your requirements.
3. Integration with Existing Tools: Langsmith integrates seamlessly with popular testing tools such as JIRA, TestRail, and PractiTest. This enables you to create, run, and manage tests within a single platform, streamlining your testing process.
4. Collaborative Testing: Langsmith supports collaborative testing, allowing multiple team members to work together on a test project. This fosters collaboration and coordination among team members, ensuring that everyone is on the same page and working towards the same goal.
5. Real-time Feedback: Langsmith provides real-time feedback on test results, enabling you to identify areas of improvement immediately. This helps you to rectify errors early on and ensure that your tests are accurate and reliable.
6. Test Case Management: Langsmith offers a comprehensive test case management system, which enables you to organize, track, and maintain your test cases. This helps you to keep your tests organized, up-to-date, and easily accessible.
7. Reporting and Analytics: Langsmith provides detailed reporting and analytics on test results, allowing you to identify trends, strengths, and weaknesses in your testing process. This enables you to optimize your testing strategy and improve overall quality.
8. Integration with Agile Methodologies: Langsmith is designed to work seamlessly with agile methodologies such as Scrum and Kanban. This ensures that your testing activities are aligned with the rest of your development process, enabling you to deliver high-quality software quickly and efficiently.
9. Customizable Workflows: Langsmith allows you to create customizable workflows tailored to your specific needs. This enables you to streamline your testing process and ensure that it aligns with your project's unique requirements.
10. User-Friendly Interface: Langsmith boasts a user-friendly interface, making it easy for team members to use and navigate the platform. This reduces the learning curve and ensures that everyone can use the platform effectively.

In summary, Langsmith is an excellent tool for testing as it provides automated testing capabilities, customizable tests, integration with existing tools, collaborative testing, real-time feedback, test case management, reporting and analytics, integration with agile methodologies, customizable workflows, and a user-friendly interface. These features work together to create a comprehensive and efficient testing platform that can help you deliver high-quality software quickly and efficiently.

4 继续加链。应该是对答案做了后除了,但也没太看出差别

from langchain_core.output_parsers import StrOutputParser

output_parser = StrOutputParser()
chain = prompt | llm | output_parser
print(chain.invoke({"input": "how can langsmith help with testing?"}))

As a world-class technical documentation writer, I can help with testing in several ways:

1. Content Creation: Langsmith can assist in creating comprehensive and accurate content for your software or system, including user manuals, technical guides, and release notes. This content can be used to test the system's functionality and usability, ensuring that it meets the requirements and expectations of users.
2. Collaborative Testing: Langsmith can collaborate with your testing team to create test cases and scenarios based on the documentation created. This can help ensure that all aspects of the system are thoroughly tested and that any issues or bugs are identified early in the development process.
3. Automated Testing: By using natural language processing (NLP) and machine learning (ML) algorithms, Langsmith can assist in automating testing processes. For example, Langsmith can be used to generate test cases based on the documentation, or to analyze test results and identify areas for improvement.
4. Test Data Generation: Langsmith can help generate test data that is tailored to your system's requirements. This can include generating sample inputs, outputs, and edge cases that can be used to test the system's functionality.
5. Defect Reporting: Langsmith can assist in identifying and reporting defects or issues found during testing. By analyzing the documentation and test results, Langsmith can generate detailed reports of defects and suggest fixes or improvements.
6. Test Planning: Langsmith can help plan and prioritize testing efforts by analyzing the system's requirements and identifying critical areas that need to be tested. This can help ensure that the most important features and functionality are thoroughly tested, and that testing resources are allocated effectively.
7. Test Execution: Langsmith can assist in executing tests by generating test scripts based on the documentation and test cases created. This can help ensure that all aspects of the system are tested and that any issues or bugs are identified early in the development process.
8. Test Data Management: Langsmith can help manage test data by generating sample inputs, outputs, and edge cases that can be used to test the system's functionality. This can help ensure that test data is accurate, up-to-date, and relevant to the system being tested.
9. Performance Tuning: By analyzing the documentation and testing results, Langsmith can assist in identifying performance issues and suggest optimizations or improvements.
10. Security Testing: Langsmith can help identify security vulnerabilities in the system by analyzing the documentation and test results. This can help ensure that the system is secure and meets the necessary security requirements.

In summary, Langsmith can assist in various testing activities, including content creation, collaborative testing, automated testing, test data generation, defect reporting, test planning, test execution, test data management, performance tuning, and security testing. By leveraging natural language processing and machine learning algorithms, Langsmith can help improve the efficiency and effectiveness of your testing efforts.

5 通过web方式载入数据

from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://docs.smith.langchain.com/user_guide")

docs = loader.load()

6 对载入的数据执行分割和嵌入

from langchain_community.embeddings import OllamaEmbeddings

embeddings = OllamaEmbeddings()
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter


text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(docs)
vector = FAISS.from_documents(documents, embeddings)

7 继续增加链,用create_stuff_documents_chain 加上prompt,形成文档查询链

from langchain.chains.combine_documents import create_stuff_documents_chain

prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context:

<context>
{context}
</context>

Question: {input}""")

document_chain = create_stuff_documents_chain(llm, prompt)

8 载入Document,然后执行查询

from langchain_core.documents import Document

document_chain.invoke({
    "input": "how can langsmith help with testing?",
    "context": [Document(page_content="langsmith can let you visualize test results")]
})

Based on the provided context, Langsmith can help with testing by providing a way to visualize test results. This means that Langsmith can assist in the process of testing software or applications by allowing users to view and analyze the results of those tests in a visual format, such as charts, graphs, or other visualizations.

9 然后使用向量增强

from langchain.chains import create_retrieval_chain

retriever = vector.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
response = retrieval_chain.invoke({"input": "how can langsmith help with testing?"})
print(response["answer"])


Based on the provided context, LangSmith can help with testing in several ways:

1. Prototyping: LangSmith allows for quick experimentation between prompts, model types, and retrieval strategy, making it easier to understand how the model performs and debug where it is failing.
2. Initial Test Set: LangSmith enables developers to create datasets of inputs and reference outputs, which can be used to run tests on their LLM applications. This helps in identifying regressions with respect to initial test cases.
3. Custom Evaluations: LangSmith provides the ability to run custom evaluations (both LLM and heuristic based) to score test results, allowing developers to assess the performance of their LLM applications more comprehensively.
4. Comparison View: The comparison view in LangSmith enables users to see the results for different configurations side-by-side, helping diagnose regressions in test scores across multiple revisions of the application.
5. Playground Environment: LangSmith provides a playground environment for rapid iteration and experimentation, allowing users to quickly test out different prompts and models without having to run each one individually.

这块应该就是很多知识库的一般流程了。

ollama的搭建的使用到这里就结束了。

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:/a/506022.html

如若内容造成侵权/违法违规/事实不符,请联系我们进行投诉反馈qq邮箱809451989@qq.com,一经查实,立即删除!

相关文章

【python】pip清华大学镜像

1、修改pip源为清华源&#xff1a; pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple2、结果是自动给我创建了一个文件&#xff1a; 3、打开这个文件所在的文件夹&#xff1a; 4、打开文件&#xff1a; 5、如果不想指定清华的&#xff0c;就删掉…

微信小程序引导组件【添加到我的小程序】

微信小程序引导组件&#xff0c;点击按钮&#xff0c;弹窗引导用户【添加到我的小程序】 源代码 https://ext.dcloud.net.cn/plugin?id17303

算法学习——LeetCode力扣图论篇3(127. 单词接龙、463. 岛屿的周长、684. 冗余连接、685. 冗余连接 II)

算法学习——LeetCode力扣图论篇3 127. 单词接龙 127. 单词接龙 - 力扣&#xff08;LeetCode&#xff09; 描述 字典 wordList 中从单词 beginWord 和 endWord 的 转换序列 是一个按下述规格形成的序列 beginWord -> s1 -> s2 -> … -> sk&#xff1a; 每一对相…

动态内存管理-错题合集讲解

空指针的解应用操作&#xff08;错误信息合集&#xff09; 越界访问 首先我们上一个代码&#xff0c;看看这个的代码的问题 这个代码的问题显而易见 &#xff0c;就是在循环里面&#xff0c;产生了越界访问的问题&#xff0c;这里你开辟了10个整形空间&#xff0c;但是从0-1…

爬虫的验证码处理

1.我们先进入chrome浏览器的审查页面找到input方法&#xff1a; 为了不少找到一个input&#xff0c;我们ctrlf的方法输入input来查找 看见我们有6个需要输入的参数。 除了上面几个的input参数&#xff0c;我们还需要获取验证码的图片&#xff0c;后续要将字母填入进去。 二.安…

XDMA windos 编译

1、先安装 Visual Studio 2019 2、Download the Windows Driver Kit (WDK) - Windows drivers | Microsoft Learn 以前的 WDK 版本和其他下载 - Windows 驱动程序 |Microsoft学习 注意版本&#xff1a;下载2004的版本 3、 选择使用10.0.19041.0 安装这个sdk. 先按vs2019 然后…

后端SpringBoot+Mybatis 查询订单数据库奇怪报错加一

排错过程&#xff1a; 看报错意思是SQL语句存在错误&#xff0c;然后使用图形化工具运行这个SQL语句 其实这里稍微细心想一下就能发现问题&#xff0c;但是当时没深入想&#xff0c;就觉得order表前加了数据库名字影响不大&#xff0c;所以感觉SQL语句是没问题的&#xff0c;然…

HarmonyOS实战开发-一次开发,多端部署-视频应用

介绍 随着智能设备类型的不断丰富&#xff0c;用户可以在不同的设备上享受同样的服务&#xff0c;但由于设备形态不尽相同&#xff0c;开发者往往需要针对具体设备修改或重构代码&#xff0c;以实现功能完整性和界面美观性的统一。OpenHarmony为开发者提供了“一次开发&#x…

【Java与数学】若不等式x^2-a*x+a<0的解集中恰有3个整数,求a的范围?

【分析】 既然不等式存在解集&#xff0c;说明一元二次方程x^2-a*xa0有解&#xff1b; 解之间横跨三个整数&#xff0c;说明Δ大于0&#xff1b; 三个整数必然是连续的&#xff0c;因为f(x)x^2-a*xa最多只与x存在两个交点&#xff0c;这是题设里的隐含条件。 【传统解法】 …

2024 3.23~3.29周报

上周工作 SVInvNet论文研读 本周计划 加入DenseNet&#xff0c;修改网络架构&#xff0c;跑代码 总结 DenseNet 密集块&#xff1a;DenseNet将网络分成多个密集块&#xff08;Dense Block)。在每个密集块内&#xff0c;每一层都连接到前面所有的层。这种跳跃连接有助于解…

Mac m1 Flink的HelloWorld

首先在官方下载Downloads | Apache Flink 下载好压缩包后解压&#xff0c;得到Flink文件夹 进入&#xff1a;cd flink-1.19.0 ls 查看里面的文件&#xff1a; 执行启动集群 ./bin/start-cluster.sh 输出显示它已经成功地启动了集群&#xff0c;并且正在启动 standalonesessio…

Vue ElementPlus Input输入框

Input 输入框 通过鼠标或键盘输入字符 input 为受控组件&#xff0c;它总会显示 Vue 绑定值。 通常情况下&#xff0c;应当处理 input 事件&#xff0c;并更新组件的绑定值&#xff08;或使用v-model&#xff09;。否则&#xff0c;输入框内显示的值将不会改变。不支持 v-mode…

Oracle 低代码平台 Apex 最新版本 23.2 安装过程

趁春节快结束前&#xff0c;安装了一把APEX &#xff0c;到目前为此&#xff0c;APEX最新版本为23.2&#xff0c;23.2和21版本有一些变化&#xff0c;只是用于验证&#xff0c;我 是使用的单独模式&#xff0c;没有安装TOMAT&#xff0c;下面列一下安装过程&#xff1a; 1.环境…

机器学习——最优化模型

最优化模型的概述&#xff1a; 从某种程度上说&#xff0c;我们的世界是由最优化问题组成的。每一天&#xff0c;我们的生活都面临无数的最优化问题&#xff1a;上班怎么选择乘车路线&#xff0c;才能舒服又快速地到达公司&#xff1b;旅游如何选择航班和宾馆&#xff0c;既省…

[flink 实时流基础] 转换算子

flink学习笔记 数据源读入数据之后&#xff0c;我们就可以使用各种转换算子&#xff0c;将一个或多个DataStream转换为新的DataStream。 文章目录 基本转换算子&#xff08;map/ filter/ flatMap&#xff09;聚合算子&#xff08;Aggregation&#xff09;按键分区&#xff08;…

【隐私计算实训营006隐语PIR介绍及开发实践】

1. 隐语实现PIR总体介绍 隐匿查询&#xff08;Private Information Retrieval PIR&#xff09;定义 按服务器数量分类 单服务器方案&#xff08;Single Server&#xff09;多服务器方案&#xff08;Multi-Server&#xff09; 按查询类型分类 Index PIRKeyword PIR 隐语目前…

基于两个单片机串行通信的电子密码锁设计

1.功能 电子号码锁在实际应用中应该有两部分&#xff0c;一部分在外部&#xff0c;有键盘部分和密码显示&#xff1b;另一部分内部&#xff0c;设置密码、显示密码。使用单片机自身带有的串口可以很方便的实现单片机之间的通信&#xff0c;使输入的密码值传送到主机检验是否是…

nginx的https与动态负载均衡

nginx的https 证书可以根据你的域名和服务器服务商去进行签发 , 比如 : 阿里云 腾讯云 百度云 华为云等 这里使用的是腾讯云 : 下载证书 : 选择 nginx: 下载之后传递到服务器上。 下面开始配置nginx的https: 1. 解压下载的证书包 cd /etc/ssl unzip xxcc.dwa_nginx.zip mv…

【A-010】基于SSH的宠物狗商城系统(含论文)

【A-010】基于SSH的宠物狗商城系统&#xff08;含论文&#xff09; 开发环境&#xff1a; Eclipse/MyEclipse、Tomcat8、Jdk1.8 数据库&#xff1a; MySQL 项目介绍&#xff1a; 在科学技术飞速发展的今天&#xff0c;互联网成为人们快速获取、发布和传递信息的重要渠道&am…

Cesium实现渐变面

一、效果图 二、实现思路 使用着色器&#xff0c;通过纹理坐标和其他参数计算出材质的颜色和透明度。通过给定的颜色、漫反射强度和透明度&#xff0c;计算出最终的反射颜色和透明度&#xff0c;并且根据给定的中心点位置和当前像素的纹理坐标&#xff0c;计算出距离中心的距离…