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docs:专栏更新
1 parent 97e29f4 commit f7fc0fe

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‎.vscode/.server-controller-port.log‎

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{
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"port": 9145,
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"time": 1728450843494,
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"time": 1729093610425,
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"version": "0.0.3"
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}

‎docs/.vuepress/config.js‎

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@@ -516,9 +516,17 @@ module.exports = {
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{
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text: 'Redis',
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items: [{
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text: 'Redis数据结构的最佳实践',
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text: '基础',
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link: '/md/redis/00-数据结构的最佳实践.md'
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}]
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},
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{
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text: '源码',
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link: '/md/redis/00-数据结构的最佳实践.md'
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},
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{
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text: '业务',
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link: '/md/redis/00-数据结构的最佳实践.md'
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},]
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},
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{
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"04-RPC框架在网络通信的网络IO模型选型",
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"熔断限流",
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"11-RPC的负载均衡",
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]
@@ -1176,7 +1185,7 @@ module.exports = {
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children: [
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"为啥要学习数据分析?",
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"correct-data-analysis-learning-methods",
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"02-数据挖掘的学习路径",
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"learning-path-data-mining",
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"企业如何利用数据打造精准用户画像?",
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"如何自动化采集数据",
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"how-to-use-octoparse-for-data-scraping",
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"03-ReentrantLock与AQS.md",
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"04-线程池以及生产环境使用.md",
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"05-京东并行框架asyncTool如何针对高并发场景进行优化?.md",
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"java21-virtual-threads-where-did-my-lock-go",
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]
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},
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{
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"01-Netty源码面试实战+原理(一)-鸿蒙篇",
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"netty-basic-components",
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"ChannelPipeline接口",
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"(06-1)-ChannelHandler 家族",
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}, ],
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"/md/redis/": [{
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title: "Redis",
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title: "基础",
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"00-数据结构的最佳实践",
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"01-Redis和ZK分布式锁优缺点对比以及生产环境使用建议",
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"02-Redisson可重入锁加锁源码分析",
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"03-Redisson公平锁加锁源码分析",
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"04-Redisson读写锁加锁机制分析",
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"05-缓存读写策略模式详解",
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"06-如何快速定位 Redis 热 key",
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"12-Redis 闭源?",
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]
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}],
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},
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{
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title: "源码",
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"02-Redisson可重入锁加锁源码分析",
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"03-Redisson公平锁加锁源码分析",
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"04-Redisson读写锁加锁机制分析",
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]
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},
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{
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title: "业务",
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collapsable: false,
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sidebarDepth: 0,
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children: [
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"00-数据结构的最佳实践",
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]
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},],
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"/md/es/": [{
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title: "ElasticSearch",
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collapsable: false,
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"11-lcel-memory-addition-method",
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"12-lcel-agent-core-components",
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"13-best-development-practices",
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"local-large-model-deployment",
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]
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},
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"01-three-minute-fastapi-ai-agent-setup",
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"02-Agent应用对话情感优化",
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"03-use-tts-to-make-your-ai-agent-speak",
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"langserve-revolutionizes-llm-app-deployment",
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"customizing-a-tool-for-your-ai-agent",
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"Complex-SQL-Joins-with-LangGraph-and-Waii",
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"AI Agent应用出路到底在哪?",
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]
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},
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children: [
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"00-introduce-to-LangGraph",
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"langgraph-studio",
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"multi_agent",
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"methods-adapting-large-language-models",
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"to-fine-tune-or-not-to-fine-tune-llm",
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"effective-datasets-fine-tuning",

‎docs/md/AI/13-best-development-practices.md‎

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优点:数据私有、更灵活、成本低
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缺点:算力设施、技术支撑
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## 3 使用 Ollama 在本地部署大模型
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### 3.1 下载并运行应用程序
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/a872adde1e96e5dbd3ddb0e910f48088.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/de0bfb92df17722ebdbb5c0696fd7666.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/daa95f47315ba60e6790d27661f85021.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/85b62d04db4c06665b1fff64de5bec87.png)
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### 3.2 从命令行中选取模型(ollama pull llam2)
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[官网支持的模型](https://ollama.com/library?sort=newest):
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![](/Users/javaedge/Downloads/IDEAProjects/java-edge-master/assets/image-20240621135627185.png)
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挑选一个比较小的试玩下:
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/46b83f44f00fb3965c35e700cb45eb85.png)
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### 3.3 运行
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[浏览器](localhost:11434):
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![](/Users/javaedge/Downloads/IDEAProjects/java-edge-master/assets/image-20240621141710055.png)
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/29fa4e05840db498501e59e03db1e63f.png)
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## 4 本地大模型调用
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既然部署本地完成了,来看看如何调用呢?
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```python
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from langchain_community.llms import Ollama
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llm = Ollama(model="qwen2:0.5b")
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llm.invoke(input="你是谁?")
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/af07e34926600fdd9946e2905c05bb7a.png)
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### 使用流式
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```python
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#使用流式
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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llm = Ollama(
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model="qwen2:0.5b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])
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)
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llm.invoke(input="第一个登上月球的人是谁?")
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/57bcab3fd266daac316d119b20199b37.png)
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## 5 模型评估
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### 5.1 远程大模型
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```python
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from langchain_openai import ChatOpenAI
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from langchain.evaluation import load_evaluator
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llm = ChatOpenAI(
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model="qwen-plus"
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)
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evaluator = load_evaluator("criteria", llm=llm, criteria="conciseness")
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eval_result = evaluator.evaluate_strings(
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prediction="four.",
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input="What's 2+2?",
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)
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print(eval_result)
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/bb4d72b250043b2ee5bd0ae82541e655.png)
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如果不简洁的回答:
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```python
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#inpt 输入的评测问题
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#prediction 预测的答案
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# 返回值 Y/N 是否符合
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# 返回值score 1-0分数,1为完全,0为不完全
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eval_result = evaluator.evaluate_strings(
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prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
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input="What's 2+2?",
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)
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print(eval_result)
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/61c1b940051d6c7b5849cf6211fceefb.png)
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### 5.2 本地大模型
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```python
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from langchain_community.chat_models import ChatOllama
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llm = ChatOllama(model="qwen2:0.5b")
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evaluator = load_evaluator("criteria", llm=llm, criteria="conciseness")
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```
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```python
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#inpt 输入的评测问题
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#prediction 预测的答案
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# 返回值 Y或者N是否符合
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# 返回值score 1-0分数,1为完全,0为不完全
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eval_result = evaluator.evaluate_strings(
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prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
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input="What's 2+2?",
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)
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print(eval_result)
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```
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### 5.3 内置评估标准
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```python
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# 内置的一些评估标准
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from langchain.evaluation import Criteria
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list(Criteria)
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/f71d5563c6a00a18f2951bb3a4e2f9cd.png)
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```python
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llm = ChatOllama(model="qwen2:0.5b")
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#使用enum格式加载标准
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from langchain.evaluation import EvaluatorType
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#自定义评估标准
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custom_criterion = {
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"幽默性": "输出的内容是否足够幽默或者包含幽默元素",
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}
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eval_chain = load_evaluator(
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EvaluatorType.CRITERIA,
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llm=llm,
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criteria=custom_criterion,
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)
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query = "给我讲一个笑话"
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prediction = "有一天,小明去买菜,结果买了一堆菜回家,结果发现自己忘了带钱。"
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eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
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print(eval_result)
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/b626bd419b59ded036872353dbd91d41.png)
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### 模型比较
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```python
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from langchain.model_laboratory import ModelLaboratory
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from langchain.prompts import PromptTemplate
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from langchain_openai import OpenAI
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from langchain_community.llms.chatglm import ChatGLM
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from langchain_community.chat_models import ChatOllama
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#比较openai、ChatGLM、ChatOllama三个模型的效果
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llms = [
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# OpenAI(temperature=0),
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ChatOllama(model="qwen2:0.5b"),
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]
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```
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```python
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model_lab = ModelLaboratory.from_llms(llms)
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model_lab.compare("齐天大圣的师傅是谁?")
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```
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![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/8c693bac93ab5309068b4a724dd9eac1.png)
30+
缺点:算力设施、技术支撑

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