docs:专栏更新

Author: Java-Edge

.vscode/.server-controller-port.log

@@ -1,5 +1,5 @@
 {
   "port": 9145,
-  "time": 1728450843494,
+  "time": 1729093610425,
   "version": "0.0.3"
 }

docs/.vuepress/config.js

@@ -516,9 +516,17 @@ module.exports = {
                             {
                                 text: 'Redis',
                                 items: [{
-                                    text: 'Redis数据结构的最佳实践',
+                                    text: '基础',
                                     link: '/md/redis/00-数据结构的最佳实践.md'
-                                }]
+                                },
+                                {
+                                    text: '源码',
+                                    link: '/md/redis/00-数据结构的最佳实践.md'
+                                },
+                                {
+                                    text: '业务',
+                                    link: '/md/redis/00-数据结构的最佳实践.md'
+                                },]
                             },
 
                             {
@@ -1110,6 +1118,7 @@ module.exports = {
                         collapsable: false,
                         sidebarDepth: 0,
                         children: [
+                            "04-RPC框架在网络通信的网络IO模型选型",
                             "熔断限流",
                             "11-RPC的负载均衡",
                         ]
@@ -1176,7 +1185,7 @@ module.exports = {
                         children: [
                             "为啥要学习数据分析？",
                             "correct-data-analysis-learning-methods",
-                            "02-数据挖掘的学习路径",
+                            "learning-path-data-mining",
                             "企业如何利用数据打造精准用户画像？",
                             "如何自动化采集数据",
                             "how-to-use-octoparse-for-data-scraping",
@@ -1194,6 +1203,7 @@ module.exports = {
                                 "03-ReentrantLock与AQS.md",
                                 "04-线程池以及生产环境使用.md",
                                 "05-京东并行框架asyncTool如何针对高并发场景进行优化？.md",
+                                "java21-virtual-threads-where-did-my-lock-go",
                             ]
                         },
                         {
@@ -1562,6 +1572,7 @@ module.exports = {
                             collapsable: false,
                             sidebarDepth: 0,
                             children: [
+                                "01-Netty源码面试实战+原理(一)-鸿蒙篇",
                                 "netty-basic-components",
                                 "ChannelPipeline接口",
                                 "(06-1)-ChannelHandler 家族",
@@ -1664,20 +1675,34 @@ module.exports = {
                     }, ],
 
                     "/md/redis/": [{
-                        title: "Redis",
+                        title: "基础",
                         collapsable: false,
                         sidebarDepth: 0,
                         children: [
-                            "00-数据结构的最佳实践",
                             "01-Redis和ZK分布式锁优缺点对比以及生产环境使用建议",
-                            "02-Redisson可重入锁加锁源码分析",
-                            "03-Redisson公平锁加锁源码分析",
-                            "04-Redisson读写锁加锁机制分析",
                             "05-缓存读写策略模式详解",
                             "06-如何快速定位 Redis 热 key",
                             "12-Redis 闭源？",
                         ]
-                    }],
+                    },
+                    {
+                        title: "源码",
+                        collapsable: false,
+                        sidebarDepth: 0,
+                        children: [
+                            "02-Redisson可重入锁加锁源码分析",
+                            "03-Redisson公平锁加锁源码分析",
+                            "04-Redisson读写锁加锁机制分析",
+                        ]
+                    },
+                    {
+                        title: "业务",
+                        collapsable: false,
+                        sidebarDepth: 0,
+                        children: [
+                            "00-数据结构的最佳实践",
+                        ]
+                    },],
                     "/md/es/": [{
                         title: "ElasticSearch",
                         collapsable: false,
@@ -2076,6 +2101,7 @@ module.exports = {
                                 "11-lcel-memory-addition-method",
                                 "12-lcel-agent-core-components",
                                 "13-best-development-practices",
+                                "local-large-model-deployment",
                             ]
                         },
 
@@ -2087,6 +2113,9 @@ module.exports = {
                                 "01-three-minute-fastapi-ai-agent-setup",
                                 "02-Agent应用对话情感优化",
                                 "03-use-tts-to-make-your-ai-agent-speak",
+                                "langserve-revolutionizes-llm-app-deployment",
+                                "customizing-a-tool-for-your-ai-agent",
+                                "Complex-SQL-Joins-with-LangGraph-and-Waii",
                                 "AI Agent应用出路到底在哪？",
                             ]
                         },
@@ -2098,6 +2127,7 @@ module.exports = {
                             children: [
                                 "00-introduce-to-LangGraph",
                                 "langgraph-studio",
+                                "multi_agent",
                                 "methods-adapting-large-language-models",
                                 "to-fine-tune-or-not-to-fine-tune-llm",
                                 "effective-datasets-fine-tuning",

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

@@ -27,205 +27,4 @@
 
 优点：数据私有、更灵活、成本低
 
-缺点：算力设施、技术支撑
-
-## 3 使用 Ollama 在本地部署大模型
-
-### 3.1 下载并运行应用程序
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/a872adde1e96e5dbd3ddb0e910f48088.png)
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/de0bfb92df17722ebdbb5c0696fd7666.png)
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/daa95f47315ba60e6790d27661f85021.png)
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/85b62d04db4c06665b1fff64de5bec87.png)
-
-### 3.2 从命令行中选取模型(ollama pull llam2)
-
-[官网支持的模型](https://ollama.com/library?sort=newest)：
-
-![](/Users/javaedge/Downloads/IDEAProjects/java-edge-master/assets/image-20240621135627185.png)
-
-挑选一个比较小的试玩下：
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/46b83f44f00fb3965c35e700cb45eb85.png)
-
-### 3.3 运行
-
-[浏览器](localhost:11434)：
-
-![](/Users/javaedge/Downloads/IDEAProjects/java-edge-master/assets/image-20240621141710055.png)
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/29fa4e05840db498501e59e03db1e63f.png)
-
-## 4 本地大模型调用
-
-既然部署本地完成了，来看看如何调用呢？
-
-```python
-from langchain_community.llms import Ollama
-
-llm = Ollama(model="qwen2:0.5b")
-llm.invoke(input="你是谁？")
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/af07e34926600fdd9946e2905c05bb7a.png)
-
-### 使用流式
-
-```python
-#使用流式
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
-llm = Ollama(
-    model="qwen2:0.5b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])
-)
-llm.invoke(input="第一个登上月球的人是谁?")
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/57bcab3fd266daac316d119b20199b37.png)
-
-## 5 模型评估
-
-### 5.1 远程大模型
-
-```python
-from langchain_openai import ChatOpenAI
-from langchain.evaluation import load_evaluator
-llm = ChatOpenAI(
-    api_key=os.getenv("DASHSCOPE_API_KEY"),
-    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-    model="qwen-plus"
-)
-
-evaluator = load_evaluator("criteria", llm=llm, criteria="conciseness")
-eval_result = evaluator.evaluate_strings(
-    prediction="four.",
-    input="What's 2+2?",
-)
-print(eval_result)
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/bb4d72b250043b2ee5bd0ae82541e655.png)
-
-如果不简洁的回答：
-
-```python
-#inpt 输入的评测问题
-#prediction 预测的答案
-# 返回值 Y/N 是否符合
-# 返回值score 1-0分数，1为完全，0为不完全
-eval_result = evaluator.evaluate_strings(
-    prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
-    input="What's 2+2?",
-)
-print(eval_result)
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/61c1b940051d6c7b5849cf6211fceefb.png)
-
-### 5.2 本地大模型
-
-```python
-from langchain_community.chat_models import ChatOllama
-llm = ChatOllama(model="qwen2:0.5b")
-evaluator = load_evaluator("criteria", llm=llm, criteria="conciseness")
-```
-
-```python
-#inpt 输入的评测问题
-#prediction 预测的答案
-# 返回值 Y或者N是否符合
-# 返回值score 1-0分数，1为完全，0为不完全
-eval_result = evaluator.evaluate_strings(
-    prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
-    input="What's 2+2?",
-)
-print(eval_result)
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/ea116b14383b6db7194d7658810767fd.png)
-
-### 5.3 内置评估标准
-
-```python
-# 内置的一些评估标准
-from langchain.evaluation import Criteria
-
-list(Criteria)
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/f71d5563c6a00a18f2951bb3a4e2f9cd.png)
-
-
-
-
-```python
-llm = ChatOllama(model="qwen2:0.5b")
-#使用enum格式加载标准
-from langchain.evaluation import EvaluatorType
-#自定义评估标准
-custom_criterion = {
-    "幽默性": "输出的内容是否足够幽默或者包含幽默元素",
-}
-eval_chain = load_evaluator(
-    EvaluatorType.CRITERIA,
-    llm=llm,
-    criteria=custom_criterion,
-)
-query = "给我讲一个笑话"
-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。"
-eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
-print(eval_result)
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/b626bd419b59ded036872353dbd91d41.png)
-
-### 模型比较
-
-```python
-from langchain.model_laboratory import ModelLaboratory
-from langchain.prompts import PromptTemplate
-from langchain_openai import OpenAI
-from langchain_community.llms.chatglm import ChatGLM
-from langchain_community.chat_models import ChatOllama
-
-#比较openai、ChatGLM、ChatOllama三个模型的效果
-llms = [
-    # OpenAI(temperature=0),
-    ChatOllama(model="qwen2:0.5b"),
-]
-```
-
-```python
-model_lab = ModelLaboratory.from_llms(llms)
-model_lab.compare("齐天大圣的师傅是谁？")
-```
-
-
-
-![](https://my-img.javaedge.com.cn/javaedge-blog/2024/06/8c693bac93ab5309068b4a724dd9eac1.png)
+缺点：算力设施、技术支撑