TRAE AI IDE 开发Cangjie(仓颉语言) Elasticsearch 客户端SDK

yunting314

608人浏览 · 2026-02-26 09:40:48

yunting314 · 2026-02-26 09:40:48 发布

代码示例gitcode地址

https://gitcode.com/yunting/es-cj.git

项目简介

基于仓颉语言实现的 Elasticsearch 客户端库，提供简洁的 ORM 风格 API 和强大的 Lambda 查询构建器。

特性

ESClient: 完整的 Elasticsearch REST API 客户端
@ESDocument/@ESField 宏: 自动生成 toJson()、fromJson()、toJsonString()、init() 方法
Mapper 类: 自动生成 CRUD 方法，包括 save()、batchSave()、createIndex() 等
LambdaQuery: Lambda 风格查询构建器，支持链式调用
字段映射: camelCase 自动转换为 snake_case
类型推断: 根据变量类型自动选择正确的 JSON 序列化方式

目录结构

es-cj/
├── src/
│   ├── client/              # ES 客户端
│   │   ├── es_client.cj         # 核心客户端
│   │   ├── es_client_builder.cj # 客户端构建器
│   │   ├── es_async_client.cj   # 异步客户端
│   │   ├── es_template.cj       # 统一操作接口
│   │   ├── es_config.cj         # 配置类
│   │   ├── transport.cj         # HTTP 传输层
│   │   ├── load_balancer.cj     # 负载均衡器
│   │   ├── bulk_queue.cj        # 批量操作队列
│   │   ├── response.cj          # 响应基类
│   │   ├── index_response.cj    # 索引响应
│   │   ├── get_response.cj      # 获取响应
│   │   ├── search_response.cj   # 搜索响应
│   │   ├── delete_response.cj   # 删除响应
│   │   ├── update_response.cj   # 更新响应
│   │   └── bulk_operation.cj    # 批量操作
│   ├── orm/                 # ORM 层
│   │   ├── lambda_query.cj      # Lambda 查询构建器
│   │   ├── query_builder.cj     # 查询条件构造器
│   │   └── aggregation.cj       # 聚合查询
│   ├── macros/              # 宏定义
│   │   └── ESDocument.cj        # @ESDocument/@ESField 宏
│   └── util/                # 工具类
│       ├── StringUtil.cj        # 字符串工具
│       ├── FieldMapping.cj      # 字段映射
│       └── AstUtil.cj           # AST 工具
└── test/                    # 测试代码

快速开始

引入依赖cjpm.toml

[dependencies]
	es_cj = { git = "https://gitcode.com/yunting/es-cj.git", branch = "main" }

[target.aarch64-apple-darwin.bin-dependencies]
	path-option = ["${CANGJIE_STDX_PATH}/darwin_aarch64_llvm/static/stdx"]

[target.aarch64-unknown-linux-gnu.bin-dependencies]
	path-option = ["${CANGJIE_STDX_PATH}/linux_aarch64_llvm/static/stdx"]

1. 创建客户端

import es_cj.client.*

let client = ESClientBuilder()
        .host("localhost:9200")
        .username("elastic")
        .password("password")
        .connectTimeout(Duration.second * 1)        // 连接超时
        .maxConnections(100)
        .build()

if (client.ping()) {
	println("Connected to Elasticsearch!")
}

集群模式

import es_cj.client.*

let client = ESClientBuilder()
        .host("xxxx:9200,xxxx:9200,xxxx:9200")
        .username("elastic")
        .password("password")
        .connectTimeout(Duration.second * 1)        // 连接超时
        .maxConnections(100)
        .build()

if (client.ping()) {
	println("Connected to Elasticsearch!")
}

2. 定义实体类

使用 @ESDocument 和 @ESField 宏自动生成序列化方法和 Mapper 类：

import es_cj.*
import es_cj.macros.*

@ESDocument[index = "test_user_file", shards=3, replicas=1]
class TestUserFile {
    @ESField[name = "user_id", type="keyword"]
    var userId: String = ""

    @ESField[name = "shoot_date", type="keyword"]
    var shootDate: String = ""

    @ESField[type = "keyword"]
    var location: String = ""

    @ESField[name = "upload_time",type="date"]
    var uploadTime: Int64 = 0
}

宏会自动生成：

public init() {
FieldMapping.register("TestUserFile", "userId", "user_id")
FieldMapping.register("TestUserFile", "shootDate", "shoot_date")
FieldMapping.register("TestUserFile", "location", "location")
FieldMapping.register("TestUserFile", "uploadTime", "uploadTime")
}

public func toJson(): JsonObject {
    ...
}
public func toJsonString(): String {
    ...
}
public static func fromJson(json: JsonValue): TestUserFile {
    ...
}

public class TestUserFileMapper {
    public let indexName: String = "test_user_file"
    public func existsIndex(): Bool
    public func createIndex(): CreateIndexResponse
    public func deleteIndex(): DeleteIndexResponse
    public func save(doc: TestUserFile): IndexResponse
    public func save(id: String, doc: TestUserFile): IndexResponse
    public func batchSave(dataList: ArrayList<TestUserFile>): BulkResponse
    public func get(id: String): ?TestUserFile
    public func delete(id: String): DeleteResponse
    public func search(query: LambdaQuery<TestUserFile>): SearchResponse
    public func searchHits(query: LambdaQuery<TestUserFile>): ArrayList<TestUserFile>
}

完整示例

1. 创建索引

import es_cj.*
import es_cj.macros.*

@ESDocument[index = "test_user_file", shards=3, replicas=1]
class TestUserFile {
    @ESField[name = "user_id", type="keyword"]
    var userId: String = ""

    @ESField[name = "shoot_date", type="keyword"]
    var shootDate: String = ""

    @ESField[type = "keyword"]
    var location: String = ""

    @ESField[name = "upload_time",type="date"]
    var uploadTime: Int64 = 0
}

func testCreateIndex(client: ESClient) {
    println("\n=== Create Index Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)
    println("IndexName: ${mapper.indexName}")

    if (mapper.existsIndex()) {
        println("Index already exists, deleting...")
        mapper.deleteIndex()
    }

    println("Creating index with shards=3, replicas=1...")
    let response = mapper.createIndex()
    println("Create index result: ${response.acknowledged}")
}

输出示例：

=== Create Index Test ===
IndexName: test_user_file
Index already exists, deleting...
Creating index with shards=3, replicas=1...
Create index result: true

2. 单个新增

import es_cj.*
import es_cj.macros.*
import std.time.DateTime

func testSave(client: ESClient) {
    println("\n=== Save Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)

    let doc = TestUserFile()
    doc.userId = "save_test"
    doc.shootDate = "2026-03"
    doc.location = "杭州"
    doc.uploadTime = DateTime.now().toUnixTimeStamp().toMilliseconds()

    println("Saving single document...")
    let response = mapper.save(doc)
    println("Save result: ${response.isSuccess()}, id: ${response.id}")

    println("\n=== Save with ID Test ===")
    let doc2 = TestUserFile()
    doc2.userId = "save_with_id"
    doc2.shootDate = "2026-04"
    doc2.location = "南京"
    doc2.uploadTime = DateTime.now().toUnixTimeStamp().toMilliseconds()

    let response2 = mapper.save("custom_id_123", doc2)
    println("Save with id result: ${response2.isSuccess()}, id: ${response2.id}")
}

输出示例：

=== Save Test ===
Saving single document...
Save result: true, id: 46JAjpwB3ytRRUVlDkBn

=== Save with ID Test ===
Save with id result: true, id: custom_id_123

3. 批量新增

import es_cj.*
import es_cj.macros.*
import std.time.DateTime

func testBatchSave(client: ESClient) {
    println("\n=== Batch Save Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)
    println("IndexName: ${mapper.indexName}")

    let dataList = ArrayList<TestUserFile>()
    let rawData = [
        ("test1", "2026-01", "深圳"),
        ("test1", "2026-01", "深圳"),
        ("test1", "2026-02", "深圳"),
        ("test1", "2026-02", "北京"),
        ("test1", "2026-02", "广州"),
        ("test1", "2026-02", "上海")
    ]

    let currentTime = DateTime.now().toUnixTimeStamp().toMilliseconds()
    var idx: Int64 = 0
    for ((userId, shootDate, location) in rawData) {
        let doc = TestUserFile()
        doc.userId = userId
        doc.shootDate = shootDate
        doc.location = location
        doc.uploadTime = currentTime + idx
        dataList.add(doc)
        idx ++
    }

    println("Batch saving ${dataList.size} documents...")
    let bulkResponse = mapper.batchSave(dataList)

    println("Bulk response status: ${bulkResponse.status}, errors: ${bulkResponse.errors}")

    if (bulkResponse.isSuccess()) {
        println("Batch save success!")
    } else {
        println("Batch save failed, errors: ${bulkResponse.hasErrors()}")
    }
}

输出示例：

=== Batch Save Test ===
IndexName: test_user_file
Batch saving 6 documents...
Bulk response status: 200, errors: false
Batch save success!

4. 条件查询（支持 IN 查询）

import es_cj.*
import es_cj.macros.*

func testQuery(client: ESClient) {
    println("\n=== Query Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)

    let locations = ArrayList<JsonValue>()
    locations.add(JsonString("深圳"))
    locations.add(JsonString("广州"))

    println("Query by userId and location (深圳 or 广州)...")
    let query = LambdaQuery<TestUserFile>()
            .forClass("TestUserFile")
            .eq("userId", "test1")
            .inArray("location", locations)
            .orderByAsc("uploadTime")
            .limit(10)

    let results = mapper.searchHits(query)
    println("Found ${results.size} documents:")
    for (doc in results) {
        println("  userId: ${doc.userId}, shootDate: ${doc.shootDate}, location: ${doc.location}")
    }
}

输出示例：

=== Query Test ===
Query by userId and location (深圳 or 广州)...
Found 4 documents:
  userId: test1, shootDate: 2026-01, location: 深圳
  userId: test1, shootDate: 2026-01, location: 深圳
  userId: test1, shootDate: 2026-02, location: 深圳
  userId: test1, shootDate: 2026-02, location: 广州

5. 多字段独立聚合查询

import es_cj.*
import es_cj.macros.*

func testAgg(client: ESClient) {
    println("\n=== Aggregation Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)

    println("Aggregation by userId, location and shootDate...")

    let locationAgg = Aggregation.terms("location_agg", "location")
            .size(10)
    let shootDateAgg = Aggregation.terms("shoot_date_agg", "shoot_date")
            .size(10)

    let query = LambdaQuery<TestUserFile>()
            .forClass("TestUserFile")
            .eq("userId", "test1")
            .aggregation("location_agg", locationAgg)
            .aggregation("shoot_date_agg", shootDateAgg)
            .limit(0)

    let response = mapper.search(query)
    println("Total hits: ${response.total}")

    if (let Some(agg) <- response.aggregations.get("location_agg")) {
        println("\n=== Location Aggregation ===")
        let aggResult = AggregationResult.fromJson("location_agg", agg)
        for (bucket in aggResult.buckets) {
            println("Location: ${bucket.key}, Count: ${bucket.docCount}")
        }
    }

    if (let Some(agg) <- response.aggregations.get("shoot_date_agg")) {
        println("\n=== ShootDate Aggregation ===")
        let aggResult = AggregationResult.fromJson("shoot_date_agg", agg)
        for (bucket in aggResult.buckets) {
            println("ShootDate: ${bucket.key}, Count: ${bucket.docCount}")
        }
    }
}

输出示例：

=== Aggregation Test ===
Aggregation by userId, location and shootDate...
Total hits: 6

=== Location Aggregation ===
Location: 深圳, Count: 3
Location: 上海, Count: 1
Location: 北京, Count: 1
Location: 广州, Count: 1

=== ShootDate Aggregation ===
ShootDate: 2026-01, Count: 2
ShootDate: 2026-02, Count: 4

6. 嵌套聚合查询（先按地点聚合，再按拍摄时间聚合）

import es_cj.*
import es_cj.macros.*

func testSubAgg(client: ESClient) {
    println("\n=== Sub Aggregation Test ===")

    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)

    println("Aggregation by userId, first location and then shootDate...")

    let locationAgg = Aggregation.terms("location_agg", "location")
            .size(10)
            .subAgg(
                    Aggregation.terms("shoot_date_agg", "shoot_date")
                    .size(10)
            )

    let query = LambdaQuery<TestUserFile>()
            .forClass("TestUserFile")
            .eq("userId", "test1")
            .aggregation("location_agg", locationAgg)
            .limit(0)

    let response = mapper.search(query)
    println("Total hits: ${response.total}")

    if (let Some(agg) <- response.aggregations.get("location_agg")) {
        println("\n=== Location Aggregation ===")
        let aggResult = AggregationResult.fromJson("location_agg", agg)
        for (bucket in aggResult.buckets) {
            println("Location: ${bucket.key}, Count: ${bucket.docCount}")

            if (let Some(shootDateResult) <- bucket.subAggregations.get("shoot_date_agg")) {
                for (sdBucket in shootDateResult.buckets) {
                    println("  ShootDate: ${sdBucket.key}, Count: ${sdBucket.docCount}")
                }
            }
        }
    }
}

输出示例：

=== Sub Aggregation Test ===
Aggregation by userId, first location and then shootDate...
Total hits: 6

=== Location Aggregation ===
Location: 深圳, Count: 3
  ShootDate: 2026-01, Count: 2
  ShootDate: 2026-02, Count: 1
Location: 上海, Count: 1
  ShootDate: 2026-02, Count: 1
Location: 北京, Count: 1
  ShootDate: 2026-02, Count: 1
Location: 广州, Count: 1
  ShootDate: 2026-02, Count: 1

7. 游标分页查询（search_after）

使用 search_after 实现深度分页，避免 from/size 的性能问题：

import es_cj.*
import es_cj.macros.*

func testSearchAfter(client: ESClient) {
    println("\n=== Search After Test ===")
    let syncOps = ESClientOperations(client)
    let mapper = TestUserFileMapper(syncOps)

    let q1 = LambdaQuery<TestUserFile>()
            .orderByDesc("uploadTime")
            .limit(2)

    let resp1 = mapper.search(q1)
    println("Page 1: ${resp1.hits.size} documents")
    for (hit in resp1.hits) {
        if (let Some(source) <- hit.source) {
            let doc = TestUserFile.fromJson(source)
            println("location: ${doc.location}, userId: ${doc.userId}, uploadTime: ${doc.uploadTime}")
        }
    }

    let lastSort = resp1.getLastSort()
    if (! lastSort.isEmpty()) {
        let q2 = LambdaQuery<TestUserFile>()
                .orderByDesc("uploadTime")
                .limit(2)
                .searchAfter(lastSort)

        let resp2 = mapper.search(q2)
        println("\nPage 2: ${resp2.hits.size} documents")
        for (hit in resp2.hits) {
            if (let Some(source) <- hit.source) {
                let doc = TestUserFile.fromJson(source)
                println("location: ${doc.location}, userId: ${doc.userId}, uploadTime: ${doc.uploadTime}")
            }
        }
    }
}

输出示例：

=== Search After Test ===
Page 1: 2 documents
  userId: user001, uploadTime: 1768380221868
  userId: user002, uploadTime: 1768380221867

Page 2: 2 documents
  userId: user003, uploadTime: 1768380221866
  userId: user004, uploadTime: 1768380221865

8. KNN 向量查询

使用 knn() 方法进行向量相似度搜索：

import es_cj.*
import es_cj.macros.*

@ESDocument[index = "test_vector_data"]
class TestVectorData {
    @ESField[name = "user_id", type="keyword"]
    var userId: String = ""

    @ESField[name = "title", type="keyword"]
    var title: String = ""

    @ESField[name = "dense_text",type="keyword"]
    var denseText: String = ""

    @ESField[name = "json_data",type="object"]
    var jsonData: JsonObject = JsonObject()

    @ESField[name = "text_vector",type="dense_vector"]
    var textVector: ArrayList<Float64> = ArrayList<Float64>()

    @ESField[name = "upload_time",type="date"]
    var uploadTime: Int64 = 0
}

func testQueryByVector(client: ESClient) {
    println("\n=== KNN Vector Search Test ===")
    let syncOps = ESClientOperations(client)
    let mapper = TestVectorDataMapper(syncOps)

    // 查询向量（1024维）
    let v = [- 0.037262, 0.048157, - 0.018295, ...]

    // KNN 查询
    let q = LambdaQuery<TestVectorData>()
            .knn("textVector", v, 3, numCandidates: 100)

    let response = mapper.search(q)
    println("Found ${response.hits.size} similar documents:")

    for (hit in response.hits) {
        if (let Some(source) <- hit.source) {
            let doc = TestVectorData.fromJson(source)
            println("userId: ${doc.userId}, title: ${doc.title}")
        }
    }
}

输出示例：

=== KNN Vector Search Test ===
Found 3 similar documents:
  userId: user001, title: 钓鱼
  userId: user002, title: 打球
  userId: user003, title: 看漫画

KNN 参数说明：

参数	说明
`field`	向量字段名
`vector`	查询向量（`Array<Float64>` 或 `ArrayList<Float64>`）
`k`	返回的最相似文档数量
`numCandidates`	候选文档数量（默认 100）

API 参考

ESClientBuilder

方法	说明	默认值
`host(host: String)`	设置 ES 主机地址（支持逗号分隔的多节点）	localhost:9200
`username(username: String)`	设置用户名	-
`password(password: String)`	设置密码	-
`maxConnections(max: Int64)`	设置连接池大小	100
`connectTimeout(t: Duration)`	设置连接超时时间	5s
`readTimeout(t: Duration)`	设置读取超时时间	30s
`writeTimeout(t: Duration)`	设置写入超时时间	10s
`build()`	构建 ESClient 实例	-

性能优化建议

let client = ESClientBuilder()
        .host("node1:9200,node2:9200,node3:9200")  // 多节点负载均衡
        .maxConnections(200)                        // 连接池大小
        .connectTimeout(Duration.second * 1)        // 连接超时
        .readTimeout(Duration.second * 30)          // 读取超时
        .writeTimeout(Duration.second * 30)         // 写入超时
        .build()

Mapper 类（自动生成）

方法	说明
`existsIndex()`	检查索引是否存在
`createIndex()`	创建索引（根据 @ESField 注解生成 mappings）
`deleteIndex()`	删除索引
`save(doc)`	保存单个文档（自动生成ID，refresh=true）
`save(id, doc)`	保存单个文档（指定ID，refresh=true）
`batchSave(dataList)`	批量保存（默认 refresh=true）
`batchSave(dataList, refresh)`	批量保存（指定 refresh 参数）
`get(id)`	获取文档
`delete(id)`	删除文档
`update(id, doc)`	更新文档
`search(query)`	搜索文档
`searchHits(query)`	搜索并返回实体列表

LambdaQuery

方法	说明
`forClass(className)`	设置实体类名
`eq(field, value)`	精确匹配（Term 查询）
`term(field, value)`	Term 查询
`match(field, value)`	Match 查询
`inArray(field, values)`	IN 查询（Terms 查询）
`gt(field, value)`	大于
`gte(field, value)`	大于等于
`lt(field, value)`	小于
`lte(field, value)`	小于等于
`between(field, min, max)`	范围查询
`like(field, value)`	模糊查询（Wildcard）
`matchQuery(field, value)`	Match 查询
`matchPhrase(field, value)`	Match Phrase 查询
`isNull(field)`	字段为空
`isNotNull(field)`	字段不为空
`and(queries)`	AND 组合查询
`or(queries)`	OR 组合查询
`aggregation(name, agg)`	添加聚合
`orderByAsc(field)`	升序排序
`orderByDesc(field)`	降序排序
`limit(size)`	返回数量
`offset(from)`	偏移量
`page(pageNum, pageSize)`	分页
`knn(field, vector, k, numCandidates?)`	KNN 向量相似度搜索
`searchAfter(values)`	游标分页（传入排序值）
`build()`	构建 JSON 查询

Aggregation

方法	说明
`terms(name, field)`	Terms 聚合
`sum(name, field)`	Sum 聚合
`avg(name, field)`	Avg 聚合
`min(name, field)`	Min 聚合
`max(name, field)`	Max 聚合
`count(name, field)`	Count 聚合
`stats(name, field)`	Stats 聚合
`topHits(name)`	Top Hits 聚合
`size(size)`	设置返回桶数量
`subAgg(agg)`	添加子聚合

@ESDocument 宏参数

参数	说明	默认值
`index`	指定索引名称	类名转下划线（如 TestUserFile -> test_user_file）
`shards`	主分片数	1
`replicas`	副本数	1

@ESField 宏参数

参数	说明	默认值
`name`	ES 字段名	变量名
`type`	ES 字段类型（keyword, text, date, integer, long, double, boolean, object, dense_vector 等）	text

工具类

StringUtil

StringUtil.toUnderlineCase("userId")    // -> "user_id"
StringUtil.toCamelCase("user_id")       // -> "userId"
StringUtil.upperFirst("name")           // -> "Name"
StringUtil.lowerFirst("Name")           // -> "name"

FieldMapping

FieldMapping.register("User", "userId", "user_id")
let esName = FieldMapping.getESFieldName("User", "userId")  // -> "user_id"

依赖

仓颉标准库
stdx.encoding.json
stdx.net.http

TRAE IDE开发Cangjie项目

下载仓颉项目的Agent模板

git clone https://gitcode.com/mumu_xsy/claudecodetemplate4cj.git

创建项目

mkdir es-cj
cd es-cj
cjpm init --name es_cj

输出

cjpm init success
# 查看
ls
# 创建情况
cjpm.toml	src
# 查看
cat cjpm.toml
# 输出
[package]
  cjc-version = "1.0.0"
  name = "es_cj"
  description = "nothing here"
  version = "1.0.0"
  target-dir = ""
  src-dir = ""
  output-type = "executable"
  compile-option = ""
  override-compile-option = ""
  link-option = ""
  package-configuration = {}

[dependencies]

TRAE IDE 打开es-cj项目

将下载的claudecodetemplate4cj项目的.claude目录、CLAUDE.md、Task.md复制到项目下
存放目录结构

es-cj/
├── .claude/
│   ├── agents/     # 
│   └── cangJie_docs/      # 
├── src/        # 
├── CLAUDE.md   # 
└── Task.md     #

TRAE IDE 设置规则和技能

项目规则
project_rules.md 复制 CLAUDE.md内容
技能
上传.claude/agents目录下的.md文件

创建技能

create_skill

配置示例

config

对话写代码

chat

最终目录结构

es-cj/
├── .claude/
│   ├── agents/                        # Agent 配置
│   │   ├── cj-architect.md            # 架构设计专家
│   │   ├── cj-developer.md            # 实现专家
│   │   ├── cj-progress-manager.md     # 进度管理专家
│   │   ├── cj-style-reviewer.md       # 代码风格审核专家
│   │   └── cj-test-fixer.md           # 测试修复专家
│   └── cangJie_docs/                  # 仓颉文档
├── .trae/
│   ├── rules/                         # 项目规则
│   │   └── project_rules.md           # 项目规则配置
│   └── skills/                        # 技能配置
│       ├── cj-architect/              # 架构设计技能
│       │   └── SKILL.md
│       ├── cj-developer/              # 实现技能
│       │   └── SKILL.md
│       ├── cj-progress-manager/       # 进度管理技能
│       │   └── SKILL.md
│       ├── cj-style-reviewer/         # 代码风格审核技能
│       │   └── SKILL.md
│       └── cj-test-fixer/             # 测试修复技能
│           └── SKILL.md
├── src/                               # 源代码
├── CLAUDE.md                          # Claude 配置
└── Task.md                            # 任务文档

备注

Trae开发cangjie项目模板

2048 AI社区

有“AI”的1024 = 2048，欢迎大家加入2048 AI社区

更多推荐

JSONL 数据怎么造，AMD 显卡微调前的数据清洗与格式规范

本文详解 AMD 显卡微调前的数据清洗与 JSONL 格式规范。针对 ROCm 环境对数据的高敏感性，文章解析 instruction、input、output 字段标准，提供 Python 批量转换脚本及正反面案例，助开发者高效构建高质量数据集，确保大模型微调稳定运行。

2048 AI社区

关于10年工作经验的程序员对OpenClaw的实战经验分享以及看法

线上出问题的时候，我直接把问题描述、相关报错日志、对应代码的本地路径、数据库只读权限给OpenClaw，它会自动关联所有上下文，先去日志系统拉取完整请求链路，再查数据库对应的数据是否异常，最后定位到具体的代码逻辑，不到1分钟就能给出问题根因和可直接落地的修复方案，比我自己翻日志查代码快很多，省了不少时间。ps:其实这种教程可以直接问现有的chat模式的AI，他们也可以直接给出教程，国内的比如dee

2048 AI社区

第06篇-Control UI 与 WebChat 使用指南

WebChat 是 Control UI 内置的聊天界面。它让你不需要安装 Telegram、不需要配置渠道，直接用浏览器就能和 AI 对话。这张图展示了 WebChat 的界面。中间白色区域是聊天窗口，包含消息历史、输入框和发送按钮。左侧列出了 WebChat 的核心特性。功能核心用途推荐场景Dashboard查看运行状态日常巡检、快速健康检查Sessions管理用户会话问题排查、数据清理Log

2048 AI社区

所有评论(0)

查看更多评论

yunting314

@yunting314

已为社区贡献3条内容

TRAE AI IDE 开发Cangjie(仓颉语言) Elasticsearch 客户端SDK

yunting314

代码示例gitcode地址

项目简介

特性

目录结构

快速开始

引入依赖cjpm.toml

1. 创建客户端

集群模式

2. 定义实体类

完整示例

1. 创建索引

2. 单个新增

3. 批量新增

4. 条件查询（支持 IN 查询）

5. 多字段独立聚合查询

6. 嵌套聚合查询（先按地点聚合，再按拍摄时间聚合）

7. 游标分页查询（search_after）

8. KNN 向量查询

API 参考

ESClientBuilder

性能优化建议

Mapper 类（自动生成）

LambdaQuery

Aggregation

@ESDocument 宏参数

@ESField 宏参数

工具类

StringUtil

FieldMapping

依赖

TRAE IDE开发Cangjie项目

下载仓颉项目的Agent模板

创建项目

TRAE IDE 打开es-cj项目

TRAE IDE 设置规则和技能

创建技能

配置示例

对话写代码

备注

所有评论(0)

温馨提示：您尚未绑定手机号

yunting314