From d8a1ad8b96c070cb93f976c70de82628bc9eea40 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=B0=91=E8=BD=BB=E7=8B=82?= <1677568218@qq.com> Date: Sat, 30 Sep 2023 13:01:14 +0800 Subject: [PATCH 1/2] docs: start.md --- docs/guide/start.md | 389 +++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 387 insertions(+), 2 deletions(-) diff --git a/docs/guide/start.md b/docs/guide/start.md index 50be46e..ed96036 100644 --- a/docs/guide/start.md +++ b/docs/guide/start.md @@ -2,20 +2,405 @@ > 本文档仅介绍基于低代码的使用方法,如果你想了解更多关于llm-ops的使用方法,请自行查阅源码示例(帮我们写写文档也是非常欢迎的)。 +可通过[Preview](https://preview.idealeap.cn/)直接上手体验低代码的使用。 + ## 安装 ```bash -npm install --save llm-ops +npm install llm-ops ``` ## 创建一个简单的pipeline +```ts +import { Pipeline,PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init();//将功能函数注册到funcStore中 +const pipelineJson = { + pipes:[] +} +const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); +const res = await pipeline.execute(""); +``` + ## 使用llm +想要使用`llm`,只需要在`pipelineJson`中添加`llm`的pipe即可。 + +在`use`字段中填写`llm`,在`params`字段中填写`llm`的参数。 + +其中,每个`pipe`都有一个`id`字段,用于标识该`pipe`。 + +`llm`的参数中,`messages`字段用于填写`llm`的历史对话和prompt,`messages`是一个数组,每个元素都是一个`message`,`message`是一个对象,其中`role`字段用于标识`message`的角色,`content`字段用于填写`message`的内容。 + +可自行查阅`API`文档,了解更多`llm`的参数。 + +> 目前通过json方式使用的`llm`不支持历史记录,如果需要历史记录,请参考相关`API`文档,使用完整的`llm`功能。 + +```ts +import { Pipeline,PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init(); +const pipelineJson = { + pipes:[{ + id: "polish", + use: "llm", + params: { + messages: [ + { + role: "system", + content: + "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。", + }, + ], + }, + }] +} +const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); +const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +``` + ## 设置环境变量 +推荐做法是直接在.env文件中设置环境变量,框架会自动读取.env文件中的环境变量。 + +若想要手动设置环境变量,可通过插桩的方式实现。 + +例如,设置`OPENAI_API_KEY`环境变量: + +```ts +import { Pipeline,PipeRegistry,LLM_OPS_CONFIG } from "llm-ops"; +LLM_OPS_CONFIG.OPENAI_API_KEY = ""; +... +``` + +在国内,通常无法直接使用OPENAI_API_KEY,因此,需要使用代理。推荐使用`Helicone`作为代理API。手动设置代理API的方法如下: + +```ts +import { Pipeline,PipeRegistry,LLM_OPS_CONFIG } from "llm-ops"; +LLM_OPS_CONFIG.OPEN_PATH = { + baseURL: "https://oai.hconeai.com/v1", + defaultHeaders: { + "Helicone-Auth": `Bearer xxx`, + }, +} +``` + ## 使用chain -## 使用自定义代码 +`chain`的使用非常简单,只需将`use`字段设置为`chain`,在`params`字段中填写`chain`的参数即可。默认使用`functionCall`作为格式化输出。如果需要使用`typeChat`,请在`params`中添加`chainName:"typeChat"`。在`struct`字段中填写格式化的输出要求。 + +以下是`functionCall`的使用示例: + +```ts +import { Pipeline,PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init(); +const pipelineJson = { + "pipes": [{ + "id": "classify", + "use": "chain", + "params": { + "struct": { + "functions": [{ + "name": "get_categories", + "description": "根据PPT文稿获取PPT类别", + "parameters": { + "type": "object", + "properties": { + "categories": { + "type": "string", + "enum": ["汇报类型", "演讲类型", "活动类型"], + "description": "PPT文稿属于哪一类型的PPT" + } + }, + "required": ["categories"] + } + }], + "function_call": { + "name": "get_categories" + } + }, + "prompt": [{ + "role": "system", + "content": "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。" + }] + } + }] +} +const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); +const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +``` + +更复杂的用法可以学习官方的[Cook Book](https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models)。 + +以下是`typeChat`的使用示例: + +```ts +import { Pipeline,PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init(); +const pipelineJson = { + "pipes": [{ + id: "subsection", + use: "chain", + params: { + chainName: "typeChat", + struct: { + schema:` +export interface subsectionSchema { + subsection: articleSchema[]; + title: string; // 整篇PPT文稿的标题 + reason: string; // 为什么要这么分段,必须要符合PPT大纲的逻辑 +} +export interface articleSchema { + content: string; //每一段的文本 + title: string; // 每一段起一个小标题,非常凝练这一段主要的内容是什么,同时与其他段落保持一个一致性的风格 +}`, + typeName: "subsectionSchema", + }, + prompt: [ + { + role: "system", + content: + "你是一个专职PPT文稿大纲处理的助手,将会对user输入的文稿进行分段,并进行中心点提取成小标题。给出整篇PPT文稿的标题,分段的理由,每段小标题和对应的内容。", + }, + ], + bound: false, + }, + }] +} +const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); +const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +``` + +从上述示例可以看出,`chain`中的`prompt`和`llm`中的`messages`是一样的,都是用于填写`chain`的历史对话和prompt。 + +可自行查阅`API`文档,了解更多`chain`的参数。 + +## Pipe使用globalParams + +有一些params可能是许多pipe都需要的,那么我们可以设置一个全局的params,这样就不用每个pipe都写一遍了。 + +```ts +const pipelineJson = { + pipes: [...], + globalParams: { + abc: "123", + ... + }, +}; +``` + +## 多个pipe组合使用 + +在pipes中添加多个pipe即可执行多个步骤。默认情况下,每个pipe的输入都是上一个pipe的输出。 + +```ts +import { Pipeline,PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init(); +const pipelineJson = { + "pipes": [{ + "id": "polish", + "use": "llm", + "params": { + "messages": [{ + "role": "system", + "content": "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。" + }] + } + }, { + "id": "classify", + "use": "chain", + "params": { + "struct": { + "functions": [{ + "name": "get_categories", + "description": "根据PPT文稿获取PPT类别", + "parameters": { + "type": "object", + "properties": { + "categories": { + "type": "string", + "enum": ["汇报类型", "演讲类型", "活动类型"], + "description": "PPT文稿属于哪一类型的PPT" + } + }, + "required": ["categories"] + } + }], + "function_call": { + "name": "get_categories" + } + }, + "prompt": [{ + "role": "system", + "content": "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。" + }] + } + }] +} +const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); +const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +``` + +## 注册自定义函数 + +除了`llm-ops`提供的函数,我们还可以注册自定义函数,实现更强大的功能。 + +下面的示例中注册了三个自定义函数,可通过`use`字段写入对应的`id`,实现自定义函数的调用。 + +```ts +const pipeRegistry = PipeRegistry.init(); +pipeRegistry.register( + "step1", + async (input: any, context: PipelineContext) => { + console.log("step1", input, context.stepParams["self_params"]); + return new Promise((resolve) => + setTimeout(() => resolve(input + "🚺"), 1000), + ); + }, +); + +pipeRegistry.register("step2", (input: any, context: PipelineContext) => { + console.log( + "step2", + input, + context.stepParams["self_params"], + context.stepResults["index_input"], + ); + return context.stepResults["index_input"]; +}); + +pipeRegistry.register( + "step3", + async (input: any, context: PipelineContext) => { + console.log("step3", input, context.stepParams["self_params"]); + return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); + }, +); + +const pipelineJson = { + pipes: [ + { + id: "step1_", + use: "step1", + params: { test: "test!!" }, + }, + { + id: "step2_", + use: "step2", + params: { test: "test22!!{{FetchData}}" }, //插槽! + }, + { + id: "step3_", + use: "step3", + }, + ], +} + +const pipeline = Pipeline.fromJSON(pipelineJson, {}, pipeRegistry); + +await pipeline.execute("我是输入").then(console.log); +``` + +`context`中包含了`stepParams`和`stepResults`,`stepParams`是所有`pipe`的`params`,`stepResults`是已执行`pipe`的执行结果。 + +也可以通过self_params字段,获取当前pipe的params。通过index_input字段,获取最初的输入。 + +## 更复杂的pipe组合使用 + +每个pipe不仅可以改变输入,还可以改变params,甚至可以将前几个pipe的执行结果放入`params`中,以此实现更复杂的功能。 + +```ts +const pipeRegistry = PipeRegistry.init(); +pipeRegistry.register( + "step1", + async (input: any, context: PipelineContext) => { + console.log("step1", input, context.stepParams["self_params"]); + return new Promise((resolve) => + setTimeout(() => resolve(input + "🚺"), 1000), + ); + }, +); + +pipeRegistry.register("step2", (input: any, context: PipelineContext) => { + console.log( + "step2", + input, + context.stepParams["self_params"], + context.stepResults["index_input"], + ); + return context.stepResults["index_input"]; +}); + +pipeRegistry.register( + "step3", + async (input: any, context: PipelineContext) => { + console.log("step3", input, context.stepParams["self_params"]); + return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); + }, +); + +const pipelineJson = { + pipes: [ + { + id: "step1_", + use: "step1", + params: { test: "test!!" }, + }, + { + id: "step2_", + use: "step2", + inputs: { + FetchData: "step1_",//获取pipe中id为step1_的pipe的执行结果,自动替换字符串"step1_"为对应的输出结果。 + }, + params: { test: "test22!!{{FetchData}}" }, //插槽,必须先在inputs中获取之前pipe的输出结果,然后在params中插槽引用inputs的对应字段! + }, + { + id: "step3_", + use: "step3", + inputs: { + input: "step1_", //这里的input是step1_的结果。默认字段名为input即直接替换当前pipe的输入。 + }, + }, + ], +}; + +const pipeline = Pipeline.fromJSON(pipelineJson, {}, pipeRegistry); + +await pipeline.execute("我是输入").then(console.log); +``` + +## 生命周期钩子 + +其实,`pipeline`的执行过程是一个`pipe`一个`pipe`的执行,每个`pipe`都有一个`execute`方法,`pipeline`的`execute`方法就是调用每个`pipe`的`execute`方法。 + +我们完全不用等到所有`pipe`都执行完毕,就可以对`pipeline`的执行过程进行干预。 + +例如,我们可以通过添加钩子函数,实现对每一个过程执行完毕后的回调,也可以实现对某个过程执行错误的回调。 + +```ts +import { EventEmitter } from "llm-ops"; +const globalEmitter = new EventEmitter(); +globalEmitter.on( + "stepComplete", + (step: any, totalSteps: any, result: any) => { + console.log( + `Step ${step}/${totalSteps} completed with result: ${result}`, + ); + }, +); +globalEmitter.on("error", (error) => { + console.log(`An error occurred: ${error}`); +}); +const pipelineJson = { + emitter: globalEmitter, + ... +} +... +``` ## 其他 + +更多更复杂的pipeline功能可以查看[@idealeap/pipeline](https://pipe.idealeap.cn/)。 + +`llm-ops`更复杂的参数和使用方法可查看[Github仓库](https://github.com/IdeaLeap/llm-ops)的测试用例以及[API文档](https://ops.idealeap.cn/api/) + +想要测试低代码功能可以访问[Preview](https://preview.idealeap.cn/)。 From 07e9737a65b92d0ad23fcac44859f2d8abf6cc28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=B0=91=E8=BD=BB=E7=8B=82?= <1677568218@qq.com> Date: Sat, 30 Sep 2023 13:48:25 +0800 Subject: [PATCH 2/2] =?UTF-8?q?docs:=20=E5=A2=9E=E5=8A=A0=E4=B8=80?= =?UTF-8?q?=E4=BA=9BTsDoc=E6=B3=A8=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 10 +- docs/.vitepress/config.ts | 63 +++- docs/api/llm-ops.chain._constructor_.md | 4 +- docs/api/llm-ops.chain.call.md | 6 +- docs/api/llm-ops.chain.exporthistory.md | 4 + docs/api/llm-ops.chain.md | 8 +- docs/api/llm-ops.chaincallschema.md | 4 + docs/api/llm-ops.chainschema.md | 4 + docs/api/llm-ops.chatcompletiontype.md | 2 + docs/api/llm-ops.chatparamstype.md | 2 + docs/api/llm-ops.chatschema.md | 4 +- docs/api/llm-ops.chatschema.messages.md | 2 + ...s.createllmschema.helicone_auth_api_key.md | 2 + docs/api/llm-ops.createllmschema.md | 8 +- docs/api/llm-ops.createllmschema.modelname.md | 2 + .../llm-ops.createllmschema.openai_api_key.md | 2 + docs/api/llm-ops.formatprompttemplate.md | 16 + docs/api/llm-ops.function_calltype.md | 2 + docs/api/llm-ops.functioncalloption.md | 2 + docs/api/llm-ops.functionstype.md | 2 + docs/api/llm-ops.llm._constructor_.md | 4 +- docs/api/llm-ops.llm.chat.md | 6 +- docs/api/llm-ops.llm.embedding.md | 6 +- docs/api/llm-ops.llm.exporthistory.md | 4 + docs/api/llm-ops.llm.log.md | 4 +- docs/api/llm-ops.llm.md | 18 +- docs/api/llm-ops.llm.moderate.md | 6 +- docs/api/llm-ops.llm.printmessage.md | 6 +- docs/api/llm-ops.llm.recall.md | 4 + ...ps.llm_ops_config.helicone_auth_api_key.md | 4 + docs/api/llm-ops.llm_ops_config.md | 14 +- .../llm-ops.llm_ops_config.milvus_address.md | 4 + .../llm-ops.llm_ops_config.milvus_password.md | 4 + .../llm-ops.llm_ops_config.milvus_username.md | 4 + docs/api/llm-ops.llm_ops_config.open_path.md | 4 + .../llm-ops.llm_ops_config.openai_api_key.md | 4 + docs/api/llm-ops.llmtype.md | 2 + docs/api/llm-ops.md | 48 +-- docs/api/llm-ops.messagefunctioncalltype.md | 2 + docs/api/llm-ops.messagestype.md | 2 + docs/api/llm-ops.messagetype.md | 2 + docs/api/llm-ops.multipromptschema.md | 4 + .../api/llm-ops.piperegistry._constructor_.md | 2 +- docs/api/llm-ops.piperegistry.md | 4 +- docs/api/llm-ops.promptsschema.md | 4 + docs/api/llm-ops.resembeddingtype.md | 2 + docs/api/llm-ops.resmessagestype.md | 2 + docs/api/llm-ops.resmoderationtype.md | 2 + docs/api/llm-ops.structschema.md | 4 + docs/guide/start.md | 312 +++++++++--------- docs/index.md | 12 +- docs/public/android-chrome-192x192.png | Bin 0 -> 12466 bytes docs/public/apple-touch-icon.png | Bin 0 -> 8674 bytes docs/public/browserconfig.xml | 9 + docs/public/favicon-16x16.png | Bin 0 -> 896 bytes docs/public/favicon-32x32.png | Bin 0 -> 1705 bytes docs/public/favicon.ico | Bin 0 -> 15086 bytes docs/public/mstile-150x150.png | Bin 0 -> 7294 bytes docs/public/og.jpg | Bin 0 -> 684176 bytes docs/public/safari-pinned-tab.svg | 49 +++ docs/public/site.webmanifest | 14 + package/chain/chain.ts | 45 +++ package/llm/index.ts | 78 ++++- package/pipeline/store.ts | 43 ++- package/prompt/promptTemplate.ts | 36 +- package/utils/config.ts | 35 +- 66 files changed, 729 insertions(+), 229 deletions(-) create mode 100644 docs/public/android-chrome-192x192.png create mode 100644 docs/public/apple-touch-icon.png create mode 100644 docs/public/browserconfig.xml create mode 100644 docs/public/favicon-16x16.png create mode 100644 docs/public/favicon-32x32.png create mode 100644 docs/public/favicon.ico create mode 100644 docs/public/mstile-150x150.png create mode 100644 docs/public/og.jpg create mode 100644 docs/public/safari-pinned-tab.svg create mode 100644 docs/public/site.webmanifest diff --git a/README.md b/README.md index aeae8cf..f3c4dbd 100644 --- a/README.md +++ b/README.md @@ -18,16 +18,15 @@ npm install llm-ops ## 特点 - - `工厂模式设计`: 选择LLM-OPS意味着选择了简便与专业!您无需深究复杂的内部结构,只需简单几步,如同开箱即用的神器。 - - `低代码开发`: 真正实现了一键式开发。只需简单地编写一段JSON代码,即可轻松实现您所想要的完整业务流程,大大减少了开发难度和学习成本。 - - `全面支持TypeScript`: LLM-OPS框架不仅基于TypeScript开发,更为开发者提供了完整的TsDoc注释。这让您在开发过程中更加流畅,无需反复查询文档,一切都变得清晰明了。 - - `AI制作的Workflow引擎`: 无论是构建复杂的企业应用还是快速打造创新项目,LLM-OPS都能为您提供稳定而高效的工作流引擎,真正做到快速开发,快速上线! +- `工厂模式设计`: 选择LLM-OPS意味着选择了简便与专业!您无需深究复杂的内部结构,只需简单几步,如同开箱即用的神器。 +- `低代码开发`: 真正实现了一键式开发。只需简单地编写一段JSON代码,即可轻松实现您所想要的完整业务流程,大大减少了开发难度和学习成本。 +- `全面支持TypeScript`: LLM-OPS框架不仅基于TypeScript开发,更为开发者提供了完整的TsDoc注释。这让您在开发过程中更加流畅,无需反复查询文档,一切都变得清晰明了。 +- `AI制作的Workflow引擎`: 无论是构建复杂的企业应用还是快速打造创新项目,LLM-OPS都能为您提供稳定而高效的工作流引擎,真正做到快速开发,快速上线! ## 低代码workflow示意图 ![pipeline示意图](https://github.com/IdeaLeap/llm-ops/assets/49270362/489d25cd-bc71-44b1-8a89-027075e3fec2) - ## 使用例程 ```ts @@ -87,6 +86,7 @@ const res = (await pipeline.execute( )) as Record; console.log(JSON.stringify(res["subsection"])); ``` + ## 框架设计结构 ![框架设计结构](https://github.com/IdeaLeap/llm-ops/assets/49270362/8834cba4-b055-41a0-a73d-f2c3346b23c5) diff --git a/docs/.vitepress/config.ts b/docs/.vitepress/config.ts index 0e218c9..141af80 100644 --- a/docs/.vitepress/config.ts +++ b/docs/.vitepress/config.ts @@ -6,8 +6,69 @@ export default defineConfig({ lang: "zh-CN", lastUpdated: true, markdown: { attrs: { disable: true } }, + head: [ + [ + "script", + { + async: "async", + src: "https://umami.marlene.top/umami.js", + "data-website-id": "8082e8f8-4d08-46ac-b089-bd883ab4c637", + "data-domains": "ops.idealeap.cn,preview.idealeap.cn", + }, + ], + ["link", { rel: "icon", href: "/favicon.ico" }], + ["meta", { name: "description", content: "LLM-OPS" }], + ["meta", { property: "og:url", content: "https://ops.idealeap.cn/" }], + ["meta", { property: "og:type", content: "website" }], + ["meta", { property: "og:title", content: "LLM-OPS | IdeaLeap" }], + ["meta", { property: "og:description", content: "LLM-OPS" }], + ["meta", { property: "og:image", content: "/og.jpg" }], + ["meta", { name: "twitter:card", content: "summary_large_image" }], + [ + "meta", + { + property: "twitter:domain", + content: "助力AIGC落地应用、高效开发llm workflow的低代码TS框架", + }, + ], + ["meta", { property: "twitter:url", content: "https://ops.idealeap.cn/" }], + ["meta", { name: "twitter:title", content: "HDU-CS-WIKI | HDU-CS-WIKI" }], + ["meta", { name: "twitter:description", content: "LLM-OPS" }], + ["meta", { name: "twitter:image", content: "/og.jpg" }], + [ + "link", + { + rel: "apple-touch-icon", + sizes: "180x180", + href: "/apple-touch-icon.png", + }, + ], + [ + "link", + { + rel: "icon", + type: "image/png", + sizes: "32x32", + href: "/favicon-32x32.png", + }, + ], + [ + "link", + { + rel: "icon", + type: "image/png", + sizes: "16x16", + href: "/favicon-16x16.png", + }, + ], + ["link", { rel: "manifest", href: "/site.webmanifest" }], + [ + "link", + { rel: "mask-icon", href: "/safari-pinned-tab.svg", color: "#5bbad5" }, + ], + ["meta", { name: "msapplication-TileColor", content: "#2b5797" }], + ], themeConfig: { - // https://vitepress.dev/reference/default-theme-config nav: [ { text: "Home", link: "/" }, { text: "Guide", link: "/guide/" }, diff --git a/docs/api/llm-ops.chain._constructor_.md b/docs/api/llm-ops.chain._constructor_.md index 76e8e60..ac58dc9 100644 --- a/docs/api/llm-ops.chain._constructor_.md +++ b/docs/api/llm-ops.chain._constructor_.md @@ -4,7 +4,7 @@ ## Chain.(constructor) -Constructs a new instance of the `Chain` class +创建一个新的 `Chain` 实例。 **Signature:** @@ -16,5 +16,5 @@ constructor(params: chainSchema); | Parameter | Type | Description | | --- | --- | --- | -| params | [chainSchema](./llm-ops.chainschema.md) | | +| params | [chainSchema](./llm-ops.chainschema.md) | 构造函数的参数对象。 | diff --git a/docs/api/llm-ops.chain.call.md b/docs/api/llm-ops.chain.call.md index f39731f..ac218ee 100644 --- a/docs/api/llm-ops.chain.call.md +++ b/docs/api/llm-ops.chain.call.md @@ -4,6 +4,8 @@ ## Chain.call() method +调用Chain + **Signature:** ```typescript @@ -14,9 +16,11 @@ call(params: chainCallSchema): Promise>; | Parameter | Type | Description | | --- | --- | --- | -| params | [chainCallSchema](./llm-ops.chaincallschema.md) | | +| params | [chainCallSchema](./llm-ops.chaincallschema.md) | 调用方法的参数对象。 | **Returns:** Promise<import("llm-ops/utils").[Result](./llm-ops.result.md)<any>> +{Promise} 返回异步处理的链调用结果。 + diff --git a/docs/api/llm-ops.chain.exporthistory.md b/docs/api/llm-ops.chain.exporthistory.md index f8f9a4a..4f8a5fe 100644 --- a/docs/api/llm-ops.chain.exporthistory.md +++ b/docs/api/llm-ops.chain.exporthistory.md @@ -4,6 +4,8 @@ ## Chain.exportHistory() method +导出链的历史记录。 + **Signature:** ```typescript @@ -13,3 +15,5 @@ exportHistory(): import("llm-ops/llm/index").messagesType; import("llm-ops/llm/index").[messagesType](./llm-ops.messagestype.md) +{any} 返回链的历史记录。 + diff --git a/docs/api/llm-ops.chain.md b/docs/api/llm-ops.chain.md index 92faa19..dd505ae 100644 --- a/docs/api/llm-ops.chain.md +++ b/docs/api/llm-ops.chain.md @@ -4,6 +4,8 @@ ## Chain class +`Chain` 类用于`llm`类的格式化输出。 + **Signature:** ```typescript @@ -14,7 +16,7 @@ export declare class Chain | Constructor | Modifiers | Description | | --- | --- | --- | -| [(constructor)(params)](./llm-ops.chain._constructor_.md) | | Constructs a new instance of the Chain class | +| [(constructor)(params)](./llm-ops.chain._constructor_.md) | | 创建一个新的 Chain 实例。 | ## Properties @@ -28,6 +30,6 @@ export declare class Chain | Method | Modifiers | Description | | --- | --- | --- | -| [call(params)](./llm-ops.chain.call.md) | | | -| [exportHistory()](./llm-ops.chain.exporthistory.md) | | | +| [call(params)](./llm-ops.chain.call.md) | | 调用Chain | +| [exportHistory()](./llm-ops.chain.exporthistory.md) | | 导出链的历史记录。 | diff --git a/docs/api/llm-ops.chaincallschema.md b/docs/api/llm-ops.chaincallschema.md index 0557c3d..59bc79c 100644 --- a/docs/api/llm-ops.chaincallschema.md +++ b/docs/api/llm-ops.chaincallschema.md @@ -4,6 +4,10 @@ ## chainCallSchema interface +`Chain`类的`call`方法的参数数据结构。 + + {messageType \| string} request - 请求消息或字符串。 {messageType\[\]} \[prompt\] - 一个可选的消息提示数组。 {structSchema} \[struct\] - 一个可选的结构模式。 {boolean} \[verbose\] - 一个可选的详细模式标志。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.chainschema.md b/docs/api/llm-ops.chainschema.md index 9d46620..e477ce8 100644 --- a/docs/api/llm-ops.chainschema.md +++ b/docs/api/llm-ops.chainschema.md @@ -4,6 +4,10 @@ ## chainSchema interface +`Chain` 构造函数的参数数据结构。 + + {LLM} \[llm\] - 一个可选的LLM实例。 {createLLMSchema} \[llmSchema\] - 一个可选的LLM模式对象。 {string} \[chainName\] - 一个可选的链名称。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.chatcompletiontype.md b/docs/api/llm-ops.chatcompletiontype.md index 27f9b00..cc4d729 100644 --- a/docs/api/llm-ops.chatcompletiontype.md +++ b/docs/api/llm-ops.chatcompletiontype.md @@ -4,6 +4,8 @@ ## chatCompletionType type +OpenAI聊天完成类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.chatparamstype.md b/docs/api/llm-ops.chatparamstype.md index b05c0ea..c233532 100644 --- a/docs/api/llm-ops.chatparamstype.md +++ b/docs/api/llm-ops.chatparamstype.md @@ -4,6 +4,8 @@ ## chatParamsType type +OpenAI聊天参数类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.chatschema.md b/docs/api/llm-ops.chatschema.md index 5a29158..d526e50 100644 --- a/docs/api/llm-ops.chatschema.md +++ b/docs/api/llm-ops.chatschema.md @@ -4,6 +4,8 @@ ## ChatSchema interface +聊天参数接口 + **Signature:** ```typescript @@ -16,5 +18,5 @@ export interface ChatSchema | --- | --- | --- | --- | | [function\_call?](./llm-ops.chatschema.function_call.md) | | [function\_callType](./llm-ops.function_calltype.md) | _(Optional)_ | | [functions?](./llm-ops.chatschema.functions.md) | | [functionsType](./llm-ops.functionstype.md) | _(Optional)_ | -| [messages](./llm-ops.chatschema.messages.md) | | [messagesType](./llm-ops.messagestype.md) | | +| [messages](./llm-ops.chatschema.messages.md) | | [messagesType](./llm-ops.messagestype.md) | 消息数组 | diff --git a/docs/api/llm-ops.chatschema.messages.md b/docs/api/llm-ops.chatschema.messages.md index 256ac5b..edccd02 100644 --- a/docs/api/llm-ops.chatschema.messages.md +++ b/docs/api/llm-ops.chatschema.messages.md @@ -4,6 +4,8 @@ ## ChatSchema.messages property +消息数组 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.createllmschema.helicone_auth_api_key.md b/docs/api/llm-ops.createllmschema.helicone_auth_api_key.md index f5b9db1..7506939 100644 --- a/docs/api/llm-ops.createllmschema.helicone_auth_api_key.md +++ b/docs/api/llm-ops.createllmschema.helicone_auth_api_key.md @@ -4,6 +4,8 @@ ## createLLMSchema.HELICONE\_AUTH\_API\_KEY property +Helicone授权API密钥 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.createllmschema.md b/docs/api/llm-ops.createllmschema.md index 5e98d68..ec17b5e 100644 --- a/docs/api/llm-ops.createllmschema.md +++ b/docs/api/llm-ops.createllmschema.md @@ -4,6 +4,8 @@ ## createLLMSchema interface +创建LLM的参数接口 + **Signature:** ```typescript @@ -16,10 +18,10 @@ export interface createLLMSchema | --- | --- | --- | --- | | [cache?](./llm-ops.createllmschema.cache.md) | | boolean | _(Optional)_ | | [choice\_num?](./llm-ops.createllmschema.choice_num.md) | | number \| 1 | _(Optional)_ | -| [HELICONE\_AUTH\_API\_KEY?](./llm-ops.createllmschema.helicone_auth_api_key.md) | | string | _(Optional)_ | +| [HELICONE\_AUTH\_API\_KEY?](./llm-ops.createllmschema.helicone_auth_api_key.md) | | string | _(Optional)_ Helicone授权API密钥 | | [history?](./llm-ops.createllmschema.history.md) | | [messagesType](./llm-ops.messagestype.md) | _(Optional)_ | -| [modelName?](./llm-ops.createllmschema.modelname.md) | | (string & object) \| "gpt-4" \| "gpt-4-0314" \| "gpt-4-0613" \| "gpt-4-32k" \| "gpt-4-32k-0314" \| "gpt-4-32k-0613" \| "gpt-3.5-turbo" \| "gpt-3.5-turbo-16k" \| "gpt-3.5-turbo-0301" \| "gpt-3.5-turbo-0613" \| "gpt-3.5-turbo-16k-0613" | _(Optional)_ | -| [OPENAI\_API\_KEY?](./llm-ops.createllmschema.openai_api_key.md) | | string | _(Optional)_ | +| [modelName?](./llm-ops.createllmschema.modelname.md) | | (string & object) \| "gpt-4" \| "gpt-4-0314" \| "gpt-4-0613" \| "gpt-4-32k" \| "gpt-4-32k-0314" \| "gpt-4-32k-0613" \| "gpt-3.5-turbo" \| "gpt-3.5-turbo-16k" \| "gpt-3.5-turbo-0301" \| "gpt-3.5-turbo-0613" \| "gpt-3.5-turbo-16k-0613" | _(Optional)_ 模型名称选项 | +| [OPENAI\_API\_KEY?](./llm-ops.createllmschema.openai_api_key.md) | | string | _(Optional)_ OpenAI API密钥 | | [stop?](./llm-ops.createllmschema.stop.md) | | string \| null \| string\[\] | _(Optional)_ | | [temperature?](./llm-ops.createllmschema.temperature.md) | | number | _(Optional)_ | | [tokens?](./llm-ops.createllmschema.tokens.md) | | number | _(Optional)_ | diff --git a/docs/api/llm-ops.createllmschema.modelname.md b/docs/api/llm-ops.createllmschema.modelname.md index 9c02751..d0e0aa8 100644 --- a/docs/api/llm-ops.createllmschema.modelname.md +++ b/docs/api/llm-ops.createllmschema.modelname.md @@ -4,6 +4,8 @@ ## createLLMSchema.modelName property +模型名称选项 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.createllmschema.openai_api_key.md b/docs/api/llm-ops.createllmschema.openai_api_key.md index 9a4be9c..1699bd6 100644 --- a/docs/api/llm-ops.createllmschema.openai_api_key.md +++ b/docs/api/llm-ops.createllmschema.openai_api_key.md @@ -4,6 +4,8 @@ ## createLLMSchema.OPENAI\_API\_KEY property +OpenAI API密钥 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.formatprompttemplate.md b/docs/api/llm-ops.formatprompttemplate.md index 86e966f..fe74958 100644 --- a/docs/api/llm-ops.formatprompttemplate.md +++ b/docs/api/llm-ops.formatprompttemplate.md @@ -4,8 +4,24 @@ ## formatPromptTemplate variable +格式化提示模板函数。 + +此函数的主要目的是为了从提供的`prompts`对象中提取出一个格式化的提示模板。 这可以是一个单一的模板,也可以是一个模板数组。 + **Signature:** ```typescript formatPromptTemplate: (prompts: PromptsSchema) => Promise<(import("openai/resources/chat").ChatCompletionMessageParam | MultiPromptSchema | undefined)[]> ``` + +## Example + + +```typescript +const promptsData = { + name: "agentPromptTemplate", + schema: { role: "user" }, +}; +const formattedPrompts = await formatPromptTemplate(promptsData); +``` + diff --git a/docs/api/llm-ops.function_calltype.md b/docs/api/llm-ops.function_calltype.md index 759acc2..fc514d6 100644 --- a/docs/api/llm-ops.function_calltype.md +++ b/docs/api/llm-ops.function_calltype.md @@ -4,6 +4,8 @@ ## function\_callType type +function\_call类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.functioncalloption.md b/docs/api/llm-ops.functioncalloption.md index 78de56d..22038f3 100644 --- a/docs/api/llm-ops.functioncalloption.md +++ b/docs/api/llm-ops.functioncalloption.md @@ -4,6 +4,8 @@ ## FunctionCallOption interface +FunctionCall的接口 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.functionstype.md b/docs/api/llm-ops.functionstype.md index b7d5e54..9fa31a6 100644 --- a/docs/api/llm-ops.functionstype.md +++ b/docs/api/llm-ops.functionstype.md @@ -4,6 +4,8 @@ ## functionsType type +FunctionInterface类型数组 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm._constructor_.md b/docs/api/llm-ops.llm._constructor_.md index cd62b77..f333507 100644 --- a/docs/api/llm-ops.llm._constructor_.md +++ b/docs/api/llm-ops.llm._constructor_.md @@ -4,7 +4,7 @@ ## LLM.(constructor) -Constructs a new instance of the `LLM` class +构造一个LLM客户端实例。 **Signature:** @@ -16,5 +16,5 @@ constructor(params: createLLMSchema); | Parameter | Type | Description | | --- | --- | --- | -| params | [createLLMSchema](./llm-ops.createllmschema.md) | | +| params | [createLLMSchema](./llm-ops.createllmschema.md) | 包含创建LLM客户端所需的参数。 | diff --git a/docs/api/llm-ops.llm.chat.md b/docs/api/llm-ops.llm.chat.md index b3f688f..1058851 100644 --- a/docs/api/llm-ops.llm.chat.md +++ b/docs/api/llm-ops.llm.chat.md @@ -4,6 +4,8 @@ ## LLM.chat() method +使用LLM进行聊天。 + **Signature:** ```typescript @@ -14,9 +16,11 @@ chat(params: ChatSchema): Promise; | Parameter | Type | Description | | --- | --- | --- | -| params | [ChatSchema](./llm-ops.chatschema.md) | | +| params | [ChatSchema](./llm-ops.chatschema.md) | 包含聊天所需的参数。 | **Returns:** Promise<[chatCompletionType](./llm-ops.chatcompletiontype.md)> +返回聊天的完成信息。 + diff --git a/docs/api/llm-ops.llm.embedding.md b/docs/api/llm-ops.llm.embedding.md index c765dae..9c694b4 100644 --- a/docs/api/llm-ops.llm.embedding.md +++ b/docs/api/llm-ops.llm.embedding.md @@ -4,6 +4,8 @@ ## LLM.embedding() method +获取输入内容的嵌入向量。 + **Signature:** ```typescript @@ -14,9 +16,11 @@ embedding(input: string | string[] | number[] | number[][]): Promise> +返回嵌入结果。 + diff --git a/docs/api/llm-ops.llm.exporthistory.md b/docs/api/llm-ops.llm.exporthistory.md index 919d4b4..b4659e9 100644 --- a/docs/api/llm-ops.llm.exporthistory.md +++ b/docs/api/llm-ops.llm.exporthistory.md @@ -4,6 +4,8 @@ ## LLM.exportHistory() method +导出历史消息。 + **Signature:** ```typescript @@ -13,3 +15,5 @@ exportHistory(): messagesType; [messagesType](./llm-ops.messagestype.md) +返回聊天的历史消息。 + diff --git a/docs/api/llm-ops.llm.log.md b/docs/api/llm-ops.llm.log.md index 28bb098..25623b2 100644 --- a/docs/api/llm-ops.llm.log.md +++ b/docs/api/llm-ops.llm.log.md @@ -4,6 +4,8 @@ ## LLM.log() method +日志输出。 + **Signature:** ```typescript @@ -14,7 +16,7 @@ static log(...args: string[]): void; | Parameter | Type | Description | | --- | --- | --- | -| args | string\[\] | | +| args | string\[\] | 要输出的内容。 | **Returns:** diff --git a/docs/api/llm-ops.llm.md b/docs/api/llm-ops.llm.md index 16af532..0397580 100644 --- a/docs/api/llm-ops.llm.md +++ b/docs/api/llm-ops.llm.md @@ -4,6 +4,8 @@ ## LLM class +LLM类代表一个与LLM API进行交互的客户端。 + **Signature:** ```typescript @@ -14,7 +16,7 @@ export declare class LLM | Constructor | Modifiers | Description | | --- | --- | --- | -| [(constructor)(params)](./llm-ops.llm._constructor_.md) | | Constructs a new instance of the LLM class | +| [(constructor)(params)](./llm-ops.llm._constructor_.md) | | 构造一个LLM客户端实例。 | ## Properties @@ -37,11 +39,11 @@ export declare class LLM | Method | Modifiers | Description | | --- | --- | --- | -| [chat(params)](./llm-ops.llm.chat.md) | | | -| [embedding(input)](./llm-ops.llm.embedding.md) | | | -| [exportHistory()](./llm-ops.llm.exporthistory.md) | | | -| [log(args)](./llm-ops.llm.log.md) | static | | -| [moderate(input)](./llm-ops.llm.moderate.md) | | | -| [printMessage(resMessages, reqMessages)](./llm-ops.llm.printmessage.md) | | | -| [recall()](./llm-ops.llm.recall.md) | | | +| [chat(params)](./llm-ops.llm.chat.md) | | 使用LLM进行聊天。 | +| [embedding(input)](./llm-ops.llm.embedding.md) | | 获取输入内容的嵌入向量。 | +| [exportHistory()](./llm-ops.llm.exporthistory.md) | | 导出历史消息。 | +| [log(args)](./llm-ops.llm.log.md) | static | 日志输出。 | +| [moderate(input)](./llm-ops.llm.moderate.md) | | 对输入内容进行审查。 | +| [printMessage(resMessages, reqMessages)](./llm-ops.llm.printmessage.md) | | 打印消息。 | +| [recall()](./llm-ops.llm.recall.md) | | 重复最后一次的聊天。 | diff --git a/docs/api/llm-ops.llm.moderate.md b/docs/api/llm-ops.llm.moderate.md index e83d449..cb6080d 100644 --- a/docs/api/llm-ops.llm.moderate.md +++ b/docs/api/llm-ops.llm.moderate.md @@ -4,6 +4,8 @@ ## LLM.moderate() method +对输入内容进行审查。 + **Signature:** ```typescript @@ -14,9 +16,11 @@ moderate(input: string | string[]): Promise; | Parameter | Type | Description | | --- | --- | --- | -| input | string \| string\[\] | | +| input | string \| string\[\] | 需要审查的输入内容。 | **Returns:** Promise<[resModerationType](./llm-ops.resmoderationtype.md)> +返回审查结果。 + diff --git a/docs/api/llm-ops.llm.printmessage.md b/docs/api/llm-ops.llm.printmessage.md index 4f6a22a..bee194b 100644 --- a/docs/api/llm-ops.llm.printmessage.md +++ b/docs/api/llm-ops.llm.printmessage.md @@ -4,6 +4,8 @@ ## LLM.printMessage() method +打印消息。 + **Signature:** ```typescript @@ -14,8 +16,8 @@ printMessage(resMessages?: resMessagesType, reqMessages?: messagesType): void; | Parameter | Type | Description | | --- | --- | --- | -| resMessages | [resMessagesType](./llm-ops.resmessagestype.md) | _(Optional)_ | -| reqMessages | [messagesType](./llm-ops.messagestype.md) | _(Optional)_ | +| resMessages | [resMessagesType](./llm-ops.resmessagestype.md) | _(Optional)_ 响应消息。 | +| reqMessages | [messagesType](./llm-ops.messagestype.md) | _(Optional)_ 请求消息。 | **Returns:** diff --git a/docs/api/llm-ops.llm.recall.md b/docs/api/llm-ops.llm.recall.md index 6e69171..2864c34 100644 --- a/docs/api/llm-ops.llm.recall.md +++ b/docs/api/llm-ops.llm.recall.md @@ -4,6 +4,8 @@ ## LLM.recall() method +重复最后一次的聊天。 + **Signature:** ```typescript @@ -13,3 +15,5 @@ recall(): Promise; Promise<[chatCompletionType](./llm-ops.chatcompletiontype.md)> +返回重复的聊天完成信息。 + diff --git a/docs/api/llm-ops.llm_ops_config.helicone_auth_api_key.md b/docs/api/llm-ops.llm_ops_config.helicone_auth_api_key.md index 02c3074..2c7be46 100644 --- a/docs/api/llm-ops.llm_ops_config.helicone_auth_api_key.md +++ b/docs/api/llm-ops.llm_ops_config.helicone_auth_api_key.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.HELICONE\_AUTH\_API\_KEY property +Helicone的授权API密钥。 + +从环境变量中获取该密钥。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm_ops_config.md b/docs/api/llm-ops.llm_ops_config.md index f72e34b..f941bc7 100644 --- a/docs/api/llm-ops.llm_ops_config.md +++ b/docs/api/llm-ops.llm_ops_config.md @@ -4,6 +4,8 @@ ## LLM\_OPS\_CONFIG class +`LLM_OPS_CONFIG` 是用于配置操作的类,它包含了与OpenAI、Helicone和Milvus相关的配置信息。 + **Signature:** ```typescript @@ -14,10 +16,10 @@ export declare class LLM_OPS_CONFIG | Property | Modifiers | Type | Description | | --- | --- | --- | --- | -| [HELICONE\_AUTH\_API\_KEY](./llm-ops.llm_ops_config.helicone_auth_api_key.md) | static | string \| undefined | | -| [MILVUS\_ADDRESS](./llm-ops.llm_ops_config.milvus_address.md) | static | string \| undefined | | -| [MILVUS\_PASSWORD](./llm-ops.llm_ops_config.milvus_password.md) | static | string \| undefined | | -| [MILVUS\_USERNAME](./llm-ops.llm_ops_config.milvus_username.md) | static | string \| undefined | | -| [OPEN\_PATH](./llm-ops.llm_ops_config.open_path.md) | static | { baseURL: string; defaultHeaders: { "Helicone-Auth": string; }; } | | -| [OPENAI\_API\_KEY](./llm-ops.llm_ops_config.openai_api_key.md) | static | string \| undefined | | +| [HELICONE\_AUTH\_API\_KEY](./llm-ops.llm_ops_config.helicone_auth_api_key.md) | static | string \| undefined |

Helicone的授权API密钥。

从环境变量中获取该密钥。

| +| [MILVUS\_ADDRESS](./llm-ops.llm_ops_config.milvus_address.md) | static | string \| undefined |

Milvus的服务地址。

从环境变量中获取此地址。

| +| [MILVUS\_PASSWORD](./llm-ops.llm_ops_config.milvus_password.md) | static | string \| undefined |

用于连接到Milvus服务的密码。

从环境变量中获取此密码。

| +| [MILVUS\_USERNAME](./llm-ops.llm_ops_config.milvus_username.md) | static | string \| undefined |

用于连接到Milvus服务的用户名。

从环境变量中获取此用户名。

| +| [OPEN\_PATH](./llm-ops.llm_ops_config.open_path.md) | static | { baseURL: string; defaultHeaders: { "Helicone-Auth": string; }; } |

OPEN\_PATH 对象定义了与Helicone API进行通信时的基础设置。

{string} baseURL - Helicone API的基础URL。 {Object} defaultHeaders - 用于与Helicone API通信时的默认请求头。

| +| [OPENAI\_API\_KEY](./llm-ops.llm_ops_config.openai_api_key.md) | static | string \| undefined |

OpenAI的API密钥。

从环境变量中获取该密钥。

| diff --git a/docs/api/llm-ops.llm_ops_config.milvus_address.md b/docs/api/llm-ops.llm_ops_config.milvus_address.md index 9c847df..1813c08 100644 --- a/docs/api/llm-ops.llm_ops_config.milvus_address.md +++ b/docs/api/llm-ops.llm_ops_config.milvus_address.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.MILVUS\_ADDRESS property +Milvus的服务地址。 + +从环境变量中获取此地址。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm_ops_config.milvus_password.md b/docs/api/llm-ops.llm_ops_config.milvus_password.md index 8dfe9a7..7df58e5 100644 --- a/docs/api/llm-ops.llm_ops_config.milvus_password.md +++ b/docs/api/llm-ops.llm_ops_config.milvus_password.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.MILVUS\_PASSWORD property +用于连接到Milvus服务的密码。 + +从环境变量中获取此密码。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm_ops_config.milvus_username.md b/docs/api/llm-ops.llm_ops_config.milvus_username.md index fadac1e..fff38bd 100644 --- a/docs/api/llm-ops.llm_ops_config.milvus_username.md +++ b/docs/api/llm-ops.llm_ops_config.milvus_username.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.MILVUS\_USERNAME property +用于连接到Milvus服务的用户名。 + +从环境变量中获取此用户名。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm_ops_config.open_path.md b/docs/api/llm-ops.llm_ops_config.open_path.md index bc97ce5..0fbebdb 100644 --- a/docs/api/llm-ops.llm_ops_config.open_path.md +++ b/docs/api/llm-ops.llm_ops_config.open_path.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.OPEN\_PATH property +OPEN\_PATH 对象定义了与Helicone API进行通信时的基础设置。 + + {string} baseURL - Helicone API的基础URL。 {Object} defaultHeaders - 用于与Helicone API通信时的默认请求头。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llm_ops_config.openai_api_key.md b/docs/api/llm-ops.llm_ops_config.openai_api_key.md index 197781c..019ed4c 100644 --- a/docs/api/llm-ops.llm_ops_config.openai_api_key.md +++ b/docs/api/llm-ops.llm_ops_config.openai_api_key.md @@ -4,6 +4,10 @@ ## LLM\_OPS\_CONFIG.OPENAI\_API\_KEY property +OpenAI的API密钥。 + +从环境变量中获取该密钥。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.llmtype.md b/docs/api/llm-ops.llmtype.md index 8248d80..83b1274 100644 --- a/docs/api/llm-ops.llmtype.md +++ b/docs/api/llm-ops.llmtype.md @@ -4,6 +4,8 @@ ## llmType type +LLM类型定义为OpenAI类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.md b/docs/api/llm-ops.md index 6dfabfc..8c57513 100644 --- a/docs/api/llm-ops.md +++ b/docs/api/llm-ops.md @@ -10,12 +10,12 @@ | --- | --- | | [AgentPromptTemplate](./llm-ops.agentprompttemplate.md) | | | [BaseAgent](./llm-ops.baseagent.md) | | -| [Chain](./llm-ops.chain.md) | | +| [Chain](./llm-ops.chain.md) | Chain 类用于llm类的格式化输出。 | | [FunctionChain](./llm-ops.functionchain.md) | | -| [LLM\_OPS\_CONFIG](./llm-ops.llm_ops_config.md) | | -| [LLM](./llm-ops.llm.md) | | +| [LLM\_OPS\_CONFIG](./llm-ops.llm_ops_config.md) | LLM_OPS_CONFIG 是用于配置操作的类,它包含了与OpenAI、Helicone和Milvus相关的配置信息。 | +| [LLM](./llm-ops.llm.md) | LLM类代表一个与LLM API进行交互的客户端。 | | [milvusVectorDB](./llm-ops.milvusvectordb.md) | | -| [PipeRegistry](./llm-ops.piperegistry.md) | | +| [PipeRegistry](./llm-ops.piperegistry.md) | 扩展原始的PipeRegistry类 | | [PolishPromptTemplate](./llm-ops.polishprompttemplate.md) | | | [TypeScriptChain](./llm-ops.typescriptchain.md) | Creates an object that can translate natural language requests into JSON objects of the given type. The specified type argument T must be the same type as typeName in the given schema. The function creates a TypeChatJsonValidator<T> and stores it in the validator property of the returned instance. | @@ -41,14 +41,14 @@ | --- | --- | | [AgentPromptTemplateSchema](./llm-ops.agentprompttemplateschema.md) | | | [BaseAgentCallSchema](./llm-ops.baseagentcallschema.md) | | -| [chainCallSchema](./llm-ops.chaincallschema.md) | | -| [chainSchema](./llm-ops.chainschema.md) | | -| [ChatSchema](./llm-ops.chatschema.md) | | -| [createLLMSchema](./llm-ops.createllmschema.md) | | +| [chainCallSchema](./llm-ops.chaincallschema.md) |

Chain类的call方法的参数数据结构。

{messageType \| string} request - 请求消息或字符串。 {messageType\[\]} \[prompt\] - 一个可选的消息提示数组。 {structSchema} \[struct\] - 一个可选的结构模式。 {boolean} \[verbose\] - 一个可选的详细模式标志。

| +| [chainSchema](./llm-ops.chainschema.md) |

Chain 构造函数的参数数据结构。

{LLM} \[llm\] - 一个可选的LLM实例。 {createLLMSchema} \[llmSchema\] - 一个可选的LLM模式对象。 {string} \[chainName\] - 一个可选的链名称。

| +| [ChatSchema](./llm-ops.chatschema.md) | 聊天参数接口 | +| [createLLMSchema](./llm-ops.createllmschema.md) | 创建LLM的参数接口 | | [createMessageSchema](./llm-ops.createmessageschema.md) | | | [Error\_2](./llm-ops.error_2.md) | An object representing an operation that failed for the reason given in message. | | [EvaluateItemSchema](./llm-ops.evaluateitemschema.md) | | -| [FunctionCallOption](./llm-ops.functioncalloption.md) | | +| [FunctionCallOption](./llm-ops.functioncalloption.md) | FunctionCall的接口 | | [FunctionCallSchema](./llm-ops.functioncallschema.md) | | | [FunctionInterface](./llm-ops.functioninterface.md) | | | [MemoryPromptTemplateSchema](./llm-ops.memoryprompttemplateschema.md) | | @@ -59,11 +59,11 @@ | [milvusVectorDBSchema](./llm-ops.milvusvectordbschema.md) | | | [milvusVectorDBSearchSchema](./llm-ops.milvusvectordbsearchschema.md) | | | [milvusVectorDBUploadSchema](./llm-ops.milvusvectordbuploadschema.md) | | -| [MultiPromptSchema](./llm-ops.multipromptschema.md) | | +| [MultiPromptSchema](./llm-ops.multipromptschema.md) |

多重提示模板数据结构。

{string} \[COLLECTION\_NAME\] - 一个可选的集合名称,用于指定向量数据库的集合。

| | [polishPromptTemplateSchema](./llm-ops.polishprompttemplateschema.md) | | -| [PromptsSchema](./llm-ops.promptsschema.md) | | +| [PromptsSchema](./llm-ops.promptsschema.md) |

主要的模板数据结构。

{string} name - 提示模板的名称。可能的值为:"polishPromptTemplate"、"agentPromptTemplate" 或其他字符串。 {MultiPromptSchema\[\] \| any\[\]} \[prompt\] - 一个可选的提示模板数组。 {Record<string, any>} \[schema\] - 一个可选的记录对象,用于存储额外的数据模板。 {string} \[COLLECTION\_NAME\] - 一个可选的集合名称,用于指定向量数据库的集合。

| | [ReflectionPromptTemplateSchema](./llm-ops.reflectionprompttemplateschema.md) | | -| [structSchema](./llm-ops.structschema.md) | | +| [structSchema](./llm-ops.structschema.md) |

call 方法的struct参数的数据结构。

{functionsType} \[functions\] - 可选的函数类型。 {function\_callType} \[function\_call\] - 可选的函数调用类型。 {string} \[schema\] - 一个可选的模式字符串。 {string} \[typeName\] - 一个可选的类型名称。

| | [Success](./llm-ops.success.md) | An object representing a successful operation with a result of type T. | | [TeacherEvaluateSchema](./llm-ops.teacherevaluateschema.md) | | | [TypeChatJsonValidator](./llm-ops.typechatjsonvalidator.md) | Represents an object that can validate JSON strings according to a given TypeScript schema. | @@ -73,28 +73,28 @@ | Variable | Description | | --- | --- | -| [formatPromptTemplate](./llm-ops.formatprompttemplate.md) | | +| [formatPromptTemplate](./llm-ops.formatprompttemplate.md) |

格式化提示模板函数。

此函数的主要目的是为了从提供的prompts对象中提取出一个格式化的提示模板。 这可以是一个单一的模板,也可以是一个模板数组。

| ## Type Aliases | Type Alias | Description | | --- | --- | -| [chatCompletionType](./llm-ops.chatcompletiontype.md) | | -| [chatParamsType](./llm-ops.chatparamstype.md) | | +| [chatCompletionType](./llm-ops.chatcompletiontype.md) | OpenAI聊天完成类型 | +| [chatParamsType](./llm-ops.chatparamstype.md) | OpenAI聊天参数类型 | | [communicationStyleType](./llm-ops.communicationstyletype.md) | | -| [function\_callType](./llm-ops.function_calltype.md) | | -| [functionsType](./llm-ops.functionstype.md) | | +| [function\_callType](./llm-ops.function_calltype.md) | function\_call类型 | +| [functionsType](./llm-ops.functionstype.md) | FunctionInterface类型数组 | | [InputValues](./llm-ops.inputvalues.md) | | | [languageType](./llm-ops.languagetype.md) | | | [levelType](./llm-ops.leveltype.md) | | -| [llmType](./llm-ops.llmtype.md) | | -| [messageFunctionCallType](./llm-ops.messagefunctioncalltype.md) | | -| [messagesType](./llm-ops.messagestype.md) | | -| [messageType](./llm-ops.messagetype.md) | | +| [llmType](./llm-ops.llmtype.md) | LLM类型定义为OpenAI类型 | +| [messageFunctionCallType](./llm-ops.messagefunctioncalltype.md) | OpenAI聊天完成消息中的FunctionCall类型 | +| [messagesType](./llm-ops.messagestype.md) | OpenAI聊天请求消息的类型数组 | +| [messageType](./llm-ops.messagetype.md) | 单一的OpenAI聊天请求消息类型 | | [reasoningType](./llm-ops.reasoningtype.md) | | -| [resEmbeddingType](./llm-ops.resembeddingtype.md) | | -| [resMessagesType](./llm-ops.resmessagestype.md) | | -| [resModerationType](./llm-ops.resmoderationtype.md) | | +| [resEmbeddingType](./llm-ops.resembeddingtype.md) | OpenAI创建嵌入响应类型 | +| [resMessagesType](./llm-ops.resmessagestype.md) | OpenAI聊天完成的消息数组类型 | +| [resModerationType](./llm-ops.resmoderationtype.md) | OpenAI内容审查响应类型 | | [Result](./llm-ops.result.md) | An object representing a successful or failed operation of type T. | | [toneStyleType](./llm-ops.tonestyletype.md) | | diff --git a/docs/api/llm-ops.messagefunctioncalltype.md b/docs/api/llm-ops.messagefunctioncalltype.md index 362dd73..39df112 100644 --- a/docs/api/llm-ops.messagefunctioncalltype.md +++ b/docs/api/llm-ops.messagefunctioncalltype.md @@ -4,6 +4,8 @@ ## messageFunctionCallType type +OpenAI聊天完成消息中的FunctionCall类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.messagestype.md b/docs/api/llm-ops.messagestype.md index c8f6295..d263575 100644 --- a/docs/api/llm-ops.messagestype.md +++ b/docs/api/llm-ops.messagestype.md @@ -4,6 +4,8 @@ ## messagesType type +OpenAI聊天请求消息的类型数组 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.messagetype.md b/docs/api/llm-ops.messagetype.md index 7cf0400..8867071 100644 --- a/docs/api/llm-ops.messagetype.md +++ b/docs/api/llm-ops.messagetype.md @@ -4,6 +4,8 @@ ## messageType type +单一的OpenAI聊天请求消息类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.multipromptschema.md b/docs/api/llm-ops.multipromptschema.md index 5470276..204cf6d 100644 --- a/docs/api/llm-ops.multipromptschema.md +++ b/docs/api/llm-ops.multipromptschema.md @@ -4,6 +4,10 @@ ## MultiPromptSchema interface +多重提示模板数据结构。 + + {string} \[COLLECTION\_NAME\] - 一个可选的集合名称,用于指定向量数据库的集合。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.piperegistry._constructor_.md b/docs/api/llm-ops.piperegistry._constructor_.md index ee6aab2..3611728 100644 --- a/docs/api/llm-ops.piperegistry._constructor_.md +++ b/docs/api/llm-ops.piperegistry._constructor_.md @@ -4,7 +4,7 @@ ## PipeRegistry.(constructor) -Constructs a new instance of the `PipeRegistry` class +PipeRegistry的构造函数 **Signature:** diff --git a/docs/api/llm-ops.piperegistry.md b/docs/api/llm-ops.piperegistry.md index b0fd15c..35a6ff8 100644 --- a/docs/api/llm-ops.piperegistry.md +++ b/docs/api/llm-ops.piperegistry.md @@ -4,6 +4,8 @@ ## PipeRegistry class +扩展原始的PipeRegistry类 + **Signature:** ```typescript @@ -15,5 +17,5 @@ export declare class PipeRegistry extends OriginalPipeline.PipeRegistry | Constructor | Modifiers | Description | | --- | --- | --- | -| [(constructor)()](./llm-ops.piperegistry._constructor_.md) | | Constructs a new instance of the PipeRegistry class | +| [(constructor)()](./llm-ops.piperegistry._constructor_.md) | | PipeRegistry的构造函数 | diff --git a/docs/api/llm-ops.promptsschema.md b/docs/api/llm-ops.promptsschema.md index 4838adf..2948d94 100644 --- a/docs/api/llm-ops.promptsschema.md +++ b/docs/api/llm-ops.promptsschema.md @@ -4,6 +4,10 @@ ## PromptsSchema interface +主要的模板数据结构。 + + {string} name - 提示模板的名称。可能的值为:"polishPromptTemplate"、"agentPromptTemplate" 或其他字符串。 {MultiPromptSchema\[\] \| any\[\]} \[prompt\] - 一个可选的提示模板数组。 {Record<string, any>} \[schema\] - 一个可选的记录对象,用于存储额外的数据模板。 {string} \[COLLECTION\_NAME\] - 一个可选的集合名称,用于指定向量数据库的集合。 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.resembeddingtype.md b/docs/api/llm-ops.resembeddingtype.md index 0e84983..91a090e 100644 --- a/docs/api/llm-ops.resembeddingtype.md +++ b/docs/api/llm-ops.resembeddingtype.md @@ -4,6 +4,8 @@ ## resEmbeddingType type +OpenAI创建嵌入响应类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.resmessagestype.md b/docs/api/llm-ops.resmessagestype.md index 899fcf3..597b169 100644 --- a/docs/api/llm-ops.resmessagestype.md +++ b/docs/api/llm-ops.resmessagestype.md @@ -4,6 +4,8 @@ ## resMessagesType type +OpenAI聊天完成的消息数组类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.resmoderationtype.md b/docs/api/llm-ops.resmoderationtype.md index da72b16..46f2f41 100644 --- a/docs/api/llm-ops.resmoderationtype.md +++ b/docs/api/llm-ops.resmoderationtype.md @@ -4,6 +4,8 @@ ## resModerationType type +OpenAI内容审查响应类型 + **Signature:** ```typescript diff --git a/docs/api/llm-ops.structschema.md b/docs/api/llm-ops.structschema.md index c92dbd9..9732ace 100644 --- a/docs/api/llm-ops.structschema.md +++ b/docs/api/llm-ops.structschema.md @@ -4,6 +4,10 @@ ## structSchema interface +`call` 方法的`struct`参数的数据结构。 + + {functionsType} \[functions\] - 可选的函数类型。 {function\_callType} \[function\_call\] - 可选的函数调用类型。 {string} \[schema\] - 一个可选的模式字符串。 {string} \[typeName\] - 一个可选的类型名称。 + **Signature:** ```typescript diff --git a/docs/guide/start.md b/docs/guide/start.md index ed96036..e9d7684 100644 --- a/docs/guide/start.md +++ b/docs/guide/start.md @@ -13,11 +13,11 @@ npm install llm-ops ## 创建一个简单的pipeline ```ts -import { Pipeline,PipeRegistry } from "llm-ops"; -const funcStore = PipeRegistry.init();//将功能函数注册到funcStore中 +import { Pipeline, PipeRegistry } from "llm-ops"; +const funcStore = PipeRegistry.init(); //将功能函数注册到funcStore中 const pipelineJson = { - pipes:[] -} + pipes: [], +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); const res = await pipeline.execute(""); ``` @@ -37,25 +37,29 @@ const res = await pipeline.execute(""); > 目前通过json方式使用的`llm`不支持历史记录,如果需要历史记录,请参考相关`API`文档,使用完整的`llm`功能。 ```ts -import { Pipeline,PipeRegistry } from "llm-ops"; +import { Pipeline, PipeRegistry } from "llm-ops"; const funcStore = PipeRegistry.init(); const pipelineJson = { - pipes:[{ - id: "polish", - use: "llm", - params: { - messages: [ - { - role: "system", - content: - "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。", - }, - ], - }, - }] -} + pipes: [ + { + id: "polish", + use: "llm", + params: { + messages: [ + { + role: "system", + content: + "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。", + }, + ], + }, + }, + ], +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); -const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +const res = await pipeline.execute( + "尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。", +); ``` ## 设置环境变量 @@ -75,13 +79,13 @@ LLM_OPS_CONFIG.OPENAI_API_KEY = ""; 在国内,通常无法直接使用OPENAI_API_KEY,因此,需要使用代理。推荐使用`Helicone`作为代理API。手动设置代理API的方法如下: ```ts -import { Pipeline,PipeRegistry,LLM_OPS_CONFIG } from "llm-ops"; +import { Pipeline, PipeRegistry, LLM_OPS_CONFIG } from "llm-ops"; LLM_OPS_CONFIG.OPEN_PATH = { baseURL: "https://oai.hconeai.com/v1", defaultHeaders: { "Helicone-Auth": `Bearer xxx`, }, -} +}; ``` ## 使用chain @@ -91,42 +95,51 @@ LLM_OPS_CONFIG.OPEN_PATH = { 以下是`functionCall`的使用示例: ```ts -import { Pipeline,PipeRegistry } from "llm-ops"; +import { Pipeline, PipeRegistry } from "llm-ops"; const funcStore = PipeRegistry.init(); const pipelineJson = { - "pipes": [{ - "id": "classify", - "use": "chain", - "params": { - "struct": { - "functions": [{ - "name": "get_categories", - "description": "根据PPT文稿获取PPT类别", - "parameters": { - "type": "object", - "properties": { - "categories": { - "type": "string", - "enum": ["汇报类型", "演讲类型", "活动类型"], - "description": "PPT文稿属于哪一类型的PPT" - } + pipes: [ + { + id: "classify", + use: "chain", + params: { + struct: { + functions: [ + { + name: "get_categories", + description: "根据PPT文稿获取PPT类别", + parameters: { + type: "object", + properties: { + categories: { + type: "string", + enum: ["汇报类型", "演讲类型", "活动类型"], + description: "PPT文稿属于哪一类型的PPT", + }, + }, + required: ["categories"], + }, }, - "required": ["categories"] - } - }], - "function_call": { - "name": "get_categories" - } + ], + function_call: { + name: "get_categories", + }, + }, + prompt: [ + { + role: "system", + content: + "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。", + }, + ], }, - "prompt": [{ - "role": "system", - "content": "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。" - }] - } - }] -} + }, + ], +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); -const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +const res = await pipeline.execute( + "尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。", +); ``` 更复杂的用法可以学习官方的[Cook Book](https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models)。 @@ -134,16 +147,17 @@ const res = await pipeline.execute("尊敬的各位评审,大家好!今天 以下是`typeChat`的使用示例: ```ts -import { Pipeline,PipeRegistry } from "llm-ops"; +import { Pipeline, PipeRegistry } from "llm-ops"; const funcStore = PipeRegistry.init(); const pipelineJson = { - "pipes": [{ - id: "subsection", - use: "chain", - params: { - chainName: "typeChat", - struct: { - schema:` + pipes: [ + { + id: "subsection", + use: "chain", + params: { + chainName: "typeChat", + struct: { + schema: ` export interface subsectionSchema { subsection: articleSchema[]; title: string; // 整篇PPT文稿的标题 @@ -153,21 +167,24 @@ export interface articleSchema { content: string; //每一段的文本 title: string; // 每一段起一个小标题,非常凝练这一段主要的内容是什么,同时与其他段落保持一个一致性的风格 }`, - typeName: "subsectionSchema", - }, - prompt: [ - { - role: "system", - content: - "你是一个专职PPT文稿大纲处理的助手,将会对user输入的文稿进行分段,并进行中心点提取成小标题。给出整篇PPT文稿的标题,分段的理由,每段小标题和对应的内容。", + typeName: "subsectionSchema", }, - ], - bound: false, + prompt: [ + { + role: "system", + content: + "你是一个专职PPT文稿大纲处理的助手,将会对user输入的文稿进行分段,并进行中心点提取成小标题。给出整篇PPT文稿的标题,分段的理由,每段小标题和对应的内容。", + }, + ], + bound: false, + }, }, - }] -} + ], +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); -const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +const res = await pipeline.execute( + "尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。", +); ``` 从上述示例可以看出,`chain`中的`prompt`和`llm`中的`messages`是一样的,都是用于填写`chain`的历史对话和prompt。 @@ -193,51 +210,64 @@ const pipelineJson = { 在pipes中添加多个pipe即可执行多个步骤。默认情况下,每个pipe的输入都是上一个pipe的输出。 ```ts -import { Pipeline,PipeRegistry } from "llm-ops"; +import { Pipeline, PipeRegistry } from "llm-ops"; const funcStore = PipeRegistry.init(); const pipelineJson = { - "pipes": [{ - "id": "polish", - "use": "llm", - "params": { - "messages": [{ - "role": "system", - "content": "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。" - }] - } - }, { - "id": "classify", - "use": "chain", - "params": { - "struct": { - "functions": [{ - "name": "get_categories", - "description": "根据PPT文稿获取PPT类别", - "parameters": { - "type": "object", - "properties": { - "categories": { - "type": "string", - "enum": ["汇报类型", "演讲类型", "活动类型"], - "description": "PPT文稿属于哪一类型的PPT" - } + pipes: [ + { + id: "polish", + use: "llm", + params: { + messages: [ + { + role: "system", + content: + "你是一个专职PPT文稿处理的助手,将会对user输入的文稿进行润色扩写,内容补充,但是原来的一些信息要点不丢失。", + }, + ], + }, + }, + { + id: "classify", + use: "chain", + params: { + struct: { + functions: [ + { + name: "get_categories", + description: "根据PPT文稿获取PPT类别", + parameters: { + type: "object", + properties: { + categories: { + type: "string", + enum: ["汇报类型", "演讲类型", "活动类型"], + description: "PPT文稿属于哪一类型的PPT", + }, + }, + required: ["categories"], + }, }, - "required": ["categories"] - } - }], - "function_call": { - "name": "get_categories" - } + ], + function_call: { + name: "get_categories", + }, + }, + prompt: [ + { + role: "system", + content: + "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。", + }, + ], }, - "prompt": [{ - "role": "system", - "content": "你是一个专职PPT文稿处理的助手,会根据user输入的PPT文稿,给出PPT的类别。" - }] - } - }] -} + }, + ], +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, funcStore); -const res = await pipeline.execute("尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。"); +const res = await pipeline.execute( + "尊敬的各位评审,大家好!今天我有幸站在这里,展示我们的项目:“ChatPPT”,一款赋能新时代、引领PPT制作革命的创新平台。我想强调的是,这不仅是一个创新平台,它更代表着我们对未来技术的展望和追求。", +); ``` ## 注册自定义函数 @@ -248,15 +278,12 @@ const res = await pipeline.execute("尊敬的各位评审,大家好!今天 ```ts const pipeRegistry = PipeRegistry.init(); -pipeRegistry.register( - "step1", - async (input: any, context: PipelineContext) => { - console.log("step1", input, context.stepParams["self_params"]); - return new Promise((resolve) => - setTimeout(() => resolve(input + "🚺"), 1000), - ); - }, -); +pipeRegistry.register("step1", async (input: any, context: PipelineContext) => { + console.log("step1", input, context.stepParams["self_params"]); + return new Promise((resolve) => + setTimeout(() => resolve(input + "🚺"), 1000), + ); +}); pipeRegistry.register("step2", (input: any, context: PipelineContext) => { console.log( @@ -268,13 +295,10 @@ pipeRegistry.register("step2", (input: any, context: PipelineContext) => { return context.stepResults["index_input"]; }); -pipeRegistry.register( - "step3", - async (input: any, context: PipelineContext) => { - console.log("step3", input, context.stepParams["self_params"]); - return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); - }, -); +pipeRegistry.register("step3", async (input: any, context: PipelineContext) => { + console.log("step3", input, context.stepParams["self_params"]); + return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); +}); const pipelineJson = { pipes: [ @@ -293,7 +317,7 @@ const pipelineJson = { use: "step3", }, ], -} +}; const pipeline = Pipeline.fromJSON(pipelineJson, {}, pipeRegistry); @@ -310,15 +334,12 @@ await pipeline.execute("我是输入").then(console.log); ```ts const pipeRegistry = PipeRegistry.init(); -pipeRegistry.register( - "step1", - async (input: any, context: PipelineContext) => { - console.log("step1", input, context.stepParams["self_params"]); - return new Promise((resolve) => - setTimeout(() => resolve(input + "🚺"), 1000), - ); - }, -); +pipeRegistry.register("step1", async (input: any, context: PipelineContext) => { + console.log("step1", input, context.stepParams["self_params"]); + return new Promise((resolve) => + setTimeout(() => resolve(input + "🚺"), 1000), + ); +}); pipeRegistry.register("step2", (input: any, context: PipelineContext) => { console.log( @@ -330,13 +351,10 @@ pipeRegistry.register("step2", (input: any, context: PipelineContext) => { return context.stepResults["index_input"]; }); -pipeRegistry.register( - "step3", - async (input: any, context: PipelineContext) => { - console.log("step3", input, context.stepParams["self_params"]); - return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); - }, -); +pipeRegistry.register("step3", async (input: any, context: PipelineContext) => { + console.log("step3", input, context.stepParams["self_params"]); + return new Promise((resolve) => setTimeout(() => resolve(input), 1000)); +}); const pipelineJson = { pipes: [ @@ -349,7 +367,7 @@ const pipelineJson = { id: "step2_", use: "step2", inputs: { - FetchData: "step1_",//获取pipe中id为step1_的pipe的执行结果,自动替换字符串"step1_"为对应的输出结果。 + FetchData: "step1_", //获取pipe中id为step1_的pipe的执行结果,自动替换字符串"step1_"为对应的输出结果。 }, params: { test: "test22!!{{FetchData}}" }, //插槽,必须先在inputs中获取之前pipe的输出结果,然后在params中插槽引用inputs的对应字段! }, diff --git a/docs/index.md b/docs/index.md index 3c0ff83..32f9ef7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,12 +15,16 @@ hero: link: https://preview.idealeap.cn/ features: - - title: 工厂模式设计 + - icon: 🔨 + title: 工厂模式设计 details: 选择LLM-OPS意味着选择了简便与专业!您无需深究复杂的内部结构,只需简单几步,如同开箱即用的神器。 - - title: 低代码开发 + - icon: 🧩 + title: 低代码开发 details: 真正实现了一键式开发。只需简单地编写一段JSON代码,即可轻松实现您所想要的完整业务流程,大大减少了开发难度和学习成本。 - - title: 全面支持TypeScript + - icon: ✈️ + title: 全面支持TypeScript details: LLM-OPS框架不仅基于TypeScript开发,更为开发者提供了完整的TsDoc注释。这让您在开发过程中更加流畅,无需反复查询文档,一切都变得清晰明了。 - - title: AI制作的Workflow引擎 + - icon: 🚀 + title: AI制作的Workflow引擎 details: 无论是构建复杂的企业应用还是快速打造创新项目,LLM-OPS都能为您提供稳定而高效的工作流引擎,真正做到快速开发,快速上线! --- diff --git a/docs/public/android-chrome-192x192.png b/docs/public/android-chrome-192x192.png new file mode 100644 index 0000000000000000000000000000000000000000..9b9bbca11ba3313c2279b386302f7600a099281d GIT binary patch literal 12466 zcmZ{Kbx<5n@b4WQ$Ds+qgG+EZ+#$F-1a~+bj^KVc0fGc~NCE_c2M>gU;BLX?fZzlV zkU-$w_f@?=Ue)h6Tf0-cvoqZ@{psG`o_?dFrA&bP92Wop5U8ps=%H)me;0mL^J^K7ySsa4*>W=0f0km06;tk03e4K zbm&T=8^E^e$_jwT|BjNbigff7Y#&ukMeIEsY8)PNBkj&l006JPs)DRR!1Axcz#MbK z+(((ycEca8GY2PbToKVei$ox7e_eC9)u5M8Y%ycL30&5Z+7V6{x(beArg;j?&h^sq 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z3_!I6CLALdYQ@<-0Lh_@wOIOVUili|Kv~(jDx0MF + + + +Created by potrace 1.14, written by Peter Selinger 2001-2017 + + + + + + + + + + + + + + diff --git a/docs/public/site.webmanifest b/docs/public/site.webmanifest new file mode 100644 index 0000000..b92edd4 --- /dev/null +++ b/docs/public/site.webmanifest @@ -0,0 +1,14 @@ +{ + "name": "", + "short_name": "", + "icons": [ + { + "src": "/android-chrome-192x192.png", + "sizes": "192x192", + "type": "image/png" + } + ], + "theme_color": "#ffffff", + "background_color": "#ffffff", + "display": "standalone" +} diff --git a/package/chain/chain.ts b/package/chain/chain.ts index f6dfa4b..a783e14 100644 --- a/package/chain/chain.ts +++ b/package/chain/chain.ts @@ -7,27 +7,60 @@ import { } from "llm-ops/llm/index"; import { FunctionChain } from "llm-ops/chain/function"; import { TypeScriptChain } from "llm-ops/chain/typechat"; +/** + * `Chain` 构造函数的参数数据结构。 + * + * @property {LLM} [llm] - 一个可选的LLM实例。 + * @property {createLLMSchema} [llmSchema] - 一个可选的LLM模式对象。 + * @property {string} [chainName] - 一个可选的链名称。 + */ export interface chainSchema { llm?: LLM; llmSchema?: createLLMSchema; chainName?: string; } +/** + * `call` 方法的`struct`参数的数据结构。 + * + * @property {functionsType} [functions] - 可选的函数类型。 + * @property {function_callType} [function_call] - 可选的函数调用类型。 + * @property {string} [schema] - 一个可选的模式字符串。 + * @property {string} [typeName] - 一个可选的类型名称。 + */ export interface structSchema { functions?: functionsType; function_call?: function_callType; schema?: string; typeName?: string; } +/** + * `Chain`类的`call`方法的参数数据结构。 + * + * @property {messageType | string} request - 请求消息或字符串。 + * @property {messageType[]} [prompt] - 一个可选的消息提示数组。 + * @property {structSchema} [struct] - 一个可选的结构模式。 + * @property {boolean} [verbose] - 一个可选的详细模式标志。 + */ export interface chainCallSchema { request: messageType | string; prompt?: messageType[]; struct?: structSchema; verbose?: boolean; } + +/** + * `Chain` 类用于`llm`类的格式化输出。 + * + */ export class Chain { llm: LLM; chainName?: string; chain: FunctionChain | TypeScriptChain; + /** + * 创建一个新的 `Chain` 实例。 + * + * @param {chainSchema} params - 构造函数的参数对象。 + */ constructor(params: chainSchema) { const { llmSchema, chainName, llm } = params; this.llm = llm || new LLM(llmSchema || {}); @@ -41,9 +74,21 @@ export class Chain { break; } } + /** + * 导出链的历史记录。 + * + * @returns {any} 返回链的历史记录。 + */ exportHistory() { return this.chain.exportHistory(); } + /** + * 调用Chain + * + * @param {chainCallSchema} params - 调用方法的参数对象。 + * + * @returns {Promise} 返回异步处理的链调用结果。 + */ async call(params: chainCallSchema) { const { request, prompt, struct, verbose } = params; switch (this.chainName) { diff --git a/package/llm/index.ts b/package/llm/index.ts index f309740..a393e92 100644 --- a/package/llm/index.ts +++ b/package/llm/index.ts @@ -1,9 +1,12 @@ import OpenAI from "openai"; import { LLM_OPS_CONFIG } from "llm-ops/utils/index"; -// only for openai-node ^4.0.0 +/** OpenAI聊天请求消息的类型数组 */ export type messagesType = OpenAI.Chat.CreateChatCompletionRequestMessage[]; +/** 单一的OpenAI聊天请求消息类型 */ export type messageType = OpenAI.Chat.CreateChatCompletionRequestMessage; +/** OpenAI聊天完成的消息数组类型 */ export type resMessagesType = OpenAI.Chat.Completions.ChatCompletion.Choice[]; +/** FunctionCall的接口 */ export interface FunctionCallOption { /** * The name of the function to call. @@ -35,19 +38,30 @@ export interface FunctionInterface { */ description?: string; } +/** FunctionInterface类型数组 */ export type functionsType = FunctionInterface[]; +/** function_call类型 */ export type function_callType = "none" | "auto" | FunctionCallOption; +/** OpenAI聊天完成消息中的FunctionCall类型 */ export type messageFunctionCallType = OpenAI.Chat.ChatCompletionMessage.FunctionCall; +/** OpenAI聊天完成类型 */ export type chatCompletionType = OpenAI.Chat.Completions.ChatCompletion; +/** LLM类型定义为OpenAI类型 */ export type llmType = OpenAI; +/** OpenAI聊天参数类型 */ export type chatParamsType = OpenAI.Chat.CompletionCreateParams; +/** OpenAI内容审查响应类型 */ export type resModerationType = OpenAI.ModerationCreateResponse; +/** OpenAI创建嵌入响应类型 */ export type resEmbeddingType = OpenAI.CreateEmbeddingResponse; - +/** 创建LLM的参数接口 */ export interface createLLMSchema { + /** Helicone授权API密钥 */ HELICONE_AUTH_API_KEY?: string; + /** OpenAI API密钥 */ OPENAI_API_KEY?: string; + /** 模型名称选项 */ modelName?: | (string & object) | "gpt-4" @@ -69,12 +83,16 @@ export interface createLLMSchema { history?: messagesType; tokens?: number; } +/** 聊天参数接口 */ export interface ChatSchema { function_call?: function_callType; + /** 消息数组 */ messages: messagesType; functions?: functionsType; } - +/** + * LLM类代表一个与LLM API进行交互的客户端。 + */ export class LLM { llm: llmType; tokens: number; @@ -106,6 +124,10 @@ export class LLM { }; user: string; + /** + * 构造一个LLM客户端实例。 + * @param params - 包含创建LLM客户端所需的参数。 + */ constructor(params: createLLMSchema) { const { HELICONE_AUTH_API_KEY = undefined, @@ -129,7 +151,10 @@ export class LLM { this.cache = cache || true; this.user = user || "LLM Ops"; } - + /** + * 导出历史消息。 + * @returns 返回聊天的历史消息。 + */ exportHistory() { return this.messages; } @@ -168,12 +193,17 @@ export class LLM { } /** - * Throws an exception for a missing environment variable. + * 抛出缺少环境变量的异常。 + * @param name - 缺少的环境变量的名称。 */ private missingEnvironmentVariable(name: string): never { throw new Error(`"Missing environment variable: ${name}`); } - + /** + * 使用LLM进行聊天。 + * @param params - 包含聊天所需的参数。 + * @returns 返回聊天的完成信息。 + */ async chat(params: ChatSchema): Promise { const { messages, function_call, functions } = params; try { @@ -234,7 +264,10 @@ export class LLM { throw err; } } - + /** + * 重复最后一次的聊天。 + * @returns 返回重复的聊天完成信息。 + */ async recall(): Promise { try { if (this.messages.length == 0) { @@ -295,14 +328,22 @@ export class LLM { throw err; } } - + /** + * 对输入内容进行审查。 + * @param input - 需要审查的输入内容。 + * @returns 返回审查结果。 + */ async moderate(input: string | string[]): Promise { return await this.llm.moderations.create({ input: input, model: "text-moderation-latest", }); } - + /** + * 获取输入内容的嵌入向量。 + * @param input - 需要嵌入的输入内容。 + * @returns 返回嵌入结果。 + */ async embedding( input: string | string[] | number[] | number[][], ): Promise { @@ -312,10 +353,17 @@ export class LLM { user: this.user, }); } - + /** + * 日志输出。 + * @param args - 要输出的内容。 + */ static log(...args: string[]) { console.log(args); } + /** + * 优雅地打印请求消息。 + * @param messages - 需要打印的消息。 + */ private prettyPrintReqMessage(messages: messagesType) { for (const message of messages) { if (message.role === "system") { @@ -350,6 +398,10 @@ export class LLM { } } } + /** + * 优雅地打印响应消息。 + * @param messages - 需要打印的消息。 + */ private prettyPrintResMessage(messages: resMessagesType) { for (const message_ of messages) { const message = message_.message; @@ -381,7 +433,11 @@ export class LLM { } } } - + /** + * 打印消息。 + * @param resMessages - 响应消息。 + * @param reqMessages - 请求消息。 + */ printMessage(resMessages?: resMessagesType, reqMessages?: messagesType) { !!reqMessages && this.prettyPrintReqMessage(reqMessages); !!resMessages && this.prettyPrintResMessage(resMessages); diff --git a/package/pipeline/store.ts b/package/pipeline/store.ts index 99d7158..c7fbe01 100644 --- a/package/pipeline/store.ts +++ b/package/pipeline/store.ts @@ -1,4 +1,7 @@ import * as OriginalPipeline from "@idealeap/pipeline"; +/** + * 从 "@idealeap/pipeline" 中导出所有内容 + */ export * from "@idealeap/pipeline"; import { createMessage } from "llm-ops/prompt/index"; import { BaseAgent } from "llm-ops/agent/index"; @@ -6,7 +9,16 @@ import { LLM } from "llm-ops/llm/index"; import { Chain } from "llm-ops/chain/index"; import { formatPromptTemplate } from "llm-ops/prompt/index"; import { milvusVectorDB } from "llm-ops/db/index"; +/** + * 对原始的管道库进行定制 + */ OriginalPipeline.PipeRegistry.customFn = { + /** + * LLM的处理函数 + * @param input - 输入数据 + * @param context - 管道上下文 + * @returns 返回LLM的输出结果 + */ llm: async (input: any, context: OriginalPipeline.PipelineContext) => { let request = input; const params = context.stepParams["self_params"]; @@ -31,6 +43,12 @@ OriginalPipeline.PipeRegistry.customFn = { }); return _.choices[0]?.message.content; }, + /** + * chain的处理功能 + * @param input - 输入数据 + * @param context - 管道上下文 + * @returns 返回chain处理的输出结果 + */ chain: async (input: any, context: OriginalPipeline.PipelineContext) => { const params = context.stepParams["self_params"]; const llm = new LLM({ ...params, cache: false }); @@ -55,6 +73,12 @@ OriginalPipeline.PipeRegistry.customFn = { return _.data; } }, + /** + * agent的处理函数 + * @param input - 输入数据 + * @param context - 管道上下文 + * @returns 返回agent调用的输出结果 + */ agent: async (input: any, context: OriginalPipeline.PipelineContext) => { const params = context.stepParams["self_params"]; const agent = new BaseAgent(params); @@ -69,6 +93,12 @@ OriginalPipeline.PipeRegistry.customFn = { return _.data; } }, + /** + * 提示模板格式化功能 + * @param input - 输入数据 + * @param context - 管道上下文 + * @returns 返回格式化后的提示模板 + */ promptTemplate: async ( input: any, context: OriginalPipeline.PipelineContext, @@ -77,6 +107,12 @@ OriginalPipeline.PipeRegistry.customFn = { const _ = await formatPromptTemplate(params); return _; }, + /** + * Milvus搜索功能的处理函数 + * @param input - 输入数据 + * @param context - 管道上下文 + * @returns 返回Milvus搜索的结果 + */ milvusSearch: async ( input: any, context: OriginalPipeline.PipelineContext, @@ -96,8 +132,13 @@ OriginalPipeline.PipeRegistry.customFn = { } }, }; - +/** + * 扩展原始的PipeRegistry类 + */ export class PipeRegistry extends OriginalPipeline.PipeRegistry { + /** + * PipeRegistry的构造函数 + */ constructor() { super(); } diff --git a/package/prompt/promptTemplate.ts b/package/prompt/promptTemplate.ts index 2296643..bb1e3d7 100644 --- a/package/prompt/promptTemplate.ts +++ b/package/prompt/promptTemplate.ts @@ -9,6 +9,14 @@ import { AgentPromptTemplate, createMessage, } from "llm-ops/prompt/index"; +/** + * 主要的模板数据结构。 + * + * @property {string} name - 提示模板的名称。可能的值为:"polishPromptTemplate"、"agentPromptTemplate" 或其他字符串。 + * @property {MultiPromptSchema[] | any[]} [prompt] - 一个可选的提示模板数组。 + * @property {Record} [schema] - 一个可选的记录对象,用于存储额外的数据模板。 + * @property {string} [COLLECTION_NAME] - 一个可选的集合名称,用于指定向量数据库的集合。 + */ export interface PromptsSchema extends Omit { name: "polishPromptTemplate" | "agentPromptTemplate" | string; @@ -16,12 +24,38 @@ export interface PromptsSchema schema?: Record; COLLECTION_NAME?: string; } +/** + * 多重提示模板数据结构。 + * + * @property {string} [COLLECTION_NAME] - 一个可选的集合名称,用于指定向量数据库的集合。 + */ export interface MultiPromptSchema extends createMessageSchema, Omit { COLLECTION_NAME?: string; } - +/** + * 格式化提示模板函数。 + * + * 此函数的主要目的是为了从提供的`prompts`对象中提取出一个格式化的提示模板。 + * 这可以是一个单一的模板,也可以是一个模板数组。 + * + * @param {PromptsSchema} prompts - 提供的提示模板对象。 + * + * @returns {Promise} 返回一个异步处理的格式化的提示模板数组。 + * + * @example + * ```typescript + * const promptsData = { + * name: "agentPromptTemplate", + * schema: { role: "user" }, + * }; + * const formattedPrompts = await formatPromptTemplate(promptsData); + * ``` + * + * @throws {Error} 当提示中缺少`COLLECTION_NAME`时抛出错误。 + * @throws {Error} 当提供的提示是无效的时抛出错误。 + */ export const formatPromptTemplate = async (prompts: PromptsSchema) => { let vectorDB: milvusVectorDB | undefined = undefined; !!prompts.COLLECTION_NAME && diff --git a/package/utils/config.ts b/package/utils/config.ts index 66f81c1..5186d37 100644 --- a/package/utils/config.ts +++ b/package/utils/config.ts @@ -1,15 +1,48 @@ import "dotenv/config"; +/** + * `LLM_OPS_CONFIG` 是用于配置操作的类,它包含了与OpenAI、Helicone和Milvus相关的配置信息。 + */ export class LLM_OPS_CONFIG { + /** + * OPEN_PATH 对象定义了与Helicone API进行通信时的基础设置。 + * + * @property {string} baseURL - Helicone API的基础URL。 + * @property {Object} defaultHeaders - 用于与Helicone API通信时的默认请求头。 + */ static OPEN_PATH = { baseURL: "https://oai.hconeai.com/v1", defaultHeaders: { - // eslint-disable-next-line @typescript-eslint/restrict-template-expressions "Helicone-Auth": `Bearer ${process.env["HELICONE_AUTH_API_KEY"]}`, }, }; + /** + * OpenAI的API密钥。 + * + * 从环境变量中获取该密钥。 + */ static OPENAI_API_KEY = process.env["OPENAI_API_KEY"]; + /** + * Helicone的授权API密钥。 + * + * 从环境变量中获取该密钥。 + */ static HELICONE_AUTH_API_KEY = process.env["HELICONE_AUTH_API_KEY"]; + /** + * Milvus的服务地址。 + * + * 从环境变量中获取此地址。 + */ static MILVUS_ADDRESS = process.env["MILVUS_ADDRESS"]; + /** + * 用于连接到Milvus服务的用户名。 + * + * 从环境变量中获取此用户名。 + */ static MILVUS_USERNAME = process.env["MILVUS_USERNAME"]; + /** + * 用于连接到Milvus服务的密码。 + * + * 从环境变量中获取此密码。 + */ static MILVUS_PASSWORD = process.env["MILVUS_PASSWORD"]; }

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