From a936f608a486d55fcae9aa2985ea5671dac13a27 Mon Sep 17 00:00:00 2001 From: zhangxu0307 Date: Wed, 20 Dec 2023 06:35:16 +0000 Subject: [PATCH] deploy: 4458d0f96ce5b146b68e8af804e00539f45c36b4 --- 404.html | 4 +-- assets/js/1bba509c.263d5a3a.js | 1 - assets/js/1bba509c.4f7cc9c9.js | 1 + assets/js/3ad3027d.7f996976.js | 1 + assets/js/7fc9262a.9db643af.js | 1 - assets/js/7fc9262a.ad3859b9.js | 1 + ...f2afb.ba3d4a35.js => 935f2afb.dbfd6821.js} | 2 +- assets/js/9fbcb257.5a8e04f9.js | 1 + assets/js/9fbcb257.6ee125b0.js | 1 - assets/js/be4af720.0c7bb83c.js | 1 + assets/js/f04cdb7e.e5bea950.js | 1 + assets/js/main.50fb8b15.js | 2 -- assets/js/main.db66bd33.js | 2 ++ ...CENSE.txt => main.db66bd33.js.LICENSE.txt} | 0 assets/js/runtime~main.1c4abcd3.js | 1 - assets/js/runtime~main.86a33690.js | 1 + blog/archive/index.html | 4 +-- blog/first-blog-post/index.html | 4 +-- blog/index.html | 4 +-- blog/long-blog-post/index.html | 4 +-- blog/mdx-blog-post/index.html | 4 +-- blog/tags/docusaurus/index.html | 4 +-- blog/tags/facebook/index.html | 4 +-- blog/tags/hello/index.html | 4 +-- blog/tags/hola/index.html | 4 +-- blog/tags/index.html | 4 +-- blog/welcome/index.html | 4 +-- docs/compression/index.html | 6 ++--- docs/configurations/index.html | 6 ++--- docs/example/index.html | 6 ++--- docs/llms/geni/index.html | 14 ++++++++++ docs/llms/index.html | 8 +++--- docs/llms/liteLLM/index.html | 6 ++--- docs/llms/ollama/index.html | 23 ++++++++++++++++ docs/llms/openai/index.html | 6 ++--- docs/llms/qwen/index.html | 26 +++++++++++++++++++ docs/overview/index.html | 6 ++--- docs/planner/index.html | 6 ++--- docs/plugin/embedding/index.html | 6 ++--- docs/plugin/plugin_intro/index.html | 8 +++--- docs/plugin/plugin_selection/index.html | 6 ++--- docs/quickstart/index.html | 6 ++--- docs/run_pytest/index.html | 6 ++--- docs/session/index.html | 6 ++--- docs/taskweaver_as_a_lib/index.html | 6 ++--- index.html | 4 +-- markdown-page/index.html | 4 +-- search-index-docs.json | 2 +- search/index.html | 4 +-- sitemap.xml | 2 +- 50 files changed, 152 insertions(+), 86 deletions(-) delete mode 100644 assets/js/1bba509c.263d5a3a.js create mode 100644 assets/js/1bba509c.4f7cc9c9.js create mode 100644 assets/js/3ad3027d.7f996976.js delete mode 100644 assets/js/7fc9262a.9db643af.js create mode 100644 assets/js/7fc9262a.ad3859b9.js rename assets/js/{935f2afb.ba3d4a35.js => 935f2afb.dbfd6821.js} (50%) create mode 100644 assets/js/9fbcb257.5a8e04f9.js delete mode 100644 assets/js/9fbcb257.6ee125b0.js create mode 100644 assets/js/be4af720.0c7bb83c.js create mode 100644 assets/js/f04cdb7e.e5bea950.js delete mode 100644 assets/js/main.50fb8b15.js create mode 100644 assets/js/main.db66bd33.js rename assets/js/{main.50fb8b15.js.LICENSE.txt => main.db66bd33.js.LICENSE.txt} (100%) delete mode 100644 assets/js/runtime~main.1c4abcd3.js create mode 100644 assets/js/runtime~main.86a33690.js create mode 100644 docs/llms/geni/index.html create mode 100644 docs/llms/ollama/index.html create mode 100644 docs/llms/qwen/index.html diff --git a/404.html b/404.html index d72ff0d5..3b2ae0dd 100644 --- a/404.html +++ b/404.html @@ -5,8 +5,8 @@ Page Not Found | TaskWeaver - - + +
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diff --git a/assets/js/1bba509c.263d5a3a.js b/assets/js/1bba509c.263d5a3a.js deleted file mode 100644 index 7417e31f..00000000 --- a/assets/js/1bba509c.263d5a3a.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[4304],{4511:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>a,contentTitle:()=>r,default:()=>d,frontMatter:()=>o,metadata:()=>l,toc:()=>c});var s=t(5893),i=t(1151);const o={},r="Auto Plugin Selection",l={id:"plugin/plugin_selection",title:"Auto Plugin Selection",description:"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request.",source:"@site/docs/plugin/plugin_selection.md",sourceDirName:"plugin",slug:"/plugin/plugin_selection",permalink:"/TaskWeaver/docs/plugin/plugin_selection",draft:!1,unlisted:!1,editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/docs/plugin/plugin_selection.md",tags:[],version:"current",frontMatter:{},sidebar:"documentSidebar",previous:{title:"Plugin Introduction",permalink:"/TaskWeaver/docs/plugin/plugin_intro"},next:{title:"Embedding",permalink:"/TaskWeaver/docs/plugin/embedding"}},a={},c=[{value:"Auto Plugin Selection Overview",id:"auto-plugin-selection-overview",level:2},{value:"Auto Plugin Selection Configuration",id:"auto-plugin-selection-configuration",level:2},{value:"Auto Plugin Selection Example",id:"auto-plugin-selection-example",level:2}];function u(e){const n={a:"a",code:"code",h1:"h1",h2:"h2",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",ul:"ul",...(0,i.a)(),...e.components};return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(n.h1,{id:"auto-plugin-selection",children:"Auto Plugin Selection"}),"\n",(0,s.jsx)(n.p,{children:"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request.\nIt targets to solve the following two problems:"}),"\n",(0,s.jsxs)(n.ol,{children:["\n",(0,s.jsx)(n.li,{children:"An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code."}),"\n",(0,s.jsx)(n.li,{children:"A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time."}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-overview",children:"Auto Plugin Selection Overview"}),"\n",(0,s.jsxs)(n.p,{children:["Below is the overview workflow of the auto plugin selection mechanism.\n",(0,s.jsx)(n.img,{alt:"Auto Plugin Selection Overview",src:t(6869).Z+"",width:"1846",height:"543"})]}),"\n",(0,s.jsx)(n.p,{children:"NOTE: the automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner."}),"\n",(0,s.jsxs)(n.p,{children:["At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions.\nThe embedding vectors are created using the specified embedding model configured in the ",(0,s.jsx)(n.code,{children:"taskweaver_config.json"})," file.\nFor more information, please refer to the ",(0,s.jsx)(n.a,{href:"embedding",children:"embedding"})," documentation."]}),"\n",(0,s.jsxs)(n.p,{children:["When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered.\nIt will first generate an embedding vector for the request using the same embedding model.\nThen, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins.\nIt will select the top-k plugins with the highest cosine similarity scores and load them into the ",(0,s.jsx)(n.code,{children:"code_generator"})," prompt."]}),"\n",(0,s.jsxs)(n.p,{children:["Upon completing the code generation, the ",(0,s.jsx)(n.code,{children:"code_generator"})," employs one or more plugins to produce the desired code.\nWe have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones.\nDuring the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones."]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-configuration",children:"Auto Plugin Selection Configuration"}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.code,{children:"code_generator.enable_auto_plugin_selection"}),": Whether to enable auto plugin selection. The default value is ",(0,s.jsx)(n.code,{children:"false"}),"."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.code,{children:"code_generator.auto_plugin_selection_topk"}),":\tThe number of auto selected plugins in each round. The default value is ",(0,s.jsx)(n.code,{children:"3"}),"."]}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-example",children:"Auto Plugin Selection Example"}),"\n",(0,s.jsx)(n.p,{children:"We show the auto plugin selection mechanism in the following example."}),"\n",(0,s.jsx)(n.p,{children:"First, we start TaskWeaver with the auto plugin selection mechanism enabled."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"=========================================================\n _____ _ _ __\n|_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____\n | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/\n | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ /\n |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/\n=========================================================\nTaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request?\nHuman: \n"})}),"\n",(0,s.jsxs)(n.p,{children:["Then we can check the log file ",(0,s.jsx)(n.code,{children:"task_weaver.log"})," in the ",(0,s.jsx)(n.code,{children:"logs"})," folder to see the auto plugin selector is initialized successfully because the ",(0,s.jsx)(n.code,{children:"Plugin embeddings generated"})," message is printed."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"2023-12-18 14:23:44,197 - INFO - Planner initialized successfully\n2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated\n2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully.\n2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized\n"})}),"\n",(0,s.jsx)(n.p,{children:'We ask TaskWeaver to "search Xbox price for me".\nThe Planner instructs the Code Interpreter to search Xbox price.'}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request?\nHuman: search xbox price for me\n>>> [INIT_PLAN]\n1. search xbox price\n2. report the result to the user \n>>> [PLAN]\n1. instruct CodeInterpreter to search xbox price\n2. report the result to the user\n>>> [CURRENT_PLAN_STEP]\n1. instruct CodeInterpreter to search xbox price\n>>> [SEND_TO]\nCodeInterpreter\n>>> [MESSAGE]\nPlease search xbox price\n>>> [PLANNER->CODEINTERPRETER]\nPlease search xbox price\n"})}),"\n",(0,s.jsxs)(n.p,{children:["Back to the Code Interpreter, the auto plugin selection mechanism is triggered.\nWe can check the log file ",(0,s.jsx)(n.code,{children:"task_weaver.log"})," again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin\n2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary']\n2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary']\n"})}),"\n",(0,s.jsx)(n.p,{children:"Then the Code Interpreter will generate the code using the selected plugins."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:">>> [THOUGHT]\nProgramApe will call the klarna_search plugin function to search for Xbox prices.\n>>> [PYTHON]\nsearch_results, description = klarna_search(query=\"xbox\")\nsearch_results, description\n>>> [VERIFICATION]\nNONE\n>>> [STATUS]\nSUCCESS\n>>> [RESULT]\nThe execution of the generated python code above has succeeded\n\nThe result of above Python code after execution is:\n( name price url attributes\n 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam...\n 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV...\n .. ... ... ... ...\n 3 Xbox Series S \u2013 Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One]\n 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam...\n\n [5 rows x 4 columns],\n 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.')\n>>> [CODEINTERPRETER->PLANNER]\nThe following python code has been executed:\n```python\nsearch_results, description = klarna_search(query=\"xbox\")\nsearch_results, description\n```\n\nThe execution of the generated python code above has succeeded\n\nThe result of above Python code after execution is:\n( name price url attributes\n 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam...\n 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV...\n .. ... ... ... ...\n 3 Xbox Series S \u2013 Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One]\n 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam...\n\n [5 rows x 4 columns],\n 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.')\n"})}),"\n",(0,s.jsx)(n.p,{children:"Finally, the Planner will report the result to the user."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:">>> [INIT_PLAN]\n1. search xbox price using klarna_search plugin\n2. report the result to the user \n>>> [PLAN]\n1. instruct CodeInterpreter to search xbox price using klarna_search plugin\n2. report the result to the user\n>>> [CURRENT_PLAN_STEP]\n2. report the result to the user\n>>> [SEND_TO]\nUser\n>>> [MESSAGE]\nHere are some Xbox prices I found:\n\n1. Microsoft Xbox Series X - Black Edition: $399.00\n2. Microsoft Xbox Series S 1TB - Black: $349.00\n3. Xbox Series S \u2013 Starter Bundle: $239.00\n4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58\n\nPlease note that these prices are subject to change and availability.\n>>> [PLANNER->USER]\nHere are some Xbox prices I found:\n\n1. Microsoft Xbox Series X - Black Edition: $399.00\n2. Microsoft Xbox Series S 1TB - Black: $349.00\n3. Xbox Series S \u2013 Starter Bundle: $239.00\n4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58\n\nPlease note that these prices are subject to change and availability.\nTaskWeaver: Here are some Xbox prices I found:\n\n1. Microsoft Xbox Series X - Black Edition: $399.00\n2. Microsoft Xbox Series S 1TB - Black: $349.00\n3. Xbox Series S \u2013 Starter Bundle: $239.00\n4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58\n\nPlease note that these prices are subject to change and availability.\n"})})]})}function d(e={}){const{wrapper:n}={...(0,i.a)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(u,{...e})}):u(e)}},6869:(e,n,t)=>{t.d(n,{Z:()=>s});const s=t.p+"assets/images/APS-1dc4d286038dedc17487146cec8214fd.png"},1151:(e,n,t)=>{t.d(n,{Z:()=>l,a:()=>r});var s=t(7294);const i={},o=s.createContext(i);function r(e){const n=s.useContext(o);return s.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function l(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:r(e.components),s.createElement(o.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/1bba509c.4f7cc9c9.js b/assets/js/1bba509c.4f7cc9c9.js new file mode 100644 index 00000000..abbe148b --- /dev/null +++ b/assets/js/1bba509c.4f7cc9c9.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[4304],{4511:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>a,contentTitle:()=>r,default:()=>d,frontMatter:()=>o,metadata:()=>l,toc:()=>c});var s=t(5893),i=t(1151);const o={},r="Auto Plugin Selection",l={id:"plugin/plugin_selection",title:"Auto Plugin Selection",description:"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request.",source:"@site/docs/plugin/plugin_selection.md",sourceDirName:"plugin",slug:"/plugin/plugin_selection",permalink:"/TaskWeaver/docs/plugin/plugin_selection",draft:!1,unlisted:!1,editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/docs/plugin/plugin_selection.md",tags:[],version:"current",frontMatter:{},sidebar:"documentSidebar",previous:{title:"Plugin Introduction",permalink:"/TaskWeaver/docs/plugin/plugin_intro"},next:{title:"Embedding",permalink:"/TaskWeaver/docs/plugin/embedding"}},a={},c=[{value:"Auto Plugin Selection Overview",id:"auto-plugin-selection-overview",level:2},{value:"Auto Plugin Selection Configuration",id:"auto-plugin-selection-configuration",level:2},{value:"Auto Plugin Selection Example",id:"auto-plugin-selection-example",level:2}];function u(e){const n={a:"a",code:"code",h1:"h1",h2:"h2",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",ul:"ul",...(0,i.a)(),...e.components};return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(n.h1,{id:"auto-plugin-selection",children:"Auto Plugin Selection"}),"\n",(0,s.jsx)(n.p,{children:"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request.\nIt targets to solve the following two problems:"}),"\n",(0,s.jsxs)(n.ol,{children:["\n",(0,s.jsx)(n.li,{children:"An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code."}),"\n",(0,s.jsx)(n.li,{children:"A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time."}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-overview",children:"Auto Plugin Selection Overview"}),"\n",(0,s.jsxs)(n.p,{children:["Below is the overview workflow of the auto plugin selection mechanism.\n",(0,s.jsx)(n.img,{alt:"Auto Plugin Selection Overview",src:t(6869).Z+"",width:"1846",height:"543"})]}),"\n",(0,s.jsx)(n.p,{children:"NOTE: the automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner."}),"\n",(0,s.jsxs)(n.p,{children:["At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions.\nThe embedding vectors are created using the specified embedding model configured in the ",(0,s.jsx)(n.code,{children:"taskweaver_config.json"})," file.\nFor more information, please refer to the ",(0,s.jsx)(n.a,{href:"/TaskWeaver/docs/plugin/embedding",children:"embedding"})," documentation."]}),"\n",(0,s.jsxs)(n.p,{children:["When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered.\nIt will first generate an embedding vector for the request using the same embedding model.\nThen, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins.\nIt will select the top-k plugins with the highest cosine similarity scores and load them into the ",(0,s.jsx)(n.code,{children:"code_generator"})," prompt."]}),"\n",(0,s.jsxs)(n.p,{children:["Upon completing the code generation, the ",(0,s.jsx)(n.code,{children:"code_generator"})," employs one or more plugins to produce the desired code.\nWe have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones.\nDuring the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones."]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-configuration",children:"Auto Plugin Selection Configuration"}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.code,{children:"code_generator.enable_auto_plugin_selection"}),": Whether to enable auto plugin selection. The default value is ",(0,s.jsx)(n.code,{children:"false"}),"."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.code,{children:"code_generator.auto_plugin_selection_topk"}),":\tThe number of auto selected plugins in each round. The default value is ",(0,s.jsx)(n.code,{children:"3"}),"."]}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"auto-plugin-selection-example",children:"Auto Plugin Selection Example"}),"\n",(0,s.jsx)(n.p,{children:"We show the auto plugin selection mechanism in the following example."}),"\n",(0,s.jsx)(n.p,{children:"First, we start TaskWeaver with the auto plugin selection mechanism enabled."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"=========================================================\n _____ _ _ __\n|_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____\n | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/\n | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ /\n |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/\n=========================================================\nTaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request?\nHuman: \n"})}),"\n",(0,s.jsxs)(n.p,{children:["Then we can check the log file ",(0,s.jsx)(n.code,{children:"task_weaver.log"})," in the ",(0,s.jsx)(n.code,{children:"logs"})," folder to see the auto plugin selector is initialized successfully because the ",(0,s.jsx)(n.code,{children:"Plugin embeddings generated"})," message is printed."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"2023-12-18 14:23:44,197 - INFO - Planner initialized successfully\n2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated\n2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully.\n2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized\n"})}),"\n",(0,s.jsx)(n.p,{children:'We ask TaskWeaver to "search Xbox price for me".\nThe Planner instructs the Code Interpreter to search Xbox price.'}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request?\nHuman: search xbox price for me\n>>> [INIT_PLAN]\n1. search xbox price\n2. report the result to the user \n>>> [PLAN]\n1. instruct CodeInterpreter to search xbox price\n2. report the result to the user\n>>> [CURRENT_PLAN_STEP]\n1. instruct CodeInterpreter to search xbox price\n>>> [SEND_TO]\nCodeInterpreter\n>>> [MESSAGE]\nPlease search xbox price\n>>> [PLANNER->CODEINTERPRETER]\nPlease search xbox price\n"})}),"\n",(0,s.jsxs)(n.p,{children:["Back to the Code Interpreter, the auto plugin selection mechanism is triggered.\nWe can check the log file ",(0,s.jsx)(n.code,{children:"task_weaver.log"})," again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin\n2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary']\n2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary']\n"})}),"\n",(0,s.jsx)(n.p,{children:"Then the Code Interpreter will generate the code using the selected plugins."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:">>> [THOUGHT]\nProgramApe will call the klarna_search plugin function to search for Xbox prices.\n>>> [PYTHON]\nsearch_results, description = klarna_search(query=\"xbox\")\nsearch_results, description\n>>> [VERIFICATION]\nNONE\n>>> [STATUS]\nSUCCESS\n>>> [RESULT]\nThe execution of the generated python code above has succeeded\n\nThe result of above Python code after execution is:\n( name price url attributes\n 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... 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The generated code (simplified) looks like\nfollows:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'df, data_description = sql_pull_data(query="pull data from time_series table") \nanomaly_df, anomaly_description = anomaly_detection(df, time_col_name="ts", value_col_name="val") \n'})}),"\n",(0,i.jsx)(n.h2,{id:"plugin-structure",children:"Plugin Structure"}),"\n",(0,i.jsx)(n.p,{children:"A plugin has two files:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Plugin Implementation"}),": a Python file that defines the plugin"]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Plugin Schema"}),": a file in yaml that defines the schema of the plugin"]}),"\n"]}),"\n",(0,i.jsx)(n.h2,{id:"plugin-implementation",children:"Plugin Implementation"}),"\n",(0,i.jsx)(n.p,{children:"The plugin function needs to be implemented in Python.\nTo be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Plugin function implementation code"}),"\n",(0,i.jsx)(n.li,{children:"TaskWeaver plugin decorator"}),"\n"]}),"\n",(0,i.jsx)(n.p,{children:"Here we exhibit an example of the anomaly detection plugin as the following code:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'import pandas as pd\nfrom pandas.api.types import is_numeric_dtype\n\nfrom taskWeaver.plugin import Plugin, register_plugin\n\n\n@register_plugin\nclass AnomalyDetectionPlugin(Plugin):\n def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str):\n\n """\n anomaly_detection function identifies anomalies from an input dataframe of time series.\n It will add a new column "Is_Anomaly", where each entry will be marked with "True" if the value is an anomaly\n or "False" otherwise.\n\n :param df: the input data, must be a dataframe\n :param time_col_name: name of the column that contains the datetime\n :param value_col_name: name of the column that contains the numeric values.\n :return df: a new df that adds an additional "Is_Anomaly" column based on the input df.\n :return description: the description about the anomaly detection results.\n """\n try:\n df[time_col_name] = pd.to_datetime(df[time_col_name])\n except Exception:\n print("Time column is not datetime")\n return\n\n if not is_numeric_dtype(df[value_col_name]):\n try:\n df[value_col_name] = df[value_col_name].astype(float)\n except ValueError:\n print("Value column is not numeric")\n return\n\n mean, std = df[value_col_name].mean(), df[value_col_name].std()\n cutoff = std * 3\n lower, upper = mean - cutoff, mean + cutoff\n df["Is_Anomaly"] = df[value_col_name].apply(lambda x: x < lower or x > upper)\n anomaly_count = df["Is_Anomaly"].sum()\n description = "There are {} anomalies in the time series data".format(anomaly_count)\n \n self.ctx.add_artifact(\n name="anomaly_detection_results", # a brief description of the artifact\n file_name="anomaly_detection_results.csv", # artifact file name\n type="df", # artifact data type, support chart/df/file/txt/svg\n val=df, # variable to be dumped\n )\n \n return df, description\n\n'})}),"\n",(0,i.jsx)(n.p,{children:"You need to go through the following steps to implement your own plugin."}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["import the TaskWeaver plugin decorator ",(0,i.jsx)(n.code,{children:"from taskWeaver.plugin import Plugin, register_plugin"})]}),"\n",(0,i.jsxs)(n.li,{children:["create your plugin class inherited from ",(0,i.jsx)(n.code,{children:"Plugin"})," parent class (e.g., ",(0,i.jsx)(n.code,{children:"AnomalyDetectionPlugin(Plugin)"}),"), which is\ndecorated by ",(0,i.jsx)(n.code,{children:"@register_plugin"})]}),"\n",(0,i.jsxs)(n.li,{children:["implement your plugin function in ",(0,i.jsx)(n.code,{children:"__call__"})," method of the plugin class. ",(0,i.jsxs)(n.strong,{children:["Most importantly, it is mandatory to\ninclude ",(0,i.jsx)(n.code,{children:"descriptions"})," of your execution results in the return values of your plugin function"]}),". These descriptions\ncan be utilized by the LLM to effectively summarize your execution results."]}),"\n"]}),"\n",(0,i.jsxs)(n.blockquote,{children:["\n",(0,i.jsx)(n.p,{children:"\ud83d\udca1A key difference in a plugin implementation and a normal python function is that it always return a description of\nthe result in natural language. As LLMs only understand natural language, it is important to let the model understand\nwhat the execution result is. In the example implementation above, the description says how many anomalies are detected.\nBehind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in\nthe above example) is not handled by the LLM."}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"important-notes",children:"Important Notes"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsx)(n.p,{children:"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they\nare installed before proceeding."}),"\n"]}),"\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsxs)(n.p,{children:["If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation,\nTaskWeaver provides an ",(0,i.jsx)(n.code,{children:"add_artifact"})," API that allows you to store these results in the workspace. In the example we\nprovide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize\nthe ",(0,i.jsx)(n.code,{children:"add_artifact"})," API to save this file as an artifact. The artifacts are stored in the ",(0,i.jsx)(n.code,{children:"project/workspace/session_id/cwd"})," folder in the project directory."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'self.ctx.add_artifact(\n name="anomaly_detection_results", # a brief description of the artifact\n file_name="anomaly_detection_results.csv", # artifact file name\n type="df", # artifact data type, support chart/df/file/txt/svg\n val=df, # variable to be dumped\n)\n'})}),"\n",(0,i.jsx)(n.h2,{id:"plugin-schema",children:"Plugin Schema"}),"\n",(0,i.jsx)(n.p,{children:"The plugin schema is composed of several parts:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"name"}),": The main function name of the Python code."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"enabled"}),": determine whether the plugin is enabled for selection during conversations. The default value is true."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"descriptions"}),": A brief description that introduces the plugin function."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"parameters"}),": This section lists all the input parameter information. It includes the parameter's name, type,\nwhether it is required or optional, and a description providing more details about the parameter."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"returns"}),": This section lists all the return value information. It includes the return value's name, type, and\ndescription that provides information about the value that is returned by the function."]}),"\n"]}),"\n",(0,i.jsxs)(n.p,{children:[(0,i.jsx)(n.strong,{children:"Note:"})," The addition of any extra fields would result in a validation failure within the plugin schema."]}),"\n",(0,i.jsx)(n.p,{children:"The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly\ndetection plugin:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-yaml",children:'name: anomaly_detection\nenabled: true\nrequired: false\ndescription: >-\n anomaly_detection function identifies anomalies from an input DataFrame of\n time series. It will add a new column "Is_Anomaly", where each entry will be marked with "True" if the value is an anomaly or "False" otherwise.\n\nparameters:\n - name: df\n type: DataFrame\n required: true\n description: >-\n the input data from which we can identify the anomalies with the 3-sigma\n algorithm.\n - name: time_col_name\n type: str\n required: true\n description: name of the column that contains the datetime\n - name: value_col_name\n type: str\n required: true\n description: name of the column that contains the numeric values.\n\nreturns:\n - name: df\n type: DataFrame\n description: >-\n This DataFrame extends the input DataFrame with a newly-added column\n "Is_Anomaly" containing the anomaly detection result.\n - name: description\n type: str\n description: This is a string describing the anomaly detection results.\n\n'})}),"\n",(0,i.jsx)(n.p,{children:"Besides, we also set two optional fields as below:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"code"}),": In cases where multiple plugins map to the same Python code (i.e., the plugin name is different from the\ncode name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"configurations"}),": When using common code that requires some configuration parameter modifications for different\nplugins, it is important to specify these configuration parameters in the plugin schema."]}),"\n"]})]})}function u(e={}){const{wrapper:n}={...(0,a.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>l,a:()=>s});var i=t(7294);const a={},o=i.createContext(a);function s(e){const n=i.useContext(o);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function l(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:s(e.components),i.createElement(o.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/9fbcb257.6ee125b0.js b/assets/js/9fbcb257.6ee125b0.js deleted file mode 100644 index 3b7fe367..00000000 --- a/assets/js/9fbcb257.6ee125b0.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[8007],{1940:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>r,contentTitle:()=>s,default:()=>u,frontMatter:()=>o,metadata:()=>l,toc:()=>c});var i=t(5893),a=t(1151);const o={id:"plugin_intro",description:"Plugin introduction",slug:"/plugin/plugin_intro"},s="Plugin Introduction",l={id:"plugin/plugin_intro",title:"Plugin Introduction",description:"Plugin introduction",source:"@site/docs/plugin/plugin_intro.md",sourceDirName:"plugin",slug:"/plugin/plugin_intro",permalink:"/TaskWeaver/docs/plugin/plugin_intro",draft:!1,unlisted:!1,editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/docs/plugin/plugin_intro.md",tags:[],version:"current",frontMatter:{id:"plugin_intro",description:"Plugin introduction",slug:"/plugin/plugin_intro"},sidebar:"documentSidebar",previous:{title:"LiteLLM",permalink:"/TaskWeaver/docs/llms/liteLLM"},next:{title:"Auto Plugin Selection",permalink:"/TaskWeaver/docs/plugin/plugin_selection"}},r={},c=[{value:"Plugin Structure",id:"plugin-structure",level:2},{value:"Plugin Implementation",id:"plugin-implementation",level:2},{value:"Important Notes",id:"important-notes",level:3},{value:"Plugin Schema",id:"plugin-schema",level:2}];function d(e){const n={blockquote:"blockquote",code:"code",h1:"h1",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,a.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(n.h1,{id:"plugin-introduction",children:"Plugin Introduction"}),"\n",(0,i.jsx)(n.p,{children:"Plugins are the units that could be orchestrated by TaskWeaver. One could view the plugins as tools that the LLM can\nutilize to accomplish certain tasks."}),"\n",(0,i.jsx)(n.p,{children:"In TaskWeaver, each plugin is represented as a Python function that can be called within a code snippet. The\norchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins.\nOne concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like\nfollows:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'df, data_description = sql_pull_data(query="pull data from time_series table") \nanomaly_df, anomaly_description = anomaly_detection(df, time_col_name="ts", value_col_name="val") \n'})}),"\n",(0,i.jsx)(n.h2,{id:"plugin-structure",children:"Plugin Structure"}),"\n",(0,i.jsx)(n.p,{children:"A plugin has two files:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Plugin Implementation"}),": a Python file that defines the plugin"]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"Plugin Schema"}),": a file in yaml that defines the schema of the plugin"]}),"\n"]}),"\n",(0,i.jsx)(n.h2,{id:"plugin-implementation",children:"Plugin Implementation"}),"\n",(0,i.jsx)(n.p,{children:"The plugin function needs to be implemented in Python.\nTo be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts:"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:"Plugin function implementation code"}),"\n",(0,i.jsx)(n.li,{children:"TaskWeaver plugin decorator"}),"\n"]}),"\n",(0,i.jsx)(n.p,{children:"Here we exhibit an example of the anomaly detection plugin as the following code:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'import pandas as pd\nfrom pandas.api.types import is_numeric_dtype\n\nfrom taskWeaver.plugin import Plugin, register_plugin\n\n\n@register_plugin\nclass AnomalyDetectionPlugin(Plugin):\n def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str):\n\n """\n anomaly_detection function identifies anomalies from an input dataframe of time series.\n It will add a new column "Is_Anomaly", where each entry will be marked with "True" if the value is an anomaly\n or "False" otherwise.\n\n :param df: the input data, must be a dataframe\n :param time_col_name: name of the column that contains the datetime\n :param value_col_name: name of the column that contains the numeric values.\n :return df: a new df that adds an additional "Is_Anomaly" column based on the input df.\n :return description: the description about the anomaly detection results.\n """\n try:\n df[time_col_name] = pd.to_datetime(df[time_col_name])\n except Exception:\n print("Time column is not datetime")\n return\n\n if not is_numeric_dtype(df[value_col_name]):\n try:\n df[value_col_name] = df[value_col_name].astype(float)\n except ValueError:\n print("Value column is not numeric")\n return\n\n mean, std = df[value_col_name].mean(), df[value_col_name].std()\n cutoff = std * 3\n lower, upper = mean - cutoff, mean + cutoff\n df["Is_Anomaly"] = df[value_col_name].apply(lambda x: x < lower or x > upper)\n anomaly_count = df["Is_Anomaly"].sum()\n description = "There are {} anomalies in the time series data".format(anomaly_count)\n \n self.ctx.add_artifact(\n name="anomaly_detection_results", # a brief description of the artifact\n file_name="anomaly_detection_results.csv", # artifact file name\n type="df", # artifact data type, support chart/df/file/txt/svg\n val=df, # variable to be dumped\n )\n \n return df, description\n\n'})}),"\n",(0,i.jsx)(n.p,{children:"You need to go through the following steps to implement your own plugin."}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["import the TaskWeaver plugin decorator ",(0,i.jsx)(n.code,{children:"from taskWeaver.plugin import Plugin, register_plugin"})]}),"\n",(0,i.jsxs)(n.li,{children:["create your plugin class inherited from ",(0,i.jsx)(n.code,{children:"Plugin"})," parent class (e.g., ",(0,i.jsx)(n.code,{children:"AnomalyDetectionPlugin(Plugin)"}),"), which is\ndecorated by ",(0,i.jsx)(n.code,{children:"@register_plugin"})]}),"\n",(0,i.jsxs)(n.li,{children:["implement your plugin function in ",(0,i.jsx)(n.code,{children:"__call__"})," method of the plugin class. ",(0,i.jsxs)(n.strong,{children:["Most importantly, it is mandatory to\ninclude ",(0,i.jsx)(n.code,{children:"descriptions"})," of your execution results in the return values of your plugin function"]}),". These descriptions\ncan be utilized by the LLM to effectively summarize your execution results."]}),"\n"]}),"\n",(0,i.jsxs)(n.blockquote,{children:["\n",(0,i.jsx)(n.p,{children:"\ud83d\udca1A key difference in a plugin implementation and a normal python function is that it always return a description of\nthe result in natural language. As LLMs only understand natural language, it is important to let the model understand\nwhat the execution result is. In the example implementation above, the description says how many anomalies are detected.\nBehind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in\nthe above example) is not handled by the LLM."}),"\n"]}),"\n",(0,i.jsx)(n.h3,{id:"important-notes",children:"Important Notes"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsx)(n.p,{children:"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they\nare installed before proceeding."}),"\n"]}),"\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsxs)(n.p,{children:["If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation,\nTaskWeaver provides an ",(0,i.jsx)(n.code,{children:"add_artifact"})," API that allows you to store these results in the workspace. In the example we\nprovide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize\nthe ",(0,i.jsx)(n.code,{children:"add_artifact"})," API to save this file as an artifact. The artifacts are stored in the ",(0,i.jsx)(n.code,{children:"project/workspace/session_id/cwd"})," folder in the project directory."]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'self.ctx.add_artifact(\n name="anomaly_detection_results", # a brief description of the artifact\n file_name="anomaly_detection_results.csv", # artifact file name\n type="df", # artifact data type, support chart/df/file/txt/svg\n val=df, # variable to be dumped\n)\n'})}),"\n",(0,i.jsx)(n.h2,{id:"plugin-schema",children:"Plugin Schema"}),"\n",(0,i.jsx)(n.p,{children:"The plugin schema is composed of several parts:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"name"}),": The main function name of the Python code."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"enabled"}),": determine whether the plugin is enabled for selection during conversations. The default value is true."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"descriptions"}),": A brief description that introduces the plugin function."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"parameters"}),": This section lists all the input parameter information. It includes the parameter's name, type,\nwhether it is required or optional, and a description providing more details about the parameter."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"returns"}),": This section lists all the return value information. It includes the return value's name, type, and\ndescription that provides information about the value that is returned by the function."]}),"\n"]}),"\n",(0,i.jsxs)(n.p,{children:[(0,i.jsx)(n.strong,{children:"Note:"})," The addition of any extra fields would result in a validation failure within the plugin schema."]}),"\n",(0,i.jsx)(n.p,{children:"The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly\ndetection plugin:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-yaml",children:'name: anomaly_detection\nenabled: true\nrequired: false\ndescription: >-\n anomaly_detection function identifies anomalies from an input DataFrame of\n time series. It will add a new column "Is_Anomaly", where each entry will be marked with "True" if the value is an anomaly or "False" otherwise.\n\nparameters:\n - name: df\n type: DataFrame\n required: true\n description: >-\n the input data from which we can identify the anomalies with the 3-sigma\n algorithm.\n - name: time_col_name\n type: str\n required: true\n description: name of the column that contains the datetime\n - name: value_col_name\n type: str\n required: true\n description: name of the column that contains the numeric values.\n\nreturns:\n - name: df\n type: DataFrame\n description: >-\n This DataFrame extends the input DataFrame with a newly-added column\n "Is_Anomaly" containing the anomaly detection result.\n - name: description\n type: str\n description: This is a string describing the anomaly detection results.\n\n'})}),"\n",(0,i.jsx)(n.p,{children:"Besides, we also set two optional fields as below:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"code"}),": In cases where multiple plugins map to the same Python code (i.e., the plugin name is different from the\ncode name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy."]}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.strong,{children:"configurations"}),": When using common code that requires some configuration parameter modifications for different\nplugins, it is important to specify these configuration parameters in the plugin schema."]}),"\n"]})]})}function u(e={}){const{wrapper:n}={...(0,a.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>l,a:()=>s});var i=t(7294);const a={},o=i.createContext(a);function s(e){const n=i.useContext(o);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function l(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:s(e.components),i.createElement(o.Provider,{value:n},e.children)}}}]); 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