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[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)}},193:(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.263d5a3a.js b/assets/js/1bba509c.263d5a3a.js new file mode 100644 index 00000000..7417e31f --- /dev/null +++ b/assets/js/1bba509c.263d5a3a.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 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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. 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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)}}}]); \ No newline at end of file diff --git a/assets/js/3d6402a8.11f8ebfb.js b/assets/js/3d6402a8.11f8ebfb.js new file mode 100644 index 00000000..2ef88567 --- /dev/null +++ b/assets/js/3d6402a8.11f8ebfb.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[889],{4497:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>O,contentTitle:()=>L,default:()=>S,frontMatter:()=>V,metadata:()=>T,toc:()=>E});var r=n(5893),a=n(1151),s=n(7294),l=n(512),o=n(2466),u=n(6550),i=n(469),c=n(1980),d=n(7392),p=n(12);function m(e){return s.Children.toArray(e).filter((e=>"\n"!==e)).map((e=>{if(!e||(0,s.isValidElement)(e)&&function(e){const{props:t}=e;return!!t&&"object"==typeof t&&"value"in t}(e))return e;throw new Error(`Docusaurus error: Bad <Tabs> child <${"string"==typeof e.type?e.type:e.type.name}>: all children of the <Tabs> component should be <TabItem>, and every <TabItem> should have a unique "value" prop.`)}))?.filter(Boolean)??[]}function f(e){const{values:t,children:n}=e;return(0,s.useMemo)((()=>{const e=t??function(e){return m(e).map((e=>{let{props:{value:t,label:n,attributes:r,default:a}}=e;return{value:t,label:n,attributes:r,default:a}}))}(n);return function(e){const t=(0,d.l)(e,((e,t)=>e.value===t.value));if(t.length>0)throw new Error(`Docusaurus error: Duplicate values "${t.map((e=>e.value)).join(", ")}" found in <Tabs>. 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n,c=this,l=[].slice.call(arguments,2);if(this.$el.empty(),n=e&&e.length,this._isEmpty=!n,!n&&this.templates.empty)this.$el.html(function(){var e=[].slice.call(arguments,0);return e=[{query:t,isEmpty:!0}].concat(e),c.templates.empty.apply(this,e)}.apply(this,l)).prepend(c.templates.header?h.apply(this,l):null).append(c.templates.footer?p.apply(this,l):null);else if(n)this.$el.html(function(){var t,n,l=[].slice.call(arguments,0),h=this,p=u.suggestions.replace("%PREFIX%",this.cssClasses.prefix).replace("%SUGGESTIONS%",this.cssClasses.suggestions);return t=a.element(p).css(this.css.suggestions),n=o.map(e,f),t.append.apply(t,n),t;function f(t){var e,n=u.suggestion.replace("%PREFIX%",h.cssClasses.prefix).replace("%SUGGESTION%",h.cssClasses.suggestion);return(e=a.element(n).attr({role:"option",id:["option",Math.floor(1e8*Math.random())].join("-")}).append(c.templates.suggestion.apply(this,[t].concat(l)))).data(i,c.name),e.data(s,c.displayFn(t)||void 0),e.data(r,JSON.stringify(t)),e.children().each((function(){a.element(this).css(h.css.suggestionChild)})),e}}.apply(this,l)).prepend(c.templates.header?h.apply(this,l):null).append(c.templates.footer?p.apply(this,l):null);else if(e&&!Array.isArray(e))throw new TypeError("suggestions must be an array");this.$menu&&this.$menu.addClass(this.cssClasses.prefix+(n?"with":"without")+"-"+this.name).removeClass(this.cssClasses.prefix+(n?"without":"with")+"-"+this.name),this.trigger("rendered",t)}function h(){var e=[].slice.call(arguments,0);return e=[{query:t,isEmpty:!n}].concat(e),c.templates.header.apply(this,e)}function p(){var e=[].slice.call(arguments,0);return e=[{query:t,isEmpty:!n}].concat(e),c.templates.footer.apply(this,e)}},getRoot:function(){return this.$el},update:function(t){function e(e){if(!this.canceled&&t===this.query){var n=[].slice.call(arguments,1);this.cacheSuggestions(t,e,n),this._render.apply(this,[t,e].concat(n))}}if(this.query=t,this.canceled=!1,this.shouldFetchFromCache(t))e.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var n=this,i=function(){n.canceled||n.source(t,e.bind(n))};if(this.debounce){clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout((function(){n.debounceTimeout=null,i()}),this.debounce)}else i()}},cacheSuggestions:function(t,e,n){this.cachedQuery=t,this.cachedSuggestions=e,this.cachedRenderExtraArgs=n},shouldFetchFromCache:function(t){return this.cache&&this.cachedQuery===t&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),t.exports=h},3354:(t,e,n)=>{"use strict";var i=n(2856),s=n(4910),r=n(3109),o=n(9050),a=n(1228);function u(t){var e,n,r,o=this;(t=t||{}).menu||i.error("menu is required"),i.isArray(t.datasets)||i.isObject(t.datasets)||i.error("1 or more datasets required"),t.datasets||i.error("datasets is required"),this.isOpen=!1,this.isEmpty=!0,this.minLength=t.minLength||0,this.templates={},this.appendTo=t.appendTo||!1,this.css=i.mixin({},a,t.appendTo?a.appendTo:{}),this.cssClasses=t.cssClasses=i.mixin({},a.defaultClasses,t.cssClasses||{}),this.cssClasses.prefix=t.cssClasses.formattedPrefix||i.formatPrefix(this.cssClasses.prefix,this.cssClasses.noPrefix),e=i.bind(this._onSuggestionClick,this),n=i.bind(this._onSuggestionMouseEnter,this),r=i.bind(this._onSuggestionMouseLeave,this);var c=i.className(this.cssClasses.prefix,this.cssClasses.suggestion);this.$menu=s.element(t.menu).on("mouseenter.aa",c,n).on("mouseleave.aa",c,r).on("click.aa",c,e),this.$container=t.appendTo?t.wrapper:this.$menu,t.templates&&t.templates.header&&(this.templates.header=i.templatify(t.templates.header),this.$menu.prepend(this.templates.header())),t.templates&&t.templates.empty&&(this.templates.empty=i.templatify(t.templates.empty),this.$empty=s.element('<div class="'+i.className(this.cssClasses.prefix,this.cssClasses.empty,!0)+'"></div>'),this.$menu.append(this.$empty),this.$empty.hide()),this.datasets=i.map(t.datasets,(function(e){return function(t,e,n){return new u.Dataset(i.mixin({$menu:t,cssClasses:n},e))}(o.$menu,e,t.cssClasses)})),i.each(this.datasets,(function(t){var e=t.getRoot();e&&0===e.parent().length&&o.$menu.append(e),t.onSync("rendered",o._onRendered,o)})),t.templates&&t.templates.footer&&(this.templates.footer=i.templatify(t.templates.footer),this.$menu.append(this.templates.footer()));var l=this;s.element(window).resize((function(){l._redraw()}))}i.mixin(u.prototype,r,{_onSuggestionClick:function(t){this.trigger("suggestionClicked",s.element(t.currentTarget))},_onSuggestionMouseEnter:function(t){var e=s.element(t.currentTarget);if(!e.hasClass(i.className(this.cssClasses.prefix,this.cssClasses.cursor,!0))){this._removeCursor();var n=this;setTimeout((function(){n._setCursor(e,!1)}),0)}},_onSuggestionMouseLeave:function(t){if(t.relatedTarget&&s.element(t.relatedTarget).closest("."+i.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).length>0)return;this._removeCursor(),this.trigger("cursorRemoved")},_onRendered:function(t,e){if(this.isEmpty=i.every(this.datasets,(function(t){return t.isEmpty()})),this.isEmpty)if(e.length>=this.minLength&&this.trigger("empty"),this.$empty)if(e.length<this.minLength)this._hide();else{var n=this.templates.empty({query:this.datasets[0]&&this.datasets[0].query});this.$empty.html(n),this.$empty.show(),this._show()}else i.any(this.datasets,(function(t){return t.templates&&t.templates.empty}))?e.length<this.minLength?this._hide():this._show():this._hide();else this.isOpen&&(this.$empty&&(this.$empty.empty(),this.$empty.hide()),e.length>=this.minLength?this._show():this._hide());this.trigger("datasetRendered")},_hide:function(){this.$container.hide()},_show:function(){this.$container.css("display","block"),this._redraw(),this.trigger("shown")},_redraw:function(){this.isOpen&&this.appendTo&&this.trigger("redrawn")},_getSuggestions:function(){return this.$menu.find(i.className(this.cssClasses.prefix,this.cssClasses.suggestion))},_getCursor:function(){return this.$menu.find(i.className(this.cssClasses.prefix,this.cssClasses.cursor)).first()},_setCursor:function(t,e){t.first().addClass(i.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).attr("aria-selected","true"),this.trigger("cursorMoved",e)},_removeCursor:function(){this._getCursor().removeClass(i.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).removeAttr("aria-selected")},_moveCursor:function(t){var e,n,i,s;this.isOpen&&(n=this._getCursor(),e=this._getSuggestions(),this._removeCursor(),-1!==(i=((i=e.index(n)+t)+1)%(e.length+1)-1)?(i<-1&&(i=e.length-1),this._setCursor(s=e.eq(i),!0),this._ensureVisible(s)):this.trigger("cursorRemoved"))},_ensureVisible:function(t){var e,n,i,s;n=(e=t.position().top)+t.height()+parseInt(t.css("margin-top"),10)+parseInt(t.css("margin-bottom"),10),i=this.$menu.scrollTop(),s=this.$menu.height()+parseInt(this.$menu.css("padding-top"),10)+parseInt(this.$menu.css("padding-bottom"),10),e<0?this.$menu.scrollTop(i+e):s<n&&this.$menu.scrollTop(i+(n-s))},close:function(){this.isOpen&&(this.isOpen=!1,this._removeCursor(),this._hide(),this.trigger("closed"))},open:function(){this.isOpen||(this.isOpen=!0,this.isEmpty||this._show(),this.trigger("opened"))},setLanguageDirection:function(t){this.$menu.css("ltr"===t?this.css.ltr:this.css.rtl)},moveCursorUp:function(){this._moveCursor(-1)},moveCursorDown:function(){this._moveCursor(1)},getDatumForSuggestion:function(t){var e=null;return t.length&&(e={raw:o.extractDatum(t),value:o.extractValue(t),datasetName:o.extractDatasetName(t)}),e},getCurrentCursor:function(){return this._getCursor().first()},getDatumForCursor:function(){return this.getDatumForSuggestion(this._getCursor().first())},getDatumForTopSuggestion:function(){return this.getDatumForSuggestion(this._getSuggestions().first())},cursorTopSuggestion:function(){this._setCursor(this._getSuggestions().first(),!1)},update:function(t){i.each(this.datasets,(function(e){e.update(t)}))},empty:function(){i.each(this.datasets,(function(t){t.clear()})),this.isEmpty=!0},isVisible:function(){return this.isOpen&&!this.isEmpty},destroy:function(){this.$menu.off(".aa"),this.$menu=null,i.each(this.datasets,(function(t){t.destroy()}))}}),u.Dataset=o,t.exports=u},50:(t,e,n)=>{"use strict";var i=n(2856),s=n(4910);function r(t){t&&t.el||i.error("EventBus initialized without el"),this.$el=s.element(t.el)}i.mixin(r.prototype,{trigger:function(t,e,n,s){var r=i.Event("autocomplete:"+t);return this.$el.trigger(r,[e,n,s]),r}}),t.exports=r},3109:(t,e,n)=>{"use strict";var i=n(624),s=/\s+/;function r(t,e,n,i){var r;if(!n)return this;for(e=e.split(s),n=i?function(t,e){return t.bind?t.bind(e):function(){t.apply(e,[].slice.call(arguments,0))}}(n,i):n,this._callbacks=this._callbacks||{};r=e.shift();)this._callbacks[r]=this._callbacks[r]||{sync:[],async:[]},this._callbacks[r][t].push(n);return this}function o(t,e,n){return function(){for(var i,s=0,r=t.length;!i&&s<r;s+=1)i=!1===t[s].apply(e,n);return!i}}t.exports={onSync:function(t,e,n){return r.call(this,"sync",t,e,n)},onAsync:function(t,e,n){return r.call(this,"async",t,e,n)},off:function(t){var e;if(!this._callbacks)return this;t=t.split(s);for(;e=t.shift();)delete this._callbacks[e];return this},trigger:function(t){var e,n,r,a,u;if(!this._callbacks)return this;t=t.split(s),r=[].slice.call(arguments,1);for(;(e=t.shift())&&(n=this._callbacks[e]);)a=o(n.sync,this,[e].concat(r)),u=o(n.async,this,[e].concat(r)),a()&&i(u);return this}}},3561:t=>{"use strict";t.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},2534:(t,e,n)=>{"use strict";var i;i={9:"tab",27:"esc",37:"left",39:"right",13:"enter",38:"up",40:"down"};var s=n(2856),r=n(4910),o=n(3109);function a(t){var e,n,o,a,u,c=this;(t=t||{}).input||s.error("input is missing"),e=s.bind(this._onBlur,this),n=s.bind(this._onFocus,this),o=s.bind(this._onKeydown,this),a=s.bind(this._onInput,this),this.$hint=r.element(t.hint),this.$input=r.element(t.input).on("blur.aa",e).on("focus.aa",n).on("keydown.aa",o),0===this.$hint.length&&(this.setHint=this.getHint=this.clearHint=this.clearHintIfInvalid=s.noop),s.isMsie()?this.$input.on("keydown.aa keypress.aa cut.aa paste.aa",(function(t){i[t.which||t.keyCode]||s.defer(s.bind(c._onInput,c,t))})):this.$input.on("input.aa",a),this.query=this.$input.val(),this.$overflowHelper=(u=this.$input,r.element('<pre aria-hidden="true"></pre>').css({position:"absolute",visibility:"hidden",whiteSpace:"pre",fontFamily:u.css("font-family"),fontSize:u.css("font-size"),fontStyle:u.css("font-style"),fontVariant:u.css("font-variant"),fontWeight:u.css("font-weight"),wordSpacing:u.css("word-spacing"),letterSpacing:u.css("letter-spacing"),textIndent:u.css("text-indent"),textRendering:u.css("text-rendering"),textTransform:u.css("text-transform")}).insertAfter(u))}function u(t){return t.altKey||t.ctrlKey||t.metaKey||t.shiftKey}a.normalizeQuery=function(t){return(t||"").replace(/^\s*/g,"").replace(/\s{2,}/g," ")},s.mixin(a.prototype,o,{_onBlur:function(){this.resetInputValue(),this.$input.removeAttr("aria-activedescendant"),this.trigger("blurred")},_onFocus:function(){this.trigger("focused")},_onKeydown:function(t){var e=i[t.which||t.keyCode];this._managePreventDefault(e,t),e&&this._shouldTrigger(e,t)&&this.trigger(e+"Keyed",t)},_onInput:function(){this._checkInputValue()},_managePreventDefault:function(t,e){var n,i,s;switch(t){case"tab":i=this.getHint(),s=this.getInputValue(),n=i&&i!==s&&!u(e);break;case"up":case"down":n=!u(e);break;default:n=!1}n&&e.preventDefault()},_shouldTrigger:function(t,e){var n;if("tab"===t)n=!u(e);else n=!0;return n},_checkInputValue:function(){var t,e,n,i,s;t=this.getInputValue(),i=t,s=this.query,n=!(!(e=a.normalizeQuery(i)===a.normalizeQuery(s))||!this.query)&&this.query.length!==t.length,this.query=t,e?n&&this.trigger("whitespaceChanged",this.query):this.trigger("queryChanged",this.query)},focus:function(){this.$input.focus()},blur:function(){this.$input.blur()},getQuery:function(){return this.query},setQuery:function(t){this.query=t},getInputValue:function(){return this.$input.val()},setInputValue:function(t,e){void 0===t&&(t=this.query),this.$input.val(t),e?this.clearHint():this._checkInputValue()},expand:function(){this.$input.attr("aria-expanded","true")},collapse:function(){this.$input.attr("aria-expanded","false")},setActiveDescendant:function(t){this.$input.attr("aria-activedescendant",t)},removeActiveDescendant:function(){this.$input.removeAttr("aria-activedescendant")},resetInputValue:function(){this.setInputValue(this.query,!0)},getHint:function(){return this.$hint.val()},setHint:function(t){this.$hint.val(t)},clearHint:function(){this.setHint("")},clearHintIfInvalid:function(){var t,e,n;n=(t=this.getInputValue())!==(e=this.getHint())&&0===e.indexOf(t),""!==t&&n&&!this.hasOverflow()||this.clearHint()},getLanguageDirection:function(){return(this.$input.css("direction")||"ltr").toLowerCase()},hasOverflow:function(){var t=this.$input.width()-2;return this.$overflowHelper.text(this.getInputValue()),this.$overflowHelper.width()>=t},isCursorAtEnd:function(){var t,e,n;return t=this.$input.val().length,e=this.$input[0].selectionStart,s.isNumber(e)?e===t:!document.selection||((n=document.selection.createRange()).moveStart("character",-t),t===n.text.length)},destroy:function(){this.$hint.off(".aa"),this.$input.off(".aa"),this.$hint=this.$input=this.$overflowHelper=null}}),t.exports=a},6549:(t,e,n)=>{"use strict";var i="aaAttrs",s=n(2856),r=n(4910),o=n(50),a=n(2534),u=n(3354),c=n(3561),l=n(1228);function h(t){var e,n;if((t=t||{}).input||s.error("missing input"),this.isActivated=!1,this.debug=!!t.debug,this.autoselect=!!t.autoselect,this.autoselectOnBlur=!!t.autoselectOnBlur,this.openOnFocus=!!t.openOnFocus,this.minLength=s.isNumber(t.minLength)?t.minLength:1,this.autoWidth=void 0===t.autoWidth||!!t.autoWidth,this.clearOnSelected=!!t.clearOnSelected,this.tabAutocomplete=void 0===t.tabAutocomplete||!!t.tabAutocomplete,t.hint=!!t.hint,t.hint&&t.appendTo)throw new Error("[autocomplete.js] hint and appendTo options can't be used at the same time");this.css=t.css=s.mixin({},l,t.appendTo?l.appendTo:{}),this.cssClasses=t.cssClasses=s.mixin({},l.defaultClasses,t.cssClasses||{}),this.cssClasses.prefix=t.cssClasses.formattedPrefix=s.formatPrefix(this.cssClasses.prefix,this.cssClasses.noPrefix),this.listboxId=t.listboxId=[this.cssClasses.root,"listbox",s.getUniqueId()].join("-");var a=function(t){var e,n,o,a;e=r.element(t.input),n=r.element(c.wrapper.replace("%ROOT%",t.cssClasses.root)).css(t.css.wrapper),t.appendTo||"block"!==e.css("display")||"table"!==e.parent().css("display")||n.css("display","table-cell");var u=c.dropdown.replace("%PREFIX%",t.cssClasses.prefix).replace("%DROPDOWN_MENU%",t.cssClasses.dropdownMenu);o=r.element(u).css(t.css.dropdown).attr({role:"listbox",id:t.listboxId}),t.templates&&t.templates.dropdownMenu&&o.html(s.templatify(t.templates.dropdownMenu)());a=e.clone().css(t.css.hint).css(function(t){return{backgroundAttachment:t.css("background-attachment"),backgroundClip:t.css("background-clip"),backgroundColor:t.css("background-color"),backgroundImage:t.css("background-image"),backgroundOrigin:t.css("background-origin"),backgroundPosition:t.css("background-position"),backgroundRepeat:t.css("background-repeat"),backgroundSize:t.css("background-size")}}(e)),a.val("").addClass(s.className(t.cssClasses.prefix,t.cssClasses.hint,!0)).removeAttr("id name placeholder required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),a.removeData&&a.removeData();e.data(i,{"aria-autocomplete":e.attr("aria-autocomplete"),"aria-expanded":e.attr("aria-expanded"),"aria-owns":e.attr("aria-owns"),autocomplete:e.attr("autocomplete"),dir:e.attr("dir"),role:e.attr("role"),spellcheck:e.attr("spellcheck"),style:e.attr("style"),type:e.attr("type")}),e.addClass(s.className(t.cssClasses.prefix,t.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":t.datasets&&t.datasets[0]&&t.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":t.ariaLabel,"aria-owns":t.listboxId}).css(t.hint?t.css.input:t.css.inputWithNoHint);try{e.attr("dir")||e.attr("dir","auto")}catch(l){}return n=t.appendTo?n.appendTo(r.element(t.appendTo).eq(0)).eq(0):e.wrap(n).parent(),n.prepend(t.hint?a:null).append(o),{wrapper:n,input:e,hint:a,menu:o}}(t);this.$node=a.wrapper;var u=this.$input=a.input;e=a.menu,n=a.hint,t.dropdownMenuContainer&&r.element(t.dropdownMenuContainer).css("position","relative").append(e.css("top","0")),u.on("blur.aa",(function(t){var n=document.activeElement;s.isMsie()&&(e[0]===n||e[0].contains(n))&&(t.preventDefault(),t.stopImmediatePropagation(),s.defer((function(){u.focus()})))})),e.on("mousedown.aa",(function(t){t.preventDefault()})),this.eventBus=t.eventBus||new o({el:u}),this.dropdown=new h.Dropdown({appendTo:t.appendTo,wrapper:this.$node,menu:e,datasets:t.datasets,templates:t.templates,cssClasses:t.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new 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\ No newline at end of file diff --git a/assets/js/8cff664c.aa1908d4.js b/assets/js/8cff664c.aa1908d4.js new file mode 100644 index 00000000..bac62049 --- /dev/null +++ b/assets/js/8cff664c.aa1908d4.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[5218],{3009:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>h,contentTitle:()=>o,default:()=>d,frontMatter:()=>r,metadata:()=>i,toc:()=>c});var a=n(5893),s=n(1151);const r={},o=void 0,i={id:"example/example",title:"example",description:"There are two types of examples: (1) planning examples and (2) code interpreter examples.",source:"@site/docs/example/example.md",sourceDirName:"example",slug:"/example/",permalink:"/TaskWeaver/docs/example/",draft:!1,unlisted:!1,editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/docs/example/example.md",tags:[],version:"current",frontMatter:{},sidebar:"documentSidebar",previous:{title:"Embedding",permalink:"/TaskWeaver/docs/plugin/embedding"},next:{title:"compression",permalink:"/TaskWeaver/docs/compression"}},h={},c=[{value:"Planning Examples",id:"planning-examples",level:4},{value:"Code Interpreter Examples",id:"code-interpreter-examples",level:4}];function l(e){const t={a:"a",code:"code",h4:"h4",li:"li",ol:"ol",p:"p",pre:"pre",...(0,s.a)(),...e.components};return(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(t.p,{children:"There are two types of examples: (1) planning examples and (2) code interpreter examples.\nPlanning examples are used to demonstrate how to use TaskWeaver to plan for a specific task.\nCode generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task."}),"\n",(0,a.jsx)(t.h4,{id:"planning-examples",children:"Planning Examples"}),"\n",(0,a.jsxs)(t.p,{children:["A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter;\nreceive the execution result from the code interpreter; and summarize the execution result.\nBefore constructing the planning example, we strongly encourage you to go through the\n",(0,a.jsx)(t.a,{href:"https://github.com/microsoft/TaskWeaver/blob/main/taskweaver/planner/planner_prompt.yaml",children:"planner prompt"}),"."]}),"\n",(0,a.jsx)(t.p,{children:"The following is an example of a planning example which contains 4 posts.\nEach post contains a message, a sender, a receiver, and a list of attachments."}),"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:'The first post is sent from the user to the planner.\nThe message is "count the rows of /home/data.csv", which is the same as the user query.'}),"\n",(0,a.jsxs)(t.li,{children:['The second post is sent from the planner to the code interpreter.\nThe message is "Please load the data file /home/data.csv and count the rows of the loaded data".\nThe attachment list contains 3 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the initial plan, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the plan, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the current plan step, which is a markdown string."}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.li,{children:'The third post is sent from the code interpreter to the planner.\nThe message is "Load the data file /home/data.csv successfully and there are 100 rows in the data file".'}),"\n",(0,a.jsxs)(t.li,{children:['The fourth post is sent from the planner to the user.\nThe message is "The data file /home/data.csv is loaded and there are 100 rows in the data file".\nThe attachment list contains 3 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the initial plan, which is the same as the second attachment of the second post."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the plan, which is the same as the third attachment of the second post."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the current plan step, which is a markdown string."}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.pre,{children:(0,a.jsx)(t.code,{className:"language-yaml",children:"enabled: True\nrounds:\n - user_query: count the rows of /home/data.csv\n state: created\n post_list:\n - message: count the rows of /home/data.csv\n send_from: User\n send_to: Planner\n attachment_list:\n - message: Please load the data file /home/data.csv and count the rows of the loaded data\n send_from: Planner\n send_to: CodeInterpreter\n attachment_list:\n - type: init_plan\n content: |-\n 1. load the data file\n 2. count the rows of the loaded data <sequentially depends on 1>\n 3. report the result to the user <interactively depends on 2>\n - type: plan\n content: |-\n 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n 2. report the result to the user\n - type: current_plan_step\n content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file\n send_from: CodeInterpreter\n send_to: Planner\n attachment_list:\n - message: The data file /home/data.csv is loaded and there are 100 rows in the data file\n send_from: Planner\n send_to: User\n attachment_list:\n - type: init_plan\n content: |-\n 1. load the data file\n 2. count the rows of the loaded data <sequentially depends on 1>\n 3. report the result to the user <interactively depends on 2>\n - type: plan\n content: |-\n 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n 2. report the result to the user\n - type: current_plan_step\n content: 2. report the result to the user\n"})}),"\n",(0,a.jsx)(t.h4,{id:"code-interpreter-examples",children:"Code Interpreter Examples"}),"\n",(0,a.jsxs)(t.p,{children:["A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task.\nThe task is from the planner. Before constructing the code interpreter example, we strongly encourage you to\nread the ",(0,a.jsx)(t.a,{href:"https://github.com/microsoft/TaskWeaver/blob/main/taskweaver/code_interpreter/code_generator/code_generator_prompt.yaml",children:"code generator prompt"}),"."]}),"\n",(0,a.jsx)(t.p,{children:"The following is an example of a code interpreter example which contains 2 posts.\nEach post contains a message, a sender, a receiver, and a list of attachments."}),"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:'The first post is sent from the planner to the code interpreter.\nThe message is "Please read file /abc/def.txt".'}),"\n",(0,a.jsxs)(t.li,{children:['The second post is sent from the code interpreter to the planner.\nThe message is "read file /abc/def.txt".\nThe attachment list contains 6 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the thought of the code interpreter, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the generated code, which is in python."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE."}),"\n",(0,a.jsx)(t.li,{children:"The fourth attachment is the verification error message, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE."}),"\n",(0,a.jsx)(t.li,{children:"The sixth attachment is the execution result, which is a markdown string."}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.pre,{children:(0,a.jsx)(t.code,{className:"language-yaml",children:'enabled: True\nrounds:\n - user_query: read file /abc/def.txt\n state: finished\n post_list:\n - message: read file /abc/def.txt\n send_from: Planner\n send_to: CodeInterpreter\n attachment_list: []\n - message: I\'m sorry, I cannot find the file /abc/def.txt. 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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. 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\ No newline at end of file diff --git a/assets/js/cf35c7d9.6728f5b2.js b/assets/js/cf35c7d9.6728f5b2.js deleted file mode 100644 index 93db8c36..00000000 --- a/assets/js/cf35c7d9.6728f5b2.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[653],{5692:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>h,contentTitle:()=>o,default:()=>d,frontMatter:()=>r,metadata:()=>i,toc:()=>c});var a=n(5893),s=n(1151);const r={},o=void 0,i={id:"Example/example",title:"example",description:"There are two types of examples: (1) planning examples and (2) code interpreter examples.",source:"@site/docs/Example/example.md",sourceDirName:"Example",slug:"/Example/",permalink:"/TaskWeaver/docs/Example/",draft:!1,unlisted:!1,editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/docs/Example/example.md",tags:[],version:"current",frontMatter:{}},h={},c=[{value:"Planning Examples",id:"planning-examples",level:4},{value:"Code Interpreter Examples",id:"code-interpreter-examples",level:4}];function l(e){const t={a:"a",code:"code",h4:"h4",li:"li",ol:"ol",p:"p",pre:"pre",...(0,s.a)(),...e.components};return(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(t.p,{children:"There are two types of examples: (1) planning examples and (2) code interpreter examples.\nPlanning examples are used to demonstrate how to use TaskWeaver to plan for a specific task.\nCode generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task."}),"\n",(0,a.jsx)(t.h4,{id:"planning-examples",children:"Planning Examples"}),"\n",(0,a.jsxs)(t.p,{children:["A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter;\nreceive the execution result from the code interpreter; and summarize the execution result.\nBefore constructing the planning example, we strongly encourage you to go through the\n",(0,a.jsx)(t.a,{href:"https://github.com/microsoft/TaskWeaver/blob/main/taskweaver/planner/planner_prompt.yaml",children:"planner prompt"}),"."]}),"\n",(0,a.jsx)(t.p,{children:"The following is an example of a planning example which contains 4 posts.\nEach post contains a message, a sender, a receiver, and a list of attachments."}),"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:'The first post is sent from the user to the planner.\nThe message is "count the rows of /home/data.csv", which is the same as the user query.'}),"\n",(0,a.jsxs)(t.li,{children:['The second post is sent from the planner to the code interpreter.\nThe message is "Please load the data file /home/data.csv and count the rows of the loaded data".\nThe attachment list contains 3 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the initial plan, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the plan, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the current plan step, which is a markdown string."}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.li,{children:'The third post is sent from the code interpreter to the planner.\nThe message is "Load the data file /home/data.csv successfully and there are 100 rows in the data file".'}),"\n",(0,a.jsxs)(t.li,{children:['The fourth post is sent from the planner to the user.\nThe message is "The data file /home/data.csv is loaded and there are 100 rows in the data file".\nThe attachment list contains 3 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the initial plan, which is the same as the second attachment of the second post."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the plan, which is the same as the third attachment of the second post."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the current plan step, which is a markdown string."}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.pre,{children:(0,a.jsx)(t.code,{className:"language-yaml",children:"enabled: True\nrounds:\n - user_query: count the rows of /home/data.csv\n state: created\n post_list:\n - message: count the rows of /home/data.csv\n send_from: User\n send_to: Planner\n attachment_list:\n - message: Please load the data file /home/data.csv and count the rows of the loaded data\n send_from: Planner\n send_to: CodeInterpreter\n attachment_list:\n - type: init_plan\n content: |-\n 1. load the data file\n 2. count the rows of the loaded data <sequentially depends on 1>\n 3. report the result to the user <interactively depends on 2>\n - type: plan\n content: |-\n 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n 2. report the result to the user\n - type: current_plan_step\n content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file\n send_from: CodeInterpreter\n send_to: Planner\n attachment_list:\n - message: The data file /home/data.csv is loaded and there are 100 rows in the data file\n send_from: Planner\n send_to: User\n attachment_list:\n - type: init_plan\n content: |-\n 1. load the data file\n 2. count the rows of the loaded data <sequentially depends on 1>\n 3. report the result to the user <interactively depends on 2>\n - type: plan\n content: |-\n 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data\n 2. report the result to the user\n - type: current_plan_step\n content: 2. report the result to the user\n"})}),"\n",(0,a.jsx)(t.h4,{id:"code-interpreter-examples",children:"Code Interpreter Examples"}),"\n",(0,a.jsxs)(t.p,{children:["A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task.\nThe task is from the planner. Before constructing the code interpreter example, we strongly encourage you to\nread the ",(0,a.jsx)(t.a,{href:"https://github.com/microsoft/TaskWeaver/blob/main/taskweaver/code_interpreter/code_generator/code_generator_prompt.yaml",children:"code generator prompt"}),"."]}),"\n",(0,a.jsx)(t.p,{children:"The following is an example of a code interpreter example which contains 2 posts.\nEach post contains a message, a sender, a receiver, and a list of attachments."}),"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:'The first post is sent from the planner to the code interpreter.\nThe message is "Please read file /abc/def.txt".'}),"\n",(0,a.jsxs)(t.li,{children:['The second post is sent from the code interpreter to the planner.\nThe message is "read file /abc/def.txt".\nThe attachment list contains 6 attachments:',"\n",(0,a.jsxs)(t.ol,{children:["\n",(0,a.jsx)(t.li,{children:"The first attachment is the thought of the code interpreter, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The second attachment is the generated code, which is in python."}),"\n",(0,a.jsx)(t.li,{children:"The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE."}),"\n",(0,a.jsx)(t.li,{children:"The fourth attachment is the verification error message, which is a markdown string."}),"\n",(0,a.jsx)(t.li,{children:"The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE."}),"\n",(0,a.jsx)(t.li,{children:"The sixth attachment is the execution result, which is a markdown string."}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,a.jsx)(t.pre,{children:(0,a.jsx)(t.code,{className:"language-yaml",children:'enabled: True\nrounds:\n - user_query: read file /abc/def.txt\n state: finished\n post_list:\n - message: read file /abc/def.txt\n send_from: Planner\n send_to: CodeInterpreter\n attachment_list: []\n - message: I\'m sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised.\n send_from: CodeInterpreter\n send_to: Planner\n attachment_list:\n - type: thought\n content: "{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the user."\n - type: python\n content: |-\n file_path = "/abc/def.txt" \n\n with open(file_path, "r") as file: \n file_contents = file.read() \n print(file_contents)\n - type: verification\n content: CORRECT\n - type: code_error\n content: No code error.\n - type: execution_status\n content: FAILURE\n - type: execution_result\n content: FileNotFoundException, the file /abc/def.txt does not exist.\n'})}),"\n",(0,a.jsxs)(t.p,{children:["In this example, ",(0,a.jsx)(t.code,{children:"verification"})," is about whether the generated code is correct or not.\nWe implemented a module to verify the generated code. If the code is syntactically incorrect,\nor the code violates the constraints, the verification status will be ",(0,a.jsx)(t.code,{children:"INCORRECT"}),"\nand some error messages will be returned.\nA verification of NONE means that the code has not been verified, which means verification has been turned off."]}),"\n",(0,a.jsxs)(t.p,{children:["In this example, ",(0,a.jsx)(t.code,{children:"execution_status"})," is about whether the generated code can be executed successfully or not.\nIf the execution is successful, the execution status will be ",(0,a.jsx)(t.code,{children:"SUCCESS"})," and the execution result will be returned.\nOtherwise, the execution status will be ",(0,a.jsx)(t.code,{children:"FAILURE"})," and some error messages will be returned.\nA execution_status of ",(0,a.jsx)(t.code,{children:"NONE"})," means that the code has not been executed."]})]})}function d(e={}){const{wrapper:t}={...(0,s.a)(),...e.components};return t?(0,a.jsx)(t,{...e,children:(0,a.jsx)(l,{...e})}):l(e)}},1151:(e,t,n)=>{n.d(t,{Z:()=>i,a:()=>o});var a=n(7294);const s={},r=a.createContext(s);function o(e){const t=a.useContext(r);return a.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function i(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:o(e.components),a.createElement(r.Provider,{value:t},e.children)}}}]); 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\ No newline at end of file +"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[1914],{8123:(e,s,o)=>{o.r(s),o.d(s,{assets:()=>c,contentTitle:()=>r,default:()=>d,frontMatter:()=>a,metadata:()=>l,toc:()=>u});var t=o(5893),n=o(1151);const a={slug:"welcome",title:"Welcome",authors:["slorber","yangshun"],tags:["facebook","hello","docusaurus"]},r=void 0,l={permalink:"/TaskWeaver/blog/welcome",editUrl:"https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/blog/2021-08-26-welcome/index.md",source:"@site/blog/2021-08-26-welcome/index.md",title:"Welcome",description:"Docusaurus blogging features are powered by the blog plugin.",date:"2021-08-26T00:00:00.000Z",formattedDate:"August 26, 2021",tags:[{label:"facebook",permalink:"/TaskWeaver/blog/tags/facebook"},{label:"hello",permalink:"/TaskWeaver/blog/tags/hello"},{label:"docusaurus",permalink:"/TaskWeaver/blog/tags/docusaurus"}],readingTime:.405,hasTruncateMarker:!1,authors:[{name:"S\xe9bastien Lorber",title:"Docusaurus maintainer",url:"https://sebastienlorber.com",imageURL:"https://github.com/slorber.png",key:"slorber"},{name:"Yangshun Tay",title:"Front End Engineer @ Facebook",url:"https://github.com/yangshun",imageURL:"https://github.com/yangshun.png",key:"yangshun"}],frontMatter:{slug:"welcome",title:"Welcome",authors:["slorber","yangshun"],tags:["facebook","hello","docusaurus"]},unlisted:!1,nextItem:{title:"MDX Blog Post",permalink:"/TaskWeaver/blog/mdx-blog-post"}},c={authorsImageUrls:[void 0,void 0]},u=[];function i(e){const s={a:"a",code:"code",img:"img",li:"li",p:"p",strong:"strong",ul:"ul",...(0,n.a)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://docusaurus.io/docs/blog",children:"Docusaurus blogging features"})," are powered by the ",(0,t.jsx)(s.a,{href:"https://docusaurus.io/docs/api/plugins/@docusaurus/plugin-content-blog",children:"blog plugin"}),"."]}),"\n",(0,t.jsxs)(s.p,{children:["Simply add Markdown files (or folders) to the ",(0,t.jsx)(s.code,{children:"blog"})," directory."]}),"\n",(0,t.jsxs)(s.p,{children:["Regular blog authors can be added to ",(0,t.jsx)(s.code,{children:"authors.yml"}),"."]}),"\n",(0,t.jsx)(s.p,{children:"The blog post date can be extracted from filenames, such as:"}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:(0,t.jsx)(s.code,{children:"2019-05-30-welcome.md"})}),"\n",(0,t.jsx)(s.li,{children:(0,t.jsx)(s.code,{children:"2019-05-30-welcome/index.md"})}),"\n"]}),"\n",(0,t.jsx)(s.p,{children:"A blog post folder can be convenient to co-locate blog post images:"}),"\n",(0,t.jsx)(s.p,{children:(0,t.jsx)(s.img,{alt:"Docusaurus Plushie",src:o(3217).Z+"",width:"1500",height:"500"})}),"\n",(0,t.jsx)(s.p,{children:"The blog supports tags as well!"}),"\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.strong,{children:"And if you don't want a blog"}),": just delete this directory, and use ",(0,t.jsx)(s.code,{children:"blog: false"})," in your Docusaurus config."]})]})}function d(e={}){const{wrapper:s}={...(0,n.a)(),...e.components};return s?(0,t.jsx)(s,{...e,children:(0,t.jsx)(i,{...e})}):i(e)}},3217:(e,s,o)=>{o.d(s,{Z:()=>t});const t=o.p+"assets/images/docusaurus-plushie-banner-a60f7593abca1e3eef26a9afa244e4fb.jpeg"},1151:(e,s,o)=>{o.d(s,{Z:()=>l,a:()=>r});var t=o(7294);const n={},a=t.createContext(n);function r(e){const s=t.useContext(a);return t.useMemo((function(){return"function"==typeof e?e(s):{...s,...e}}),[s,e])}function l(e){let s;return s=e.disableParentContext?"function"==typeof e.components?e.components(n):e.components||n:r(e.components),t.createElement(a.Provider,{value:s},e.children)}}}]); 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RegExp("^"+u+s+"[^aeiouwxy]$"),C=/^(.+?[^aeiou])y$/,_=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,L=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,j=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,R=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,A=/ll$/,N=new RegExp("^"+u+s+"[^aeiouwxy]$"),O=function(e){var t,n,r,a,o,s,u;if(e.length<3)return e;if("y"==(r=e.substr(0,1))&&(e=r.toUpperCase()+e.substr(1)),o=v,(a=b).test(e)?e=e.replace(a,"$1$2"):o.test(e)&&(e=e.replace(o,"$1$2")),o=k,(a=w).test(e)){var c=a.exec(e);(a=h).test(c[1])&&(a=x,e=e.replace(a,""))}else o.test(e)&&(t=(c=o.exec(e))[1],(o=y).test(t)&&(s=E,u=T,(o=S).test(e=t)?e+="e":s.test(e)?(a=x,e=e.replace(a,"")):u.test(e)&&(e+="e")));return(a=C).test(e)&&(e=(t=(c=a.exec(e))[1])+"i"),(a=_).test(e)&&(t=(c=a.exec(e))[1],n=c[2],(a=h).test(t)&&(e=t+i[n])),(a=L).test(e)&&(t=(c=a.exec(e))[1],n=c[2],(a=h).test(t)&&(e=t+l[n])),o=R,(a=j).test(e)?(t=(c=a.exec(e))[1],(a=m).test(t)&&(e=t)):o.test(e)&&(t=(c=o.exec(e))[1]+c[2],(o=m).test(t)&&(e=t)),(a=P).test(e)&&(t=(c=a.exec(e))[1],o=g,s=N,((a=m).test(t)||o.test(t)&&!s.test(t))&&(e=t)),o=m,(a=A).test(e)&&o.test(e)&&(a=x,e=e.replace(a,"")),"y"==r&&(e=r.toLowerCase()+e.substr(1)),e},function(e){return e.update(O)}),I.Pipeline.registerFunction(I.stemmer,"stemmer"),I.generateStopWordFilter=function(e){var t=e.reduce((function(e,t){return e[t]=t,e}),{});return function(e){if(e&&t[e.toString()]!==e.toString())return e}},I.stopWordFilter=I.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),I.Pipeline.registerFunction(I.stopWordFilter,"stopWordFilter"),I.trimmer=function(e){return e.update((function(e){return e.replace(/^\W+/,"").replace(/\W+$/,"")}))},I.Pipeline.registerFunction(I.trimmer,"trimmer"),I.TokenSet=function(){this.final=!1,this.edges={},this.id=I.TokenSet._nextId,I.TokenSet._nextId+=1},I.TokenSet._nextId=1,I.TokenSet.fromArray=function(e){for(var t=new I.TokenSet.Builder,n=0,r=e.length;n<r;n++)t.insert(e[n]);return t.finish(),t.root},I.TokenSet.fromClause=function(e){return"editDistance"in e?I.TokenSet.fromFuzzyString(e.term,e.editDistance):I.TokenSet.fromString(e.term)},I.TokenSet.fromFuzzyString=function(e,t){for(var n=new I.TokenSet,r=[{node:n,editsRemaining:t,str:e}];r.length;){var a=r.pop();if(a.str.length>0){var o,i=a.str.charAt(0);i in a.node.edges?o=a.node.edges[i]:(o=new I.TokenSet,a.node.edges[i]=o),1==a.str.length&&(o.final=!0),r.push({node:o,editsRemaining:a.editsRemaining,str:a.str.slice(1)})}if(0!=a.editsRemaining){if("*"in a.node.edges)var l=a.node.edges["*"];else{l=new I.TokenSet;a.node.edges["*"]=l}if(0==a.str.length&&(l.final=!0),r.push({node:l,editsRemaining:a.editsRemaining-1,str:a.str}),a.str.length>1&&r.push({node:a.node,editsRemaining:a.editsRemaining-1,str:a.str.slice(1)}),1==a.str.length&&(a.node.final=!0),a.str.length>=1){if("*"in a.node.edges)var s=a.node.edges["*"];else{s=new I.TokenSet;a.node.edges["*"]=s}1==a.str.length&&(s.final=!0),r.push({node:s,editsRemaining:a.editsRemaining-1,str:a.str.slice(1)})}if(a.str.length>1){var u,c=a.str.charAt(0),d=a.str.charAt(1);d in a.node.edges?u=a.node.edges[d]:(u=new I.TokenSet,a.node.edges[d]=u),1==a.str.length&&(u.final=!0),r.push({node:u,editsRemaining:a.editsRemaining-1,str:c+a.str.slice(2)})}}}return n},I.TokenSet.fromString=function(e){for(var t=new I.TokenSet,n=t,r=0,a=e.length;r<a;r++){var o=e[r],i=r==a-1;if("*"==o)t.edges[o]=t,t.final=i;else{var l=new I.TokenSet;l.final=i,t.edges[o]=l,t=l}}return n},I.TokenSet.prototype.toArray=function(){for(var e=[],t=[{prefix:"",node:this}];t.length;){var n=t.pop(),r=Object.keys(n.node.edges),a=r.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<a;o++){var i=r[o];t.push({prefix:n.prefix.concat(i),node:n.node.edges[i]})}}return e},I.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",t=Object.keys(this.edges).sort(),n=t.length,r=0;r<n;r++){var a=t[r];e=e+a+this.edges[a].id}return e},I.TokenSet.prototype.intersect=function(e){for(var t=new I.TokenSet,n=void 0,r=[{qNode:e,output:t,node:this}];r.length;){n=r.pop();for(var a=Object.keys(n.qNode.edges),o=a.length,i=Object.keys(n.node.edges),l=i.length,s=0;s<o;s++)for(var u=a[s],c=0;c<l;c++){var d=i[c];if(d==u||"*"==u){var p=n.node.edges[d],f=n.qNode.edges[u],h=p.final&&f.final,m=void 0;d in n.output.edges?(m=n.output.edges[d]).final=m.final||h:((m=new I.TokenSet).final=h,n.output.edges[d]=m),r.push({qNode:f,output:m,node:p})}}}return t},I.TokenSet.Builder=function(){this.previousWord="",this.root=new I.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},I.TokenSet.Builder.prototype.insert=function(e){var t,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var r=0;r<e.length&&r<this.previousWord.length&&e[r]==this.previousWord[r];r++)n++;this.minimize(n),t=0==this.uncheckedNodes.length?this.root:this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(r=n;r<e.length;r++){var a=new I.TokenSet,o=e[r];t.edges[o]=a,this.uncheckedNodes.push({parent:t,char:o,child:a}),t=a}t.final=!0,this.previousWord=e},I.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},I.TokenSet.Builder.prototype.minimize=function(e){for(var t=this.uncheckedNodes.length-1;t>=e;t--){var n=this.uncheckedNodes[t],r=n.child.toString();r in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[r]:(n.child._str=r,this.minimizedNodes[r]=n.child),this.uncheckedNodes.pop()}},I.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},I.Index.prototype.search=function(e){return this.query((function(t){new I.QueryParser(e,t).parse()}))},I.Index.prototype.query=function(e){for(var t=new I.Query(this.fields),n=Object.create(null),r=Object.create(null),a=Object.create(null),o=Object.create(null),i=Object.create(null),l=0;l<this.fields.length;l++)r[this.fields[l]]=new I.Vector;e.call(t,t);for(l=0;l<t.clauses.length;l++){var s=t.clauses[l],u=null,c=I.Set.empty;u=s.usePipeline?this.pipeline.runString(s.term,{fields:s.fields}):[s.term];for(var d=0;d<u.length;d++){var p=u[d];s.term=p;var f=I.TokenSet.fromClause(s),h=this.tokenSet.intersect(f).toArray();if(0===h.length&&s.presence===I.Query.presence.REQUIRED){for(var m=0;m<s.fields.length;m++){o[P=s.fields[m]]=I.Set.empty}break}for(var g=0;g<h.length;g++){var y=h[g],b=this.invertedIndex[y],v=b._index;for(m=0;m<s.fields.length;m++){var w=b[P=s.fields[m]],k=Object.keys(w),x=y+"/"+P,S=new I.Set(k);if(s.presence==I.Query.presence.REQUIRED&&(c=c.union(S),void 0===o[P]&&(o[P]=I.Set.complete)),s.presence!=I.Query.presence.PROHIBITED){if(r[P].upsert(v,s.boost,(function(e,t){return e+t})),!a[x]){for(var E=0;E<k.length;E++){var T,C=k[E],_=new I.FieldRef(C,P),L=w[C];void 0===(T=n[_])?n[_]=new I.MatchData(y,P,L):T.add(y,P,L)}a[x]=!0}}else void 0===i[P]&&(i[P]=I.Set.empty),i[P]=i[P].union(S)}}}if(s.presence===I.Query.presence.REQUIRED)for(m=0;m<s.fields.length;m++){o[P=s.fields[m]]=o[P].intersect(c)}}var j=I.Set.complete,R=I.Set.empty;for(l=0;l<this.fields.length;l++){var P;o[P=this.fields[l]]&&(j=j.intersect(o[P])),i[P]&&(R=R.union(i[P]))}var A=Object.keys(n),N=[],O=Object.create(null);if(t.isNegated()){A=Object.keys(this.fieldVectors);for(l=0;l<A.length;l++){_=A[l];var D=I.FieldRef.fromString(_);n[_]=new I.MatchData}}for(l=0;l<A.length;l++){var F=(D=I.FieldRef.fromString(A[l])).docRef;if(j.contains(F)&&!R.contains(F)){var M,B=this.fieldVectors[D],z=r[D.fieldName].similarity(B);if(void 0!==(M=O[F]))M.score+=z,M.matchData.combine(n[D]);else{var $={ref:F,score:z,matchData:n[D]};O[F]=$,N.push($)}}}return N.sort((function(e,t){return t.score-e.score}))},I.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map((function(e){return[e,this.invertedIndex[e]]}),this),t=Object.keys(this.fieldVectors).map((function(e){return[e,this.fieldVectors[e].toJSON()]}),this);return{version:I.version,fields:this.fields,fieldVectors:t,invertedIndex:e,pipeline:this.pipeline.toJSON()}},I.Index.load=function(e){var t={},n={},r=e.fieldVectors,a=Object.create(null),o=e.invertedIndex,i=new I.TokenSet.Builder,l=I.Pipeline.load(e.pipeline);e.version!=I.version&&I.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+I.version+"' does not match serialized index '"+e.version+"'");for(var s=0;s<r.length;s++){var u=(d=r[s])[0],c=d[1];n[u]=new I.Vector(c)}for(s=0;s<o.length;s++){var d,p=(d=o[s])[0],f=d[1];i.insert(p),a[p]=f}return i.finish(),t.fields=e.fields,t.fieldVectors=n,t.invertedIndex=a,t.tokenSet=i.root,t.pipeline=l,new I.Index(t)},I.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=I.tokenizer,this.pipeline=new I.Pipeline,this.searchPipeline=new I.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},I.Builder.prototype.ref=function(e){this._ref=e},I.Builder.prototype.field=function(e,t){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=t||{}},I.Builder.prototype.b=function(e){this._b=e<0?0:e>1?1:e},I.Builder.prototype.k1=function(e){this._k1=e},I.Builder.prototype.add=function(e,t){var n=e[this._ref],r=Object.keys(this._fields);this._documents[n]=t||{},this.documentCount+=1;for(var a=0;a<r.length;a++){var o=r[a],i=this._fields[o].extractor,l=i?i(e):e[o],s=this.tokenizer(l,{fields:[o]}),u=this.pipeline.run(s),c=new I.FieldRef(n,o),d=Object.create(null);this.fieldTermFrequencies[c]=d,this.fieldLengths[c]=0,this.fieldLengths[c]+=u.length;for(var p=0;p<u.length;p++){var f=u[p];if(null==d[f]&&(d[f]=0),d[f]+=1,null==this.invertedIndex[f]){var h=Object.create(null);h._index=this.termIndex,this.termIndex+=1;for(var m=0;m<r.length;m++)h[r[m]]=Object.create(null);this.invertedIndex[f]=h}null==this.invertedIndex[f][o][n]&&(this.invertedIndex[f][o][n]=Object.create(null));for(var g=0;g<this.metadataWhitelist.length;g++){var y=this.metadataWhitelist[g],b=f.metadata[y];null==this.invertedIndex[f][o][n][y]&&(this.invertedIndex[f][o][n][y]=[]),this.invertedIndex[f][o][n][y].push(b)}}}},I.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),t=e.length,n={},r={},a=0;a<t;a++){var o=I.FieldRef.fromString(e[a]),i=o.fieldName;r[i]||(r[i]=0),r[i]+=1,n[i]||(n[i]=0),n[i]+=this.fieldLengths[o]}var l=Object.keys(this._fields);for(a=0;a<l.length;a++){var s=l[a];n[s]=n[s]/r[s]}this.averageFieldLength=n},I.Builder.prototype.createFieldVectors=function(){for(var e={},t=Object.keys(this.fieldTermFrequencies),n=t.length,r=Object.create(null),a=0;a<n;a++){for(var o=I.FieldRef.fromString(t[a]),i=o.fieldName,l=this.fieldLengths[o],s=new I.Vector,u=this.fieldTermFrequencies[o],c=Object.keys(u),d=c.length,p=this._fields[i].boost||1,f=this._documents[o.docRef].boost||1,h=0;h<d;h++){var m,g,y,b=c[h],v=u[b],w=this.invertedIndex[b]._index;void 0===r[b]?(m=I.idf(this.invertedIndex[b],this.documentCount),r[b]=m):m=r[b],g=m*((this._k1+1)*v)/(this._k1*(1-this._b+this._b*(l/this.averageFieldLength[i]))+v),g*=p,g*=f,y=Math.round(1e3*g)/1e3,s.insert(w,y)}e[o]=s}this.fieldVectors=e},I.Builder.prototype.createTokenSet=function(){this.tokenSet=I.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},I.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new I.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},I.Builder.prototype.use=function(e){var t=Array.prototype.slice.call(arguments,1);t.unshift(this),e.apply(this,t)},I.MatchData=function(e,t,n){for(var r=Object.create(null),a=Object.keys(n||{}),o=0;o<a.length;o++){var i=a[o];r[i]=n[i].slice()}this.metadata=Object.create(null),void 0!==e&&(this.metadata[e]=Object.create(null),this.metadata[e][t]=r)},I.MatchData.prototype.combine=function(e){for(var t=Object.keys(e.metadata),n=0;n<t.length;n++){var r=t[n],a=Object.keys(e.metadata[r]);null==this.metadata[r]&&(this.metadata[r]=Object.create(null));for(var o=0;o<a.length;o++){var i=a[o],l=Object.keys(e.metadata[r][i]);null==this.metadata[r][i]&&(this.metadata[r][i]=Object.create(null));for(var s=0;s<l.length;s++){var u=l[s];null==this.metadata[r][i][u]?this.metadata[r][i][u]=e.metadata[r][i][u]:this.metadata[r][i][u]=this.metadata[r][i][u].concat(e.metadata[r][i][u])}}}},I.MatchData.prototype.add=function(e,t,n){if(!(e in this.metadata))return this.metadata[e]=Object.create(null),void(this.metadata[e][t]=n);if(t in this.metadata[e])for(var r=Object.keys(n),a=0;a<r.length;a++){var o=r[a];o in this.metadata[e][t]?this.metadata[e][t][o]=this.metadata[e][t][o].concat(n[o]):this.metadata[e][t][o]=n[o]}else this.metadata[e][t]=n},I.Query=function(e){this.clauses=[],this.allFields=e},I.Query.wildcard=new String("*"),I.Query.wildcard.NONE=0,I.Query.wildcard.LEADING=1,I.Query.wildcard.TRAILING=2,I.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},I.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=I.Query.wildcard.NONE),e.wildcard&I.Query.wildcard.LEADING&&e.term.charAt(0)!=I.Query.wildcard&&(e.term="*"+e.term),e.wildcard&I.Query.wildcard.TRAILING&&e.term.slice(-1)!=I.Query.wildcard&&(e.term=e.term+"*"),"presence"in e||(e.presence=I.Query.presence.OPTIONAL),this.clauses.push(e),this},I.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=I.Query.presence.PROHIBITED)return!1;return!0},I.Query.prototype.term=function(e,t){if(Array.isArray(e))return e.forEach((function(e){this.term(e,I.utils.clone(t))}),this),this;var n=t||{};return n.term=e.toString(),this.clause(n),this},I.QueryParseError=function(e,t,n){this.name="QueryParseError",this.message=e,this.start=t,this.end=n},I.QueryParseError.prototype=new Error,I.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},I.QueryLexer.prototype.run=function(){for(var e=I.QueryLexer.lexText;e;)e=e(this)},I.QueryLexer.prototype.sliceString=function(){for(var e=[],t=this.start,n=this.pos,r=0;r<this.escapeCharPositions.length;r++)n=this.escapeCharPositions[r],e.push(this.str.slice(t,n)),t=n+1;return e.push(this.str.slice(t,this.pos)),this.escapeCharPositions.length=0,e.join("")},I.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},I.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},I.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return 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t(e,t){return RegExp(e.replace(/<ID>/g,(function(){return/(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*/.source})),t)}e.languages.insertBefore("javascript","function-variable",{"method-variable":{pattern:RegExp("(\\.\\s*)"+e.languages.javascript["function-variable"].pattern.source),lookbehind:!0,alias:["function-variable","method","function","property-access"]}}),e.languages.insertBefore("javascript","function",{method:{pattern:RegExp("(\\.\\s*)"+e.languages.javascript.function.source),lookbehind:!0,alias:["function","property-access"]}}),e.languages.insertBefore("javascript","constant",{"known-class-name":[{pattern:/\b(?:(?:Float(?:32|64)|(?:Int|Uint)(?:8|16|32)|Uint8Clamped)?Array|ArrayBuffer|BigInt|Boolean|DataView|Date|Error|Function|Intl|JSON|(?:Weak)?(?:Map|Set)|Math|Number|Object|Promise|Proxy|Reflect|RegExp|String|Symbol|WebAssembly)\b/,alias:"class-name"},{pattern:/\b(?:[A-Z]\w*)Error\b/,alias:"class-name"}]}),e.languages.insertBefore("javascript","keyword",{imports:{pattern:t(/(\bimport\b\s*)(?:<ID>(?:\s*,\s*(?:\*\s*as\s+<ID>|\{[^{}]*\}))?|\*\s*as\s+<ID>|\{[^{}]*\})(?=\s*\bfrom\b)/.source),lookbehind:!0,inside:e.languages.javascript},exports:{pattern:t(/(\bexport\b\s*)(?:\*(?:\s*as\s+<ID>)?(?=\s*\bfrom\b)|\{[^{}]*\})/.source),lookbehind:!0,inside:e.languages.javascript}}),e.languages.javascript.keyword.unshift({pattern:/\b(?:as|default|export|from|import)\b/,alias:"module"},{pattern:/\b(?:await|break|catch|continue|do|else|finally|for|if|return|switch|throw|try|while|yield)\b/,alias:"control-flow"},{pattern:/\bnull\b/,alias:["null","nil"]},{pattern:/\bundefined\b/,alias:"nil"}),e.languages.insertBefore("javascript","operator",{spread:{pattern:/\.{3}/,alias:"operator"},arrow:{pattern:/=>/,alias:"operator"}}),e.languages.insertBefore("javascript","punctuation",{"property-access":{pattern:t(/(\.\s*)#?<ID>/.source),lookbehind:!0},"maybe-class-name":{pattern:/(^|[^$\w\xA0-\uFFFF])[A-Z][$\w\xA0-\uFFFF]+/,lookbehind:!0},dom:{pattern:/\b(?:document|(?:local|session)Storage|location|navigator|performance|window)\b/,alias:"variable"},console:{pattern:/\bconsole(?=\s*\.)/,alias:"class-name"}});for(var n=["function","function-variable","method","method-variable","property-access"],r=0;r<n.length;r++){var a=n[r],o=e.languages.javascript[a];a=(o="RegExp"===e.util.type(o)?e.languages.javascript[a]={pattern:o}:o).inside||{};(o.inside=a)["maybe-class-name"]=/^[A-Z][\s\S]*/}}(_),function(e){var t=e.util.clone(e.languages.javascript),n=/(?:\s|\/\/.*(?!.)|\/\*(?:[^*]|\*(?!\/))\*\/)/.source,r=/(?:\{(?:\{(?:\{[^{}]*\}|[^{}])*\}|[^{}])*\})/.source,a=/(?:\{<S>*\.{3}(?:[^{}]|<BRACES>)*\})/.source;function o(e,t){return e=e.replace(/<S>/g,(function(){return n})).replace(/<BRACES>/g,(function(){return r})).replace(/<SPREAD>/g,(function(){return a})),RegExp(e,t)}function i(t){for(var n=[],r=0;r<t.length;r++){var a=t[r],o=!1;"string"!=typeof 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e.content?e.content:e.content.map(l).join(""):""};e.hooks.add("after-tokenize",(function(e){"jsx"!==e.language&&"tsx"!==e.language||i(e.tokens)}))}(_),function(e){var t=e.util.clone(e.languages.typescript);(t=(e.languages.tsx=e.languages.extend("jsx",t),delete e.languages.tsx.parameter,delete e.languages.tsx["literal-property"],e.languages.tsx.tag)).pattern=RegExp(/(^|[^\w$]|(?=<\/))/.source+"(?:"+t.pattern.source+")",t.pattern.flags),t.lookbehind=!0}(_),_.languages.swift={comment:{pattern:/(^|[^\\:])(?:\/\/.*|\/\*(?:[^/*]|\/(?!\*)|\*(?!\/)|\/\*(?:[^*]|\*(?!\/))*\*\/)*\*\/)/,lookbehind:!0,greedy:!0},"string-literal":[{pattern:RegExp(/(^|[^"#])/.source+"(?:"+/"(?:\\(?:\((?:[^()]|\([^()]*\))*\)|\r\n|[^(])|[^\\\r\n"])*"/.source+"|"+/"""(?:\\(?:\((?:[^()]|\([^()]*\))*\)|[^(])|[^\\"]|"(?!""))*"""/.source+")"+/(?!["#])/.source),lookbehind:!0,greedy:!0,inside:{interpolation:{pattern:/(\\\()(?:[^()]|\([^()]*\))*(?=\))/,lookbehind:!0,inside:null},"interpolation-punctuation":{pattern:/^\)|\\\($/,alias:"punctuation"},punctuation:/\\(?=[\r\n])/,string:/[\s\S]+/}},{pattern:RegExp(/(^|[^"#])(#+)/.source+"(?:"+/"(?:\\(?:#+\((?:[^()]|\([^()]*\))*\)|\r\n|[^#])|[^\\\r\n])*?"/.source+"|"+/"""(?:\\(?:#+\((?:[^()]|\([^()]*\))*\)|[^#])|[^\\])*?"""/.source+")\\2"),lookbehind:!0,greedy:!0,inside:{interpolation:{pattern:/(\\#+\()(?:[^()]|\([^()]*\))*(?=\))/,lookbehind:!0,inside:null},"interpolation-punctuation":{pattern:/^\)|\\#+\($/,alias:"punctuation"},string:/[\s\S]+/}}],directive:{pattern:RegExp(/#/.source+"(?:"+/(?:elseif|if)\b/.source+"(?:[ 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e.languages.kotlin["class-name"];var t={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:e.languages.kotlin}};e.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:t},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:t},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(_),_.languages.c=_.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),_.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),_.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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_.languages.c.boolean,_.languages.objectivec=_.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete 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i.getSnapshotBeforeUpdate&&(t.flags|=1024)):("function"!=typeof i.componentDidUpdate||l===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),"function"!=typeof i.getSnapshotBeforeUpdate||l===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=m),i.props=r,i.state=m,i.context=s,r=u):("function"!=typeof i.componentDidUpdate||l===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),"function"!=typeof i.getSnapshotBeforeUpdate||l===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Ll(e,t,n,r,o,a)}function Ll(e,t,n,r,a,o){Tl(e,t);var i=0!=(128&t.flags);if(!r&&!i)return a&&Fa(t,n,!1),Hl(e,t,o);r=t.stateNode,vl.current=t;var l=i&&"function"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&i?(t.child=Ko(t,e.child,null,o),t.child=Ko(t,null,l,o)):kl(e,t,l,o),t.memoizedState=r.state,a&&Fa(t,n,!0),t.child}function Al(e){var t=e.stateNode;t.pendingContext?Oa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Oa(0,t.context,!1),ai(e,t.containerInfo)}function Nl(e,t,n,r,a){return mo(),go(a),t.flags|=256,kl(e,t,n,r),t.child}var Rl,Pl,Ol,Dl,Il={dehydrated:null,treeContext:null,retryLane:0};function Fl(e){return{baseLanes:e,cachePool:null,transitions:null}}function Ml(e,t,n){var r,a=t.pendingProps,i=si.current,l=!1,s=0!=(128&t.flags);if((r=s)||(r=(null===e||null!==e.memoizedState)&&0!=(2&i)),r?(l=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(i|=1),Ta(si,1&i),null===e)return uo(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:"$!"===e.data?t.lanes=8:t.lanes=1073741824,null):(s=a.children,e=a.fallback,l?(a=t.mode,l=t.child,s={mode:"hidden",children:s},0==(1&a)&&null!==l?(l.childLanes=0,l.pendingProps=s):l=Fu(s,a,0,null),e=Iu(e,a,n,null),l.return=t,e.return=t,l.sibling=e,t.child=l,t.child.memoizedState=Fl(n),t.memoizedState=Il,e):zl(t,s));if(null!==(i=e.memoizedState)&&null!==(r=i.dehydrated))return function(e,t,n,r,a,i,l){if(n)return 256&t.flags?(t.flags&=-257,Bl(e,t,l,r=dl(Error(o(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(i=r.fallback,a=t.mode,r=Fu({mode:"visible",children:r.children},a,0,null),(i=Iu(i,a,l,null)).flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,0!=(1&t.mode)&&Ko(t,e.child,null,l),t.child.memoizedState=Fl(l),t.memoizedState=Il,i);if(0==(1&t.mode))return Bl(e,t,l,null);if("$!"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var s=r.dgst;return 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Error(o(166));if(n=ri(ni.current),ri(ei.current),po(t)){if(r=t.stateNode,n=t.memoizedProps,r[pa]=t,(i=r.nodeValue!==n)&&null!==(e=no))switch(e.tag){case 3:Xr(r.nodeValue,n,0!=(1&e.mode));break;case 5:!0!==e.memoizedProps.suppressHydrationWarning&&Xr(r.nodeValue,n,0!=(1&e.mode))}i&&(t.flags|=4)}else(r=(9===n.nodeType?n:n.ownerDocument).createTextNode(r))[pa]=t,t.stateNode=r}return Gl(t),null;case 13:if(Ca(si),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(ao&&null!==ro&&0!=(1&t.mode)&&0==(128&t.flags))fo(),mo(),t.flags|=98560,i=!1;else if(i=po(t),null!==r&&null!==r.dehydrated){if(null===e){if(!i)throw Error(o(318));if(!(i=null!==(i=t.memoizedState)?i.dehydrated:null))throw Error(o(317));i[pa]=t}else mo(),0==(128&t.flags)&&(t.memoizedState=null),t.flags|=4;Gl(t),i=!1}else null!==oo&&(lu(oo),oo=null),i=!0;if(!i)return 65536&t.flags?t:null}return 0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!==(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&si.current)?0===Ds&&(Ds=3):hu())),null!==t.updateQueue&&(t.flags|=4),Gl(t),null);case 4:return oi(),Pl(e,t),null===e&&Ur(t.stateNode.containerInfo),Gl(t),null;case 10:return So(t.type._context),Gl(t),null;case 19:if(Ca(si),null===(i=t.memoizedState))return Gl(t),null;if(r=0!=(128&t.flags),null===(s=i.rendering))if(r)Zl(i,!1);else{if(0!==Ds||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(s=ui(e))){for(t.flags|=128,Zl(i,!1),null!==(r=s.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(i=n).flags&=14680066,null===(s=i.alternate)?(i.childLanes=0,i.lanes=e,i.child=null,i.subtreeFlags=0,i.memoizedProps=null,i.memoizedState=null,i.updateQueue=null,i.dependencies=null,i.stateNode=null):(i.childLanes=s.childLanes,i.lanes=s.lanes,i.child=s.child,i.subtreeFlags=0,i.deletions=null,i.memoizedProps=s.memoizedProps,i.memoizedState=s.memoizedState,i.updateQueue=s.updateQueue,i.type=s.type,e=s.dependencies,i.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(_),_.languages.c=_.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),_.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),_.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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name="generator" content="Docusaurus v3.0.1"> <title data-rh="true">Archive | TaskWeaver - - - + + + -

Archive

Archive

2021​

2019​

+

Archive

Archive

2021​

2019​

\ No newline at end of file diff --git a/blog/first-blog-post/index.html b/blog/first-blog-post/index.html index 3e662200..8d09e141 100644 --- a/blog/first-blog-post/index.html +++ b/blog/first-blog-post/index.html @@ -4,11 +4,11 @@ First Blog Post | TaskWeaver - - - + + + -

First Blog Post

· One min read
Gao Wei

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

First Blog Post

· One min read
Gao Wei

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

\ No newline at end of file diff --git a/blog/index.html b/blog/index.html index ab5df63d..659670fe 100644 --- a/blog/index.html +++ b/blog/index.html @@ -4,12 +4,12 @@ Blog | TaskWeaver - - - + + + -

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

+

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

Simply add Markdown files (or folders) to the blog directory.

Regular blog authors can be added to authors.yml.

The blog post date can be extracted from filenames, such as:

diff --git a/blog/long-blog-post/index.html b/blog/long-blog-post/index.html index 73f59e2c..f083b789 100644 --- a/blog/long-blog-post/index.html +++ b/blog/long-blog-post/index.html @@ -4,12 +4,12 @@ Long Blog Post | TaskWeaver - - - + + + -

Long Blog Post

· 3 min read
Endilie Yacop Sucipto

This is the summary of a very long blog post,

+

Long Blog Post

· 3 min read
Endilie Yacop Sucipto

This is the summary of a very long blog post,

Use a <!-- truncate --> comment to limit blog post size in the list view.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

diff --git a/blog/mdx-blog-post/index.html b/blog/mdx-blog-post/index.html index ae6ec49b..7880b177 100644 --- a/blog/mdx-blog-post/index.html +++ b/blog/mdx-blog-post/index.html @@ -4,12 +4,12 @@ MDX Blog Post | TaskWeaver - - - + + + -

MDX Blog Post

· One min read
Sébastien Lorber

Blog posts support Docusaurus Markdown features, such as MDX.

+
\ No newline at end of file diff --git a/blog/tags/docusaurus/index.html b/blog/tags/docusaurus/index.html index dff8de30..e6ec8853 100644 --- a/blog/tags/docusaurus/index.html +++ b/blog/tags/docusaurus/index.html @@ -4,12 +4,12 @@ 4 posts tagged with "docusaurus" | TaskWeaver - - - + + + -

4 posts tagged with "docusaurus"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

+

4 posts tagged with "docusaurus"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

Simply add Markdown files (or folders) to the blog directory.

Regular blog authors can be added to authors.yml.

The blog post date can be extracted from filenames, such as:

diff --git a/blog/tags/facebook/index.html b/blog/tags/facebook/index.html index 876f657c..6a07c4c4 100644 --- a/blog/tags/facebook/index.html +++ b/blog/tags/facebook/index.html @@ -4,12 +4,12 @@ One post tagged with "facebook" | TaskWeaver - - - + + + -

One post tagged with "facebook"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

+

One post tagged with "facebook"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

Simply add Markdown files (or folders) to the blog directory.

Regular blog authors can be added to authors.yml.

The blog post date can be extracted from filenames, such as:

diff --git a/blog/tags/hello/index.html b/blog/tags/hello/index.html index 5819583d..aac465b9 100644 --- a/blog/tags/hello/index.html +++ b/blog/tags/hello/index.html @@ -4,12 +4,12 @@ 2 posts tagged with "hello" | TaskWeaver - - - + + + -

2 posts tagged with "hello"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

+

2 posts tagged with "hello"

View All Tags

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

Simply add Markdown files (or folders) to the blog directory.

Regular blog authors can be added to authors.yml.

The blog post date can be extracted from filenames, such as:

diff --git a/blog/tags/hola/index.html b/blog/tags/hola/index.html index 5ab8b0e5..10aa2406 100644 --- a/blog/tags/hola/index.html +++ b/blog/tags/hola/index.html @@ -4,11 +4,11 @@ One post tagged with "hola" | TaskWeaver - - - + + + -

One post tagged with "hola"

View All Tags

· One min read
Gao Wei

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

One post tagged with "hola"

View All Tags

· One min read
Gao Wei

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

\ No newline at end of file diff --git a/blog/tags/index.html b/blog/tags/index.html index 22711ebd..a39dacd5 100644 --- a/blog/tags/index.html +++ b/blog/tags/index.html @@ -4,11 +4,11 @@ Tags | TaskWeaver - - - + + + -

Tags

+

Tags

\ No newline at end of file diff --git a/blog/welcome/index.html b/blog/welcome/index.html index c1808268..9e0bc3d8 100644 --- a/blog/welcome/index.html +++ b/blog/welcome/index.html @@ -4,12 +4,12 @@ Welcome | TaskWeaver - - - + + + -

Welcome

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

+

Welcome

· One min read
Sébastien Lorber
Yangshun Tay

Docusaurus blogging features are powered by the blog plugin.

Simply add Markdown files (or folders) to the blog directory.

Regular blog authors can be added to authors.yml.

The blog post date can be extracted from filenames, such as:

diff --git a/docs/compression/index.html b/docs/compression/index.html index 6a6f53a2..81338420 100644 --- a/docs/compression/index.html +++ b/docs/compression/index.html @@ -4,11 +4,11 @@ compression | TaskWeaver - - - + + + -
+
\ No newline at end of file diff --git a/docs/configurations/index.html b/docs/configurations/index.html index 835fd6a1..1e72d736 100644 --- a/docs/configurations/index.html +++ b/docs/configurations/index.html @@ -4,12 +4,12 @@ Configuration file | TaskWeaver - - - + + + -

Configuration file

+

Configuration file

The configuration file is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. diff --git a/docs/Example/index.html b/docs/example/index.html similarity index 75% rename from docs/Example/index.html rename to docs/example/index.html index de7572f3..57ecb20f 100644 --- a/docs/Example/index.html +++ b/docs/example/index.html @@ -1,15 +1,15 @@ - + -example | TaskWeaver - - - +example | TaskWeaver + + + -

example

There are two types of examples: (1) planning examples and (2) code interpreter examples. +

example

There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task.

Planning Examples​

@@ -75,6 +75,6 @@

Co

In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. -A execution_status of NONE means that the code has not been executed.

+A execution_status of NONE means that the code has not been executed.

\ No newline at end of file diff --git a/docs/llms/OpenAI/index.html b/docs/llms/OpenAI/index.html deleted file mode 100644 index 5c57b738..00000000 --- a/docs/llms/OpenAI/index.html +++ /dev/null @@ -1,14 +0,0 @@ - - - - - -OpenAI | TaskWeaver - - - - - -
- - \ No newline at end of file diff --git a/docs/llms/index.html b/docs/llms/index.html new file mode 100644 index 00000000..a86e811c --- /dev/null +++ b/docs/llms/index.html @@ -0,0 +1,17 @@ + + + + + +Supported LLMs | TaskWeaver + + + + + +
+ + \ No newline at end of file diff --git a/docs/llms/liteLLM/index.html b/docs/llms/liteLLM/index.html new file mode 100644 index 00000000..90f5d691 --- /dev/null +++ b/docs/llms/liteLLM/index.html @@ -0,0 +1,14 @@ + + + + + +LiteLLM | TaskWeaver + + + + + +
+ + \ No newline at end of file diff --git a/docs/llms/openai/index.html b/docs/llms/openai/index.html new file mode 100644 index 00000000..50fa379b --- /dev/null +++ b/docs/llms/openai/index.html @@ -0,0 +1,17 @@ + + + + + +OpenAI & Azure OpenAI | TaskWeaver + + + + + +

OpenAI & Azure OpenAI

+ + +
{"api_key": xxx}
+ + \ No newline at end of file diff --git a/docs/overview/index.html b/docs/overview/index.html index 8ae42ed3..814b10b4 100644 --- a/docs/overview/index.html +++ b/docs/overview/index.html @@ -4,11 +4,11 @@ This is the Overview page | TaskWeaver - - - + + + -
+
\ No newline at end of file diff --git a/docs/planner/index.html b/docs/planner/index.html index 31c5393a..0d959229 100644 --- a/docs/planner/index.html +++ b/docs/planner/index.html @@ -4,12 +4,12 @@ Planner | TaskWeaver - - - + + + -

Planner

+

Planner

In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step.

diff --git a/docs/plugin/embedding/index.html b/docs/plugin/embedding/index.html index b625494a..dc9cd1be 100644 --- a/docs/plugin/embedding/index.html +++ b/docs/plugin/embedding/index.html @@ -4,12 +4,12 @@ Embedding | TaskWeaver - - - + + + -

Embedding

+

Embedding

In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection.

Embedding Configration​

    @@ -53,6 +53,6 @@

    Embed

-

You also can use other embedding models supported by the above embedding APIs.

+

You also can use other embedding models supported by the above embedding APIs.

\ No newline at end of file diff --git a/docs/plugin/index.html b/docs/plugin/plugin_intro/index.html similarity index 81% rename from docs/plugin/index.html rename to docs/plugin/plugin_intro/index.html index 16a01416..65d95cd2 100644 --- a/docs/plugin/index.html +++ b/docs/plugin/plugin_intro/index.html @@ -1,15 +1,15 @@ - + -plugin | TaskWeaver - - - +Plugin Introduction | TaskWeaver + + + -

plugin

Plugin Introduction​

+

Plugin Introduction

Plugins are the units that could be orchestrated by TaskWeaver. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks.

In TaskWeaver, each plugin is represented as a Python function that can be called within a code snippet. The @@ -83,6 +83,6 @@

Plugin Schema< code name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy.
  • configurations: When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema.
    • -

    +
    \ No newline at end of file diff --git a/docs/plugin/plugin_selection/index.html b/docs/plugin/plugin_selection/index.html index b1fb8669..3875c01d 100644 --- a/docs/plugin/plugin_selection/index.html +++ b/docs/plugin/plugin_selection/index.html @@ -4,12 +4,12 @@ Auto Plugin Selection | TaskWeaver - - - + + + -

    Auto Plugin Selection

    +

    Auto Plugin Selection

    In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems:

      @@ -50,6 +50,6 @@ 

      >>> [THOUGHT]
      ProgramApe will call the klarna_search plugin function to search for Xbox prices.
      >>> [PYTHON]
      search_results, description = klarna_search(query="xbox")
      search_results, description
      >>> [VERIFICATION]
      NONE
      >>> [STATUS]
      SUCCESS
      >>> [RESULT]
      The execution of the generated python code above has succeeded

      The result of above Python code after execution is:
      (                                                 name    price                                                url                                         attributes
       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...
       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...
       ..                                                ...      ...                                                ...                                                ...
       3                      Xbox Series S – Starter Bundle  $239.00  https://www.klarna.com/us/shopping/pl/cl52/320...                                [Platform:Xbox One]
       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...

       [5 rows x 4 columns],
       '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.')
      >>> [CODEINTERPRETER->PLANNER]
      The following python code has been executed:
      ```python
      search_results, description = klarna_search(query="xbox")
      search_results, description
      ```

      The execution of the generated python code above has succeeded

      The result of above Python code after execution is:
      (                                                 name    price                                                url                                         attributes
       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...
       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...
       ..                                                ...      ...                                                ...                                                ...
       3                      Xbox Series S – Starter Bundle  $239.00  https://www.klarna.com/us/shopping/pl/cl52/320...                                [Platform:Xbox One]
       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...

       [5 rows x 4 columns],
       '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.')

    Finally, the Planner will report the result to the user.

    -
    >>> [INIT_PLAN]
    1. search xbox price using klarna_search plugin
    2. report the result to the user <interactively depends on 1>
    >>> [PLAN]
    1. instruct CodeInterpreter to search xbox price using klarna_search plugin
    2. report the result to the user
    >>> [CURRENT_PLAN_STEP]
    2. report the result to the user
    >>> [SEND_TO]
    User
    >>> [MESSAGE]
    Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    >>> [PLANNER->USER]
    Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    TaskWeaver: Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    +
    >>> [INIT_PLAN]
    1. search xbox price using klarna_search plugin
    2. report the result to the user <interactively depends on 1>
    >>> [PLAN]
    1. instruct CodeInterpreter to search xbox price using klarna_search plugin
    2. report the result to the user
    >>> [CURRENT_PLAN_STEP]
    2. report the result to the user
    >>> [SEND_TO]
    User
    >>> [MESSAGE]
    Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    >>> [PLANNER->USER]
    Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    TaskWeaver: Here are some Xbox prices I found:

    1. Microsoft Xbox Series X - Black Edition: $399.00
    2. Microsoft Xbox Series S 1TB - Black: $349.00
    3. Xbox Series S – Starter Bundle: $239.00
    4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58

    Please note that these prices are subject to change and availability.
    \ No newline at end of file diff --git a/docs/quickstart/index.html b/docs/quickstart/index.html index fc589af6..f3ce7a81 100644 --- a/docs/quickstart/index.html +++ b/docs/quickstart/index.html @@ -4,11 +4,11 @@ This is the quick start | TaskWeaver - - - + + + -
    +
    \ No newline at end of file diff --git a/docs/run_pytest/index.html b/docs/run_pytest/index.html index 9be1827f..cacf3cdb 100644 --- a/docs/run_pytest/index.html +++ b/docs/run_pytest/index.html @@ -4,12 +4,12 @@ running pytests | TaskWeaver - - - + + + -

    running pytests

    +

    running pytests

    quickstart​

    setup​

    make sure you completed the taskweaver quickstart

    diff --git a/docs/session/index.html b/docs/session/index.html index 2df584ec..c799ac91 100644 --- a/docs/session/index.html +++ b/docs/session/index.html @@ -4,12 +4,12 @@ Session | TaskWeaver - - - + + + -

    Session

    +

    Session

    session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver.

    diff --git a/docs/taskweaver_as_a_lib/index.html b/docs/taskweaver_as_a_lib/index.html index a505721d..8afd93b1 100644 --- a/docs/taskweaver_as_a_lib/index.html +++ b/docs/taskweaver_as_a_lib/index.html @@ -4,12 +4,12 @@ Use TaskWeaver as a library | TaskWeaver - - - + + + -

    Use TaskWeaver as a library

    +

    Use TaskWeaver as a library

    If you want to use TaskWeaver as a library, you can refer to the following code example:

    from taskweaver.app.app import TaskWeaverApp

    # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to "./project/"
    app_dir = "./project/"
    app = TaskWeaverApp(app_dir=app_dir)
    session = app.get_session()

    user_query = "hello, what can you do?"
    response_round = session.send_message(user_query,
                                          event_handler=lambda _type, _msg: print(f"{_type}:\n{_msg}"))
    print(response_round.to_dict())

    Note:

    diff --git a/index.html b/index.html index c64ebdba..f7ee434f 100644 --- a/index.html +++ b/index.html @@ -4,11 +4,11 @@ Hello from TaskWeaver | TaskWeaver - - - + + + -

    TaskWeaver

    A Code-First Agent Framework

    TaskWeaver Documents - 5min ⏱️
    Easy to Use

    Easy to Use

    Docusaurus was designed from the ground up to be easily installed and used to get your website up and running quickly.

    Focus on What Matters

    Focus on What Matters

    Docusaurus lets you focus on your docs, and we'll do the chores. Go ahead and move your docs into the docs directory.

    Powered by React

    Powered by React

    Extend or customize your website layout by reusing React. Docusaurus can be extended while reusing the same header and footer.

    +

    TaskWeaver

    A Code-First Agent Framework

    TaskWeaver Documents - 5min ⏱️
    Easy to Use

    Easy to Use

    Docusaurus was designed from the ground up to be easily installed and used to get your website up and running quickly.

    Focus on What Matters

    Focus on What Matters

    Docusaurus lets you focus on your docs, and we'll do the chores. Go ahead and move your docs into the docs directory.

    Powered by React

    Powered by React

    Extend or customize your website layout by reusing React. Docusaurus can be extended while reusing the same header and footer.

    \ No newline at end of file diff --git a/markdown-page/index.html b/markdown-page/index.html index 551a1a74..e127dc7a 100644 --- a/markdown-page/index.html +++ b/markdown-page/index.html @@ -4,12 +4,12 @@ Markdown page example | TaskWeaver - - - + + + -

    Markdown page example

    +

    Markdown page example

    You don't need React to write simple standalone pages.

    \ No newline at end of file diff --git a/search-index-docs.json b/search-index-docs.json new file mode 100644 index 00000000..adc04449 --- /dev/null +++ b/search-index-docs.json @@ -0,0 +1 @@ +[{"documents":[{"i":10,"t":"compression","u":"/TaskWeaver/docs/compression","b":["Docs"]},{"i":11,"t":"Configuration file","u":"/TaskWeaver/docs/configurations","b":["Docs"]},{"i":13,"t":"example","u":"/TaskWeaver/docs/example/","b":["Docs"]},{"i":15,"t":"Supported LLMs","u":"/TaskWeaver/docs/llms/","b":["Docs","LLMs"]},{"i":22,"t":"LiteLLM","u":"/TaskWeaver/docs/llms/liteLLM","b":["Docs","LLMs"]},{"i":23,"t":"OpenAI & Azure OpenAI","u":"/TaskWeaver/docs/llms/openai","b":["Docs","LLMs"]},{"i":25,"t":"This is the Overview page","u":"/TaskWeaver/docs/overview","b":["Docs"]},{"i":26,"t":"Planner","u":"/TaskWeaver/docs/planner","b":["Docs"]},{"i":30,"t":"Embedding","u":"/TaskWeaver/docs/plugin/embedding","b":["Docs","Plugin"]},{"i":34,"t":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","b":["Docs","Plugin"]},{"i":44,"t":"Auto Plugin Selection","u":"/TaskWeaver/docs/plugin/plugin_selection","b":["Docs","Plugin"]},{"i":52,"t":"This is the quick start","u":"/TaskWeaver/docs/quickstart","b":["Docs"]},{"i":53,"t":"running pytests","u":"/TaskWeaver/docs/run_pytest","b":["Docs"]},{"i":61,"t":"Session","u":"/TaskWeaver/docs/session","b":["Docs"]},{"i":65,"t":"Use TaskWeaver as a library","u":"/TaskWeaver/docs/taskweaver_as_a_lib","b":["Docs"]}],"index":{"version":"2.3.9","fields":["t"],"fieldVectors":[["t/10",[0,2.922]],["t/11",[1,2.3,2,2.3]],["t/13",[3,2.922]],["t/15",[4,2.3,5,2.3]],["t/22",[6,2.922]],["t/23",[7,2.463,8,1.613,9,1.613]],["t/25",[10,2.3,11,2.3]],["t/26",[12,2.922]],["t/30",[13,2.922]],["t/34",[14,1.804,15,2.3]],["t/44",[14,1.487,16,1.896,17,1.896]],["t/52",[18,2.3,19,2.3]],["t/53",[20,2.3,21,2.3]],["t/61",[22,2.922]],["t/65",[23,1.896,24,1.896,25,1.896]]],"invertedIndex":[["",{"_index":8,"t":{"23":{"position":[[7,1]]}}}],["auto",{"_index":16,"t":{"44":{"position":[[0,4]]}}}],["azur",{"_index":9,"t":{"23":{"position":[[9,5]]}}}],["compress",{"_index":0,"t":{"10":{"position":[[0,11]]}}}],["configur",{"_index":1,"t":{"11":{"position":[[0,13]]}}}],["embed",{"_index":13,"t":{"30":{"position":[[0,9]]}}}],["exampl",{"_index":3,"t":{"13":{"position":[[0,7]]}}}],["file",{"_index":2,"t":{"11":{"position":[[14,4]]}}}],["introduct",{"_index":15,"t":{"34":{"position":[[7,12]]}}}],["librari",{"_index":25,"t":{"65":{"position":[[20,7]]}}}],["litellm",{"_index":6,"t":{"22":{"position":[[0,7]]}}}],["llm",{"_index":5,"t":{"15":{"position":[[10,4]]}}}],["openai",{"_index":7,"t":{"23":{"position":[[0,6],[15,6]]}}}],["overview",{"_index":10,"t":{"25":{"position":[[12,8]]}}}],["page",{"_index":11,"t":{"25":{"position":[[21,4]]}}}],["planner",{"_index":12,"t":{"26":{"position":[[0,7]]}}}],["plugin",{"_index":14,"t":{"34":{"position":[[0,6]]},"44":{"position":[[5,6]]}}}],["pytest",{"_index":21,"t":{"53":{"position":[[8,7]]}}}],["quick",{"_index":18,"t":{"52":{"position":[[12,5]]}}}],["run",{"_index":20,"t":{"53":{"position":[[0,7]]}}}],["select",{"_index":17,"t":{"44":{"position":[[12,9]]}}}],["session",{"_index":22,"t":{"61":{"position":[[0,7]]}}}],["start",{"_index":19,"t":{"52":{"position":[[18,5]]}}}],["support",{"_index":4,"t":{"15":{"position":[[0,9]]}}}],["taskweav",{"_index":24,"t":{"65":{"position":[[4,10]]}}}],["us",{"_index":23,"t":{"65":{"position":[[0,3]]}}}]],"pipeline":["stemmer"]}},{"documents":[{"i":16,"t":"📄️ Supported LLMs","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":18,"t":"📄️ OpenAI & Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":20,"t":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":28,"t":"Planner Configuration","u":"/TaskWeaver/docs/planner","h":"#planner-configuration","p":26},{"i":32,"t":"Embedding Configration","u":"/TaskWeaver/docs/plugin/embedding","h":"#embedding-configration","p":30},{"i":36,"t":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":34},{"i":38,"t":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":34},{"i":40,"t":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":34},{"i":42,"t":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":34},{"i":46,"t":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":44},{"i":48,"t":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":44},{"i":50,"t":"Auto Plugin Selection Example","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-example","p":44},{"i":54,"t":"quickstart","u":"/TaskWeaver/docs/run_pytest","h":"#quickstart","p":53},{"i":55,"t":"setup","u":"/TaskWeaver/docs/run_pytest","h":"#setup","p":53},{"i":57,"t":"run tests","u":"/TaskWeaver/docs/run_pytest","h":"#run-tests","p":53},{"i":59,"t":"notes","u":"/TaskWeaver/docs/run_pytest","h":"#notes","p":53},{"i":63,"t":"Session 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configuration file is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. For null values, use null instead of None or \"null\". All other values should be strings in double quotes. The following table lists the parameters in the configuration file: Parameter Description Default Value llm.model The model name used by the language model. gpt-4 llm.backup_model The model name used for self-correction purposes. null llm.api_base The base URL of the OpenAI API. https://api.openai.com/v1 llm.api_key The API key of the OpenAI API. null llm.api_type The type of the OpenAI API, could be openai or azure. openai llm.api_version The version of the OpenAI API. 2023-07-01-preview llm.response_format The response format of the OpenAI API, could be json_object, text or null. json_object code_interpreter.code_verification_on Whether to enable code verification. false code_interpreter.plugin_only Whether to turn on the plugin only mode. false code_interpreter.allowed_modules The list of allowed modules to import in code generation. \"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\" logging.log_file The name of the log file. taskweaver.log logging.log_folder The folder to store the log file. logs plugin.base_path The folder to store plugins. ${AppBaseDir}/plugins planner.example_base_path The folder to store planner examples. ${AppBaseDir}/planner_examples planner.prompt_compression Whether to compress the chat history for planner. false planner.skip_planning Whether to skip LLM planning process and enable the default plan false code_generator.example_base_path The folder to store code interpreter examples. ${AppBaseDir}/codeinterpreter_examples code_generator.prompt_compression Whether to compress the chat history for code interpreter. false code_generator.enable_auto_plugin_selection Whether to enable auto plugin selection. false code_generator.auto_plugin_selection_topk The number of auto selected plugins in each round. 3 session.max_internal_chat_round_num The maximum number of internal chat rounds between Planner and Code Interpreter. 10 session.code_interpreter_only Allow users to directly communicate with the Code Interpreter. false 💡 ${AppBaseDir} is the project directory. 💡 Up to 11/30/2023, the json_object and text options of llm.response_format is only supported by the OpenAI models later than 1106. If you are using an older version of OpenAI model, you need to set the llm.response_format to null.","s":"Configuration file","u":"/TaskWeaver/docs/configurations","h":"","p":11},{"i":14,"t":"There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task. Planning Examples​ A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter; receive the execution result from the code interpreter; and summarize the execution result. Before constructing the planning example, we strongly encourage you to go through the planner prompt. The following is an example of a planning example which contains 4 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the user to the planner. The message is \"count the rows of /home/data.csv\", which is the same as the user query. The second post is sent from the planner to the code interpreter. The message is \"Please load the data file /home/data.csv and count the rows of the loaded data\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is a markdown string. The second attachment is the plan, which is a markdown string. The third attachment is the current plan step, which is a markdown string. The third post is sent from the code interpreter to the planner. The message is \"Load the data file /home/data.csv successfully and there are 100 rows in the data file\". The fourth post is sent from the planner to the user. The message is \"The data file /home/data.csv is loaded and there are 100 rows in the data file\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is the same as the second attachment of the second post. The second attachment is the plan, which is the same as the third attachment of the second post. The third attachment is the current plan step, which is a markdown string. enabled: True rounds: - user_query: count the rows of /home/data.csv state: created post_list: - message: count the rows of /home/data.csv send_from: User send_to: Planner attachment_list: - message: Please load the data file /home/data.csv and count the rows of the loaded data send_from: Planner send_to: CodeInterpreter attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the user - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file send_from: CodeInterpreter send_to: Planner attachment_list: - message: The data file /home/data.csv is loaded and there are 100 rows in the data file send_from: Planner send_to: User attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the user - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 2. report the result to the user Code Interpreter Examples​ A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task. The task is from the planner. Before constructing the code interpreter example, we strongly encourage you to read the code generator prompt. The following is an example of a code interpreter example which contains 2 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the planner to the code interpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the code interpreter to the planner. The message is \"read file /abc/def.txt\". The attachment list contains 6 attachments: The first attachment is the thought of the code interpreter, which is a markdown string. The second attachment is the generated code, which is in python. The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE. The fourth attachment is the verification error message, which is a markdown string. The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE. The sixth attachment is the execution result, which is a markdown string. enabled: True rounds: - user_query: read file /abc/def.txt state: finished post_list: - message: read file /abc/def.txt send_from: Planner send_to: CodeInterpreter attachment_list: [] - message: I'm sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised. send_from: CodeInterpreter send_to: Planner attachment_list: - type: thought content: \"{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the user.\" - type: python content: |- file_path = \"/abc/def.txt\" with open(file_path, \"r\") as file: file_contents = file.read() print(file_contents) - type: verification content: CORRECT - type: code_error content: No code error. - type: execution_status content: FAILURE - type: execution_result content: FileNotFoundException, the file /abc/def.txt does not exist. In this example, verification is about whether the generated code is correct or not. We implemented a module to verify the generated code. If the code is syntactically incorrect, or the code violates the constraints, the verification status will be INCORRECT and some error messages will be returned. A verification of NONE means that the code has not been verified, which means verification has been turned off. In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. A execution_status of NONE means that the code has not been executed.","s":"example","u":"/TaskWeaver/docs/example/","h":"","p":13},{"i":17,"t":"List for all supported LLMs","s":"📄️ Supported LLMs","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":19,"t":"Using LLMs from OpenAI/AOAI","s":"📄️ OpenAI & Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":21,"t":"Using LLMs from LiteLLM","s":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":15},{"i":24,"t":"OpenAI Azure OpenAI {\"api_key\": xxx} This is AOAI","s":"OpenAI & Azure OpenAI","u":"/TaskWeaver/docs/llms/openai","h":"","p":23},{"i":27,"t":"In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step.","s":"Planner","u":"/TaskWeaver/docs/planner","h":"","p":26},{"i":29,"t":"planner.example_base_path: The folder to store planner examples. The default value is ${AppBaseDir}/planner_examples. If you want to create your own planner examples, you can add them to this folder. More details about example can referred to example. planner.prompt_compression: At times, lengthy conversations with the Planner may exceed the input limitations of the LLM model. To address this issue, we can compress the chat history and send it to the LLM model. The default value for this setting is false. More details about prompt_compression can be referred to prompt_compression. planner.skip_planning: In certain scenarios, there may be no need to use the Planner to generate complex plans for simple tasks. For instance, if a user wants to count the rows in a data file, the request can be sent directly to the Code Interpreter. When the majority of user requests involve simple tasks, enabling this option will create a dummy plan that is sent alongside the user request to the Code Interpreter directly, without LLM generation process. The fixed dummy plan is shown in dummy_plan.json. Here is an chat example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [MESSAGE]eparing... <=�=> Please process this request: generate 10 random numbers >>> [SEND_TO] CodeInterpreter >>> [INIT_PLAN] 1. ask Code Interpreter to handle the request; 2. report the result to user >>> [PLAN] 1. ask Code Interpreter to handle user's request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 1. ask Code Interpreter to handle the request >>> [PLANNER->CODEINTERPRETER] Please process this request: generate 10 random numbers >>> [PYTHON]tarting... <=�=> random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]tarting... <=�=> SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [INIT_PLAN]ting... <=�=> 1. ask Code Interpreter to handle the request; 2. report the result to user >>> [PLAN] 1. ask Code Interpreter to handle user's request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 2. report the result to user >>> [SEND_TO] User >>> [MESSAGE] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] >>> [PLANNER->USER] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] TaskWeaver: The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]","s":"Planner Configuration","u":"/TaskWeaver/docs/planner","h":"#planner-configuration","p":26},{"i":31,"t":"In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection.","s":"Embedding","u":"/TaskWeaver/docs/plugin/embedding","h":"","p":30},{"i":33,"t":"llm.embedding_api_type: The type of the embedding API. We support the following types: openai qwen ollama sentence_transformers llm.embedding_model: The embedding model name. The model name should be aligned with llm.embedding_api_type. We only list some embedding models we have tested below: openai text-embedding-ada-002 qwen text-embedding-v1 ollama llama2 sentence_transformers all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 all-distilroberta-v1 all-MiniLM-L12-v2 multi-qa-MiniLM-L6-cos-v1 You also can use other embedding models supported by the above embedding APIs.","s":"Embedding Configration","u":"/TaskWeaver/docs/plugin/embedding","h":"#embedding-configration","p":30},{"i":35,"t":"Plugins are the units that could be orchestrated by TaskWeaver. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks. In TaskWeaver, each plugin is represented as a Python function that can be called within a code snippet. The orchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins. One concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like follows: df, data_description = sql_pull_data(query=\"pull data from time_series table\") anomaly_df, anomaly_description = anomaly_detection(df, time_col_name=\"ts\", value_col_name=\"val\")","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":34},{"i":37,"t":"A plugin has two files: Plugin Implementation: a Python file that defines the plugin Plugin Schema: a file in yaml that defines the schema of the plugin","s":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":34},{"i":39,"t":"The plugin function needs to be implemented in Python. To be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts: Plugin function implementation code TaskWeaver plugin decorator Here we exhibit an example of the anomaly detection plugin as the following code: import pandas as pd from pandas.api.types import is_numeric_dtype from taskWeaver.plugin import Plugin, register_plugin @register_plugin class AnomalyDetectionPlugin(Plugin): def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str): \"\"\" anomaly_detection function identifies anomalies from an input dataframe of 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. :param df: the input data, must be a dataframe :param time_col_name: name of the column that contains the datetime :param value_col_name: name of the column that contains the numeric values. :return df: a new df that adds an additional \"Is_Anomaly\" column based on the input df. :return description: the description about the anomaly detection results. \"\"\" try: df[time_col_name] = pd.to_datetime(df[time_col_name]) except Exception: print(\"Time column is not datetime\") return if not is_numeric_dtype(df[value_col_name]): try: df[value_col_name] = df[value_col_name].astype(float) except ValueError: print(\"Value column is not numeric\") return mean, std = df[value_col_name].mean(), df[value_col_name].std() cutoff = std * 3 lower, upper = mean - cutoff, mean + cutoff df[\"Is_Anomaly\"] = df[value_col_name].apply(lambda x: x < lower or x > upper) anomaly_count = df[\"Is_Anomaly\"].sum() description = \"There are {} anomalies in the time series data\".format(anomaly_count) self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped ) return df, description You need to go through the following steps to implement your own plugin. import the TaskWeaver plugin decorator from taskWeaver.plugin import Plugin, register_plugin create your plugin class inherited from Plugin parent class (e.g., AnomalyDetectionPlugin(Plugin)), which is decorated by @register_plugin implement your plugin function in __call__ method of the plugin class. Most importantly, it is mandatory to include descriptions of your execution results in the return values of your plugin function. These descriptions can be utilized by the LLM to effectively summarize your execution results. 💡A key difference in a plugin implementation and a normal python function is that it always return a description of the result in natural language. As LLMs only understand natural language, it is important to let the model understand what the execution result is. In the example implementation above, the description says how many anomalies are detected. Behind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in the above example) is not handled by the LLM.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":34},{"i":41,"t":"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they are installed before proceeding. If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation, TaskWeaver provides an add_artifact API that allows you to store these results in the workspace. In the example we provide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize the add_artifact API to save this file as an artifact. The artifacts are stored in the project/workspace/session_id/cwd folder in the project directory. self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped )","s":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":34},{"i":43,"t":"The plugin schema is composed of several parts: name: The main function name of the Python code. enabled: determine whether the plugin is enabled for selection during conversations. The default value is true. descriptions: A brief description that introduces the plugin function. parameters: This section lists all the input parameter information. It includes the parameter's name, type, whether it is required or optional, and a description providing more details about the parameter. returns: This section lists all the return value information. It includes the return value's name, type, and description that provides information about the value that is returned by the function. Note: The addition of any extra fields would result in a validation failure within the plugin schema. The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly detection plugin: name: anomaly_detection enabled: true required: false description: >- anomaly_detection function identifies anomalies from an input DataFrame of 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. parameters: - name: df type: DataFrame required: true description: >- the input data from which we can identify the anomalies with the 3-sigma algorithm. - name: time_col_name type: str required: true description: name of the column that contains the datetime - name: value_col_name type: str required: true description: name of the column that contains the numeric values. returns: - name: df type: DataFrame description: >- This DataFrame extends the input DataFrame with a newly-added column \"Is_Anomaly\" containing the anomaly detection result. - name: description type: str description: This is a string describing the anomaly detection results. Besides, we also set two optional fields as below: code: In cases where multiple plugins map to the same Python code (i.e., the plugin name is different from the code name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy. configurations: When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema.","s":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":34},{"i":45,"t":"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems: An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code. A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time.","s":"Auto Plugin Selection","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"","p":44},{"i":47,"t":"Below is the overview workflow of the auto plugin selection mechanism. 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. At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions. The embedding vectors are created using the specified embedding model configured in the taskweaver_config.json file. For more information, please refer to the embedding documentation. When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered. It will first generate an embedding vector for the request using the same embedding model. Then, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins. It will select the top-k plugins with the highest cosine similarity scores and load them into the code_generator prompt. Upon completing the code generation, the code_generator employs one or more plugins to produce the desired code. We have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones. During the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones.","s":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":44},{"i":49,"t":"code_generator.enable_auto_plugin_selection: Whether to enable auto plugin selection. The default value is false. code_generator.auto_plugin_selection_topk: The number of auto selected plugins in each round. The default value is 3.","s":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":44},{"i":51,"t":"We show the auto plugin selection mechanism in the following example. First, we start TaskWeaver with the auto plugin selection mechanism enabled. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: Then we can check the log file task_weaver.log in the logs folder to see the auto plugin selector is initialized successfully because the Plugin embeddings generated message is printed. 2023-12-18 14:23:44,197 - INFO - Planner initialized successfully 2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated 2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully. 2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized We ask TaskWeaver to \"search Xbox price for me\". The Planner instructs the Code Interpreter to search Xbox price. TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: search xbox price for me >>> [INIT_PLAN] 1. search xbox price 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price 2. report the result to the user >>> [CURRENT_PLAN_STEP] 1. instruct CodeInterpreter to search xbox price >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please search xbox price >>> [PLANNER->CODEINTERPRETER] Please search xbox price Back to the Code Interpreter, the auto plugin selection mechanism is triggered. We can check the log file task_weaver.log again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores. 023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin 2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary'] 2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary'] Then the Code Interpreter will generate the code using the selected plugins. >>> [THOUGHT] ProgramApe will call the klarna_search plugin function to search for Xbox prices. >>> [PYTHON] search_results, description = klarna_search(query=\"xbox\") search_results, description >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 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... 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... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 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... [5 rows x 4 columns], '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.') >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python search_results, description = klarna_search(query=\"xbox\") search_results, description ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 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... 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... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 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... [5 rows x 4 columns], '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.') Finally, the Planner will report the result to the user. >>> [INIT_PLAN] 1. search xbox price using klarna_search plugin 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price using klarna_search plugin 2. report the result to the user >>> [CURRENT_PLAN_STEP] 2. report the result to the user >>> [SEND_TO] User >>> [MESSAGE] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. >>> [PLANNER->USER] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. TaskWeaver: Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability.","s":"Auto Plugin Selection Example","u":"/TaskWeaver/docs/plugin/plugin_selection","h":"#auto-plugin-selection-example","p":44},{"i":56,"t":"make sure you completed the taskweaver quickstart","s":"setup","u":"/TaskWeaver/docs/run_pytest","h":"#setup","p":53},{"i":58,"t":"execute ./scripts/run_pytest.sh from the project root","s":"run tests","u":"/TaskWeaver/docs/run_pytest","h":"#run-tests","p":53},{"i":60,"t":"the script temporarily sets PYTHONPATH and runs pytest no permanent environment changes pass additional pytest arguments as needed done! 🚀","s":"notes","u":"/TaskWeaver/docs/run_pytest","h":"#notes","p":53},{"i":62,"t":"session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver.","s":"Session","u":"/TaskWeaver/docs/session","h":"","p":61},{"i":64,"t":"max_internal_chat_round_num: the maximum number of internal chat rounds between Planner and Code Interpreter. If the number of internal chat rounds exceeds this number, the session will be terminated. The default value is 10. code_interpreter_only: allow users to directly communicate with the Code Interpreter. In this mode, users can only send messages to the Code Interpreter and receive messages from the Code Interpreter. Here is an example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [PYTHON]tarting... <=�=> > import numpy as np random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]tarting... <=�=> SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) TaskWeaver: The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers \\``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194])","s":"Session Configration","u":"/TaskWeaver/docs/session","h":"#session-configration","p":61},{"i":66,"t":"If you want to use TaskWeaver as a library, you can refer to the following code example: from taskweaver.app.app import TaskWeaverApp # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to \"./project/\" app_dir = \"./project/\" app = TaskWeaverApp(app_dir=app_dir) session = app.get_session() user_query = \"hello, what can you do?\" response_round = session.send_message(user_query, event_handler=lambda _type, _msg: print(f\"{_type}:\\n{_msg}\")) print(response_round.to_dict()) Note: event_handler: a callback function that is utilized to display the internal planning and execution steps of TaskWeaver. It takes two arguments: the message type (e.g., plan) and the message body. response_round: the response from TaskWeaver. which is an object of the Round class. An example of the Round object is shown below: { \"id\": \"round-20231201-043134-218a2681\", \"user_query\": \"hello, what can you do?\", \"state\": \"finished\", \"post_list\": [ { \"id\": \"post-20231201-043134-10eedcca\", \"message\": \"hello, what can you do?\", \"send_from\": \"User\", \"send_to\": \"Planner\", \"attachment_list\": [] }, { \"id\": \"post-20231201-043141-86a2aaff\", \"message\": \"I can help you with various tasks, such as counting rows in a data file, detecting anomalies in a dataset, searching for products on Klarna, summarizing research papers, and pulling data from a SQL database. Please provide more information about the task you want to accomplish, and I'll guide you through the process.\", \"send_from\": \"Planner\", \"send_to\": \"User\", \"attachment_list\": [ { \"id\": \"atta-20231201-043141-6bc4da86\", \"type\": \"init_plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-6f29f6c9\", \"type\": \"plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-76186c7a\", \"type\": \"current_plan_step\", \"content\": \"1. list the available functions\" } ] } ] }","s":"Use TaskWeaver as a 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