diff --git a/404.html b/404.html index d6b93cea5b..af2b1180df 100644 --- a/404.html +++ b/404.html @@ -21,7 +21,7 @@ - + diff --git a/agents-overview.html b/agents-overview.html index 2d139949bd..9e384236a0 100644 --- a/agents-overview.html +++ b/agents-overview.html @@ -21,7 +21,7 @@ - + diff --git a/agents-tool-calling-agent-component.html b/agents-tool-calling-agent-component.html index 10636a0889..d0a92ad186 100644 --- a/agents-tool-calling-agent-component.html +++ b/agents-tool-calling-agent-component.html @@ -21,7 +21,7 @@ - + diff --git a/api-reference-api-examples.html b/api-reference-api-examples.html index 3d7e25988b..0b72ddb893 100644 --- a/api-reference-api-examples.html +++ b/api-reference-api-examples.html @@ -21,7 +21,7 @@ - + diff --git a/api.html b/api.html index 9908cf78e5..0f7cac66e3 100644 --- a/api.html +++ b/api.html @@ -21,7 +21,7 @@ - + diff --git a/api/add-user.html b/api/add-user.html index 551e5be690..a4286c97bd 100644 --- a/api/add-user.html +++ b/api/add-user.html @@ -21,7 +21,7 @@ - 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+ diff --git a/api/update-folder-redirect.html b/api/update-folder-redirect.html index 98998c73ac..4d4ef989f3 100644 --- a/api/update-folder-redirect.html +++ b/api/update-folder-redirect.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-message.html b/api/update-message.html index e173ee4103..9e248b24f5 100644 --- a/api/update-message.html +++ b/api/update-message.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-project-mcp-settings.html b/api/update-project-mcp-settings.html index 7f0d2d8670..f081fc6681 100644 --- a/api/update-project-mcp-settings.html +++ b/api/update-project-mcp-settings.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-project.html b/api/update-project.html index 2eaaa9b2e5..6758f94919 100644 --- a/api/update-project.html +++ b/api/update-project.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-session-id.html b/api/update-session-id.html index 1acf4a9ca4..0323e58277 100644 --- a/api/update-session-id.html +++ b/api/update-session-id.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-shared-component.html b/api/update-shared-component.html index 2a2786bc1a..3c64c6c248 100644 --- a/api/update-shared-component.html +++ b/api/update-shared-component.html @@ -21,7 +21,7 @@ - + diff --git a/api/update-variable.html b/api/update-variable.html index efa5f67e91..43ab7ad9db 100644 --- a/api/update-variable.html +++ b/api/update-variable.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-file-1.html b/api/upload-file-1.html index 7892f050cd..36ffcbd4a4 100644 --- a/api/upload-file-1.html +++ b/api/upload-file-1.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-file-2.html b/api/upload-file-2.html index 4c7e7ac12b..795e4ba692 100644 --- a/api/upload-file-2.html +++ b/api/upload-file-2.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-file-redirect.html b/api/upload-file-redirect.html index 010c4b3f95..176d531be7 100644 --- a/api/upload-file-redirect.html +++ b/api/upload-file-redirect.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-file.html b/api/upload-file.html index 94ed5c7f38..575483b418 100644 --- a/api/upload-file.html +++ b/api/upload-file.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-user-file-1.html b/api/upload-user-file-1.html index 383bfa5add..562245d6bc 100644 --- a/api/upload-user-file-1.html +++ b/api/upload-user-file-1.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-user-file.html b/api/upload-user-file.html index 98b49b79a7..ad279aaa6c 100644 --- a/api/upload-user-file.html +++ b/api/upload-user-file.html @@ -21,7 +21,7 @@ - + diff --git a/api/webhook-run-flow.html b/api/webhook-run-flow.html index a9bd5a8729..3758b14b50 100644 --- a/api/webhook-run-flow.html +++ b/api/webhook-run-flow.html @@ -21,7 +21,7 @@ - + diff --git a/assets/js/2ab0d4f5.82b3b12f.js b/assets/js/2ab0d4f5.82b3b12f.js deleted file mode 100644 index f2ff5ce669..0000000000 --- a/assets/js/2ab0d4f5.82b3b12f.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[1845],{28453:(e,n,t)=>{t.d(n,{R:()=>a,x:()=>o});var i=t(96540);const s={},r=i.createContext(s);function a(e){const n=i.useContext(r);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:a(e.components),i.createElement(r.Provider,{value:n},e.children)}},83640:(e,n,t)=>{t.r(n),t.d(n,{contentTitle:()=>o,default:()=>c,frontMatter:()=>a,metadata:()=>i,toc:()=>l});const i=JSON.parse('{"type":"api","id":"simplified-run-flow","title":"Simplified Run Flow","description":"","slug":"/simplified-run-flow","frontMatter":{},"api":{"tags":["Base"],"description":"Executes a specified flow by ID with support for streaming and telemetry.\\n\\nThis endpoint executes a flow identified by ID or name, with options for streaming the response\\nand tracking execution metrics. 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input."]}),"\n",(0,r.jsxs)(t.li,{children:["Connect the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component's ",(0,r.jsx)(t.strong,{children:"Batch Results"})," output to a ",(0,r.jsx)(t.strong,{children:"Parser"})," component's ",(0,r.jsx)(t.strong,{children:"DataFrame"})," input.\nThe flow looks like this:"]}),"\n"]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"A batch run component connected to OpenAI and a Parser",src:n(88103).A+"",width:"1776",height:"1370"})}),"\n",(0,r.jsxs)(t.ol,{start:"4",children:["\n",(0,r.jsxs)(t.li,{children:["In the ",(0,r.jsx)(t.strong,{children:"Column Name"})," field of the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component, enter a column name based on the data you're loading from the ",(0,r.jsx)(t.strong,{children:"File"})," loader. For example, to process a column of ",(0,r.jsx)(t.code,{children:"name"}),", enter ",(0,r.jsx)(t.code,{children:"name"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["Optionally, in the ",(0,r.jsx)(t.strong,{children:"System Message"})," field of the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component, enter a ",(0,r.jsx)(t.strong,{children:"System Message"})," to instruct the connected LLM on how to process your file. For example, ",(0,r.jsx)(t.code,{children:"Create a business card for each name."})]}),"\n",(0,r.jsxs)(t.li,{children:["In the ",(0,r.jsx)(t.strong,{children:"Template"})," field of the ",(0,r.jsx)(t.strong,{children:"Parser"})," component, enter a template for using the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component's new DataFrame columns.\nTo use all three columns from the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component, include them like this:"]}),"\n"]}),"\n",(0,r.jsx)(a.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"record_number: {batch_index}, name: {text_input}, summary: {model_response}",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,r.jsxs)(t.ol,{start:"7",children:["\n",(0,r.jsxs)(t.li,{children:["To run the flow, in the ",(0,r.jsx)(t.strong,{children:"Parser"})," component, click ",(0,r.jsx)(o.A,{name:"Play","aria-label":"Play icon"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["To view your created DataFrame, in the ",(0,r.jsx)(t.strong,{children:"Parser"})," component, click ",(0,r.jsx)(o.A,{name:"TextSearch","aria-label":"Inspect icon"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["Optionally, connect a ",(0,r.jsx)(t.strong,{children:"Chat Output"})," component, and open the ",(0,r.jsx)(t.strong,{children:"Playground"})," to see the output."]}),"\n"]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"model"}),(0,r.jsx)(t.td,{children:"HandleInput"}),(0,r.jsx)(t.td,{children:"Connect the 'Language Model' output from your LLM component here. Required."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"system_message"}),(0,r.jsx)(t.td,{children:"MultilineInput"}),(0,r.jsx)(t.td,{children:"A multi-line system instruction for all rows in the DataFrame."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"df"}),(0,r.jsx)(t.td,{children:"DataFrameInput"}),(0,r.jsx)(t.td,{children:"The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"column_name"}),(0,r.jsx)(t.td,{children:"MessageTextInput"}),(0,r.jsx)(t.td,{children:"The name of the DataFrame column to treat as text messages. If empty, all columns are formatted in TOML."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"output_column_name"}),(0,r.jsx)(t.td,{children:"MessageTextInput"}),(0,r.jsxs)(t.td,{children:["Name of the column where the model's response is stored. Default=",(0,r.jsx)(t.code,{children:"model_response"}),"."]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"enable_metadata"}),(0,r.jsx)(t.td,{children:"BoolInput"}),(0,r.jsx)(t.td,{children:"If True, add metadata to the output DataFrame."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"batch_results"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsx)(t.td,{children:"A DataFrame with all original columns plus the model's response column."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"current-date",children:"Current date"}),"\n",(0,r.jsx)(t.p,{children:"The Current Date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"timezone"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The timezone for the current date and time."})]})})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"current_date"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The resulting current date and time in the selected timezone."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"id-generator",children:"ID Generator"}),"\n",(0,r.jsx)(t.p,{children:"This component generates a unique ID."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"unique_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"message-history",children:"Message history"}),"\n",(0,r.jsx)(t.admonition,{type:"info",children:(0,r.jsx)(t.p,{children:"Prior to Langflow 1.1, this component was known as the Chat Memory component."})}),"\n",(0,r.jsx)(t.p,{children:"This component retrieves chat messages from Langflow tables or external memory."}),"\n",(0,r.jsxs)(t.p,{children:["In this example, the ",(0,r.jsx)(t.strong,{children:"Message Store"})," component stores the complete chat history in a local Langflow table, which the ",(0,r.jsx)(t.strong,{children:"Message History"})," component retrieves as context for the LLM to answer each question."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Message store and history components",src:n(77111).A+"",width:"2778",height:"1166"})}),"\n",(0,r.jsxs)(t.p,{children:["For more information on configuring memory in Langflow, see ",(0,r.jsx)(t.a,{href:"/memory",children:"Memory"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"memory"}),(0,r.jsx)(t.td,{children:"Memory"}),(0,r.jsx)(t.td,{children:"Retrieve messages from an external memory. If empty, the Langflow tables are used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"Filter by sender type."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"Filter by sender name."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"n_messages"}),(0,r.jsx)(t.td,{children:"Integer"}),(0,r.jsx)(t.td,{children:"The number of messages to retrieve."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"session_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"order"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The order of the messages."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"template"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsxs)(t.td,{children:["The template to use for formatting the data. It can contain the keys ",(0,r.jsx)(t.code,{children:"{text}"}),", ",(0,r.jsx)(t.code,{children:"{sender}"})," or any other key in the message data."]})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"messages"}),(0,r.jsx)(t.td,{children:"Data"}),(0,r.jsx)(t.td,{children:"The retrieved messages as Data objects."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"messages_text"}),(0,r.jsx)(t.td,{children:"Message"}),(0,r.jsx)(t.td,{children:"The retrieved messages formatted as text."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"dataframe"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsx)(t.td,{children:"A DataFrame containing the message data."})]})]})]})]}),"\n",(0,r.jsx)(t.h2,{id:"message-store",children:"Message store"}),"\n",(0,r.jsx)(t.p,{children:"This component stores chat messages or text in Langflow tables or external memory."}),"\n",(0,r.jsxs)(t.p,{children:["In this example, the ",(0,r.jsx)(t.strong,{children:"Message Store"})," component stores the complete chat history in a local Langflow table, which the ",(0,r.jsx)(t.strong,{children:"Message History"})," component retrieves as context for the LLM to answer each question."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Message store and history components",src:n(77111).A+"",width:"2778",height:"1166"})}),"\n",(0,r.jsxs)(t.p,{children:["For more information on configuring memory in Langflow, see ",(0,r.jsx)(t.a,{href:"/memory",children:"Memory"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"message"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The chat message to be stored. (Required)"})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"memory"}),(0,r.jsx)(t.td,{children:"Memory"}),(0,r.jsx)(t.td,{children:"The external memory to store the message. If empty, the Langflow tables are used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The sender of the message. Can be Machine or User. If empty, the current sender parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name of the sender. Can be AI or User. If empty, the current sender parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"session_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter is used."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"stored_messages"}),(0,r.jsx)(t.td,{children:"List[Data]"}),(0,r.jsx)(t.td,{children:"The list of stored messages after the current message has been added."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"structured-output",children:"Structured output"}),"\n",(0,r.jsx)(t.p,{children:"This component transforms LLM responses into structured data formats."}),"\n",(0,r.jsxs)(t.p,{children:["In this example from the ",(0,r.jsx)(t.strong,{children:"Financial Support Parser"})," template, the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component transforms unstructured financial reports into structured data."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Structured output example",src:n(82798).A+"",width:"2554",height:"1548"})}),"\n",(0,r.jsxs)(t.p,{children:["The connected LLM model is prompted by the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component's ",(0,r.jsx)(t.code,{children:"Format Instructions"})," parameter to extract structured output from the unstructured text. ",(0,r.jsx)(t.code,{children:"Format Instructions"})," is utilized as the system prompt for the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component."]}),"\n",(0,r.jsxs)(t.p,{children:["In the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component, click the ",(0,r.jsx)(t.strong,{children:"Open table"})," button to view the ",(0,r.jsx)(t.code,{children:"Output Schema"})," table.\nThe ",(0,r.jsx)(t.code,{children:"Output Schema"})," parameter defines the structure and data types for the model's output using a table with the following fields:"]}),"\n",(0,r.jsxs)(t.ul,{children:["\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Name"}),": The name of the output field."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Description"}),": The purpose of the output field."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Type"}),": The data type of the output field. The available types are ",(0,r.jsx)(t.code,{children:"str"}),", ",(0,r.jsx)(t.code,{children:"int"}),", ",(0,r.jsx)(t.code,{children:"float"}),", ",(0,r.jsx)(t.code,{children:"bool"}),", ",(0,r.jsx)(t.code,{children:"list"}),", or ",(0,r.jsx)(t.code,{children:"dict"}),". The default is ",(0,r.jsx)(t.code,{children:"text"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Multiple"}),": This feature is deprecated. Currently, it is set to ",(0,r.jsx)(t.code,{children:"True"})," by default if you expect multiple values for a single field. For example, a ",(0,r.jsx)(t.code,{children:"list"})," of ",(0,r.jsx)(t.code,{children:"features"})," is set to ",(0,r.jsx)(t.code,{children:"True"})," to contain multiple values, such as ",(0,r.jsx)(t.code,{children:'["waterproof", "durable", "lightweight"]'}),". Default: ",(0,r.jsx)(t.code,{children:"True"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Parse DataFrame"})," component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the ",(0,r.jsx)(t.code,{children:"output_schema"})," table with curly braces."]}),"\n",(0,r.jsxs)(t.p,{children:["For example, the template ",(0,r.jsx)(t.code,{children:"EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT}"})," presents the extracted values in the ",(0,r.jsx)(t.strong,{children:"Playground"})," as ",(0,r.jsx)(t.code,{children:"EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"llm"}),(0,r.jsx)(t.td,{children:"LanguageModel"}),(0,r.jsx)(t.td,{children:"The language model to use to generate the structured output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"input_value"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The input message to the language model."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"system_prompt"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The instructions to the language model for formatting the output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"schema_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name for the output data schema."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"output_schema"}),(0,r.jsx)(t.td,{children:"Table"}),(0,r.jsx)(t.td,{children:"The structure and data types for the model's output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"multiple"}),(0,r.jsx)(t.td,{children:"Boolean"}),(0,r.jsxs)(t.td,{children:["[Deprecated] Always set to ",(0,r.jsx)(t.code,{children:"True"}),"."]})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output"}),(0,r.jsx)(t.td,{children:"Data"}),(0,r.jsx)(t.td,{children:"The structured output is a Data object based on the defined schema."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"legacy-components",children:"Legacy components"}),"\n",(0,r.jsx)(t.p,{children:"Legacy components are available for use but are no longer supported."}),"\n",(0,r.jsx)(t.h3,{id:"create-list",children:"Create List"}),"\n",(0,r.jsx)(t.p,{children:"This component dynamically creates a record with a specified number of fields."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"n_fields"}),(0,r.jsx)(t.td,{children:"Integer"}),(0,r.jsx)(t.td,{children:"The number of fields to be added to the record."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"text_key"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The key used as text."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"list"}),(0,r.jsx)(t.td,{children:"List"}),(0,r.jsx)(t.td,{children:"The dynamically created list with the specified number of fields."})]})})]})]}),"\n",(0,r.jsx)(t.h3,{id:"output-parser",children:"Output Parser"}),"\n",(0,r.jsxs)(t.p,{children:["This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's ",(0,r.jsx)(t.code,{children:"CommaSeparatedListOutputParser"}),"."]}),"\n",(0,r.jsx)(t.admonition,{type:"note",children:(0,r.jsx)(t.p,{children:"This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use."})}),"\n",(0,r.jsxs)(t.p,{children:["Both the ",(0,r.jsx)(t.strong,{children:"Output Parser"})," and ",(0,r.jsx)(t.strong,{children:"Structured Output"})," components format LLM responses, but they have different use cases.\nThe ",(0,r.jsx)(t.strong,{children:"Output Parser"})," is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example ",(0,r.jsx)(t.code,{children:'["item1", "item2", "item3"]'}),".\nThe ",(0,r.jsx)(t.strong,{children:"Structured Output"})," is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types."]}),"\n",(0,r.jsx)(t.p,{children:"To use this component:"}),"\n",(0,r.jsxs)(t.ol,{children:["\n",(0,r.jsxs)(t.li,{children:["Create a Prompt component and connect the Output Parser's ",(0,r.jsx)(t.code,{children:"format_instructions"})," output to it. This ensures the LLM knows how to format its response."]}),"\n",(0,r.jsxs)(t.li,{children:["Write your actual prompt text in the Prompt component, including the ",(0,r.jsx)(t.code,{children:"{format_instructions}"})," variable.\nFor example, in your Prompt component, the template might look like:"]}),"\n"]}),"\n",(0,r.jsx)(a.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"{format_instructions}",props:{}}]},{tokens:[{content:"Please list three fruits.",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,r.jsxs)(t.ol,{start:"3",children:["\n",(0,r.jsxs)(t.li,{children:["\n",(0,r.jsxs)(t.p,{children:["Connect the ",(0,r.jsx)(t.code,{children:"output_parser"})," output to your LLM model."]}),"\n"]}),"\n",(0,r.jsxs)(t.li,{children:["\n",(0,r.jsxs)(t.p,{children:["The output parser converts this into a Python list: ",(0,r.jsx)(t.code,{children:'["apple", "banana", "orange"]'}),"."]}),"\n"]}),"\n"]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"parser_type"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:'The parser type. Currently supports "CSV".'})]})})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"format_instructions"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"Pass to a prompt template to include formatting instructions for LLM responses."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"output_parser"}),(0,r.jsx)(t.td,{children:"Parser"}),(0,r.jsx)(t.td,{children:"The constructed output parser that can be used to parse LLM responses."})]})]})]})]})]})}function u(e={}){const{wrapper:t}={...(0,d.R)(),...e.components};return t?(0,r.jsx)(t,{...e,children:(0,r.jsx)(p,{...e})}):p(e)}function m(e,t){throw new Error("Expected "+(t?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}},72654:(e,t,n)=>{n.d(t,{A:()=>s});const s=n.p+"assets/images/astra_db_chat_memory_rounded-9746ca2bb69d3b07ac0a071f4b9471b3.png"},77111:(e,t,n)=>{n.d(t,{A:()=>s});const s=n.p+"assets/images/component-message-history-message-store-be0396aefa69496e8bc21452d240e04d.png"},82798:(e,t,n)=>{n.d(t,{A:()=>s});const s=n.p+"assets/images/component-structured-output-7bb5a996464cb83d4c94a893419bb176.png"},84443:(e,t,n)=>{n.d(t,{A:()=>d});n(96540);var s=n(64058),r=n(74848);function d(e){let{name:t,...n}=e;const d=s[t];return d?(0,r.jsx)(d,{...n}):null}},88103:(e,t,n)=>{n.d(t,{A:()=>s});const s=n.p+"assets/images/component-batch-run-0ad3fa6f082eb01504dae7df3cc4daad.png"}}]); \ No newline at end of file +"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[7338],{31154:(e,t,n)=>{n.r(t),n.d(t,{CH:()=>a,assets:()=>l,chCodeConfig:()=>x,contentTitle:()=>h,default:()=>u,frontMatter:()=>c,metadata:()=>s,toc:()=>j});const s=JSON.parse('{"id":"Components/components-helpers","title":"Helpers","description":"Helper components provide utility functions to help manage data, tasks, and other components in your flow.","source":"@site/docs/Components/components-helpers.md","sourceDirName":"Components","slug":"/components-helpers","permalink":"/components-helpers","draft":false,"unlisted":false,"tags":[],"version":"current","frontMatter":{"title":"Helpers","slug":"/components-helpers"},"sidebar":"docs","previous":{"title":"Embeddings","permalink":"/components-embedding-models"},"next":{"title":"Inputs and outputs","permalink":"/components-io"}}');var r=n(74848),d=n(28453),i=n(24754),o=n(84443);const c={title:"Helpers",slug:"/components-helpers"},h="Helper components in Langflow",l={},a={annotations:i.hk,Code:i.Cy},x={staticMediaQuery:"not screen, (max-width: 768px)",lineNumbers:!0,showCopyButton:!0,themeName:"github-dark"},j=[{value:"Use a helper component in a flow",id:"use-a-helper-component-in-a-flow",level:2},{value:"Batch Run",id:"batch-run",level:2},{value:"Current date",id:"current-date",level:2},{value:"ID Generator",id:"id-generator",level:2},{value:"Message history",id:"message-history",level:2},{value:"Message store",id:"message-store",level:2},{value:"Structured output",id:"structured-output",level:2},{value:"Legacy components",id:"legacy-components",level:2},{value:"Create List",id:"create-list",level:3},{value:"Output Parser",id:"output-parser",level:3}];function p(e){const t={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",h3:"h3",header:"header",img:"img",li:"li",ol:"ol",p:"p",strong:"strong",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",ul:"ul",...(0,d.R)(),...e.components},{Details:s}=t;return a||m("CH",!1),a.Code||m("CH.Code",!0),s||m("Details",!0),(0,r.jsxs)(r.Fragment,{children:[(0,r.jsx)("style",{dangerouslySetInnerHTML:{__html:'[data-ch-theme="github-dark"] { --ch-t-colorScheme: dark;--ch-t-foreground: #c9d1d9;--ch-t-background: #0d1117;--ch-t-lighter-inlineBackground: #0d1117e6;--ch-t-editor-background: #0d1117;--ch-t-editor-foreground: #c9d1d9;--ch-t-editor-lineHighlightBackground: #6e76811a;--ch-t-editor-rangeHighlightBackground: #ffffff0b;--ch-t-editor-infoForeground: #3794FF;--ch-t-editor-selectionBackground: #264F78;--ch-t-focusBorder: #1f6feb;--ch-t-tab-activeBackground: #0d1117;--ch-t-tab-activeForeground: #c9d1d9;--ch-t-tab-inactiveBackground: #010409;--ch-t-tab-inactiveForeground: #8b949e;--ch-t-tab-border: #30363d;--ch-t-tab-activeBorder: #0d1117;--ch-t-editorGroup-border: #30363d;--ch-t-editorGroupHeader-tabsBackground: #010409;--ch-t-editorLineNumber-foreground: #6e7681;--ch-t-input-background: #0d1117;--ch-t-input-foreground: #c9d1d9;--ch-t-input-border: #30363d;--ch-t-icon-foreground: #8b949e;--ch-t-sideBar-background: #010409;--ch-t-sideBar-foreground: #c9d1d9;--ch-t-sideBar-border: #30363d;--ch-t-list-activeSelectionBackground: #6e768166;--ch-t-list-activeSelectionForeground: #c9d1d9;--ch-t-list-hoverBackground: #6e76811a;--ch-t-list-hoverForeground: #c9d1d9; }'}}),"\n","\n",(0,r.jsx)(t.header,{children:(0,r.jsx)(t.h1,{id:"helper-components-in-langflow",children:"Helper components in Langflow"})}),"\n",(0,r.jsx)(t.p,{children:"Helper components provide utility functions to help manage data, tasks, and other components in your flow."}),"\n",(0,r.jsx)(t.h2,{id:"use-a-helper-component-in-a-flow",children:"Use a helper component in a flow"}),"\n",(0,r.jsxs)(t.p,{children:["Chat memory in Langflow is stored either in local Langflow tables with ",(0,r.jsx)(t.code,{children:"LCBufferMemory"}),", or connected to an external database."]}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Store Message"})," helper component stores chat memories as ",(0,r.jsx)(t.a,{href:"/concepts-objects",children:"Data"})," objects, and the ",(0,r.jsx)(t.strong,{children:"Message History"})," helper component retrieves chat messages as data objects or strings."]}),"\n",(0,r.jsxs)(t.p,{children:["This example flow stores and retrieves chat history from an ",(0,r.jsx)(t.a,{href:"/components-memories#astradbchatmemory-component",children:"AstraDBChatMemory"})," component with ",(0,r.jsx)(t.strong,{children:"Store Message"})," and ",(0,r.jsx)(t.strong,{children:"Chat Memory"})," components."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Sample Flow storing Chat Memory in AstraDB",src:n(72654).A+"",width:"3178",height:"1228"})}),"\n",(0,r.jsx)(t.h2,{id:"batch-run",children:"Batch Run"}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component runs a language model over ",(0,r.jsx)(t.strong,{children:"each row"})," of a ",(0,r.jsx)(t.a,{href:"/concepts-objects#dataframe-object",children:"DataFrame"})," text column and returns a new DataFrame with the original text and an LLM response."]}),"\n",(0,r.jsx)(t.p,{children:"The response contains the following columns:"}),"\n",(0,r.jsxs)(t.ul,{children:["\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.code,{children:"text_input"}),": The original text from the input DataFrame."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.code,{children:"model_response"}),": The model's response for each input."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.code,{children:"batch_index"}),": The processing order, with a ",(0,r.jsx)(t.code,{children:"0"}),"-based index."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.code,{children:"metadata"})," (optional): Additional information about the processing."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["These columns, when connected to a ",(0,r.jsx)(t.strong,{children:"Parser"})," component, can be used as variables within curly braces."]}),"\n",(0,r.jsxs)(t.p,{children:["To use the Batch Run component with a ",(0,r.jsx)(t.strong,{children:"Parser"})," component, do the following:"]}),"\n",(0,r.jsxs)(t.ol,{children:["\n",(0,r.jsxs)(t.li,{children:["Connect a ",(0,r.jsx)(t.strong,{children:"Model"})," component to the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component's ",(0,r.jsx)(t.strong,{children:"Language model"})," port."]}),"\n",(0,r.jsxs)(t.li,{children:["Connect a component that outputs DataFrame, like ",(0,r.jsx)(t.strong,{children:"File"})," component, to the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component's ",(0,r.jsx)(t.strong,{children:"DataFrame"})," input."]}),"\n",(0,r.jsxs)(t.li,{children:["Connect the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component's ",(0,r.jsx)(t.strong,{children:"Batch Results"})," output to a ",(0,r.jsx)(t.strong,{children:"Parser"})," component's ",(0,r.jsx)(t.strong,{children:"DataFrame"})," input.\nThe flow looks like this:"]}),"\n"]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"A batch run component connected to OpenAI and a Parser",src:n(88103).A+"",width:"1776",height:"1370"})}),"\n",(0,r.jsxs)(t.ol,{start:"4",children:["\n",(0,r.jsxs)(t.li,{children:["In the ",(0,r.jsx)(t.strong,{children:"Column Name"})," field of the ",(0,r.jsx)(t.strong,{children:"Batch Run"})," component, enter a column name based on the data you're loading from the ",(0,r.jsx)(t.strong,{children:"File"})," loader. 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Required."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"system_message"}),(0,r.jsx)(t.td,{children:"MultilineInput"}),(0,r.jsx)(t.td,{children:"A multi-line system instruction for all rows in the DataFrame."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"df"}),(0,r.jsx)(t.td,{children:"DataFrameInput"}),(0,r.jsx)(t.td,{children:"The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"column_name"}),(0,r.jsx)(t.td,{children:"MessageTextInput"}),(0,r.jsx)(t.td,{children:"The name of the DataFrame column to treat as text messages. If empty, all columns are formatted in TOML."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"output_column_name"}),(0,r.jsx)(t.td,{children:"MessageTextInput"}),(0,r.jsxs)(t.td,{children:["Name of the column where the model's response is stored. Default=",(0,r.jsx)(t.code,{children:"model_response"}),"."]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"enable_metadata"}),(0,r.jsx)(t.td,{children:"BoolInput"}),(0,r.jsx)(t.td,{children:"If True, add metadata to the output DataFrame."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"batch_results"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsx)(t.td,{children:"A DataFrame with all original columns plus the model's response column."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"current-date",children:"Current date"}),"\n",(0,r.jsx)(t.p,{children:"The Current Date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"timezone"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The timezone for the current date and time."})]})})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"current_date"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The resulting current date and time in the selected timezone."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"id-generator",children:"ID Generator"}),"\n",(0,r.jsx)(t.p,{children:"This component generates a unique ID."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"unique_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"message-history",children:"Message history"}),"\n",(0,r.jsx)(t.admonition,{type:"info",children:(0,r.jsx)(t.p,{children:"Prior to Langflow 1.1, this component was known as the Chat Memory component."})}),"\n",(0,r.jsx)(t.p,{children:"This component retrieves chat messages from Langflow tables or external memory."}),"\n",(0,r.jsxs)(t.p,{children:["In this example, the ",(0,r.jsx)(t.strong,{children:"Message Store"})," component stores the complete chat history in a local Langflow table, which the ",(0,r.jsx)(t.strong,{children:"Message History"})," component retrieves as context for the LLM to answer each question."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Message store and history components",src:n(77111).A+"",width:"2778",height:"1166"})}),"\n",(0,r.jsxs)(t.p,{children:["For more information on configuring memory in Langflow, see ",(0,r.jsx)(t.a,{href:"/memory",children:"Memory"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"memory"}),(0,r.jsx)(t.td,{children:"Memory"}),(0,r.jsx)(t.td,{children:"Retrieve messages from an external memory. If empty, the Langflow tables are used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"Filter by sender type."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"Filter by sender name."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"n_messages"}),(0,r.jsx)(t.td,{children:"Integer"}),(0,r.jsx)(t.td,{children:"The number of messages to retrieve."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"session_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"order"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The order of the messages."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"template"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsxs)(t.td,{children:["The template to use for formatting the data. It can contain the keys ",(0,r.jsx)(t.code,{children:"{text}"}),", ",(0,r.jsx)(t.code,{children:"{sender}"})," or any other key in the message data."]})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"messages"}),(0,r.jsx)(t.td,{children:"Data"}),(0,r.jsx)(t.td,{children:"The retrieved messages as Data objects."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"messages_text"}),(0,r.jsx)(t.td,{children:"Message"}),(0,r.jsx)(t.td,{children:"The retrieved messages formatted as text."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"dataframe"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsx)(t.td,{children:"A DataFrame containing the message data."})]})]})]})]}),"\n",(0,r.jsx)(t.h2,{id:"message-store",children:"Message store"}),"\n",(0,r.jsx)(t.p,{children:"This component stores chat messages or text in Langflow tables or external memory."}),"\n",(0,r.jsxs)(t.p,{children:["In this example, the ",(0,r.jsx)(t.strong,{children:"Message Store"})," component stores the complete chat history in a local Langflow table, which the ",(0,r.jsx)(t.strong,{children:"Message History"})," component retrieves as context for the LLM to answer each question."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Message store and history components",src:n(77111).A+"",width:"2778",height:"1166"})}),"\n",(0,r.jsxs)(t.p,{children:["For more information on configuring memory in Langflow, see ",(0,r.jsx)(t.a,{href:"/memory",children:"Memory"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"message"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The chat message to be stored. (Required)"})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"memory"}),(0,r.jsx)(t.td,{children:"Memory"}),(0,r.jsx)(t.td,{children:"The external memory to store the message. If empty, the Langflow tables are used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The sender of the message. Can be Machine or User. If empty, the current sender parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name of the sender. Can be AI or User. If empty, the current sender parameter is used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"session_id"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter is used."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"stored_messages"}),(0,r.jsx)(t.td,{children:"List[Data]"}),(0,r.jsx)(t.td,{children:"The list of stored messages after the current message has been added."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"structured-output",children:"Structured output"}),"\n",(0,r.jsx)(t.p,{children:"This component transforms LLM responses into structured data formats."}),"\n",(0,r.jsxs)(t.p,{children:["In this example from the ",(0,r.jsx)(t.strong,{children:"Financial Report Parser"})," template, the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component transforms unstructured financial reports into structured data."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Structured output example",src:n(82798).A+"",width:"2554",height:"1548"})}),"\n",(0,r.jsxs)(t.p,{children:["The connected LLM model is prompted by the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component's ",(0,r.jsx)(t.code,{children:"Format Instructions"})," parameter to extract structured output from the unstructured text. ",(0,r.jsx)(t.code,{children:"Format Instructions"})," is utilized as the system prompt for the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component."]}),"\n",(0,r.jsxs)(t.p,{children:["In the ",(0,r.jsx)(t.strong,{children:"Structured Output"})," component, click the ",(0,r.jsx)(t.strong,{children:"Open table"})," button to view the ",(0,r.jsx)(t.code,{children:"Output Schema"})," table.\nThe ",(0,r.jsx)(t.code,{children:"Output Schema"})," parameter defines the structure and data types for the model's output using a table with the following fields:"]}),"\n",(0,r.jsxs)(t.ul,{children:["\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Name"}),": The name of the output field."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Description"}),": The purpose of the output field."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Type"}),": The data type of the output field. The available types are ",(0,r.jsx)(t.code,{children:"str"}),", ",(0,r.jsx)(t.code,{children:"int"}),", ",(0,r.jsx)(t.code,{children:"float"}),", ",(0,r.jsx)(t.code,{children:"bool"}),", ",(0,r.jsx)(t.code,{children:"list"}),", or ",(0,r.jsx)(t.code,{children:"dict"}),". The default is ",(0,r.jsx)(t.code,{children:"text"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:[(0,r.jsx)(t.strong,{children:"Multiple"}),": This feature is deprecated. Currently, it is set to ",(0,r.jsx)(t.code,{children:"True"})," by default if you expect multiple values for a single field. For example, a ",(0,r.jsx)(t.code,{children:"list"})," of ",(0,r.jsx)(t.code,{children:"features"})," is set to ",(0,r.jsx)(t.code,{children:"True"})," to contain multiple values, such as ",(0,r.jsx)(t.code,{children:'["waterproof", "durable", "lightweight"]'}),". Default: ",(0,r.jsx)(t.code,{children:"True"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Parser"})," component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the ",(0,r.jsx)(t.code,{children:"output_schema"})," table with curly braces."]}),"\n",(0,r.jsxs)(t.p,{children:["For example, the template ",(0,r.jsx)(t.code,{children:"EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT}"})," presents the extracted values in the ",(0,r.jsx)(t.strong,{children:"Playground"})," as ",(0,r.jsx)(t.code,{children:"EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"llm"}),(0,r.jsx)(t.td,{children:"LanguageModel"}),(0,r.jsx)(t.td,{children:"The language model to use to generate the structured output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"input_value"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The input message to the language model."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"system_prompt"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The instructions to the language model for formatting the output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"schema_name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name for the output data schema."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"output_schema"}),(0,r.jsx)(t.td,{children:"Table"}),(0,r.jsx)(t.td,{children:"The structure and data types for the model's output."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"multiple"}),(0,r.jsx)(t.td,{children:"Boolean"}),(0,r.jsxs)(t.td,{children:["[Deprecated] Always set to ",(0,r.jsx)(t.code,{children:"True"}),"."]})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output"}),(0,r.jsx)(t.td,{children:"Data"}),(0,r.jsx)(t.td,{children:"The structured output is a Data object based on the defined schema."})]})})]})]}),"\n",(0,r.jsx)(t.h2,{id:"legacy-components",children:"Legacy components"}),"\n",(0,r.jsx)(t.p,{children:"Legacy components are available for use but are no longer supported."}),"\n",(0,r.jsx)(t.h3,{id:"create-list",children:"Create List"}),"\n",(0,r.jsx)(t.p,{children:"This component dynamically creates a record with a specified number of fields."}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Parameters"}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Inputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"n_fields"}),(0,r.jsx)(t.td,{children:"Integer"}),(0,r.jsx)(t.td,{children:"The number of fields to be added to the record."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"text_key"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The key used as text."})]})]})]}),(0,r.jsx)(t.p,{children:(0,r.jsx)(t.strong,{children:"Outputs"})}),(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Name"}),(0,r.jsx)(t.th,{children:"Type"}),(0,r.jsx)(t.th,{children:"Description"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"list"}),(0,r.jsx)(t.td,{children:"List"}),(0,r.jsx)(t.td,{children:"The dynamically created list with the specified number of fields."})]})})]})]}),"\n",(0,r.jsx)(t.h3,{id:"output-parser",children:"Output Parser"}),"\n",(0,r.jsxs)(t.p,{children:["This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's ",(0,r.jsx)(t.code,{children:"CommaSeparatedListOutputParser"}),"."]}),"\n",(0,r.jsx)(t.admonition,{type:"note",children:(0,r.jsx)(t.p,{children:"This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use."})}),"\n",(0,r.jsxs)(t.p,{children:["Both the ",(0,r.jsx)(t.strong,{children:"Output Parser"})," and ",(0,r.jsx)(t.strong,{children:"Structured Output"})," components format LLM responses, but they have different use cases.\nThe ",(0,r.jsx)(t.strong,{children:"Output Parser"})," is simpler and focused on converting responses into comma-separated lists. 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b/components-bundle-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-custom-components.html b/components-custom-components.html index be5d63d471..8c2531e56d 100644 --- a/components-custom-components.html +++ b/components-custom-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-data.html b/components-data.html index f3bad2f15b..209f0e7f08 100644 --- a/components-data.html +++ b/components-data.html @@ -21,7 +21,7 @@ - + diff --git a/components-embedding-models.html b/components-embedding-models.html index 0e382de903..cd5476b4cb 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -21,7 +21,7 @@ - + diff --git a/components-helpers.html b/components-helpers.html index b0a2a51418..27de9e47b0 100644 --- a/components-helpers.html +++ b/components-helpers.html @@ -21,7 +21,7 @@ - + @@ -94,7 +94,7 @@ To use all three columns from the Batch Run component, include
Parameters

Inputs

NameTypeDescription
messageStringThe chat message to be stored. (Required)
memoryMemoryThe external memory to store the message. If empty, the Langflow tables are used.
senderStringThe sender of the message. Can be Machine or User. If empty, the current sender parameter is used.
sender_nameStringThe name of the sender. Can be AI or User. If empty, the current sender parameter is used.
session_idStringThe session ID of the chat. If empty, the current session ID parameter is used.

Outputs

NameTypeDescription
stored_messagesList[Data]The list of stored messages after the current message has been added.

Structured output​

This component transforms LLM responses into structured data formats.

-

In this example from the Financial Support Parser template, the Structured Output component transforms unstructured financial reports into structured data.

+

In this example from the Financial Report Parser template, the Structured Output component transforms unstructured financial reports into structured data.

Structured output example

The connected LLM model is prompted by the Structured Output component's Format Instructions parameter to extract structured output from the unstructured text. Format Instructions is utilized as the system prompt for the Structured Output component.

In the Structured Output component, click the Open table button to view the Output Schema table. @@ -105,7 +105,7 @@ The Output Schema parameter defines the structure and data types fo

  • Type: The data type of the output field. The available types are str, int, float, bool, list, or dict. The default is text.
  • Multiple: This feature is deprecated. Currently, it is set to True by default if you expect multiple values for a single field. For example, a list of features is set to True to contain multiple values, such as ["waterproof", "durable", "lightweight"]. Default: True.
    • -

    The Parse DataFrame component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the output_schema table with curly braces.

    +

    The Parser component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the output_schema table with curly braces.

    For example, the template EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT} presents the extracted values in the Playground as EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion.

    Parameters

    Inputs

    NameTypeDescription
    llmLanguageModelThe language model to use to generate the structured output.
    input_valueStringThe input message to the language model.
    system_promptStringThe instructions to the language model for formatting the output.
    schema_nameStringThe name for the output data schema.
    output_schemaTableThe structure and data types for the model's output.
    multipleBoolean[Deprecated] Always set to True.

    Outputs

    NameTypeDescription
    structured_outputDataThe structured output is a Data object based on the defined schema.

    Legacy components​

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