From 7b329177fbcafdf90272a82c170fc896b85841fc Mon Sep 17 00:00:00 2001 From: "github-merge-queue[bot]" Date: Mon, 5 May 2025 16:08:40 +0000 Subject: [PATCH] deploy: 45826ce1c43ad0bd3608dc673803a23fe0eea7bd --- 404.html | 2 +- agents-overview.html | 2 +- agents-tool-calling-agent-component.html | 2 +- api-reference-api-examples.html | 2 +- api.html | 2 +- api/add-user.html | 2 +- api/auto-login.html | 2 +- api/build-flow.html | 2 +- api/build-public-tmp.html | 2 +- api/build-vertex-stream.html | 2 +- api/build-vertex.html | 2 +- api/cancel-build.html | 2 +- api/check-if-store-has-api-key.html | 2 +- api/check-if-store-is-enabled.html | 2 +- api/create-api-key-route.html | 2 +- api/create-flow.html | 2 +- api/create-flows.html | 2 +- api/create-folder.html | 2 +- api/create-upload-file.html | 2 +- api/create-variable.html | 2 +- api/custom-component-update.html | 2 +- api/custom-component.html | 2 +- api/delete-all-files-1.html | 2 +- api/delete-all-files.html | 2 +- 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Agents use LLMs as a reasoning engine to decide which of the connected tool components to use to solve a problem."]}),"\n",(0,r.jsxs)(t.p,{children:["Tools in agentic functions are, essentially, functions that the agent can call to perform tasks or access external resources.\nA function is wrapped as a ",(0,r.jsx)(t.code,{children:"Tool"})," object, with a common interface the agent understands.\nAgents become aware of tools through tool registration, where the agent is provided a list of available tools, typically at agent initialization. The ",(0,r.jsx)(t.code,{children:"Tool"})," object's description tells the agent what the tool can do."]}),"\n",(0,r.jsx)(t.p,{children:"The agent then uses a connected LLM to reason through the problem to decide which tool is best for the job."}),"\n",(0,r.jsx)(t.h2,{id:"use-a-tool-in-a-flow",children:"Use a tool in a flow"}),"\n",(0,r.jsxs)(t.p,{children:["Tools are typically connected to agent components at the ",(0,r.jsx)(t.strong,{children:"Tools"})," port."]}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.a,{href:"/starter-projects-simple-agent",children:"simple agent starter project"})," uses URL and Calculator tools connected to an ",(0,r.jsx)(t.a,{href:"/components-agents#agent-component",children:"agent component"})," to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use."]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Simple agent starter flow",src:s(19680).A+"",width:"1732",height:"1748"})}),"\n",(0,r.jsxs)(t.p,{children:["To make a component into a tool that an agent can use, enable ",(0,r.jsx)(t.strong,{children:"Tool mode"})," in the component. Enabling ",(0,r.jsx)(t.strong,{children:"Tool mode"})," modifies a component input to accept calls from an agent.\nIf the component you want to connect to an agent doesn't have a ",(0,r.jsx)(t.strong,{children:"Tool mode"})," option, you can modify the component's inputs to become a tool.\nFor an example, see ",(0,r.jsx)(t.a,{href:"/agents-tool-calling-agent-component#make-any-component-a-tool",children:"Make any component a tool"}),"."]}),"\n",(0,r.jsx)(t.h2,{id:"arxiv",children:"arXiv"}),"\n",(0,r.jsxs)(t.p,{children:["This component searches and retrieves papers from ",(0,r.jsx)(t.a,{href:"https://arXiv.org",children:"arXiv.org"}),"."]}),"\n",(0,r.jsx)(t.h3,{id:"inputs",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"search_query"}),(0,r.jsx)(t.td,{children:"Search Query"}),(0,r.jsxs)(t.td,{children:["The search query for arXiv papers (for example, ",(0,r.jsx)(t.code,{children:"quantum computing"}),")"]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"search_type"}),(0,r.jsx)(t.td,{children:"Search Field"}),(0,r.jsx)(t.td,{children:"The field to search in"})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"max_results"}),(0,r.jsx)(t.td,{children:"Max Results"}),(0,r.jsx)(t.td,{children:"Maximum number of results to return"})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"papers"}),(0,r.jsx)(t.td,{children:"Papers"}),(0,r.jsx)(t.td,{children:"List of retrieved arXiv papers"})]})})]}),"\n",(0,r.jsx)(t.h2,{id:"astra-db-tool",children:"Astra DB tool"}),"\n",(0,r.jsx)(t.p,{children:"This component allows agents to query data from Astra DB collections."}),"\n",(0,r.jsxs)(t.p,{children:["To use this tool in a flow, connect it to an ",(0,r.jsx)(t.strong,{children:"Agent"})," component.\nThe flow looks like this:"]}),"\n",(0,r.jsx)(t.p,{children:(0,r.jsx)(t.img,{alt:"Astra DB JSON tool connected to an Agent",src:s(68335).A+"",width:"4000",height:"2742"})}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Tool Name"})," and ",(0,r.jsx)(t.strong,{children:"Tool Description"})," fields are required for the Agent to decide when to use the tool.\n",(0,r.jsx)(t.strong,{children:"Tool Name"})," cannot contain spaces."]}),"\n",(0,r.jsxs)(t.p,{children:["The values for ",(0,r.jsx)(t.strong,{children:"Collection Name"}),", ",(0,r.jsx)(t.strong,{children:"Astra DB Application Token"}),", and ",(0,r.jsx)(t.strong,{children:"Astra DB API Endpoint"})," are found in your Astra DB deployment. For more information, see the ",(0,r.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html",children:"DataStax documentation"}),"."]}),"\n",(0,r.jsxs)(t.p,{children:["In this example, an ",(0,r.jsx)(t.strong,{children:"OpenAI"})," embeddings component is connected to use the Astra DB tool component's ",(0,r.jsx)(t.strong,{children:"Semantic Search"})," capability.\nTo use ",(0,r.jsx)(t.strong,{children:"Semantic Search"}),", you must have an embedding model or Astra DB Vectorize enabled.\nIf you try to run the flow without an embedding model, you will get an error."]}),"\n",(0,r.jsxs)(t.p,{children:["Open the ",(0,r.jsx)(t.strong,{children:"Playground"})," and ask a question about your data.\nThe Agent uses the ",(0,r.jsx)(t.strong,{children:"Astra DB Tool"})," to return information about your collection."]}),"\n",(0,r.jsx)(t.h3,{id:"define-astra-db-tool-parameters",children:"Define Astra DB tool parameters"}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.strong,{children:"Tool Parameters"})," configuration pane allows you to define parameters for ",(0,r.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters",children:"filter conditions"})," for the component's ",(0,r.jsx)(t.strong,{children:"Find"})," command."]}),"\n",(0,r.jsxs)(t.p,{children:["These filters become available as parameters that the LLM can use when calling the tool, with a better understanding of each parameter provided by the ",(0,r.jsx)(t.strong,{children:"Description"})," field."]}),"\n",(0,r.jsxs)(t.ol,{children:["\n",(0,r.jsxs)(t.li,{children:["To define a parameter for your query, in the ",(0,r.jsx)(t.strong,{children:"Tool Parameters"})," pane, click ",(0,r.jsx)(l.A,{name:"Plus","aria-label":"Add"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["Complete the fields based on your data. For example, with this filter, the LLM can filter by unique ",(0,r.jsx)(t.code,{children:"customer_id"})," values."]}),"\n"]}),"\n",(0,r.jsxs)(t.ul,{children:["\n",(0,r.jsxs)(t.li,{children:["Name: ",(0,r.jsx)(t.code,{children:"customer_id"})]}),"\n",(0,r.jsx)(t.li,{children:"Attribute Name: Leave empty if the attribute matches the field name in the database."}),"\n",(0,r.jsxs)(t.li,{children:["Description: ",(0,r.jsx)(t.code,{children:'"The unique identifier of the customer to filter by"'}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["Is Metadata: ",(0,r.jsx)(t.code,{children:"False"})," unless the value stored in the metadata field."]}),"\n",(0,r.jsxs)(t.li,{children:["Is Mandatory: ",(0,r.jsx)(t.code,{children:"True"})," to require this filter."]}),"\n",(0,r.jsxs)(t.li,{children:["Is Timestamp: ",(0,r.jsx)(t.code,{children:"False"})," since the value is an ID, not a timestamp."]}),"\n",(0,r.jsxs)(t.li,{children:["Operator: ",(0,r.jsx)(t.code,{children:"$eq"})," to look for an exact match."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["If you want to apply filters regardless of the LLM's input, use the ",(0,r.jsx)(t.strong,{children:"Static Filters"})," option, which is available in the component's ",(0,r.jsx)(t.strong,{children:"Controls"})," pane."]}),"\n",(0,r.jsxs)(t.table,{children:[(0,r.jsx)(t.thead,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.th,{children:"Parameter"}),(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:"Name"}),(0,r.jsx)(t.td,{children:"The name of the parameter that is exposed to the LLM. It can be the same as the underlying field name or a more descriptive label. The LLM uses this name, along with the description, to infer what value to provide during execution."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Attribute Name"}),(0,r.jsxs)(t.td,{children:["When the parameter name shown to the LLM differs from the actual field or property in the database, use this setting to map the user-facing name to the correct attribute. For example, to apply a range filter to the timestamp field, define two separate parameters, such as ",(0,r.jsx)(t.code,{children:"start_date"})," and ",(0,r.jsx)(t.code,{children:"end_date"}),", that both reference the same timestamp attribute."]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Description"}),(0,r.jsxs)(t.td,{children:["Provides instructions to the LLM on how the parameter should be used. Clear and specific guidance helps the LLM provide valid input. For example, if a field such as ",(0,r.jsx)(t.code,{children:"specialty"})," is stored in lowercase, the description should indicate that the input must be lowercase."]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Is Metadata"}),(0,r.jsxs)(t.td,{children:["When loading data using LangChain or Langflow, additional attributes may be stored under a metadata object. If the target attribute is stored this way, enable this option. It adjusts the query by generating a filter in the format: ",(0,r.jsx)(t.code,{children:'{"metadata.": ""}'})]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Is Timestamp"}),(0,r.jsx)(t.td,{children:"For date or time-based filters, enable this option to automatically convert values to the timestamp format that the Astrapy client expects. This ensures compatibility with the underlying API without requiring manual formatting."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Operator"}),(0,r.jsxs)(t.td,{children:["Defines the filtering logic applied to the attribute. You can use any valid ",(0,r.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/api-reference/filter-operator-collections.html",children:"Data API filter operator"}),". For example, to filter a time range on the timestamp attribute, use two parameters: one with the ",(0,r.jsx)(t.code,{children:"$gt"}),' operator for "greater than", and another with the ',(0,r.jsx)(t.code,{children:"$lt"}),' operator for "less than".']})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"inputs-1",children:"Inputs"}),"\n",(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:"Tool Name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name used to reference the tool in the agent's prompt."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Tool Description"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"A brief description of the tool. 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For more information, see ",(0,r.jsx)(t.a,{href:"/integrations-mcp",children:"MCP integrations"}),"."]}),"\n",(0,r.jsx)(t.p,{children:"To use the MCP server component with an agent component, follow these steps:"}),"\n",(0,r.jsxs)(t.ol,{children:["\n",(0,r.jsx)(t.li,{children:"Add the MCP server component to your workflow."}),"\n",(0,r.jsxs)(t.li,{children:["In the MCP server component, in the ",(0,r.jsx)(t.strong,{children:"MCP Command"})," field, enter the command to start your MCP server. For example, to start a ",(0,r.jsx)(t.a,{href:"https://github.com/modelcontextprotocol/servers/tree/main/src/fetch",children:"Fetch"})," server, the command is:"]}),"\n"]}),"\n",(0,r.jsx)(a.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"uvx ",props:{style:{color:"#FFA657"}}},{content:"mcp-server-fetch",props:{style:{color:"#A5D6FF"}}}]}],lang:"bash"},annotations:[]}]}),"\n",(0,r.jsxs)(t.p,{children:[(0,r.jsx)(t.code,{children:"uvx"})," is included with ",(0,r.jsx)(t.code,{children:"uv"})," in the Langflow package.\nTo use ",(0,r.jsx)(t.code,{children:"npx"})," server commands, you must first install an LTS release of ",(0,r.jsx)(t.a,{href:"https://docs.npmjs.com/downloading-and-installing-node-js-and-npm",children:"Node.js"}),".\nFor an example of starting ",(0,r.jsx)(t.code,{children:"npx"})," MCP servers, see ",(0,r.jsx)(t.a,{href:"/mcp-component-astra",children:"Connect an Astra DB MCP server to Langflow"}),"."]}),"\n",(0,r.jsxs)(t.ol,{start:"3",children:["\n",(0,r.jsxs)(t.li,{children:["Click ",(0,r.jsx)(l.A,{name:"RefreshCw","aria-label":"Refresh"})," to get the server's list of ",(0,r.jsx)(t.strong,{children:"Tools"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["In the ",(0,r.jsx)(t.strong,{children:"Tool"})," field, select the server tool you want the component to use.\nThe available fields change based on the selected tool.\nFor information on the parameters, see the MCP server's documentation."]}),"\n",(0,r.jsxs)(t.li,{children:["In the MCP server component, enable ",(0,r.jsx)(t.strong,{children:"Tool mode"}),".\nConnect the MCP server component's ",(0,r.jsx)(t.strong,{children:"Toolset"})," port to an ",(0,r.jsx)(t.strong,{children:"Agent"})," component's ",(0,r.jsx)(t.strong,{children:"Tools"})," port."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["The flow looks similar to this:\n",(0,r.jsx)(t.img,{alt:"MCP server component",src:s(78567).A+"",width:"2546",height:"1474"})]}),"\n",(0,r.jsxs)(t.ol,{start:"6",children:["\n",(0,r.jsxs)(t.li,{children:["Open the ",(0,r.jsx)(t.strong,{children:"Playground"}),".\nAsk the agent to summarize recent tech news. 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Agents use LLMs as a reasoning engine to decide which of the connected tool components to use to solve a problem."]}),"\n",(0,r.jsxs)(t.p,{children:["Tools in agentic functions are, essentially, functions that the agent can call to perform tasks or access external resources.\nA function is wrapped as a ",(0,r.jsx)(t.code,{children:"Tool"})," object, with a common interface the agent understands.\nAgents become aware of tools through tool registration, where the agent is provided a list of available tools, typically at agent initialization. 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Enabling ",(0,r.jsx)(t.strong,{children:"Tool mode"})," modifies a component input to accept calls from an agent.\nIf the component you want to connect to an agent doesn't have a ",(0,r.jsx)(t.strong,{children:"Tool mode"})," option, you can modify the component's inputs to become a tool.\nFor an example, see ",(0,r.jsx)(t.a,{href:"/agents-tool-calling-agent-component#make-any-component-a-tool",children:"Make any component a tool"}),"."]}),"\n",(0,r.jsx)(t.h2,{id:"arxiv",children:"arXiv"}),"\n",(0,r.jsxs)(t.p,{children:["This component searches and retrieves papers from ",(0,r.jsx)(t.a,{href:"https://arXiv.org",children:"arXiv.org"}),"."]}),"\n",(0,r.jsx)(t.h3,{id:"inputs",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"search_query"}),(0,r.jsx)(t.td,{children:"Search Query"}),(0,r.jsxs)(t.td,{children:["The search query for arXiv papers (for example, ",(0,r.jsx)(t.code,{children:"quantum computing"}),")"]})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"search_type"}),(0,r.jsx)(t.td,{children:"Search Field"}),(0,r.jsx)(t.td,{children:"The field to search in"})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"max_results"}),(0,r.jsx)(t.td,{children:"Max Results"}),(0,r.jsx)(t.td,{children:"Maximum number of results to return"})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsx)(t.tbody,{children:(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"papers"}),(0,r.jsx)(t.td,{children:"Papers"}),(0,r.jsx)(t.td,{children:"List of retrieved arXiv papers"})]})})]}),"\n",(0,r.jsx)(t.h2,{id:"astra-db-tool",children:"Astra DB Tool"}),"\n",(0,r.jsxs)(t.p,{children:["The ",(0,r.jsx)(t.code,{children:"Astra DB Tool"})," allows agents to connect to and query data from Astra DB collections."]}),"\n",(0,r.jsx)(t.h3,{id:"inputs-1",children:"Inputs"}),"\n",(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:"Tool Name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name used to reference the tool in the agent's prompt."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Tool Description"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"A brief description of the tool. This helps the model decide when to use it."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Collection Name"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The name of the Astra DB collection to query."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Token"}),(0,r.jsx)(t.td,{children:"SecretString"}),(0,r.jsx)(t.td,{children:"The authentication token for accessing Astra DB."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"API Endpoint"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:"The Astra DB API endpoint."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Projection Fields"}),(0,r.jsx)(t.td,{children:"String"}),(0,r.jsx)(t.td,{children:'The attributes to return, separated by commas. Default: "*".'})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"Tool Parameters"}),(0,r.jsx)(t.td,{children:"Dict"}),(0,r.jsxs)(t.td,{children:["Parameters the model needs to fill to execute the tool. 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in ",(0,r.jsx)(t.strong,{children:"Legacy"}),", which means it is no longer in active development as of Langflow version 1.3."]})}),"\n",(0,r.jsx)(t.p,{children:"This component allows you to call the Google Search API."}),"\n",(0,r.jsx)(t.h3,{id:"inputs-9",children:"Inputs"}),"\n",(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:"google_api_key"}),(0,r.jsx)(t.td,{children:"SecretString"}),(0,r.jsx)(t.td,{children:"Google API key for authentication"})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"google_cse_id"}),(0,r.jsx)(t.td,{children:"SecretString"}),(0,r.jsx)(t.td,{children:"Google Custom Search Engine 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client that can leverage MCP servers, Langflow is also an MCP server that exposes flows as tools through the ",(0,r.jsx)(t.code,{children:"/api/v1/mcp/sse"})," API endpoint. For more information, see ",(0,r.jsx)(t.a,{href:"/integrations-mcp",children:"MCP integrations"}),"."]}),"\n",(0,r.jsx)(t.p,{children:"To use the MCP server component with an agent component, follow these steps:"}),"\n",(0,r.jsxs)(t.ol,{children:["\n",(0,r.jsx)(t.li,{children:"Add the MCP server component to your workflow."}),"\n",(0,r.jsxs)(t.li,{children:["In the MCP server component, in the ",(0,r.jsx)(t.strong,{children:"MCP Command"})," field, enter the command to start your MCP server. For example, to start a ",(0,r.jsx)(t.a,{href:"https://github.com/modelcontextprotocol/servers/tree/main/src/fetch",children:"Fetch"})," server, the command is:"]}),"\n"]}),"\n",(0,r.jsx)(a.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"uvx ",props:{style:{color:"#FFA657"}}},{content:"mcp-server-fetch",props:{style:{color:"#A5D6FF"}}}]}],lang:"bash"},annotations:[]}]}),"\n",(0,r.jsxs)(t.p,{children:[(0,r.jsx)(t.code,{children:"uvx"})," is included with ",(0,r.jsx)(t.code,{children:"uv"})," in the Langflow package.\nTo use ",(0,r.jsx)(t.code,{children:"npx"})," server commands, you must first install an LTS release of ",(0,r.jsx)(t.a,{href:"https://docs.npmjs.com/downloading-and-installing-node-js-and-npm",children:"Node.js"}),".\nFor an example of starting ",(0,r.jsx)(t.code,{children:"npx"})," MCP servers, see ",(0,r.jsx)(t.a,{href:"/mcp-component-astra",children:"Connect an Astra DB MCP server to Langflow"}),"."]}),"\n",(0,r.jsxs)(t.ol,{start:"3",children:["\n",(0,r.jsxs)(t.li,{children:["Click ",(0,r.jsx)(l.A,{name:"RefreshCw","aria-label":"Refresh"})," to get the server's list of ",(0,r.jsx)(t.strong,{children:"Tools"}),"."]}),"\n",(0,r.jsxs)(t.li,{children:["In the ",(0,r.jsx)(t.strong,{children:"Tool"})," field, select the server tool you want the component to use.\nThe available fields change based on the selected tool.\nFor information on the parameters, see the MCP server's documentation."]}),"\n",(0,r.jsxs)(t.li,{children:["In the MCP server component, enable ",(0,r.jsx)(t.strong,{children:"Tool mode"}),".\nConnect the MCP server component's ",(0,r.jsx)(t.strong,{children:"Toolset"})," port to an ",(0,r.jsx)(t.strong,{children:"Agent"})," component's ",(0,r.jsx)(t.strong,{children:"Tools"})," port."]}),"\n"]}),"\n",(0,r.jsxs)(t.p,{children:["The flow looks similar to this:\n",(0,r.jsx)(t.img,{alt:"MCP server component",src:s(78567).A+"",width:"2546",height:"1474"})]}),"\n",(0,r.jsxs)(t.ol,{start:"6",children:["\n",(0,r.jsxs)(t.li,{children:["Open the ",(0,r.jsx)(t.strong,{children:"Playground"}),".\nAsk the agent to summarize recent tech news. 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parameters."}),"\n",(0,r.jsx)(t.h3,{id:"outputs-22",children:"Outputs"}),"\n",(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:"tool"}),(0,r.jsx)(t.td,{children:"Tool"}),(0,r.jsx)(t.td,{children:"Yahoo Finance News tool for use in LangChain"})]})})]})]})}function p(e={}){const{wrapper:t}={...(0,i.R)(),...e.components};return t?(0,r.jsx)(t,{...e,children:(0,r.jsx)(u,{...e})}):u(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.")}},19680:(e,t,s)=>{s.d(t,{A:()=>n});const n=s.p+"assets/images/starter-flow-simple-agent-71d0dec218a5da8e3110c51716f1c91e.png"},78567:(e,t,s)=>{s.d(t,{A:()=>n});const 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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:"System Message"}),(0,r.jsx)(t.td,{children:"MultilineInput"}),(0,r.jsx)(t.td,{children:"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:"DataFrame"}),(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:"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. Default='text'. 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This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline."}),"\n",(0,r.jsx)(t.h3,{id:"inputs-1",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Timezone"}),(0,r.jsx)(t.td,{children:"Select the timezone for the current date and time."})]})})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-1",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Current Date"}),(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.jsx)(t.h3,{id:"inputs-2",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Value"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-2",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"ID"}),(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.jsx)(t.h3,{id:"inputs-3",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"External Memory"}),(0,r.jsx)(t.td,{children:"Retrieve messages from an external memory. If empty, it will use the Langflow tables."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"Sender Type"}),(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:"Sender Name"}),(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:"Number of Messages"}),(0,r.jsx)(t.td,{children:"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:"Session ID"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter will be used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"order"}),(0,r.jsx)(t.td,{children:"Order"}),(0,r.jsx)(t.td,{children:"Order of the messages."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"template"}),(0,r.jsx)(t.td,{children:"Template"}),(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."]})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-3",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Messages (Data)"}),(0,r.jsx)(t.td,{children:"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:"Messages (Text)"}),(0,r.jsx)(t.td,{children:"Retrieved messages formatted as text."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"lc_memory"}),(0,r.jsx)(t.td,{children:"Memory"}),(0,r.jsxs)(t.td,{children:["A constructed Langchain ",(0,r.jsx)(t.a,{href:"https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html",children:"ConversationBufferMemory"})," object"]})]})]})]}),"\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.jsx)(t.h3,{id:"inputs-4",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Message"}),(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:"External Memory"}),(0,r.jsx)(t.td,{children:"The external memory to store the message. If empty, it will use the Langflow tables."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"Sender"}),(0,r.jsx)(t.td,{children:"The sender of the message. Can be Machine or User. If empty, the current sender parameter will be used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender_name"}),(0,r.jsx)(t.td,{children:"Sender Name"}),(0,r.jsx)(t.td,{children:"The name of the sender. Can be AI or User. If empty, the current sender parameter will be used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"session_id"}),(0,r.jsx)(t.td,{children:"Session ID"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter will be used."})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-4",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Stored Messages"}),(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.jsx)(t.h3,{id:"inputs-5",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Language Model"}),(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:"Input Message"}),(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:"Format Instructions"}),(0,r.jsx)(t.td,{children:"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:"Schema Name"}),(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:"Output Schema"}),(0,r.jsx)(t.td,{children:"Defines 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:"Generate Multiple"}),(0,r.jsxs)(t.td,{children:["[Deprecated] Always set to ",(0,r.jsx)(t.code,{children:"True"}),"."]})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-5",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output"}),(0,r.jsx)(t.td,{children:"Structured Output"}),(0,r.jsx)(t.td,{children:"The structured output is a Data object based on the defined schema."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output_dataframe"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsxs)(t.td,{children:["The structured output converted to a ",(0,r.jsx)(t.a,{href:"/concepts-objects#dataframe-object",children:"DataFrame"})," format."]})]})]})]}),"\n",(0,r.jsx)(t.h2,{id:"legacy-components",children:"Legacy components"}),"\n",(0,r.jsx)(t.p,{children:"Legacy components are no longer in active development but are backward compatible."}),"\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.jsx)(t.h4,{id:"inputs-6",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Number of Fields"}),(0,r.jsx)(t.td,{children:"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:"Text Key"}),(0,r.jsx)(t.td,{children:"Key used as text."})]})]})]}),"\n",(0,r.jsx)(t.h4,{id:"outputs-6",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:u,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.jsx)(t.h4,{id:"inputs-7",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Parser"}),(0,r.jsx)(t.td,{children:'Select the parser type. Currently supports "CSV".'})]})})]}),"\n",(0,r.jsx)(t.h4,{id:"outputs-7",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Format Instructions"}),(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:"Output Parser"}),(0,r.jsx)(t.td,{children:"The constructed output parser that can be used to parse LLM responses."})]})]})]})]})}function p(e={}){const{wrapper:t}={...(0,d.R)(),...e.components};return t?(0,r.jsx)(t,{...e,children:(0,r.jsx)(j,{...e})}):j(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:()=>h,chCodeConfig:()=>u,contentTitle:()=>c,default:()=>p,frontMatter:()=>l,metadata:()=>s,toc:()=>x});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 l={title:"Helpers",slug:"/components-helpers"},c="Helper components in Langflow",h={},a={annotations:i.hk,Code:i.Cy},u={staticMediaQuery:"not screen, (max-width: 768px)",lineNumbers:!0,showCopyButton:!0,themeName:"github-dark"},x=[{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:"Inputs",id:"inputs",level:3},{value:"Outputs",id:"outputs",level:3},{value:"Current date",id:"current-date",level:2},{value:"Inputs",id:"inputs-1",level:3},{value:"Outputs",id:"outputs-1",level:3},{value:"ID Generator",id:"id-generator",level:2},{value:"Inputs",id:"inputs-2",level:3},{value:"Outputs",id:"outputs-2",level:3},{value:"Message history",id:"message-history",level:2},{value:"Inputs",id:"inputs-3",level:3},{value:"Outputs",id:"outputs-3",level:3},{value:"Message store",id:"message-store",level:2},{value:"Inputs",id:"inputs-4",level:3},{value:"Outputs",id:"outputs-4",level:3},{value:"Structured output",id:"structured-output",level:2},{value:"Inputs",id:"inputs-5",level:3},{value:"Outputs",id:"outputs-5",level:3},{value:"Legacy components",id:"legacy-components",level:2},{value:"Create List",id:"create-list",level:3},{value:"Inputs",id:"inputs-6",level:4},{value:"Outputs",id:"outputs-6",level:4},{value:"Output Parser",id:"output-parser",level:3},{value:"Inputs",id:"inputs-7",level:4},{value:"Outputs",id:"outputs-7",level:4}];function j(e){const t={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",h3:"h3",h4:"h4",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};return a||m("CH",!1),a.Code||m("CH.Code",!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. 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. 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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:"System Message"}),(0,r.jsx)(t.td,{children:"MultilineInput"}),(0,r.jsx)(t.td,{children:"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:"DataFrame"}),(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:"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. Default='text'. Required."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"enable_metadata"}),(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."})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Method"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Batch Results"}),(0,r.jsx)(t.td,{children:"run_batch"}),(0,r.jsx)(t.td,{children:"A DataFrame with columns: 'text_input', 'model_response', 'batch_index', and optional 'metadata' containing processing information."})]})})]}),"\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.jsx)(t.h3,{id:"inputs-1",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Timezone"}),(0,r.jsx)(t.td,{children:"Select the timezone for the current date and time."})]})})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-1",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Current Date"}),(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.jsx)(t.h3,{id:"inputs-2",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Value"}),(0,r.jsx)(t.td,{children:"The generated unique ID."})]})})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-2",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"ID"}),(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.jsx)(t.h3,{id:"inputs-3",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"External Memory"}),(0,r.jsx)(t.td,{children:"Retrieve messages from an external memory. If empty, it will use the Langflow tables."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"sender"}),(0,r.jsx)(t.td,{children:"Sender Type"}),(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:"Sender Name"}),(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:"Number of Messages"}),(0,r.jsx)(t.td,{children:"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:"Session ID"}),(0,r.jsx)(t.td,{children:"The session ID of the chat. If empty, the current session ID parameter will be used."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"order"}),(0,r.jsx)(t.td,{children:"Order"}),(0,r.jsx)(t.td,{children:"Order of the messages."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"template"}),(0,r.jsx)(t.td,{children:"Template"}),(0,r.jsxs)(t.td,{children:["The template to use for formatting the data. 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If empty, the current session ID parameter will be used."})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-4",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Stored Messages"}),(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.jsx)(t.h3,{id:"inputs-5",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Language Model"}),(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:"Input Message"}),(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:"Format Instructions"}),(0,r.jsx)(t.td,{children:"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:"Schema Name"}),(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:"Output Schema"}),(0,r.jsx)(t.td,{children:"Defines 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:"Generate Multiple"}),(0,r.jsxs)(t.td,{children:["[Deprecated] Always set to ",(0,r.jsx)(t.code,{children:"True"}),"."]})]})]})]}),"\n",(0,r.jsx)(t.h3,{id:"outputs-5",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(0,r.jsxs)(t.tbody,{children:[(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output"}),(0,r.jsx)(t.td,{children:"Structured Output"}),(0,r.jsx)(t.td,{children:"The structured output is a Data object based on the defined schema."})]}),(0,r.jsxs)(t.tr,{children:[(0,r.jsx)(t.td,{children:"structured_output_dataframe"}),(0,r.jsx)(t.td,{children:"DataFrame"}),(0,r.jsxs)(t.td,{children:["The structured output converted to a ",(0,r.jsx)(t.a,{href:"/concepts-objects#dataframe-object",children:"DataFrame"})," format."]})]})]})]}),"\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.jsx)(t.h4,{id:"inputs-6",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Number of Fields"}),(0,r.jsx)(t.td,{children:"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:"Text Key"}),(0,r.jsx)(t.td,{children:"Key used as text."})]})]})]}),"\n",(0,r.jsx)(t.h4,{id:"outputs-6",children:"Outputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:u,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.jsx)(t.h4,{id:"inputs-7",children:"Inputs"}),"\n",(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:"Display Name"}),(0,r.jsx)(t.th,{children:"Info"})]})}),(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:"Parser"}),(0,r.jsx)(t.td,{children:'Select the parser type. 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Each input can target specific components and provide custom values.\\n- `outputs` (List[str], optional): A list of output names to retrieve from the executed flow.\\n If not provided, all outputs are returned.\\n- `tweaks` (Optional[Tweaks], optional): A dictionary of tweaks to customize the flow execution.\\n The tweaks can be used to modify the flow\'s parameters and components.\\n Tweaks can be overridden by the input values.\\n- `stream` (bool, optional): Specifies whether the results should be streamed. Defaults to False.\\n- `session_id` (Union[None, str], optional): An optional session ID to utilize existing session data for the flow\\n execution.\\n- `api_key_user` (User): The user associated with the current API key. 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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 0c8fd84787..037597c470 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 2debeb0e50..e814c059c5 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 534d6410aa..e08b77c2cf 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 8e5d1ddcf4..e2e352f903 100644 --- a/components-helpers.html +++ b/components-helpers.html @@ -21,7 +21,7 @@ - + @@ -127,7 +127,7 @@ The Output Schema parameter defines the structure and data types fo

    Outputs​

    NameDisplay NameInfo
    structured_outputStructured OutputThe structured output is a Data object based on the defined schema.
    structured_output_dataframeDataFrameThe structured output converted to a DataFrame format.

    Legacy components​

    -

    Legacy components are no longer in active development but are backward compatible.

    +

    Legacy components are available for use but are no longer supported.

    Create List​

    This component dynamically creates a record with a specified number of fields.

    Inputs​

    diff --git a/components-io.html b/components-io.html index 0fe7529713..698b32a5d7 100644 --- a/components-io.html +++ b/components-io.html @@ -21,7 +21,7 @@ - + diff --git a/components-loaders.html b/components-loaders.html index c9bb131e04..e2e72e0961 100644 --- a/components-loaders.html +++ b/components-loaders.html @@ -21,7 +21,7 @@ - + diff --git a/components-logic.html b/components-logic.html index 4c21ba9709..b642945bdf 100644 --- a/components-logic.html +++ b/components-logic.html @@ -21,7 +21,7 @@ - + diff --git a/components-memories.html b/components-memories.html index a25c654232..55269f407c 100644 --- a/components-memories.html +++ b/components-memories.html @@ -21,7 +21,7 @@ - + diff --git a/components-models.html b/components-models.html index 5602c67dd2..a6cbf88844 100644 --- a/components-models.html +++ b/components-models.html @@ -21,7 +21,7 @@ - + diff --git a/components-processing.html b/components-processing.html index 9f8a963531..480188dfa7 100644 --- a/components-processing.html +++ b/components-processing.html @@ -21,7 +21,7 @@ - + diff --git a/components-prompts.html b/components-prompts.html index 8967054d82..26d08e4406 100644 --- a/components-prompts.html +++ b/components-prompts.html @@ -21,7 +21,7 @@ - + diff --git a/components-tools.html b/components-tools.html index 58e52ef4de..58855701e8 100644 --- a/components-tools.html +++ b/components-tools.html @@ -21,7 +21,7 @@ - + @@ -54,12 +54,41 @@ For an example, see Outputs​
    NameDisplay NameInfo
    papersPapersList of retrieved arXiv papers
    -

    Astra DB Tool​

    -

    The Astra DB Tool allows agents to connect to and query data from Astra DB collections.

    +

    Astra DB tool​

    +

    This component allows agents to query data from Astra DB collections.

    +

    To use this tool in a flow, connect it to an Agent component. +The flow looks like this:

    +

    Astra DB JSON tool connected to an Agent

    +

    The Tool Name and Tool Description fields are required for the Agent to decide when to use the tool. +Tool Name cannot contain spaces.

    +

    The values for Collection Name, Astra DB Application Token, and Astra DB API Endpoint are found in your Astra DB deployment. For more information, see the DataStax documentation.

    +

    In this example, an OpenAI embeddings component is connected to use the Astra DB tool component's Semantic Search capability. +To use Semantic Search, you must have an embedding model or Astra DB Vectorize enabled. +If you try to run the flow without an embedding model, you will get an error.

    +

    Open the Playground and ask a question about your data. +The Agent uses the Astra DB Tool to return information about your collection.

    +

    Define Astra DB tool parameters​

    +

    The Tool Parameters configuration pane allows you to define parameters for filter conditions for the component's Find command.

    +

    These filters become available as parameters that the LLM can use when calling the tool, with a better understanding of each parameter provided by the Description field.

    +
      +
    1. To define a parameter for your query, in the Tool Parameters pane, click .
      2. +
    2. Complete the fields based on your data. For example, with this filter, the LLM can filter by unique customer_id values.
      3. +
    +
      +
    • Name: customer_id
      • +
    • Attribute Name: Leave empty if the attribute matches the field name in the database.
      • +
    • Description: "The unique identifier of the customer to filter by".
      • +
    • Is Metadata: False unless the value stored in the metadata field.
      • +
    • Is Mandatory: True to require this filter.
      • +
    • Is Timestamp: False since the value is an ID, not a timestamp.
      • +
    • Operator: $eq to look for an exact match.
      • +
    +

    If you want to apply filters regardless of the LLM's input, use the Static Filters option, which is available in the component's Controls pane.

    +
    ParameterDescription
    NameThe name of the parameter that is exposed to the LLM. It can be the same as the underlying field name or a more descriptive label. The LLM uses this name, along with the description, to infer what value to provide during execution.
    Attribute NameWhen the parameter name shown to the LLM differs from the actual field or property in the database, use this setting to map the user-facing name to the correct attribute. For example, to apply a range filter to the timestamp field, define two separate parameters, such as start_date and end_date, that both reference the same timestamp attribute.
    DescriptionProvides instructions to the LLM on how the parameter should be used. Clear and specific guidance helps the LLM provide valid input. For example, if a field such as specialty is stored in lowercase, the description should indicate that the input must be lowercase.
    Is MetadataWhen loading data using LangChain or Langflow, additional attributes may be stored under a metadata object. If the target attribute is stored this way, enable this option. It adjusts the query by generating a filter in the format: {"metadata.<attribute_name>": "<value>"}
    Is TimestampFor date or time-based filters, enable this option to automatically convert values to the timestamp format that the Astrapy client expects. This ensures compatibility with the underlying API without requiring manual formatting.
    OperatorDefines the filtering logic applied to the attribute. You can use any valid Data API filter operator. For example, to filter a time range on the timestamp attribute, use two parameters: one with the $gt operator for "greater than", and another with the $lt operator for "less than".

    Inputs​

    -
    NameTypeDescription
    Tool NameStringThe name used to reference the tool in the agent's prompt.
    Tool DescriptionStringA brief description of the tool. This helps the model decide when to use it.
    Collection NameStringThe name of the Astra DB collection to query.
    TokenSecretStringThe authentication token for accessing Astra DB.
    API EndpointStringThe Astra DB API endpoint.
    Projection FieldsStringThe attributes to return, separated by commas. Default: "*".
    Tool ParametersDictParameters the model needs to fill to execute the tool. For required parameters, use an exclamation mark (for example, !customer_id).
    Static FiltersDictAttribute-value pairs used to filter query results.
    LimitStringThe number of documents to return.
    +
    NameTypeDescription
    Tool NameStringThe name used to reference the tool in the agent's prompt.
    Tool DescriptionStringA brief description of the tool. This helps the model decide when to use it.
    Collection NameStringThe name of the Astra DB collection to query.
    TokenSecretStringThe authentication token for accessing Astra DB.
    API EndpointStringThe Astra DB API endpoint.
    Projection FieldsStringThe attributes to return, separated by commas. The default is *.
    Tool ParametersDictParameters the model needs to fill to execute the tool. For required parameters, use an exclamation mark, for example !customer_id.
    Static FiltersDictAttribute-value pairs used to filter query results.
    LimitStringThe number of documents to return.

    Outputs​

    -

    The Data output is primarily used when directly querying Astra DB, while the Tool output is used when integrating with LangChain agents or chains.

    +

    The Data output is used when directly querying Astra DB, while the Tool output is used when integrating with agents.

    NameTypeDescription
    DataList[Data]A list of Data objects containing the query results from Astra DB. Each Data object contains the document fields specified by the projection attributes. Limited by the number_of_results parameter.
    ToolStructuredToolA LangChain StructuredTool object that can be used in agent workflows. Contains the tool name, description, argument schema based on tool parameters, and the query function.

    Astra DB CQL Tool​

    The Astra DB CQL Tool allows agents to query data from CQL tables in Astra DB.

    @@ -82,7 +111,7 @@ For an example, see Combinatorial Reasoner​ -

    This component runs Icosa's Combinatorial Reasoning (CR) pipeline on an input to create an optimized prompt with embedded reasons. Sign up for access here: https://forms.gle/oWNv2NKjBNaqqvCx6

    +

    This component runs Icosa's Combinatorial Reasoning (CR) pipeline on an input to create an optimized prompt with embedded reasons. For more information, see Icosa Computing.

    Inputs​

    NameDisplay NameDescription
    promptPromptInput to run CR on
    openai_api_keyOpenAI API KeyOpenAI API key for authentication
    usernameUsernameUsername for Icosa API authentication
    passwordPasswordPassword for Icosa API authentication
    model_nameModel NameOpenAI LLM to use for reason generation

    Outputs​

    @@ -242,7 +271,7 @@ Instead, use the MCP server component

    Inputs​

    This component does not have any input parameters.

    Outputs​

    -
    NameTypeDescription
    toolToolYahoo Finance News tool for use in LangChain

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