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+ + \ No newline at end of file diff --git a/agents/index.html b/agents/index.html new file mode 100644 index 00000000..f87c2c61 --- /dev/null +++ b/agents/index.html @@ -0,0 +1,40 @@ + + + + + +Agents powered by Langflow | OpenRAG + + + + + + + +
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Agents powered by Langflow

OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow.

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This flow intelligently chats with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM.

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The Agent component shines here in its ability to make decisions on not only what query should be sent, but when a query is necessary to solve the problem at hand.

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How do agents work?

Agents extend Large Language Models (LLMs) by integrating tools, which are functions that provide additional context and enable autonomous task execution. These integrations make agents more specialized and powerful than standalone LLMs.

Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action. For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.

Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response. The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API.

In an agentic context, tools are functions that the agent can run to perform tasks or access external resources. A function is wrapped as a Tool object with a common interface that the agent understands. Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization. The Tool object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request.

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Use the OpenRAG OpenSearch Agent flow​

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If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG OpenSearch Agent chat flow. +To view the flow, click Settings, and then click Edit in Langflow. +This flow contains seven components:

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  • The Agent component orchestrates the entire flow by deciding when to search the knowledge base, how to formulate search queries, and how to combine retrieved information with the user's question to generate a comprehensive response. +The Agent behaves according to the prompt in the Agent Instructions field.
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  • The Chat Input component is connected to the Agent component's Input port. This allows to flow to be triggered by an incoming prompt from a user or application.
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  • The OpenSearch component is connected to the Agent component's Tools port. The agent may not use this database for every request; the agent only uses this connection if it decides the knowledge can help respond to the prompt.
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  • The Language Model component is connected to the Agent component's Language Model port. The agent uses the connected LLM to reason through the request sent through Chat Input.
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  • The Embedding Model component is connected to the OpenSearch component's Embedding port. This component converts text queries into vector representations that are compared with document embeddings stored in OpenSearch for semantic similarity matching. This gives your Agent's queries context.
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  • The Text Input component is populated with the global variable OPENRAG-QUERY-FILTER. +This filter is the Knowledge filter, and filters which knowledge sources to search through.
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  • The Agent component's Output port is connected to the Chat Output component, which returns the final response to the user or application.
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All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic. +To modify a flow, click Settings, and click Edit in Langflow. +Flows are edited in the same way as in the Langflow visual editor.

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For an example of changing out the agent's LLM in OpenRAG, see the Quickstart.

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To restore the flow to its initial state, in OpenRAG, click Settings, and then click Restore Flow. +OpenRAG warns you that this discards all custom settings. Click Restore to restore the flow.

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strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[571],{3227:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>g,contentTitle:()=>h,default:()=>x,frontMatter:()=>a,metadata:()=>i,toc:()=>u});const i=JSON.parse('{"id":"core-components/knowledge","title":"OpenSearch Knowledge","description":"OpenRAG uses OpenSearch for its vector-backed knowledge store.","source":"@site/docs/core-components/knowledge.mdx","sourceDirName":"core-components","slug":"/knowledge","permalink":"/openrag/knowledge","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/core-components/knowledge.mdx","tags":[],"version":"current","frontMatter":{"title":"OpenSearch Knowledge","slug":"/knowledge"},"sidebar":"tutorialSidebar","previous":{"title":"Langflow Agents","permalink":"/openrag/agents"},"next":{"title":"Docling Ingestion","permalink":"/openrag/ingestion"}}');var o=s(4848),t=s(8453),r=s(9179),l=s(1470),c=s(9365),d=s(3782);const a={title:"OpenSearch Knowledge",slug:"/knowledge"},h=void 0,g={},u=[{value:"Explore knowledge",id:"explore-knowledge",level:2},...d.RM,{value:"Ingest knowledge",id:"ingest-knowledge",level:2},{value:"Direct file ingestion",id:"direct-file-ingestion",level:3},{value:"Ingest files through OAuth connectors",id:"oauth-ingestion",level:3},{value:"Sync cloud connectors",id:"sync-cloud-connectors",level:3},{value:"Knowledge ingestion settings",id:"knowledge-ingestion-settings",level:3},{value:"Create knowledge filters",id:"create-knowledge-filters",level:2},{value:"OpenRAG default configuration",id:"openrag-default-configuration",level:2}];function p(e){const n={a:"a",code:"code",h2:"h2",h3:"h3",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,t.R)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsxs)(n.p,{children:["OpenRAG uses ",(0,o.jsx)(n.a,{href:"https://docs.opensearch.org/latest/",children:"OpenSearch"})," for its vector-backed knowledge store.\nOpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support."]}),"\n",(0,o.jsx)(n.h2,{id:"explore-knowledge",children:"Explore knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["The Knowledge page lists the documents OpenRAG has ingested into the OpenSearch vector database's ",(0,o.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,o.jsxs)(n.p,{children:["To explore your current knowledge, click ",(0,o.jsx)(r.A,{name:"Library","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Knowledge"}),".\nClick on a document to display the chunks derived from splitting the default documents into the vector database."]}),"\n",(0,o.jsxs)(n.p,{children:["Documents are processed with the default ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow, so if you want to split your documents differently, edit the ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow."]}),"\n",(0,o.jsx)(d.Ay,{}),"\n",(0,o.jsx)(n.h2,{id:"ingest-knowledge",children:"Ingest knowledge"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors."}),"\n",(0,o.jsx)(n.h3,{id:"direct-file-ingestion",children:"Direct file ingestion"}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow uses Langflow's ",(0,o.jsxs)(n.a,{href:"https://docs.langflow.org/components-data#file",children:[(0,o.jsx)(n.strong,{children:"File"})," component"]})," to split and embed files loaded from your local machine into the OpenSearch database."]}),"\n",(0,o.jsxs)(n.p,{children:["The default path to your local folder is mounted from the ",(0,o.jsx)(n.code,{children:"./documents"})," folder in your OpenRAG project directory to the ",(0,o.jsx)(n.code,{children:"/app/documents/"})," directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the ",(0,o.jsx)(n.strong,{children:"Documents Paths"})," variable in either the TUI's ",(0,o.jsx)(n.a,{href:"/install#advanced-setup",children:"Advanced Setup"})," or in the ",(0,o.jsx)(n.code,{children:".env"})," used by Docker Compose."]}),"\n",(0,o.jsxs)(n.p,{children:["To load and process a single file from the mapped location, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then click ",(0,o.jsx)(n.strong,{children:"Add File"}),".\nThe file is loaded into your OpenSearch database, and appears in the Knowledge page."]}),"\n",(0,o.jsxs)(n.p,{children:["To load and process a directory from the mapped location, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then click ",(0,o.jsx)(n.strong,{children:"Process Folder"}),".\nThe files are loaded into your OpenSearch database, and appear in the Knowledge page."]}),"\n",(0,o.jsx)(n.h3,{id:"oauth-ingestion",children:"Ingest files through OAuth connectors"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG supports Google Drive, OneDrive, and AWS S3 as OAuth connectors for seamless document synchronization."}),"\n",(0,o.jsx)(n.p,{children:"OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage."}),"\n",(0,o.jsx)(n.p,{children:"Before users can connect their cloud storage accounts, you must configure OAuth credentials in OpenRAG. This requires registering OpenRAG as an OAuth application with a cloud provider and obtaining client ID and secret keys for each service you want to support."}),"\n",(0,o.jsx)(n.p,{children:"To add an OAuth connector to OpenRAG, do the following.\nThis example uses Google OAuth.\nIf you wish to use another provider, add the secrets to another provider."}),"\n",(0,o.jsxs)(l.A,{groupId:"Installation type",children:[(0,o.jsx)(c.A,{value:"TUI",label:"TUI",default:!0,children:(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["If OpenRAG is running, stop it with ",(0,o.jsx)(n.strong,{children:"Status"})," > ",(0,o.jsx)(n.strong,{children:"Stop Services"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Advanced Setup"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["Add the OAuth provider's client and secret key in the ",(0,o.jsx)(n.a,{href:"/install#advanced-setup",children:"Advanced Setup"})," menu."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Save Configuration"}),".\nThe TUI generates a new ",(0,o.jsx)(n.code,{children:".env"})," file with your OAuth values."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Start Container Services"}),"."]}),"\n"]})}),(0,o.jsxs)(c.A,{value:".env",label:".env",children:[(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsx)(n.li,{children:"Stop the Docker deployment."}),"\n",(0,o.jsxs)(n.li,{children:["Add the OAuth provider's client and secret key in the ",(0,o.jsx)(n.code,{children:".env"})," file for Docker Compose."]}),"\n"]}),(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:"GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'\nGOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'\n"})}),(0,o.jsxs)(n.ol,{start:"3",children:["\n",(0,o.jsxs)(n.li,{children:["Save your ",(0,o.jsx)(n.code,{children:".env"}),". file."]}),"\n",(0,o.jsx)(n.li,{children:"Start the Docker deployment."}),"\n"]})]})]}),"\n",(0,o.jsxs)(n.p,{children:["The OpenRAG frontend at ",(0,o.jsx)(n.code,{children:"http://localhost:3000"})," now redirects to an OAuth callback login page for your OAuth provider.\nA successful authentication opens OpenRAG with the required scopes for your connected storage."]}),"\n",(0,o.jsx)(n.p,{children:"To add knowledge from an OAuth-connected storage provider, do the following:"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then select the storage provider, for example, ",(0,o.jsx)(n.strong,{children:"Google Drive"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Add Cloud Knowledge"})," page opens."]}),"\n",(0,o.jsxs)(n.li,{children:["To add files or folders from the connected storage, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Files"}),".\nSelect the files or folders you want and click ",(0,o.jsx)(n.strong,{children:"Select"}),".\nYou can select multiples."]}),"\n",(0,o.jsxs)(n.li,{children:["When your files are selected, click ",(0,o.jsx)(n.strong,{children:"Ingest Files"}),".\nThe ingestion process may take some time, depending on the size of your documents."]}),"\n",(0,o.jsx)(n.li,{children:"When ingestion is complete, your documents are available in the Knowledge screen."}),"\n"]}),"\n",(0,o.jsx)(n.h3,{id:"sync-cloud-connectors",children:"Sync cloud connectors"}),"\n",(0,o.jsxs)(n.p,{children:["Your connected data sources are found in the ",(0,o.jsx)(r.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"})," page."]}),"\n",(0,o.jsxs)(n.p,{children:["When you click ",(0,o.jsx)(n.strong,{children:"Sync Now"})," for a connected cloud service like Google Drive, OpenRAG scans your connected Google Drive account to find files that match your sync criteria. Sync criteria are controlled in ",(0,o.jsx)(n.strong,{children:"Sync Settings"})," on the same page. You can sync all files, or select a maximum number of files to sync."]}),"\n",(0,o.jsx)(n.p,{children:"For each file found, OpenRAG downloads, converts, and embeds the processed content into OpenSearch."}),"\n",(0,o.jsxs)(n.p,{children:["You can monitor the sync progress in the ",(0,o.jsx)(r.A,{name:"Bell","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Tasks"})," sidebar."]}),"\n",(0,o.jsx)(n.p,{children:"Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page."}),"\n",(0,o.jsx)(n.h3,{id:"knowledge-ingestion-settings",children:"Knowledge ingestion settings"}),"\n",(0,o.jsxs)(n.p,{children:["To configure the knowledge ingestion pipeline parameters, see ",(0,o.jsx)(n.a,{href:"/ingestion",children:"Docling Ingestion"}),"."]}),"\n",(0,o.jsx)(n.h2,{id:"create-knowledge-filters",children:"Create knowledge filters"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG includes a knowledge filter system for organizing and managing document collections.\nKnowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of OpenRAG."}),"\n",(0,o.jsx)(n.p,{children:"Knowledge filters help agents work more efficiently with large document collections by focusing their context within relevant documents sets."}),"\n",(0,o.jsx)(n.p,{children:"To create a knowledge filter, do the following:"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create New Filter"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Create New Knowledge Filter"})," pane appears."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Enter a ",(0,o.jsx)(n.strong,{children:"Name"})," and ",(0,o.jsx)(n.strong,{children:"Description"}),", and then click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create Filter"}),".\nA new filter is created with default settings that match everything."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To modify the default filter, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click your new filter to edit it in the ",(0,o.jsx)(n.strong,{children:"Knowledge Filter"})," pane."]}),"\n",(0,o.jsx)(n.p,{children:"The following filter options are configurable."}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Search Query"}),': Enter text for semantic search, such as "financial reports from Q4".']}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Data Sources"}),": Select specific data sources or folders to include."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Document Types"}),": Filter by file type."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Owners"}),": Filter by who uploaded the documents."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Sources"}),": Filter by connector types, such as local upload or Google Drive."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Result Limit"}),": Set maximum number of results. The default is ",(0,o.jsx)(n.code,{children:"10"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Score Threshold"}),": Set minimum relevance score. The default score is ",(0,o.jsx)(n.code,{children:"0"}),"."]}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["When you're done editing the filter, click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Save Configuration"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To apply the filter to OpenRAG globally, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then select the filter to apply."]}),"\n",(0,o.jsxs)(n.p,{children:["To apply the filter to a single chat session, in the ",(0,o.jsx)(r.A,{name:"MessageSquare","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Chat"})," window, click ",(0,o.jsx)(n.strong,{children:"@"}),", and then select the filter to apply."]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"openrag-default-configuration",children:"OpenRAG default configuration"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility."}),"\n",(0,o.jsxs)(n.p,{children:["The complete list of supported models is available at ",(0,o.jsx)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py",children:"/src/services/models_service.py"}),"."]}),"\n",(0,o.jsx)(n.p,{children:"You can use custom embedding models by specifying them in your configuration."}),"\n",(0,o.jsxs)(n.p,{children:["If you use an unknown embedding model, OpenRAG will automatically fall back to ",(0,o.jsx)(n.code,{children:"1536"})," dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from ",(0,o.jsx)(n.code,{children:"1536"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["The default embedding dimension is ",(0,o.jsx)(n.code,{children:"1536"})," and the default model is ",(0,o.jsx)(n.code,{children:"text-embedding-3-small"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["For models with known vector dimensions, see ",(0,o.jsx)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py",children:"/src/config/settings.py"}),"."]})]})}function x(e={}){const{wrapper:n}={...(0,t.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(p,{...e})}):p(e)}},3782:(e,n,s)=>{s.d(n,{Ay:()=>c,RM:()=>r});var i=s(4848),o=s(8453),t=s(9179);const r=[];function l(e){const n={a:"a",p:"p",strong:"strong",...(0,o.R)(),...e.components};return(0,i.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and 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type."}),"\n",(0,t.jsx)(n.p,{children:"For GPU-enabled systems, run the following command:"}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-bash",children:"docker compose up -d\n"})}),"\n",(0,t.jsx)(n.p,{children:"For CPU-only systems, run the following command:"}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-bash",children:"docker compose -f docker-compose-cpu.yml up -d\n"})}),"\n",(0,t.jsx)(n.p,{children:"The OpenRAG Docker Compose file starts five containers:"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Container Name"}),(0,t.jsx)(n.th,{children:"Default Address"}),(0,t.jsx)(n.th,{children:"Purpose"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"OpenRAG Backend"}),(0,t.jsx)(n.td,{children:(0,t.jsx)(n.a,{href:"http://localhost:8000",children:"http://localhost:8000"})}),(0,t.jsx)(n.td,{children:"FastAPI server and core 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t,BlindsIcon:()=>Lt,Blocks:()=>xt,BlocksIcon:()=>xt,Bluetooth:()=>It,BluetoothConnected:()=>wt,BluetoothConnectedIcon:()=>wt,BluetoothIcon:()=>It,BluetoothOff:()=>ft,BluetoothOffIcon:()=>ft,BluetoothSearching:()=>Ct,BluetoothSearchingIcon:()=>Ct,Bold:()=>bt,BoldIcon:()=>bt,Bolt:()=>qt,BoltIcon:()=>qt,Bomb:()=>St,BombIcon:()=>St,Bone:()=>zt,BoneIcon:()=>zt,Book:()=>eh,BookA:()=>At,BookAIcon:()=>At,BookAlert:()=>Ht,BookAlertIcon:()=>Ht,BookAudio:()=>Vt,BookAudioIcon:()=>Vt,BookCheck:()=>Pt,BookCheckIcon:()=>Pt,BookCopy:()=>jt,BookCopyIcon:()=>jt,BookDashed:()=>Bt,BookDashedIcon:()=>Bt,BookDown:()=>Dt,BookDownIcon:()=>Dt,BookHeadphones:()=>Ft,BookHeadphonesIcon:()=>Ft,BookHeart:()=>Rt,BookHeartIcon:()=>Rt,BookIcon:()=>eh,BookImage:()=>Tt,BookImageIcon:()=>Tt,BookKey:()=>Ut,BookKeyIcon:()=>Ut,BookLock:()=>Ot,BookLockIcon:()=>Ot,BookMarked:()=>Gt,BookMarkedIcon:()=>Gt,BookMinus:()=>Zt,BookMinusIcon:()=>Zt,BookOpen:()=>Xt,BookOpenCheck:()=>Wt,BookOpenCheckIcon:()=>Wt,BookOpenIcon:()=>Xt,BookOpenText:()=>Et,BookOpenTextIcon:()=>Et,BookPlus:()=>Nt,BookPlusIcon:()=>Nt,BookTemplate:()=>Bt,BookTemplateIcon:()=>Bt,BookText:()=>Kt,BookTextIcon:()=>Kt,BookType:()=>Qt,BookTypeIcon:()=>Qt,BookUp:()=>Yt,BookUp2:()=>Jt,BookUp2Icon:()=>Jt,BookUpIcon:()=>Yt,BookUser:()=>_t,BookUserIcon:()=>_t,BookX:()=>$t,BookXIcon:()=>$t,Bookmark:()=>ch,BookmarkCheck:()=>ah,BookmarkCheckIcon:()=>ah,BookmarkIcon:()=>ch,BookmarkMinus:()=>th,BookmarkMinusIcon:()=>th,BookmarkPlus:()=>hh,BookmarkPlusIcon:()=>hh,BookmarkX:()=>dh,BookmarkXIcon:()=>dh,BoomBox:()=>rh,BoomBoxIcon:()=>rh,Bot:()=>yh,BotIcon:()=>yh,BotMessageSquare:()=>ih,BotMessageSquareIcon:()=>ih,BotOff:()=>oh,BotOffIcon:()=>oh,BottleWine:()=>kh,BottleWineIcon:()=>kh,BowArrow:()=>ph,BowArrowIcon:()=>ph,Box:()=>nh,BoxIcon:()=>nh,BoxSelect:()=>zC,BoxSelectIcon:()=>zC,Boxes:()=>lh,BoxesIcon:()=>lh,Braces:()=>Mh,BracesIcon:()=>Mh,Brackets:()=>uh,BracketsIcon:()=>uh,Brain:()=>mh,BrainCircuit:()=>sh,BrainCircuitIcon:()=>sh,BrainCog:()=>vh,BrainCogIcon:()=>vh,BrainIcon:()=>mh,BrickWall:()=>xh,BrickWallFire:()=>gh,BrickWallFireIcon:()=>gh,BrickWallIcon:()=>xh,BrickWallShield:()=>Lh,BrickWallShieldIcon:()=>Lh,Briefcase:()=>Ih,BriefcaseBusiness:()=>wh,BriefcaseBusinessIcon:()=>wh,BriefcaseConveyorBelt:()=>fh,BriefcaseConveyorBeltIcon:()=>fh,BriefcaseIcon:()=>Ih,BriefcaseMedical:()=>Ch,BriefcaseMedicalIcon:()=>Ch,BringToFront:()=>bh,BringToFrontIcon:()=>bh,Brush:()=>Sh,BrushCleaning:()=>qh,BrushCleaningIcon:()=>qh,BrushIcon:()=>Sh,Bubbles:()=>zh,BubblesIcon:()=>zh,Bug:()=>Vh,BugIcon:()=>Vh,BugOff:()=>Hh,BugOffIcon:()=>Hh,BugPlay:()=>Ah,BugPlayIcon:()=>Ah,Building:()=>jh,Building2:()=>Ph,Building2Icon:()=>Ph,BuildingIcon:()=>jh,Bus:()=>Dh,BusFront:()=>Bh,BusFrontIcon:()=>Bh,BusIcon:()=>Dh,Cable:()=>Rh,CableCar:()=>Fh,CableCarIcon:()=>Fh,CableIcon:()=>Rh,Cake:()=>Uh,CakeIcon:()=>Uh,CakeSlice:()=>Th,CakeSliceIcon:()=>Th,Calculator:()=>Oh,CalculatorIcon:()=>Oh,Calendar:()=>yd,Calendar1:()=>Gh,Calendar1Icon:()=>Gh,CalendarArrowDown:()=>Zh,CalendarArrowDownIcon:()=>Zh,CalendarArrowUp:()=>Wh,CalendarArrowUpIcon:()=>Wh,CalendarCheck:()=>Xh,CalendarCheck2:()=>Eh,CalendarCheck2Icon:()=>Eh,CalendarCheckIcon:()=>Xh,CalendarClock:()=>Nh,CalendarClockIcon:()=>Nh,CalendarCog:()=>Kh,CalendarCogIcon:()=>Kh,CalendarDays:()=>Qh,CalendarDaysIcon:()=>Qh,CalendarFold:()=>Yh,CalendarFoldIcon:()=>Yh,CalendarHeart:()=>Jh,CalendarHeartIcon:()=>Jh,CalendarIcon:()=>yd,CalendarMinus:()=>$h,CalendarMinus2:()=>_h,CalendarMinus2Icon:()=>_h,CalendarMinusIcon:()=>$h,CalendarOff:()=>ed,CalendarOffIcon:()=>ed,CalendarPlus:()=>td,CalendarPlus2:()=>ad,CalendarPlus2Icon:()=>ad,CalendarPlusIcon:()=>td,CalendarRange:()=>hd,CalendarRangeIcon:()=>hd,CalendarSearch:()=>dd,CalendarSearchIcon:()=>dd,CalendarSync:()=>cd,CalendarSyncIcon:()=>cd,CalendarX:()=>rd,CalendarX2:()=>id,CalendarX2Icon:()=>id,CalendarXIcon:()=>rd,Camera:()=>kd,CameraIcon:()=>kd,CameraOff:()=>od,CameraOffIcon:()=>od,CandlestickChart:()=>Fd,CandlestickChartIcon:()=>Fd,Candy:()=>ld,CandyCane:()=>pd,CandyCaneIcon:()=>pd,CandyIcon:()=>ld,CandyOff:()=>nd,CandyOffIcon:()=>nd,Cannabis:()=>ud,CannabisIcon:()=>ud,Captions:()=>sd,CaptionsIcon:()=>sd,CaptionsOff:()=>Md,CaptionsOffIcon:()=>Md,Car:()=>gd,CarFront:()=>vd,CarFrontIcon:()=>vd,CarIcon:()=>gd,CarTaxiFront:()=>md,CarTaxiFrontIcon:()=>md,Caravan:()=>Ld,CaravanIcon:()=>Ld,CardSim:()=>xd,CardSimIcon:()=>xd,Carrot:()=>wd,CarrotIcon:()=>wd,CaseLower:()=>fd,CaseLowerIcon:()=>fd,CaseSensitive:()=>Cd,CaseSensitiveIcon:()=>Cd,CaseUpper:()=>Id,CaseUpperIcon:()=>Id,CassetteTape:()=>bd,CassetteTapeIcon:()=>bd,Cast:()=>qd,CastIcon:()=>qd,Castle:()=>Sd,CastleIcon:()=>Sd,Cat:()=>zd,CatIcon:()=>zd,Cctv:()=>Ad,CctvIcon:()=>Ad,ChartArea:()=>Hd,ChartAreaIcon:()=>Hd,ChartBar:()=>Dd,ChartBarBig:()=>Vd,ChartBarBigIcon:()=>Vd,ChartBarDecreasing:()=>Pd,ChartBarDecreasingIcon:()=>Pd,ChartBarIcon:()=>Dd,ChartBarIncreasing:()=>jd,ChartBarIncreasingIcon:()=>jd,ChartBarStacked:()=>Bd,ChartBarStackedIcon:()=>Bd,ChartCandlestick:()=>Fd,ChartCandlestickIcon:()=>Fd,ChartColumn:()=>Gd,ChartColumnBig:()=>Rd,ChartColumnBigIcon:()=>Rd,ChartColumnDecreasing:()=>Td,ChartColumnDecreasingIcon:()=>Td,ChartColumnIcon:()=>Gd,ChartColumnIncreasing:()=>Ud,ChartColumnIncreasingIcon:()=>Ud,ChartColumnStacked:()=>Od,ChartColumnStackedIcon:()=>Od,ChartGantt:()=>Zd,ChartGanttIcon:()=>Zd,ChartLine:()=>Wd,ChartLineIcon:()=>Wd,ChartNetwork:()=>Ed,ChartNetworkIcon:()=>Ed,ChartNoAxesColumn:()=>Kd,ChartNoAxesColumnDecreasing:()=>Xd,ChartNoAxesColumnDecreasingIcon:()=>Xd,ChartNoAxesColumnIcon:()=>Kd,ChartNoAxesColumnIncreasing:()=>Nd,ChartNoAxesColumnIncreasingIcon:()=>Nd,ChartNoAxesCombined:()=>Qd,ChartNoAxesCombinedIcon:()=>Qd,ChartNoAxesGantt:()=>Jd,ChartNoAxesGanttIcon:()=>Jd,ChartPie:()=>Yd,ChartPieIcon:()=>Yd,ChartScatter:()=>_d,ChartScatterIcon:()=>_d,ChartSpline:()=>$d,ChartSplineIcon:()=>$d,Check:()=>tc,CheckCheck:()=>ec,CheckCheckIcon:()=>ec,CheckCircle:()=>Pc,CheckCircle2:()=>jc,CheckCircle2Icon:()=>jc,CheckCircleIcon:()=>Pc,CheckIcon:()=>tc,CheckLine:()=>ac,CheckLineIcon:()=>ac,CheckSquare:()=>vC,CheckSquare2:()=>mC,CheckSquare2Icon:()=>mC,CheckSquareIcon:()=>vC,ChefHat:()=>hc,ChefHatIcon:()=>hc,Cherry:()=>dc,CherryIcon:()=>dc,ChevronDown:()=>cc,ChevronDownCircle:()=>Bc,ChevronDownCircleIcon:()=>Bc,ChevronDownIcon:()=>cc,ChevronDownSquare:()=>gC,ChevronDownSquareIcon:()=>gC,ChevronFirst:()=>ic,ChevronFirstIcon:()=>ic,ChevronLast:()=>rc,ChevronLastIcon:()=>rc,ChevronLeft:()=>yc,ChevronLeftCircle:()=>Dc,ChevronLeftCircleIcon:()=>Dc,ChevronLeftIcon:()=>yc,ChevronLeftSquare:()=>LC,ChevronLeftSquareIcon:()=>LC,ChevronRight:()=>oc,ChevronRightCircle:()=>Fc,ChevronRightCircleIcon:()=>Fc,ChevronRightIcon:()=>oc,ChevronRightSquare:()=>xC,ChevronRightSquareIcon:()=>xC,ChevronUp:()=>kc,ChevronUpCircle:()=>Rc,ChevronUpCircleIcon:()=>Rc,ChevronUpIcon:()=>kc,ChevronUpSquare:()=>wC,ChevronUpSquareIcon:()=>wC,ChevronsDown:()=>nc,ChevronsDownIcon:()=>nc,ChevronsDownUp:()=>pc,ChevronsDownUpIcon:()=>pc,ChevronsLeft:()=>Mc,ChevronsLeftIcon:()=>Mc,ChevronsLeftRight:()=>uc,ChevronsLeftRightEllipsis:()=>lc,ChevronsLeftRightEllipsisIcon:()=>lc,ChevronsLeftRightIcon:()=>uc,ChevronsRight:()=>vc,ChevronsRightIcon:()=>vc,ChevronsRightLeft:()=>sc,ChevronsRightLeftIcon:()=>sc,ChevronsUp:()=>gc,ChevronsUpDown:()=>mc,ChevronsUpDownIcon:()=>mc,ChevronsUpIcon:()=>gc,Chrome:()=>Lc,ChromeIcon:()=>Lc,Chromium:()=>Lc,ChromiumIcon:()=>Lc,Church:()=>xc,ChurchIcon:()=>xc,Cigarette:()=>fc,CigaretteIcon:()=>fc,CigaretteOff:()=>wc,CigaretteOffIcon:()=>wc,Circle:()=>ui,CircleAlert:()=>Cc,CircleAlertIcon:()=>Cc,CircleArrowDown:()=>Ic,CircleArrowDownIcon:()=>Ic,CircleArrowLeft:()=>bc,CircleArrowLeftIcon:()=>bc,CircleArrowOutDownLeft:()=>qc,CircleArrowOutDownLeftIcon:()=>qc,CircleArrowOutDownRight:()=>Sc,CircleArrowOutDownRightIcon:()=>Sc,CircleArrowOutUpLeft:()=>zc,CircleArrowOutUpLeftIcon:()=>zc,CircleArrowOutUpRight:()=>Ac,CircleArrowOutUpRightIcon:()=>Ac,CircleArrowRight:()=>Hc,CircleArrowRightIcon:()=>Hc,CircleArrowUp:()=>Vc,CircleArrowUpIcon:()=>Vc,CircleCheck:()=>jc,CircleCheckBig:()=>Pc,CircleCheckBigIcon:()=>Pc,CircleCheckIcon:()=>jc,CircleChevronDown:()=>Bc,CircleChevronDownIcon:()=>Bc,CircleChevronLeft:()=>Dc,CircleChevronLeftIcon:()=>Dc,CircleChevronRight:()=>Fc,CircleChevronRightIcon:()=>Fc,CircleChevronUp:()=>Rc,CircleChevronUpIcon:()=>Rc,CircleDashed:()=>Uc,CircleDashedIcon:()=>Uc,CircleDivide:()=>Tc,CircleDivideIcon:()=>Tc,CircleDollarSign:()=>Oc,CircleDollarSignIcon:()=>Oc,CircleDot:()=>Zc,CircleDotDashed:()=>Gc,CircleDotDashedIcon:()=>Gc,CircleDotIcon:()=>Zc,CircleEllipsis:()=>Wc,CircleEllipsisIcon:()=>Wc,CircleEqual:()=>Xc,CircleEqualIcon:()=>Xc,CircleFadingArrowUp:()=>Ec,CircleFadingArrowUpIcon:()=>Ec,CircleFadingPlus:()=>Nc,CircleFadingPlusIcon:()=>Nc,CircleGauge:()=>Kc,CircleGaugeIcon:()=>Kc,CircleHelp:()=>ci,CircleHelpIcon:()=>ci,CircleIcon:()=>ui,CircleMinus:()=>Qc,CircleMinusIcon:()=>Qc,CircleOff:()=>Jc,CircleOffIcon:()=>Jc,CircleParking:()=>_c,CircleParkingIcon:()=>_c,CircleParkingOff:()=>Yc,CircleParkingOffIcon:()=>Yc,CirclePause:()=>$c,CirclePauseIcon:()=>$c,CirclePercent:()=>ei,CirclePercentIcon:()=>ei,CirclePlay:()=>ai,CirclePlayIcon:()=>ai,CirclePlus:()=>ti,CirclePlusIcon:()=>ti,CirclePoundSterling:()=>hi,CirclePoundSterlingIcon:()=>hi,CirclePower:()=>di,CirclePowerIcon:()=>di,CircleQuestionMark:()=>ci,CircleQuestionMarkIcon:()=>ci,CircleSlash:()=>ri,CircleSlash2:()=>ii,CircleSlash2Icon:()=>ii,CircleSlashIcon:()=>ri,CircleSlashed:()=>ii,CircleSlashedIcon:()=>ii,CircleSmall:()=>yi,CircleSmallIcon:()=>yi,CircleStar:()=>oi,CircleStarIcon:()=>oi,CircleStop:()=>ki,CircleStopIcon:()=>ki,CircleUser:()=>ni,CircleUserIcon:()=>ni,CircleUserRound:()=>pi,CircleUserRoundIcon:()=>pi,CircleX:()=>li,CircleXIcon:()=>li,CircuitBoard:()=>Mi,CircuitBoardIcon:()=>Mi,Citrus:()=>si,CitrusIcon:()=>si,Clapperboard:()=>vi,ClapperboardIcon:()=>vi,Clipboard:()=>zi,ClipboardCheck:()=>mi,ClipboardCheckIcon:()=>mi,ClipboardClock:()=>gi,ClipboardClockIcon:()=>gi,ClipboardCopy:()=>Li,ClipboardCopyIcon:()=>Li,ClipboardEdit:()=>Ii,ClipboardEditIcon:()=>Ii,ClipboardIcon:()=>zi,ClipboardList:()=>xi,ClipboardListIcon:()=>xi,ClipboardMinus:()=>wi,ClipboardMinusIcon:()=>wi,ClipboardPaste:()=>fi,ClipboardPasteIcon:()=>fi,ClipboardPen:()=>Ii,ClipboardPenIcon:()=>Ii,ClipboardPenLine:()=>Ci,ClipboardPenLineIcon:()=>Ci,ClipboardPlus:()=>bi,ClipboardPlusIcon:()=>bi,ClipboardSignature:()=>Ci,ClipboardSignatureIcon:()=>Ci,ClipboardType:()=>qi,ClipboardTypeIcon:()=>qi,ClipboardX:()=>Si,ClipboardXIcon:()=>Si,Clock:()=>Ni,Clock1:()=>Ai,Clock10:()=>Hi,Clock10Icon:()=>Hi,Clock11:()=>Vi,Clock11Icon:()=>Vi,Clock12:()=>Pi,Clock12Icon:()=>Pi,Clock1Icon:()=>Ai,Clock2:()=>ji,Clock2Icon:()=>ji,Clock3:()=>Bi,Clock3Icon:()=>Bi,Clock4:()=>Di,Clock4Icon:()=>Di,Clock5:()=>Fi,Clock5Icon:()=>Fi,Clock6:()=>Ri,Clock6Icon:()=>Ri,Clock7:()=>Ti,Clock7Icon:()=>Ti,Clock8:()=>Ui,Clock8Icon:()=>Ui,Clock9:()=>Oi,Clock9Icon:()=>Oi,ClockAlert:()=>Gi,ClockAlertIcon:()=>Gi,ClockArrowDown:()=>Zi,ClockArrowDownIcon:()=>Zi,ClockArrowUp:()=>Wi,ClockArrowUpIcon:()=>Wi,ClockFading:()=>Ei,ClockFadingIcon:()=>Ei,ClockIcon:()=>Ni,ClockPlus:()=>Xi,ClockPlusIcon:()=>Xi,ClosedCaption:()=>Ki,ClosedCaptionIcon:()=>Ki,Cloud:()=>nr,CloudAlert:()=>Qi,CloudAlertIcon:()=>Qi,CloudCheck:()=>Ji,CloudCheckIcon:()=>Ji,CloudCog:()=>Yi,CloudCogIcon:()=>Yi,CloudDownload:()=>_i,CloudDownloadIcon:()=>_i,CloudDrizzle:()=>$i,CloudDrizzleIcon:()=>$i,CloudFog:()=>er,CloudFogIcon:()=>er,CloudHail:()=>ar,CloudHailIcon:()=>ar,CloudIcon:()=>nr,CloudLightning:()=>tr,CloudLightningIcon:()=>tr,CloudMoon:()=>dr,CloudMoonIcon:()=>dr,CloudMoonRain:()=>hr,CloudMoonRainIcon:()=>hr,CloudOff:()=>cr,CloudOffIcon:()=>cr,CloudRain:()=>rr,CloudRainIcon:()=>rr,CloudRainWind:()=>ir,CloudRainWindIcon:()=>ir,CloudSnow:()=>yr,CloudSnowIcon:()=>yr,CloudSun:()=>kr,CloudSunIcon:()=>kr,CloudSunRain:()=>or,CloudSunRainIcon:()=>or,CloudUpload:()=>pr,CloudUploadIcon:()=>pr,Cloudy:()=>lr,CloudyIcon:()=>lr,Clover:()=>ur,CloverIcon:()=>ur,Club:()=>Mr,ClubIcon:()=>Mr,Code:()=>vr,Code2:()=>sr,Code2Icon:()=>sr,CodeIcon:()=>vr,CodeSquare:()=>fC,CodeSquareIcon:()=>fC,CodeXml:()=>sr,CodeXmlIcon:()=>sr,Codepen:()=>mr,CodepenIcon:()=>mr,Codesandbox:()=>gr,CodesandboxIcon:()=>gr,Coffee:()=>Lr,CoffeeIcon:()=>Lr,Cog:()=>xr,CogIcon:()=>xr,Coins:()=>wr,CoinsIcon:()=>wr,Columns:()=>fr,Columns2:()=>fr,Columns2Icon:()=>fr,Columns3:()=>Ir,Columns3Cog:()=>Cr,Columns3CogIcon:()=>Cr,Columns3Icon:()=>Ir,Columns4:()=>qr,Columns4Icon:()=>qr,ColumnsIcon:()=>fr,ColumnsSettings:()=>Cr,ColumnsSettingsIcon:()=>Cr,Combine:()=>br,CombineIcon:()=>br,Command:()=>Sr,CommandIcon:()=>Sr,Compass:()=>zr,CompassIcon:()=>zr,Component:()=>Ar,ComponentIcon:()=>Ar,Computer:()=>Hr,ComputerIcon:()=>Hr,ConciergeBell:()=>Vr,ConciergeBellIcon:()=>Vr,Cone:()=>Pr,ConeIcon:()=>Pr,Construction:()=>jr,ConstructionIcon:()=>jr,Contact:()=>Dr,Contact2:()=>Br,Contact2Icon:()=>Br,ContactIcon:()=>Dr,ContactRound:()=>Br,ContactRoundIcon:()=>Br,Container:()=>Fr,ContainerIcon:()=>Fr,Contrast:()=>Rr,ContrastIcon:()=>Rr,Cookie:()=>Tr,CookieIcon:()=>Tr,CookingPot:()=>Ur,CookingPotIcon:()=>Ur,Copy:()=>Xr,CopyCheck:()=>Or,CopyCheckIcon:()=>Or,CopyIcon:()=>Xr,CopyMinus:()=>Gr,CopyMinusIcon:()=>Gr,CopyPlus:()=>Zr,CopyPlusIcon:()=>Zr,CopySlash:()=>Wr,CopySlashIcon:()=>Wr,CopyX:()=>Er,CopyXIcon:()=>Er,Copyleft:()=>Nr,CopyleftIcon:()=>Nr,Copyright:()=>Kr,CopyrightIcon:()=>Kr,CornerDownLeft:()=>Qr,CornerDownLeftIcon:()=>Qr,CornerDownRight:()=>Jr,CornerDownRightIcon:()=>Jr,CornerLeftDown:()=>_r,CornerLeftDownIcon:()=>_r,CornerLeftUp:()=>Yr,CornerLeftUpIcon:()=>Yr,CornerRightDown:()=>$r,CornerRightDownIcon:()=>$r,CornerRightUp:()=>ey,CornerRightUpIcon:()=>ey,CornerUpLeft:()=>ay,CornerUpLeftIcon:()=>ay,CornerUpRight:()=>ty,CornerUpRightIcon:()=>ty,Cpu:()=>hy,CpuIcon:()=>hy,CreativeCommons:()=>dy,CreativeCommonsIcon:()=>dy,CreditCard:()=>cy,CreditCardIcon:()=>cy,Croissant:()=>iy,CroissantIcon:()=>iy,Crop:()=>ry,CropIcon:()=>ry,Cross:()=>yy,CrossIcon:()=>yy,Crosshair:()=>oy,CrosshairIcon:()=>oy,Crown:()=>ky,CrownIcon:()=>ky,Cuboid:()=>py,CuboidIcon:()=>py,CupSoda:()=>ny,CupSodaIcon:()=>ny,CurlyBraces:()=>Mh,CurlyBracesIcon:()=>Mh,Currency:()=>ly,CurrencyIcon:()=>ly,Cylinder:()=>uy,CylinderIcon:()=>uy,Dam:()=>My,DamIcon:()=>My,Database:()=>my,DatabaseBackup:()=>sy,DatabaseBackupIcon:()=>sy,DatabaseIcon:()=>my,DatabaseZap:()=>vy,DatabaseZapIcon:()=>vy,DecimalsArrowLeft:()=>gy,DecimalsArrowLeftIcon:()=>gy,DecimalsArrowRight:()=>Ly,DecimalsArrowRightIcon:()=>Ly,Delete:()=>xy,DeleteIcon:()=>xy,Dessert:()=>wy,DessertIcon:()=>wy,Diameter:()=>fy,DiameterIcon:()=>fy,Diamond:()=>qy,DiamondIcon:()=>qy,DiamondMinus:()=>Cy,DiamondMinusIcon:()=>Cy,DiamondPercent:()=>Iy,DiamondPercentIcon:()=>Iy,DiamondPlus:()=>by,DiamondPlusIcon:()=>by,Dice1:()=>Sy,Dice1Icon:()=>Sy,Dice2:()=>Ay,Dice2Icon:()=>Ay,Dice3:()=>zy,Dice3Icon:()=>zy,Dice4:()=>Hy,Dice4Icon:()=>Hy,Dice5:()=>Vy,Dice5Icon:()=>Vy,Dice6:()=>Py,Dice6Icon:()=>Py,Dices:()=>jy,DicesIcon:()=>jy,Diff:()=>By,DiffIcon:()=>By,Disc:()=>Ty,Disc2:()=>Dy,Disc2Icon:()=>Dy,Disc3:()=>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eiptText:()=>YL,ReceiptTextIcon:()=>YL,ReceiptTurkishLira:()=>JL,ReceiptTurkishLiraIcon:()=>JL,RectangleCircle:()=>$L,RectangleCircleIcon:()=>$L,RectangleEllipsis:()=>ex,RectangleEllipsisIcon:()=>ex,RectangleGoggles:()=>ax,RectangleGogglesIcon:()=>ax,RectangleHorizontal:()=>tx,RectangleHorizontalIcon:()=>tx,RectangleVertical:()=>hx,RectangleVerticalIcon:()=>hx,Recycle:()=>dx,RecycleIcon:()=>dx,Redo:()=>rx,Redo2:()=>cx,Redo2Icon:()=>cx,RedoDot:()=>ix,RedoDotIcon:()=>ix,RedoIcon:()=>rx,RefreshCcw:()=>ox,RefreshCcwDot:()=>yx,RefreshCcwDotIcon:()=>yx,RefreshCcwIcon:()=>ox,RefreshCw:()=>px,RefreshCwIcon:()=>px,RefreshCwOff:()=>kx,RefreshCwOffIcon:()=>kx,Refrigerator:()=>nx,RefrigeratorIcon:()=>nx,Regex:()=>lx,RegexIcon:()=>lx,RemoveFormatting:()=>ux,RemoveFormattingIcon:()=>ux,Repeat:()=>sx,Repeat1:()=>vx,Repeat1Icon:()=>vx,Repeat2:()=>Mx,Repeat2Icon:()=>Mx,RepeatIcon:()=>sx,Replace:()=>Lx,ReplaceAll:()=>mx,ReplaceAllIcon:()=>mx,ReplaceIcon:()=>Lx,Reply:()=>xx,ReplyAll:()=>gx,ReplyAllIcon:()=>gx,ReplyIcon:()=>xx,Rewind:()=>wx,RewindIcon:()=>wx,Ribbon:()=>fx,RibbonIcon:()=>fx,Rocket:()=>Cx,RocketIcon:()=>Cx,RockingChair:()=>Ix,RockingChairIcon:()=>Ix,RollerCoaster:()=>bx,RollerCoasterIcon:()=>bx,Rose:()=>qx,RoseIcon:()=>qx,Rotate3D:()=>Sx,Rotate3DIcon:()=>Sx,Rotate3d:()=>Sx,Rotate3dIcon:()=>Sx,RotateCcw:()=>Hx,RotateCcwIcon:()=>Hx,RotateCcwKey:()=>zx,RotateCcwKeyIcon:()=>zx,RotateCcwSquare:()=>Ax,RotateCcwSquareIcon:()=>Ax,RotateCw:()=>Px,RotateCwIcon:()=>Px,RotateCwSquare:()=>Vx,RotateCwSquareIcon:()=>Vx,Route:()=>Bx,RouteIcon:()=>Bx,RouteOff:()=>jx,RouteOffIcon:()=>jx,Router:()=>Fx,RouterIcon:()=>Fx,Rows:()=>Dx,Rows2:()=>Dx,Rows2Icon:()=>Dx,Rows3:()=>Rx,Rows3Icon:()=>Rx,Rows4:()=>Tx,Rows4Icon:()=>Tx,RowsIcon:()=>Dx,Rss:()=>Ux,RssIcon:()=>Ux,Ruler:()=>Gx,RulerDimensionLine:()=>Ox,RulerDimensionLineIcon:()=>Ox,RulerIcon:()=>Gx,RussianRuble:()=>Wx,RussianRubleIcon:()=>Wx,Sailboat:()=>Zx,SailboatIcon:()=>Zx,Salad:()=>Ex,SaladIcon:()=>Ex,Sandwich:()=>Xx,SandwichIcon:()=>Xx,Satellite:()=>Qx,SatelliteDish:()=>Nx,SatelliteDishIcon:()=>Nx,SatelliteIcon:()=>Qx,SaudiRiyal:()=>Kx,SaudiRiyalIcon:()=>Kx,Save:()=>_x,SaveAll:()=>Jx,SaveAllIcon:()=>Jx,SaveIcon:()=>_x,SaveOff:()=>Yx,SaveOffIcon:()=>Yx,Scale:()=>ew,Scale3D:()=>$x,Scale3DIcon:()=>$x,Scale3d:()=>$x,Scale3dIcon:()=>$x,ScaleIcon:()=>ew,Scaling:()=>aw,ScalingIcon:()=>aw,Scan:()=>kw,ScanBarcode:()=>tw,ScanBarcodeIcon:()=>tw,ScanEye:()=>hw,ScanEyeIcon:()=>hw,ScanFace:()=>dw,ScanFaceIcon:()=>dw,ScanHeart:()=>cw,ScanHeartIcon:()=>cw,ScanIcon:()=>kw,ScanLine:()=>iw,ScanLineIcon:()=>iw,ScanQrCode:()=>rw,ScanQrCodeIcon:()=>rw,ScanSearch:()=>yw,ScanSearchIcon:()=>yw,ScanText:()=>ow,ScanTextIcon:()=>ow,ScatterChart:()=>_d,ScatterChartIcon:()=>_d,School:()=>pw,School2:()=>Kq,School2Icon:()=>Kq,SchoolIcon:()=>pw,Scissors:()=>lw,ScissorsIcon:()=>lw,ScissorsLineDashed:()=>nw,ScissorsLineDashedIcon:()=>nw,ScissorsSquare:()=>$C,ScissorsSquareDashedBottom:()=>MC,ScissorsSquareDashedBottomIcon:()=>MC,ScissorsSquareIcon:()=>$C,ScreenShare:()=>Mw,ScreenShareIcon:()=>Mw,ScreenShareOff:()=>uw,ScreenShareOffIcon:()=>uw,Scroll:()=>vw,ScrollIcon:()=>vw,ScrollText:()=>sw,ScrollTextIcon:()=>sw,Search:()=>ww,SearchCheck:()=>mw,SearchCheckIcon:()=>mw,SearchCode:()=>gw,SearchCodeIcon:()=>gw,SearchIcon:()=>ww,SearchSlash:()=>Lw,SearchSlashIcon:()=>Lw,SearchX:()=>xw,SearchXIcon:()=>xw,Section:()=>fw,SectionIcon:()=>fw,Send:()=>bw,SendHorizonal:()=>Cw,SendHorizonalIcon:()=>Cw,SendHorizontal:()=>Cw,SendHorizontalIcon:()=>Cw,SendIcon:()=>bw,SendToBack:()=>Iw,SendToBackIcon:()=>Iw,SeparatorHorizontal:()=>qw,SeparatorHorizontalIcon:()=>qw,SeparatorVertical:()=>Sw,SeparatorVerticalIcon:()=>Sw,Server:()=>Vw,ServerCog:()=>zw,ServerCogIcon:()=>zw,ServerCrash:()=>Aw,ServerCrashIcon:()=>Aw,ServerIcon:()=>Vw,ServerOff:()=>Hw,ServerOffIcon:()=>Hw,Settings:()=>Pw,Settings2:()=>jw,Settings2Icon:()=>jw,SettingsIcon:()=>Pw,Shapes:()=>Fw,ShapesIcon:()=>Fw,Share:()=>Dw,Share2:()=>Bw,Share2Icon:()=>Bw,ShareIcon:()=>Dw,Sheet:()=>Rw,SheetIcon:()=>Rw,Shell:()=>Tw,ShellIcon:()=>Tw,Shield:()=>Yw,ShieldAlert:()=>Uw,ShieldAlertIcon:()=>Uw,ShieldBan:()=>Ow,ShieldBanIcon:()=>Ow,ShieldCheck:()=>Gw,ShieldCheckIcon:()=>Gw,ShieldClose:()=>Jw,ShieldCloseIcon:()=>Jw,ShieldEllipsis:()=>Zw,ShieldEllipsisIcon:()=>Zw,ShieldHalf:()=>Ww,ShieldHalfIcon:()=>Ww,ShieldIcon:()=>Yw,ShieldMinus:()=>Ew,ShieldMinusIcon:()=>Ew,ShieldOff:()=>Xw,ShieldOffIcon:()=>Xw,ShieldPlus:()=>Nw,ShieldPlusIcon:()=>Nw,ShieldQuestion:()=>Kw,ShieldQuestionIcon:()=>Kw,ShieldQuestionMark:()=>Kw,ShieldQuestionMarkIcon:()=>Kw,ShieldUser:()=>Qw,ShieldUserIcon:()=>Qw,ShieldX:()=>Jw,ShieldXIcon:()=>Jw,Ship:()=>$w,ShipIcon:()=>$w,ShipWheel:()=>_w,ShipWheelIcon:()=>_w,Shirt:()=>ef,ShirtIcon:()=>ef,ShoppingBag:()=>af,ShoppingBagIcon:()=>af,ShoppingBasket:()=>tf,ShoppingBasketIcon:()=>tf,ShoppingCart:()=>hf,ShoppingCartIcon:()=>hf,Shovel:()=>df,ShovelIcon:()=>df,ShowerHead:()=>cf,ShowerHeadIcon:()=>cf,Shredder:()=>rf,ShredderIcon:()=>rf,Shrimp:()=>yf,ShrimpIcon:()=>yf,Shrink: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2v4",key:"4m81vk"}],["path",{d:"M21 15V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h8.5",key:"1scpom"}],["path",{d:"M3 10h18",key:"8toen8"}],["path",{d:"M8 2v4",key:"1cmpym"}]]),ed=k("calendar-off",[["path",{d:"M4.2 4.2A2 2 0 0 0 3 6v14a2 2 0 0 0 2 2h14a2 2 0 0 0 1.82-1.18",key:"16swn3"}],["path",{d:"M21 15.5V6a2 2 0 0 0-2-2H9.5",key:"yhw86o"}],["path",{d:"M16 2v4",key:"4m81vk"}],["path",{d:"M3 10h7",key:"1wap6i"}],["path",{d:"M21 10h-5.5",key:"quycpq"}],["path",{d:"m2 2 20 20",key:"1ooewy"}]]),ad=k("calendar-plus-2",[["path",{d:"M8 2v4",key:"1cmpym"}],["path",{d:"M16 2v4",key:"4m81vk"}],["rect",{width:"18",height:"18",x:"3",y:"4",rx:"2",key:"1hopcy"}],["path",{d:"M3 10h18",key:"8toen8"}],["path",{d:"M10 16h4",key:"17e571"}],["path",{d:"M12 14v4",key:"1thi36"}]]),td=k("calendar-plus",[["path",{d:"M16 19h6",key:"xwg31i"}],["path",{d:"M16 2v4",key:"4m81vk"}],["path",{d:"M19 16v6",key:"tddt3s"}],["path",{d:"M21 12.598V6a2 2 0 0 0-2-2H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 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10h18",key:"8toen8"}]]),od=k("camera-off",[["path",{d:"M14.564 14.558a3 3 0 1 1-4.122-4.121",key:"1rnrzw"}],["path",{d:"m2 2 20 20",key:"1ooewy"}],["path",{d:"M20 20H4a2 2 0 0 1-2-2V9a2 2 0 0 1 2-2h1.997a2 2 0 0 0 .819-.175",key:"1x3arw"}],["path",{d:"M9.695 4.024A2 2 0 0 1 10.004 4h3.993a2 2 0 0 1 1.76 1.05l.486.9A2 2 0 0 0 18.003 7H20a2 2 0 0 1 2 2v7.344",key:"1i84u0"}]]),kd=k("camera",[["path",{d:"M13.997 4a2 2 0 0 1 1.76 1.05l.486.9A2 2 0 0 0 18.003 7H20a2 2 0 0 1 2 2v9a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V9a2 2 0 0 1 2-2h1.997a2 2 0 0 0 1.759-1.048l.489-.904A2 2 0 0 1 10.004 4z",key:"18u6gg"}],["circle",{cx:"12",cy:"13",r:"3",key:"1vg3eu"}]]),pd=k("candy-cane",[["path",{d:"M5.7 21a2 2 0 0 1-3.5-2l8.6-14a6 6 0 0 1 10.4 6 2 2 0 1 1-3.464-2 2 2 0 1 0-3.464-2Z",key:"isaq8g"}],["path",{d:"M17.75 7 15 2.1",key:"12x7e8"}],["path",{d:"M10.9 4.8 13 9",key:"100a87"}],["path",{d:"m7.9 9.7 2 4.4",key:"ntfhaj"}],["path",{d:"M4.9 14.7 7 18.9",key:"1x43jy"}]]),nd=k("candy-off",[["path",{d:"M10 10v7.9",key:"m8g9tt"}],["path",{d:"M11.802 6.145a5 5 0 0 1 6.053 6.053",key:"dn87i3"}],["path",{d:"M14 6.1v2.243",key:"1kzysn"}],["path",{d:"m15.5 15.571-.964.964a5 5 0 0 1-7.071 0 5 5 0 0 1 0-7.07l.964-.965",key:"3sxy18"}],["path",{d:"M16 7V3a1 1 0 0 1 1.707-.707 2.5 2.5 0 0 0 2.152.717 1 1 0 0 1 1.131 1.131 2.5 2.5 0 0 0 .717 2.152A1 1 0 0 1 21 8h-4",key:"gpb6xx"}],["path",{d:"m2 2 20 20",key:"1ooewy"}],["path",{d:"M8 17v4a1 1 0 0 1-1.707.707 2.5 2.5 0 0 0-2.152-.717 1 1 0 0 1-1.131-1.131 2.5 2.5 0 0 0-.717-2.152A1 1 0 0 1 3 16h4",key:"qexcha"}]]),ld=k("candy",[["path",{d:"M10 7v10.9",key:"1gynux"}],["path",{d:"M14 6.1V17",key:"116kdf"}],["path",{d:"M16 7V3a1 1 0 0 1 1.707-.707 2.5 2.5 0 0 0 2.152.717 1 1 0 0 1 1.131 1.131 2.5 2.5 0 0 0 .717 2.152A1 1 0 0 1 21 8h-4",key:"gpb6xx"}],["path",{d:"M16.536 7.465a5 5 0 0 0-7.072 0l-2 2a5 5 0 0 0 0 7.07 5 5 0 0 0 7.072 0l2-2a5 5 0 0 0 0-7.07",key:"1tsln4"}],["path",{d:"M8 17v4a1 1 0 0 1-1.707.707 2.5 2.5 0 0 0-2.152-.717 1 1 0 0 1-1.131-1.131 2.5 2.5 0 0 0-.717-2.152A1 1 0 0 1 3 16h4",key:"qexcha"}]]),ud=k("cannabis",[["path",{d:"M12 22v-4",key:"1utk9m"}],["path",{d:"M7 12c-1.5 0-4.5 1.5-5 3 3.5 1.5 6 1 6 1-1.5 1.5-2 3.5-2 5 2.5 0 4.5-1.5 6-3 1.5 1.5 3.5 3 6 3 0-1.5-.5-3.5-2-5 0 0 2.5.5 6-1-.5-1.5-3.5-3-5-3 1.5-1 4-4 4-6-2.5 0-5.5 1.5-7 3 0-2.5-.5-5-2-7-1.5 2-2 4.5-2 7-1.5-1.5-4.5-3-7-3 0 2 2.5 5 4 6",key:"1mezod"}]]),Md=k("captions-off",[["path",{d:"M10.5 5H19a2 2 0 0 1 2 2v8.5",key:"jqtk4d"}],["path",{d:"M17 11h-.5",key:"1961ue"}],["path",{d:"M19 19H5a2 2 0 0 1-2-2V7a2 2 0 0 1 2-2",key:"1keqsi"}],["path",{d:"m2 2 20 20",key:"1ooewy"}],["path",{d:"M7 11h4",key:"1o1z6v"}],["path",{d:"M7 15h2.5",key:"1ina1g"}]]),sd=k("captions",[["rect",{width:"18",height:"14",x:"3",y:"5",rx:"2",ry:"2",key:"12ruh7"}],["path",{d:"M7 15h4M15 15h2M7 11h2M13 11h4",key:"1ueiar"}]]),vd=k("car-front",[["path",{d:"m21 8-2 2-1.5-3.7A2 2 0 0 0 15.646 5H8.4a2 2 0 0 0-1.903 1.257L5 10 3 8",key:"1imjwt"}],["path",{d:"M7 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0 2 2h2",key:"19jm3t"}],["path",{d:"M2 9h3a1 1 0 0 1 1 1v2a1 1 0 0 1-1 1H2",key:"13hakp"}],["path",{d:"M22 17v1a1 1 0 0 1-1 1H10v-9a1 1 0 0 1 1-1h2a1 1 0 0 1 1 1v9",key:"1crci8"}],["circle",{cx:"8",cy:"19",r:"2",key:"t8fc5s"}]]),xd=k("card-sim",[["path",{d:"M12 14v4",key:"1thi36"}],["path",{d:"M14.172 2a2 2 0 0 1 1.414.586l3.828 3.828A2 2 0 0 1 20 7.828V20a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2z",key:"1o66bk"}],["path",{d:"M8 14h8",key:"1fgep2"}],["rect",{x:"8",y:"10",width:"8",height:"8",rx:"1",key:"1aonk6"}]]),wd=k("carrot",[["path",{d:"M2.27 21.7s9.87-3.5 12.73-6.36a4.5 4.5 0 0 0-6.36-6.37C5.77 11.84 2.27 21.7 2.27 21.7zM8.64 14l-2.05-2.04M15.34 15l-2.46-2.46",key:"rfqxbe"}],["path",{d:"M22 9s-1.33-2-3.5-2C16.86 7 15 9 15 9s1.33 2 3.5 2S22 9 22 9z",key:"6b25w4"}],["path",{d:"M15 2s-2 1.33-2 3.5S15 9 15 9s2-1.84 2-3.5C17 3.33 15 2 15 2z",key:"fn65lo"}]]),fd=k("case-lower",[["path",{d:"M10 9v7",key:"ylp826"}],["path",{d:"M14 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2h-6",key:"3zrzxg"}],["path",{d:"M2 12a9 9 0 0 1 8 8",key:"g6cvee"}],["path",{d:"M2 16a5 5 0 0 1 4 4",key:"1y1dii"}],["line",{x1:"2",x2:"2.01",y1:"20",y2:"20",key:"xu2jvo"}]]),Sd=k("castle",[["path",{d:"M10 5V3",key:"1y54qe"}],["path",{d:"M14 5V3",key:"m6isi"}],["path",{d:"M15 21v-3a3 3 0 0 0-6 0v3",key:"lbp5hj"}],["path",{d:"M18 3v8",key:"2ollhf"}],["path",{d:"M18 5H6",key:"98imr9"}],["path",{d:"M22 11H2",key:"1lmjae"}],["path",{d:"M22 9v10a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V9",key:"1rly83"}],["path",{d:"M6 3v8",key:"csox7g"}]]),zd=k("cat",[["path",{d:"M12 5c.67 0 1.35.09 2 .26 1.78-2 5.03-2.84 6.42-2.26 1.4.58-.42 7-.42 7 .57 1.07 1 2.24 1 3.44C21 17.9 16.97 21 12 21s-9-3-9-7.56c0-1.25.5-2.4 1-3.44 0 0-1.89-6.42-.5-7 1.39-.58 4.72.23 6.5 2.23A9.04 9.04 0 0 1 12 5Z",key:"x6xyqk"}],["path",{d:"M8 14v.5",key:"1nzgdb"}],["path",{d:"M16 14v.5",key:"1lajdz"}],["path",{d:"M11.25 16.25h1.5L12 17l-.75-.75Z",key:"12kq1m"}]]),Ad=k("cctv",[["path",{d:"M16.75 12h3.632a1 1 0 0 1 .894 1.447l-2.034 4.069a1 1 0 0 1-1.708.134l-2.124-2.97",key:"ir91b5"}],["path",{d:"M17.106 9.053a1 1 0 0 1 .447 1.341l-3.106 6.211a1 1 0 0 1-1.342.447L3.61 12.3a2.92 2.92 0 0 1-1.3-3.91L3.69 5.6a2.92 2.92 0 0 1 3.92-1.3z",key:"jlp8i1"}],["path",{d:"M2 19h3.76a2 2 0 0 0 1.8-1.1L9 15",key:"19bib8"}],["path",{d:"M2 21v-4",key:"l40lih"}],["path",{d:"M7 9h.01",key:"19b3jx"}]]),Hd=k("chart-area",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M7 11.207a.5.5 0 0 1 .146-.353l2-2a.5.5 0 0 1 .708 0l3.292 3.292a.5.5 0 0 0 .708 0l4.292-4.292a.5.5 0 0 1 .854.353V16a1 1 0 0 1-1 1H8a1 1 0 0 1-1-1z",key:"q0gr47"}]]),Vd=k("chart-bar-big",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["rect",{x:"7",y:"13",width:"9",height:"4",rx:"1",key:"1iip1u"}],["rect",{x:"7",y:"5",width:"12",height:"4",rx:"1",key:"1anskk"}]]),Pd=k("chart-bar-decreasing",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M7 11h8",key:"1feolt"}],["path",{d:"M7 16h3",key:"ur6vzw"}],["path",{d:"M7 6h12",key:"sz5b0d"}]]),jd=k("chart-bar-increasing",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M7 11h8",key:"1feolt"}],["path",{d:"M7 16h12",key:"wsnu98"}],["path",{d:"M7 6h3",key:"w9rmul"}]]),Bd=k("chart-bar-stacked",[["path",{d:"M11 13v4",key:"vyy2rb"}],["path",{d:"M15 5v4",key:"1gx88a"}],["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["rect",{x:"7",y:"13",width:"9",height:"4",rx:"1",key:"1iip1u"}],["rect",{x:"7",y:"5",width:"12",height:"4",rx:"1",key:"1anskk"}]]),Dd=k("chart-bar",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M7 16h8",key:"srdodz"}],["path",{d:"M7 11h12",key:"127s9w"}],["path",{d:"M7 6h3",key:"w9rmul"}]]),Fd=k("chart-candlestick",[["path",{d:"M9 5v4",key:"14uxtq"}],["rect",{width:"4",height:"6",x:"7",y:"9",rx:"1",key:"f4fvz0"}],["path",{d:"M9 15v2",key:"r5rk32"}],["path",{d:"M17 3v2",key:"1l2re6"}],["rect",{width:"4",height:"8",x:"15",y:"5",rx:"1",key:"z38je5"}],["path",{d:"M17 13v3",key:"5l0wba"}],["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}]]),Rd=k("chart-column-big",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["rect",{x:"15",y:"5",width:"4",height:"12",rx:"1",key:"q8uenq"}],["rect",{x:"7",y:"8",width:"4",height:"9",rx:"1",key:"sr5ea"}]]),Td=k("chart-column-decreasing",[["path",{d:"M13 17V9",key:"1fwyjl"}],["path",{d:"M18 17v-3",key:"1sqioe"}],["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M8 17V5",key:"1wzmnc"}]]),Ud=k("chart-column-increasing",[["path",{d:"M13 17V9",key:"1fwyjl"}],["path",{d:"M18 17V5",key:"sfb6ij"}],["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M8 17v-3",key:"17ska0"}]]),Od=k("chart-column-stacked",[["path",{d:"M11 13H7",key:"t0o9gq"}],["path",{d:"M19 9h-4",key:"rera1j"}],["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["rect",{x:"15",y:"5",width:"4",height:"12",rx:"1",key:"q8uenq"}],["rect",{x:"7",y:"8",width:"4",height:"9",rx:"1",key:"sr5ea"}]]),Gd=k("chart-column",[["path",{d:"M3 3v16a2 2 0 0 0 2 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2z",key:"18887p"}]]),pv=k("messages-square",[["path",{d:"M16 10a2 2 0 0 1-2 2H6.828a2 2 0 0 0-1.414.586l-2.202 2.202A.71.71 0 0 1 2 14.286V4a2 2 0 0 1 2-2h10a2 2 0 0 1 2 2z",key:"1n2ejm"}],["path",{d:"M20 9a2 2 0 0 1 2 2v10.286a.71.71 0 0 1-1.212.502l-2.202-2.202A2 2 0 0 0 17.172 19H10a2 2 0 0 1-2-2v-1",key:"1qfcsi"}]]),nv=k("mic-off",[["path",{d:"M12 19v3",key:"npa21l"}],["path",{d:"M15 9.34V5a3 3 0 0 0-5.68-1.33",key:"1gzdoj"}],["path",{d:"M16.95 16.95A7 7 0 0 1 5 12v-2",key:"cqa7eg"}],["path",{d:"M18.89 13.23A7 7 0 0 0 19 12v-2",key:"16hl24"}],["path",{d:"m2 2 20 20",key:"1ooewy"}],["path",{d:"M9 9v3a3 3 0 0 0 5.12 2.12",key:"r2i35w"}]]),lv=k("mic-vocal",[["path",{d:"m11 7.601-5.994 8.19a1 1 0 0 0 .1 1.298l.817.818a1 1 0 0 0 1.314.087L15.09 12",key:"80a601"}],["path",{d:"M16.5 21.174C15.5 20.5 14.372 20 13 20c-2.058 0-3.928 2.356-6 2-2.072-.356-2.775-3.369-1.5-4.5",key:"j0ngtp"}],["circle",{cx:"16",cy:"7",r:"5",key:"d08jfb"}]]),uv=k("mic",[["path",{d:"M12 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1v3",key:"1drr47"}]]),vv=k("microwave",[["rect",{width:"20",height:"15",x:"2",y:"4",rx:"2",key:"2no95f"}],["rect",{width:"8",height:"7",x:"6",y:"8",rx:"1",key:"zh9wx"}],["path",{d:"M18 8v7",key:"o5zi4n"}],["path",{d:"M6 19v2",key:"1loha6"}],["path",{d:"M18 19v2",key:"1dawf0"}]]),mv=k("milestone",[["path",{d:"M12 13v8",key:"1l5pq0"}],["path",{d:"M12 3v3",key:"1n5kay"}],["path",{d:"M4 6a1 1 0 0 0-1 1v5a1 1 0 0 0 1 1h13a2 2 0 0 0 1.152-.365l3.424-2.317a1 1 0 0 0 0-1.635l-3.424-2.318A2 2 0 0 0 17 6z",key:"1btarq"}]]),gv=k("milk-off",[["path",{d:"M8 2h8",key:"1ssgc1"}],["path",{d:"M9 2v1.343M15 2v2.789a4 4 0 0 0 .672 2.219l.656.984a4 4 0 0 1 .672 2.22v1.131M7.8 7.8l-.128.192A4 4 0 0 0 7 10.212V20a2 2 0 0 0 2 2h6a2 2 0 0 0 2-2v-3",key:"y0ejgx"}],["path",{d:"M7 15a6.47 6.47 0 0 1 5 0 6.472 6.472 0 0 0 3.435.435",key:"iaxqsy"}],["line",{x1:"2",x2:"22",y1:"2",y2:"22",key:"a6p6uj"}]]),Lv=k("milk",[["path",{d:"M8 2h8",key:"1ssgc1"}],["path",{d:"M9 2v2.789a4 4 0 0 1-.672 2.219l-.656.984A4 4 0 0 0 7 10.212V20a2 2 0 0 0 2 2h6a2 2 0 0 0 2-2v-9.789a4 4 0 0 0-.672-2.219l-.656-.984A4 4 0 0 1 15 4.788V2",key:"qtp12x"}],["path",{d:"M7 15a6.472 6.472 0 0 1 5 0 6.47 6.47 0 0 0 5 0",key:"ygeh44"}]]),xv=k("minimize-2",[["path",{d:"m14 10 7-7",key:"oa77jy"}],["path",{d:"M20 10h-6V4",key:"mjg0md"}],["path",{d:"m3 21 7-7",key:"tjx5ai"}],["path",{d:"M4 14h6v6",key:"rmj7iw"}]]),wv=k("minimize",[["path",{d:"M8 3v3a2 2 0 0 1-2 2H3",key:"hohbtr"}],["path",{d:"M21 8h-3a2 2 0 0 1-2-2V3",key:"5jw1f3"}],["path",{d:"M3 16h3a2 2 0 0 1 2 2v3",key:"198tvr"}],["path",{d:"M16 21v-3a2 2 0 0 1 2-2h3",key:"ph8mxp"}]]),fv=k("minus",[["path",{d:"M5 12h14",key:"1ays0h"}]]),Cv=k("monitor-check",[["path",{d:"m9 10 2 2 4-4",key:"1gnqz4"}],["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}]]),Iv=k("monitor-cog",[["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"m14.305 7.53.923-.382",key:"1mlnsw"}],["path",{d:"m15.228 4.852-.923-.383",key:"82mpwg"}],["path",{d:"m16.852 3.228-.383-.924",key:"ln4sir"}],["path",{d:"m16.852 8.772-.383.923",key:"1dejw0"}],["path",{d:"m19.148 3.228.383-.924",key:"192kgf"}],["path",{d:"m19.53 9.696-.382-.924",key:"fiavlr"}],["path",{d:"m20.772 4.852.924-.383",key:"1j8mgp"}],["path",{d:"m20.772 7.148.924.383",key:"zix9be"}],["path",{d:"M22 13v2a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h7",key:"1tnzv8"}],["path",{d:"M8 21h8",key:"1ev6f3"}],["circle",{cx:"18",cy:"6",r:"3",key:"1h7g24"}]]),bv=k("monitor-dot",[["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M22 12.307V15a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h8.693",key:"1dx6ho"}],["path",{d:"M8 21h8",key:"1ev6f3"}],["circle",{cx:"19",cy:"6",r:"3",key:"108a5v"}]]),qv=k("monitor-down",[["path",{d:"M12 13V7",key:"h0r20n"}],["path",{d:"m15 10-3 3-3-3",key:"lzhmyn"}],["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}]]),Sv=k("monitor-off",[["path",{d:"M17 17H4a2 2 0 0 1-2-2V5c0-1.5 1-2 1-2",key:"k0q8oc"}],["path",{d:"M22 15V5a2 2 0 0 0-2-2H9",key:"cp1ac0"}],["path",{d:"M8 21h8",key:"1ev6f3"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"m2 2 20 20",key:"1ooewy"}]]),zv=k("monitor-pause",[["path",{d:"M10 13V7",key:"1u13u9"}],["path",{d:"M14 13V7",key:"1vj9om"}],["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}]]),Av=k("monitor-play",[["path",{d:"M15.033 9.44a.647.647 0 0 1 0 1.12l-4.065 2.352a.645.645 0 0 1-.968-.56V7.648a.645.645 0 0 1 .967-.56z",key:"vbtd3f"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}],["rect",{x:"2",y:"3",width:"20",height:"14",rx:"2",key:"x3v2xh"}]]),Hv=k("monitor-smartphone",[["path",{d:"M18 8V6a2 2 0 0 0-2-2H4a2 2 0 0 0-2 2v7a2 2 0 0 0 2 2h8",key:"10dyio"}],["path",{d:"M10 19v-3.96 3.15",key:"1irgej"}],["path",{d:"M7 19h5",key:"qswx4l"}],["rect",{width:"6",height:"10",x:"16",y:"12",rx:"2",key:"1egngj"}]]),Vv=k("monitor-speaker",[["path",{d:"M5.5 20H8",key:"1k40s5"}],["path",{d:"M17 9h.01",key:"1j24nn"}],["rect",{width:"10",height:"16",x:"12",y:"4",rx:"2",key:"ixliua"}],["path",{d:"M8 6H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h4",key:"1mp6e1"}],["circle",{cx:"17",cy:"15",r:"1",key:"tqvash"}]]),Pv=k("monitor-stop",[["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}],["rect",{x:"2",y:"3",width:"20",height:"14",rx:"2",key:"x3v2xh"}],["rect",{x:"9",y:"7",width:"6",height:"6",rx:"1",key:"5m2oou"}]]),jv=k("monitor-up",[["path",{d:"m9 10 3-3 3 3",key:"11gsxs"}],["path",{d:"M12 13V7",key:"h0r20n"}],["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}],["path",{d:"M12 17v4",key:"1riwvh"}],["path",{d:"M8 21h8",key:"1ev6f3"}]]),Bv=k("monitor-x",[["path",{d:"m14.5 12.5-5-5",key:"1jahn5"}],["path",{d:"m9.5 12.5 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1h11",key:"c9qhm2"}]]),aL=k("pin",[["path",{d:"M12 17v5",key:"bb1du9"}],["path",{d:"M9 10.76a2 2 0 0 1-1.11 1.79l-1.78.9A2 2 0 0 0 5 15.24V16a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-.76a2 2 0 0 0-1.11-1.79l-1.78-.9A2 2 0 0 1 15 10.76V7a1 1 0 0 1 1-1 2 2 0 0 0 0-4H8a2 2 0 0 0 0 4 1 1 0 0 1 1 1z",key:"1nkz8b"}]]),tL=k("pipette",[["path",{d:"m12 9-8.414 8.414A2 2 0 0 0 3 18.828v1.344a2 2 0 0 1-.586 1.414A2 2 0 0 1 3.828 21h1.344a2 2 0 0 0 1.414-.586L15 12",key:"1y3wsu"}],["path",{d:"m18 9 .4.4a1 1 0 1 1-3 3l-3.8-3.8a1 1 0 1 1 3-3l.4.4 3.4-3.4a1 1 0 1 1 3 3z",key:"110lr1"}],["path",{d:"m2 22 .414-.414",key:"jhxm08"}]]),hL=k("pizza",[["path",{d:"m12 14-1 1",key:"11onhr"}],["path",{d:"m13.75 18.25-1.25 1.42",key:"1yisr3"}],["path",{d:"M17.775 5.654a15.68 15.68 0 0 0-12.121 12.12",key:"1qtqk6"}],["path",{d:"M18.8 9.3a1 1 0 0 0 2.1 7.7",key:"fbbbr2"}],["path",{d:"M21.964 20.732a1 1 0 0 1-1.232 1.232l-18-5a1 1 0 0 1-.695-1.232A19.68 19.68 0 0 1 15.732 2.037a1 1 0 0 1 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6h.01",key:"1v4wsw"}],["path",{d:"M6 18h.01",key:"uhywen"}],["path",{d:"M20.83 8.83a4 4 0 0 0-5.66-5.66l-12 12a4 4 0 1 0 5.66 5.66Z",key:"6fykxj"}],["path",{d:"M18 11.66V22a4 4 0 0 0 4-4V6",key:"1utzek"}]]),lL=k("pocket",[["path",{d:"M20 3a2 2 0 0 1 2 2v6a1 1 0 0 1-20 0V5a2 2 0 0 1 2-2z",key:"1uodqw"}],["path",{d:"m8 10 4 4 4-4",key:"1mxd5q"}]]),uL=k("podcast",[["path",{d:"M13 17a1 1 0 1 0-2 0l.5 4.5a0.5 0.5 0 0 0 1 0z",fill:"currentColor",key:"x1mxqr"}],["path",{d:"M16.85 18.58a9 9 0 1 0-9.7 0",key:"d71mpg"}],["path",{d:"M8 14a5 5 0 1 1 8 0",key:"fc81rn"}],["circle",{cx:"12",cy:"11",r:"1",fill:"currentColor",key:"vqiwd"}]]),ML=k("pointer-off",[["path",{d:"M10 4.5V4a2 2 0 0 0-2.41-1.957",key:"jsi14n"}],["path",{d:"M13.9 8.4a2 2 0 0 0-1.26-1.295",key:"hirc7f"}],["path",{d:"M21.7 16.2A8 8 0 0 0 22 14v-3a2 2 0 1 0-4 0v-1a2 2 0 0 0-3.63-1.158",key:"1jxb2e"}],["path",{d:"m7 15-1.8-1.8a2 2 0 0 0-2.79 2.86L6 19.7a7.74 7.74 0 0 0 6 2.3h2a8 8 0 0 0 5.657-2.343",key:"10r7hm"}],["path",{d:"M6 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0-2 2v14a2 2 0 0 0 2 2h6",key:"14rsvq"}],["path",{d:"m21 21-9-9",key:"1et2py"}],["path",{d:"M21 15v6h-6",key:"1jko0i"}]]),yC=k("square-arrow-out-up-left",[["path",{d:"M13 3h6a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-6",key:"14mv1t"}],["path",{d:"m3 3 9 9",key:"rks13r"}],["path",{d:"M3 9V3h6",key:"ira0h2"}]]),oC=k("square-arrow-out-up-right",[["path",{d:"M21 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h6",key:"y09zxi"}],["path",{d:"m21 3-9 9",key:"mpx6sq"}],["path",{d:"M15 3h6v6",key:"1q9fwt"}]]),kC=k("square-arrow-right",[["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M8 12h8",key:"1wcyev"}],["path",{d:"m12 16 4-4-4-4",key:"1i9zcv"}]]),pC=k("square-arrow-up-left",[["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M8 16V8h8",key:"19xb1h"}],["path",{d:"M16 16 8 8",key:"1qdy8n"}]]),nC=k("square-arrow-up-right",[["rect",{width:"18",height:"18",x:"3",y:"3",rx:"2",key:"afitv7"}],["path",{d:"M8 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key"})]}),"\n",(0,o.jsxs)(s,{children:[(0,o.jsx)("summary",{children:"Create a Langflow API key"}),(0,o.jsxs)(n.p,{children:["A Langflow API key is a user-specific token you can use with Langflow.\nIt is ",(0,o.jsx)(n.strong,{children:"only"})," used for sending requests to the Langflow server.\nIt does ",(0,o.jsx)(n.strong,{children:"not"})," access to OpenRAG."]}),(0,o.jsx)(n.p,{children:"To create a Langflow API key, do the following:"}),(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In Langflow, click your user icon, and then select ",(0,o.jsx)(n.strong,{children:"Settings"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(n.strong,{children:"Langflow API Keys"}),", and then click ",(0,o.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add New"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Name your key, and then click 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Click ",(0,o.jsx)(n.strong,{children:"Proceed"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["The OpenRAG OpenSearch Agent flow appears.\n",(0,o.jsx)(n.img,{alt:"OpenRAG Open Search Agent Flow",src:t(3982).A+"",width:"4108",height:"2250"})]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In the ",(0,o.jsx)(n.strong,{children:"Language Model"})," component, under ",(0,o.jsx)(n.strong,{children:"Model"}),", select a different OpenAI model."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Save your flow with ",(0,o.jsx)("kbd",{children:"Command+S"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In OpenRAG, start a new conversation by clicking the ",(0,o.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," in the ",(0,o.jsx)(n.strong,{children:"Conversations"})," tab."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Ask the same question as before to demonstrate how a different language model changes the results."}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"integrate-openrag-into-your-application",children:"Integrate OpenRAG into your application"}),"\n",(0,o.jsxs)(n.p,{children:["To integrate OpenRAG into your application, use the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-reference-api-examples",children:"Langflow API"}),".\nMake requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results."]}),"\n",(0,o.jsx)(n.p,{children:"Langflow provides code snippets to help you get started with the Langflow API."}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To navigate to the OpenRAG OpenSearch Agent flow, click ",(0,o.jsx)(a.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and then click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"})," in the OpenRAG OpenSearch Agent flow."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(n.strong,{children:"Share"}),", and then click ",(0,o.jsx)(n.strong,{children:"API access"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["The default code in the API access pane constructs a request with the Langflow server ",(0,o.jsx)(n.code,{children:"url"}),", ",(0,o.jsx)(n.code,{children:"headers"}),", and a ",(0,o.jsx)(n.code,{children:"payload"})," of request data. The code snippets automatically include the ",(0,o.jsx)(n.code,{children:"LANGFLOW_SERVER_ADDRESS"})," and ",(0,o.jsx)(n.code,{children:"FLOW_ID"})," values for the flow. Replace these values if you're using the code for a different server or flow. The default Langflow server address is ",(0,o.jsx)(n.a,{href:"http://localhost:7860",children:"http://localhost:7860"}),"."]}),"\n",(0,o.jsxs)(i.A,{children:[(0,o.jsx)(l.A,{value:"python",label:"Python",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-python",children:'import requests\nimport os\nimport uuid\n\napi_key = \'LANGFLOW_API_KEY\'\nurl = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow\n\n# Request payload configuration\npayload = {\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "hello world!"\n}\npayload["session_id"] = str(uuid.uuid4())\n\nheaders = {"x-api-key": api_key}\n\ntry:\n # Send API request\n response = requests.request("POST", url, json=payload, headers=headers)\n response.raise_for_status() # Raise exception for bad status codes\n\n # Print response\n print(response.text)\n\nexcept requests.exceptions.RequestException as e:\n print(f"Error making API request: {e}")\nexcept ValueError as e:\n print(f"Error parsing response: {e}")\n'})})}),(0,o.jsx)(l.A,{value:"typescript",label:"TypeScript",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-typescript",children:'const crypto = require(\'crypto\');\nconst apiKey = \'LANGFLOW_API_KEY\';\nconst payload = {\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "hello world!"\n};\npayload.session_id = crypto.randomUUID();\n\nconst options = {\n method: \'POST\',\n headers: {\n \'Content-Type\': \'application/json\',\n "x-api-key": apiKey\n },\n body: JSON.stringify(payload)\n};\n\nfetch(\'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID\', options)\n .then(response => response.json())\n .then(response => console.warn(response))\n .catch(err => console.error(err));\n'})})}),(0,o.jsx)(l.A,{value:"curl",label:"curl",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:'curl --request POST \\\n --url \'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false\' \\\n --header \'Content-Type: application/json\' \\\n --header "x-api-key: LANGFLOW_API_KEY" \\ \n --data \'{\n\t\t "output_type": "chat",\n\t\t "input_type": "chat",\n\t\t "input_value": "hello world!",\n\t\t }\'\n'})})})]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Copy the snippet, paste it in a script file, and then run the script to send the request. If you are using the curl snippet, you can run the command directly in your terminal."}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.p,{children:["If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.\nThe following is an example of a response from running the ",(0,o.jsx)(n.strong,{children:"Simple Agent"})," template flow:"]}),"\n",(0,o.jsxs)(s,{children:[(0,o.jsx)("summary",{children:"Result"}),(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-json",children:'{\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "outputs": [\n {\n "inputs": {\n "input_value": "hello world!"\n },\n "outputs": [\n {\n "results": {\n "message": {\n "text_key": "text",\n "data": {\n "timestamp": "2025-06-16 19:58:23 UTC",\n "sender": "Machine",\n "sender_name": "AI",\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "text": "Hello world! \ud83c\udf0d How can I assist you today?",\n "files": [],\n "error": false,\n "edit": false,\n "properties": {\n "text_color": "",\n "background_color": "",\n "edited": false,\n "source": {\n "id": "Agent-ZOknz",\n "display_name": "Agent",\n "source": "gpt-4o-mini"\n },\n "icon": "bot",\n "allow_markdown": false,\n "positive_feedback": null,\n "state": "complete",\n "targets": []\n },\n "category": "message",\n "content_blocks": [\n {\n "title": "Agent Steps",\n "contents": [\n {\n "type": "text",\n "duration": 2,\n "header": {\n "title": "Input",\n "icon": "MessageSquare"\n },\n "text": "**Input**: hello world!"\n },\n {\n "type": "text",\n "duration": 226,\n "header": {\n "title": "Output",\n "icon": "MessageSquare"\n },\n "text": "Hello world! \ud83c\udf0d How can I assist you today?"\n }\n ],\n "allow_markdown": true,\n "media_url": null\n }\n ],\n "id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",\n "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "duration": null\n },\n "default_value": "",\n "text": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "files": [],\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "timestamp": "2025-06-16T19:58:23+00:00",\n "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "error": false,\n "edit": false,\n "properties": {\n "text_color": "",\n "background_color": "",\n "edited": false,\n "source": {\n "id": "Agent-ZOknz",\n "display_name": "Agent",\n "source": "gpt-4o-mini"\n },\n "icon": "bot",\n "allow_markdown": false,\n "positive_feedback": null,\n "state": "complete",\n "targets": []\n },\n "category": "message",\n "content_blocks": [\n {\n "title": "Agent Steps",\n "contents": [\n {\n "type": "text",\n "duration": 2,\n "header": {\n "title": "Input",\n "icon": "MessageSquare"\n },\n "text": "**Input**: hello world!"\n },\n {\n "type": "text",\n "duration": 226,\n "header": {\n "title": "Output",\n "icon": "MessageSquare"\n },\n "text": "Hello world! \ud83c\udf0d How can I assist you today?"\n }\n ],\n "allow_markdown": true,\n "media_url": null\n }\n ],\n "duration": null\n }\n },\n "artifacts": {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "files": [],\n "type": "object"\n },\n "outputs": {\n "message": {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "type": "text"\n }\n },\n "logs": {\n "message": []\n },\n "messages": [\n {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "stream_url": null,\n "component_id": "ChatOutput-aF5lw",\n "files": [],\n "type": "text"\n }\n ],\n "timedelta": null,\n "duration": null,\n "component_display_name": "Chat Output",\n "component_id": "ChatOutput-aF5lw",\n "used_frozen_result": false\n }\n ]\n }\n ]\n}\n'})})]}),"\n",(0,o.jsx)(n.p,{children:"To further explore the API, see:"}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:["The Langflow ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/quickstart#extract-data-from-the-response",children:"Quickstart"})," extends this example with extracting fields from the response."]}),"\n",(0,o.jsx)(n.li,{children:(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-reference-api-examples",children:"Get started with the Langflow API"})}),"\n"]})]})}function g(e={}){const{wrapper:n}={...(0,r.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(p,{...e})}):p(e)}},3982:(e,n,t)=>{t.d(n,{A:()=>s});const s=t.p+"assets/images/opensearch-agent-flow-a3c183a9f2d5ec513819aba1f1b49c39.png"},9179:(e,n,t)=>{t.d(n,{A:()=>r});t(6540);var s=t(4827),o=t(4848);function r({name:e,...n}){const t=s[e];return t?(0,o.jsx)(t,{...n}):null}}}]); \ No newline at end of file diff --git a/assets/js/ca2c3c0c.40e07761.js b/assets/js/ca2c3c0c.40e07761.js new file mode 100644 index 00000000..46baa793 --- /dev/null +++ b/assets/js/ca2c3c0c.40e07761.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[919],{3782:(e,n,s)=>{s.d(n,{Ay:()=>a,RM:()=>r});var i=s(4848),o=s(8453),t=s(9179);const r=[];function c(e){const n={a:"a",p:"p",strong:"strong",...(0,o.R)(),...e.components};return(0,i.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.\nTo modify a flow, click ",(0,i.jsx)(t.A,{name:"Settings2","aria-hidden":"true"})," ",(0,i.jsx)(n.strong,{children:"Settings"}),", and click ",(0,i.jsx)(n.strong,{children:"Edit in Langflow"}),".\nFlows are edited in the same way as in the ",(0,i.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),"."]})}function a(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(c,{...e})}):c(e)}},7125:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>a,contentTitle:()=>c,default:()=>g,frontMatter:()=>r,metadata:()=>i,toc:()=>l});const i=JSON.parse('{"id":"core-components/ingestion","title":"Docling Ingestion","description":"OpenRAG uses Docling for its document ingestion pipeline.","source":"@site/docs/core-components/ingestion.mdx","sourceDirName":"core-components","slug":"/ingestion","permalink":"/openrag/ingestion","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/core-components/ingestion.mdx","tags":[],"version":"current","frontMatter":{"title":"Docling Ingestion","slug":"/ingestion"},"sidebar":"tutorialSidebar","previous":{"title":"OpenSearch Knowledge","permalink":"/openrag/knowledge"},"next":{"title":"Environment Variables","permalink":"/openrag/configure/configuration"}}');var o=s(4848),t=s(8453);s(9179),s(1470),s(9365),s(3782);const r={title:"Docling Ingestion",slug:"/ingestion"},c=void 0,a={},l=[{value:"Docling ingestion settings",id:"docling-ingestion-settings",level:2},{value:"Use OpenRAG default ingestion instead of Docling serve",id:"use-openrag-default-ingestion-instead-of-docling-serve",level:2}];function d(e){const n={a:"a",code:"code",h2:"h2",p:"p",strong:"strong",...(0,t.R)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsxs)(n.p,{children:["OpenRAG uses ",(0,o.jsx)(n.a,{href:"https://docling-project.github.io/docling/",children:"Docling"})," for its document ingestion pipeline.\nMore specifically, OpenRAG uses ",(0,o.jsx)(n.a,{href:"https://github.com/docling-project/docling-serve",children:"Docling Serve"}),", which starts a ",(0,o.jsx)(n.code,{children:"docling-serve"})," process on your local machine and runs Docling ingestion through an API service."]}),"\n",(0,o.jsxs)(n.p,{children:["Docling ingests documents from your local machine or OAuth connectors, splits them into chunks, and stores them as separate, structured documents in the OpenSearch ",(0,o.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images."}),"\n",(0,o.jsx)(n.h2,{id:"docling-ingestion-settings",children:"Docling ingestion settings"}),"\n",(0,o.jsx)(n.p,{children:"These settings configure the Docling ingestion parameters."}),"\n",(0,o.jsxs)(n.p,{children:["OpenRAG will warn you if ",(0,o.jsx)(n.code,{children:"docling-serve"})," is not running.\nTo start or stop ",(0,o.jsx)(n.code,{children:"docling-serve"})," or any other native services, in the TUI main menu, click ",(0,o.jsx)(n.strong,{children:"Start Native Services"})," or ",(0,o.jsx)(n.strong,{children:"Stop Native Services"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:[(0,o.jsx)(n.strong,{children:"Embedding model"})," determines which AI model is used to create vector embeddings. 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commands."]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.a,{href:"get-started/docker",children:(0,t.jsx)(n.strong,{children:"Docker Compose"})}),": Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies."]}),"\n"]}),"\n"]}),"\n",(0,t.jsx)(n.h2,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.a,{href:"https://www.python.org/downloads/release/python-3100/",children:"Python Version 3.10 to 3.13"})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.a,{href:"https://docs.astral.sh/uv/getting-started/installation/",children:"uv"})}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.a,{href:"https://docs.docker.com/get-docker/",children:"Docker"})," or ",(0,t.jsx)(n.a,{href:"https://podman.io/docs/installation",children:"Podman"})," installed"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.a,{href:"https://docs.docker.com/compose/install/",children:"Docker Compose"})," installed. 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For more, see the ",(0,t.jsx)(n.a,{href:"https://docs.langflow.org/api-keys-and-authentication#langflow-enable-superuser-cli",children:"Langflow docs"}),"."]})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:(0,t.jsx)(n.code,{children:"DOCUMENTS_PATH"})}),(0,t.jsx)(n.td,{children:"Set your local path"}),(0,t.jsx)(n.td,{children:"Path to your document storage directory."})]})]})]})]}),"\n",(0,t.jsxs)(n.p,{children:["To complete credentials, click ",(0,t.jsx)(n.strong,{children:"Save Configuration"}),"."]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:["To start OpenRAG with your credentials, click ",(0,t.jsx)(n.strong,{children:"Start Container Services"}),".\nStartup pulls container images and starts them, so it can take some time.\nThe operation has completed when the ",(0,t.jsx)(n.strong,{children:"Close"})," button is available and the terminal displays:"]}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-bash",children:"Services started successfully\nCommand completed successfully\n"})}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:["To open the OpenRAG application, click ",(0,t.jsx)(n.strong,{children:"Open App"}),", press ",(0,t.jsx)("kbd",{children:"6"}),", or navigate to ",(0,t.jsx)(n.code,{children:"http://localhost:3000"}),".\nThe application opens."]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:["Select your language model and embedding model provider, and complete the required fields.\n",(0,t.jsx)(n.strong,{children:"Your provider can only be selected once, and you must use the same provider for your language model and embedding model."}),"\nThe language model can be changed, but the embeddings model cannot be changed.\nTo change your provider selection, you must restart OpenRAG and delete the ",(0,t.jsx)(n.code,{children:"config.yml"})," file."]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(a.A,{groupId:"Embedding provider",children:[(0,t.jsx)(o.A,{value:"OpenAI",label:"OpenAI",default:!0,children:(0,t.jsxs)(n.ol,{start:"9",children:["\n",(0,t.jsxs)(n.li,{children:["If you already entered a value for ",(0,t.jsx)(n.code,{children:"OPENAI_API_KEY"})," in the TUI in Step 5, enable ",(0,t.jsx)(n.strong,{children:"Get API key from environment variable"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["Under ",(0,t.jsx)(n.strong,{children:"Advanced settings"}),", select your ",(0,t.jsx)(n.strong,{children:"Embedding Model"})," and ",(0,t.jsx)(n.strong,{children:"Language Model"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["To load 2 sample PDFs, enable ",(0,t.jsx)(n.strong,{children:"Sample dataset"}),".\nThis is recommended, but not required."]}),"\n",(0,t.jsxs)(n.li,{children:["Click ",(0,t.jsx)(n.strong,{children:"Complete"}),"."]}),"\n"]})}),(0,t.jsx)(o.A,{value:"IBM watsonx.ai",label:"IBM watsonx.ai",children:(0,t.jsxs)(n.ol,{start:"9",children:["\n",(0,t.jsxs)(n.li,{children:["Complete the fields for ",(0,t.jsx)(n.strong,{children:"watsonx.ai API Endpoint"}),", ",(0,t.jsx)(n.strong,{children:"IBM API key"}),", and ",(0,t.jsx)(n.strong,{children:"IBM Project ID"}),".\nThese values are found in your IBM watsonx deployment."]}),"\n",(0,t.jsxs)(n.li,{children:["Under ",(0,t.jsx)(n.strong,{children:"Advanced settings"}),", select your ",(0,t.jsx)(n.strong,{children:"Embedding Model"})," and ",(0,t.jsx)(n.strong,{children:"Language Model"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["To load 2 sample PDFs, enable ",(0,t.jsx)(n.strong,{children:"Sample dataset"}),".\nThis is recommended, but not required."]}),"\n",(0,t.jsxs)(n.li,{children:["Click ",(0,t.jsx)(n.strong,{children:"Complete"}),"."]}),"\n"]})}),(0,t.jsx)(o.A,{value:"Ollama",label:"Ollama",children:(0,t.jsxs)(n.ol,{start:"9",children:["\n",(0,t.jsxs)(n.li,{children:["Enter your Ollama server's base URL address.\nThe default Ollama server address is ",(0,t.jsx)(n.code,{children:"http://localhost:11434"}),".\nSince OpenRAG is running in a container, you may need to change ",(0,t.jsx)(n.code,{children:"localhost"})," to access services outside of the container. 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a?(sl(),a=o.fallback,u=t.mode,c=(s=e.child).sibling,(o=Ir(s,{mode:"hidden",children:o.children})).subtreeFlags=65011712&s.subtreeFlags,null!==c?a=Ir(c,a):(a=$r(a,u,n,null)).flags|=2,a.return=t,o.return=t,o.sibling=a,t.child=o,o=a,a=t.child,null===(u=e.child.memoizedState)?u=Bl(n):(null!==(s=u.cachePool)?(c=Oo._currentValue,s=s.parent!==c?{parent:c,pool:c}:s):s=Go(),u={baseLanes:u.baseLanes|n,cachePool:s}),a.memoizedState=u,a.childLanes=$l(e,r,n),t.memoizedState=zl,o):(il(t),e=(n=e.child).sibling,(n=Ir(n,{mode:"visible",children:o.children})).return=t,n.sibling=null,null!==e&&(null===(r=t.deletions)?(t.deletions=[e],t.flags|=16):r.push(e)),t.child=n,t.memoizedState=null,n)}function ql(e,t){return(t=Hl({mode:"visible",children:t},e.mode)).return=e,e.child=t}function Hl(e,t){return(e=Fr(22,e,null,t)).lanes=0,e.stateNode={_visibility:1,_pendingMarkers:null,_retryCache:null,_transitions:null},e}function Gl(e,t,n){return nl(t,e.child,null,n),(e=ql(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function Vl(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),So(e.return,t,n)}function Wl(e,t,n,r,o){var a=e.memoizedState;null===a?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:o}:(a.isBackwards=t,a.rendering=null,a.renderingStartTime=0,a.last=r,a.tail=n,a.tailMode=o)}function Ql(e,t,n){var r=t.pendingProps,o=r.revealOrder,a=r.tail;if(Tl(e,t,r.children,n),2&(r=cl.current))r=1&r|2,t.flags|=128;else{if(null!==e&&128&e.flags)e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&Vl(e,n,t);else if(19===e.tag)Vl(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break 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0!==(e.lanes&t)||!(null===(e=e.dependencies)||!Co(e))}function Xl(e,t,n){if(null!==e)if(e.memoizedProps!==t.pendingProps)_l=!0;else{if(!(Yl(e,n)||128&t.flags))return _l=!1,function(e,t,n){switch(t.tag){case 3:V(t,t.stateNode.containerInfo),wo(0,Oo,e.memoizedState.cache),mo();break;case 27:case 5:Q(t);break;case 4:V(t,t.stateNode.containerInfo);break;case 10:wo(0,t.type,t.memoizedProps.value);break;case 13:var r=t.memoizedState;if(null!==r)return null!==r.dehydrated?(il(t),t.flags|=128,null):0!==(n&t.child.childLanes)?Ul(e,t,n):(il(t),null!==(e=Kl(e,t,n))?e.sibling:null);il(t);break;case 19:var o=!!(128&e.flags);if((r=0!==(n&t.childLanes))||(Eo(e,t,n,!1),r=0!==(n&t.childLanes)),o){if(r)return Ql(e,t,n);t.flags|=128}if(null!==(o=t.memoizedState)&&(o.rendering=null,o.tail=null,o.lastEffect=null),$(cl,cl.current),r)break;return null;case 22:case 23:return t.lanes=0,Ll(e,t,n);case 24:wo(0,Oo,e.memoizedState.cache)}return Kl(e,t,n)}(e,t,n);_l=!!(131072&e.flags)}else 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o=n;if(e.nodeName.toLowerCase()!==t.toLowerCase()){if(!r&&("INPUT"!==e.nodeName||"hidden"!==e.type))break}else if(r){if(!e[ze])switch(t){case"meta":if(!e.hasAttribute("itemprop"))break;return e;case"link":if("stylesheet"===(a=e.getAttribute("rel"))&&e.hasAttribute("data-precedence"))break;if(a!==o.rel||e.getAttribute("href")!==(null==o.href||""===o.href?null:o.href)||e.getAttribute("crossorigin")!==(null==o.crossOrigin?null:o.crossOrigin)||e.getAttribute("title")!==(null==o.title?null:o.title))break;return e;case"style":if(e.hasAttribute("data-precedence"))break;return e;case"script":if(((a=e.getAttribute("src"))!==(null==o.src?null:o.src)||e.getAttribute("type")!==(null==o.type?null:o.type)||e.getAttribute("crossorigin")!==(null==o.crossOrigin?null:o.crossOrigin))&&a&&e.hasAttribute("async")&&!e.hasAttribute("itemprop"))break;return e;default:return e}}else{if("input"!==t||"hidden"!==e.type)return e;var a=null==o.name?null:""+o.name;if("hidden"===o.type&&e.getAttribute("name")===a)return e}if(null===(e=yd(e.nextSibling)))break}return null}(r,t.type,t.pendingProps,lo))?(t.stateNode=r,ro=t,oo=yd(r.firstChild),lo=!1,o=!0):o=!1),o||uo(t)),Q(t),o=t.type,a=t.pendingProps,l=null!==e?e.memoizedProps:null,r=a.children,id(o,a)?r=null:null!==l&&id(o,l)&&(t.flags|=32),null!==t.memoizedState&&(o=La(e,t,Da,null,null,n),Qd._currentValue=o),Rl(e,t),Tl(e,t,r,n),t.child;case 6:return null===e&&ao&&((e=n=oo)&&(null!==(n=function(e,t,n){if(""===t)return null;for(;3!==e.nodeType;){if((1!==e.nodeType||"INPUT"!==e.nodeName||"hidden"!==e.type)&&!n)return null;if(null===(e=yd(e.nextSibling)))return null}return e}(n,t.pendingProps,lo))?(t.stateNode=n,ro=t,oo=null,e=!0):e=!1),e||uo(t)),null;case 13:return Ul(e,t,n);case 4:return V(t,t.stateNode.containerInfo),r=t.pendingProps,null===e?t.child=nl(t,null,r,n):Tl(e,t,r,n),t.child;case 11:return jl(e,t,t.type,t.pendingProps,n);case 7:return Tl(e,t,t.pendingProps,n),t.child;case 8:case 12:return Tl(e,t,t.pendingProps.children,n),t.child;case 10:return r=t.pendingProps,wo(0,t.type,r.value),Tl(e,t,r.children,n),t.child;case 9:return o=t.type._context,r=t.pendingProps.children,Ao(t),r=r(o=_o(o)),t.flags|=1,Tl(e,t,r,n),t.child;case 14:return Pl(e,t,t.type,t.pendingProps,n);case 15:return Nl(e,t,t.type,t.pendingProps,n);case 19:return Ql(e,t,n);case 31:return r=t.pendingProps,n=t.mode,r={mode:r.mode,children:r.children},null===e?((n=Hl(r,n)).ref=t.ref,t.child=n,n.return=t,t=n):((n=Ir(e.child,r)).ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return Ll(e,t,n);case 24:return Ao(t),r=_o(Oo),null===e?(null===(o=qo())&&(o=ru,a=Ro(),o.pooledCache=a,a.refCount++,null!==a&&(o.pooledCacheLanes|=n),o=a),t.memoizedState={parent:r,cache:o},ra(t),wo(0,Oo,o)):(0!==(e.lanes&n)&&(oa(e,t),da(t,null,null,n),ca()),o=e.memoizedState,a=t.memoizedState,o.parent!==r?(o={parent:r,cache:r},t.memoizedState=o,0===t.lanes&&(t.memoizedState=t.updateQueue.baseState=o),wo(0,Oo,r)):(r=a.cache,wo(0,Oo,r),r!==o.cache&&xo(t,[Oo],n,!0))),Tl(e,t,t.pendingProps.children,n),t.child;case 29:throw t.pendingProps}throw Error(i(156,t.tag))}function Zl(e){e.flags|=4}function Jl(e,t){if("stylesheet"!==t.type||4&t.state.loading)e.flags&=-16777217;else if(e.flags|=16777216,!$d(t)){if(null!==(t=ol.current)&&((4194048&au)===au?null!==al:(62914560&au)!==au&&!(536870912&au)||t!==al))throw Jo=Ko,Wo;e.flags|=8192}}function es(e,t){null!==t&&(e.flags|=4),16384&e.flags&&(t=22!==e.tag?xe():536870912,e.lanes|=t,yu|=t)}function ts(e,t){if(!ao)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function ns(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var o=e.child;null!==o;)n|=o.lanes|o.childLanes,r|=65011712&o.subtreeFlags,r|=65011712&o.flags,o.return=e,o=o.sibling;else for(o=e.child;null!==o;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags,r|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function rs(e,t,n){var r=t.pendingProps;switch(no(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:case 1:return ns(t),null;case 3:return n=t.stateNode,r=null,null!==e&&(r=e.memoizedState.cache),t.memoizedState.cache!==r&&(t.flags|=2048),ko(Oo),W(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),null!==e&&null!==e.child||(po(t)?Zl(t):null===e||e.memoizedState.isDehydrated&&!(256&t.flags)||(t.flags|=1024,ho())),ns(t),null;case 26:return n=t.memoizedState,null===e?(Zl(t),null!==n?(ns(t),Jl(t,n)):(ns(t),t.flags&=-16777217)):n?n!==e.memoizedState?(Zl(t),ns(t),Jl(t,n)):(ns(t),t.flags&=-16777217):(e.memoizedProps!==r&&Zl(t),ns(t),t.flags&=-16777217),null;case 27:K(t),n=H.current;var o=t.type;if(null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zl(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return ns(t),null}e=U.current,po(t)?co(t):(e=wd(o,r,n),t.stateNode=e,Zl(t))}return ns(t),null;case 5:if(K(t),n=t.type,null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zl(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return ns(t),null}if(e=U.current,po(t))co(t);else{switch(o=rd(H.current),e){case 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Configuration

+

OpenRAG supports multiple configuration methods with the following priority:

+
    +
  1. Environment Variables (highest priority)
    2. +
  2. Configuration File (config.yaml)
    3. +
  3. Langflow Flow Settings (runtime override)
    4. +
  4. Default Values (fallback)
    5. +
+

Configuration File​

+

Create a config.yaml file in the project root to configure OpenRAG:

+
# OpenRAG Configuration File
provider:
  model_provider: "openai" # openai, anthropic, azure, etc.
  api_key: "your-api-key" # or use OPENAI_API_KEY env var

knowledge:
  embedding_model: "text-embedding-3-small"
  chunk_size: 1000
  chunk_overlap: 200
  ocr: true
  picture_descriptions: false

agent:
  llm_model: "gpt-4o-mini"
  system_prompt: "You are a helpful AI assistant..."
+

Environment Variables​

+

Environment variables will override configuration file settings. You can still use .env files:

+
cp .env.example .env
+

Required Variables​

+
VariableDescription
OPENAI_API_KEYYour OpenAI API key
OPENSEARCH_PASSWORDPassword for OpenSearch admin user
LANGFLOW_SUPERUSERLangflow admin username
LANGFLOW_SUPERUSER_PASSWORDLangflow admin password
LANGFLOW_CHAT_FLOW_IDID of your Langflow chat flow
LANGFLOW_INGEST_FLOW_IDID of your Langflow ingestion flow
NUDGES_FLOW_IDID of your Langflow nudges/suggestions flow
+

Ingestion Configuration​

+
VariableDescription
DISABLE_INGEST_WITH_LANGFLOWDisable Langflow ingestion pipeline (default: false)
+
    +
  • false or unset: Uses Langflow pipeline (upload → ingest → delete)
    • +
  • true: Uses traditional OpenRAG processor for document ingestion
    • +
+

Optional Variables​

+
VariableDescription
LANGFLOW_PUBLIC_URLPublic URL for Langflow (default: http://localhost:7860)
GOOGLE_OAUTH_CLIENT_ID / GOOGLE_OAUTH_CLIENT_SECRETGoogle OAuth authentication
MICROSOFT_GRAPH_OAUTH_CLIENT_ID / MICROSOFT_GRAPH_OAUTH_CLIENT_SECRETMicrosoft OAuth
WEBHOOK_BASE_URLBase URL for webhook endpoints
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEYAWS integrations
SESSION_SECRETSession management (default: auto-generated, change in production)
LANGFLOW_KEYExplicit Langflow API key (auto-generated if not provided)
LANGFLOW_SECRET_KEYSecret key for Langflow internal operations
+

OpenRAG Configuration Variables​

+

These environment variables override settings in config.yaml:

+

Provider Settings​

+
VariableDescriptionDefault
MODEL_PROVIDERModel provider (openai, anthropic, etc.)openai
PROVIDER_API_KEYAPI key for the model provider
OPENAI_API_KEYOpenAI API key (backward compatibility)
+

Knowledge Settings​

+
VariableDescriptionDefault
EMBEDDING_MODELEmbedding model for vector searchtext-embedding-3-small
CHUNK_SIZEText chunk size for document processing1000
CHUNK_OVERLAPOverlap between chunks200
OCR_ENABLEDEnable OCR for image processingtrue
PICTURE_DESCRIPTIONS_ENABLEDEnable picture descriptionsfalse
+

Agent Settings​

+
VariableDescriptionDefault
LLM_MODELLanguage model for the chat agentgpt-4o-mini
SYSTEM_PROMPTSystem prompt for the agentDefault assistant prompt
+

See .env.example for a complete list with descriptions, and docker-compose*.yml for runtime usage.

+ + \ No newline at end of file diff --git a/get-started/docker/index.html b/get-started/docker/index.html new file mode 100644 index 00000000..5adfbc00 --- /dev/null +++ b/get-started/docker/index.html @@ -0,0 +1,70 @@ + + + + + +Docker deployment | OpenRAG + + + + + + + +
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Docker deployment

There are two different Docker Compose files. +They deploy the same applications and containers, but to different environments.

+
    +
  • +

    docker-compose.yml is an OpenRAG deployment with GPU support for accelerated AI processing.

    +
    • +
  • +

    docker-compose-cpu.yml is a CPU-only version of OpenRAG for systems without GPU support. Use this Docker compose file for environments where GPU drivers aren't available.

    +
    • +
+

To install OpenRAG with Docker Compose:

+
    +
  1. +

    Clone the OpenRAG repository.

    +
    git clone https://github.com/langflow-ai/openrag.git
    cd openrag
    +
    2. +
  2. +

    Copy the example .env file that is included in the repository root. +The example file includes all environment variables with comments to guide you in finding and setting their values.

    +
    cp .env.example .env
    +

    Alternatively, create a new .env file in the repository root.

    +
    touch .env
    +
    3. +
  3. +

    Set environment variables. The Docker Compose files are populated with values from your .env, so the following values are required to be set:

    +
    OPENSEARCH_PASSWORD=your_secure_password
    OPENAI_API_KEY=your_openai_api_key

    LANGFLOW_SUPERUSER=admin
    LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
    LANGFLOW_SECRET_KEY=your_secret_key
    +

    For more information on configuring OpenRAG with environment variables, see Environment variables. +For additional configuration values, including config.yaml, see Configuration.

    +
    4. +
  4. +

    Deploy OpenRAG with Docker Compose based on your deployment type.

    +

    For GPU-enabled systems, run the following command:

    +
    docker compose up -d
    +

    For CPU-only systems, run the following command:

    +
    docker compose -f docker-compose-cpu.yml up -d
    +

    The OpenRAG Docker Compose file starts five containers:

    +
    Container NameDefault AddressPurpose
    OpenRAG Backendhttp://localhost:8000FastAPI server and core functionality.
    OpenRAG Frontendhttp://localhost:3000React web interface for users.
    Langflowhttp://localhost:7860AI workflow engine and flow management.
    OpenSearchhttp://localhost:9200Vector database for document storage.
    OpenSearch Dashboardshttp://localhost:5601Database administration interface.
    +
    5. +
  5. +

    Verify installation by confirming all services are running.

    +
    docker compose ps
    +

    You can now access the application at:

    + +
    6. +
+

Continue with the Quickstart.

+

Rebuild all Docker containers​

+

If you need to reset state and rebuild all of your containers, run the following command. +Your OpenSearch and Langflow databases will be lost. +Documents stored in the ./documents directory will persist, since the directory is mounted as a volume in the OpenRAG backend container.

+
docker compose up --build --force-recreate --remove-orphans
+ + \ No newline at end of file diff --git a/get-started/tui/index.html b/get-started/tui/index.html new file mode 100644 index 00000000..4bdc6e4d --- /dev/null +++ b/get-started/tui/index.html @@ -0,0 +1,69 @@ + + + + + +Terminal User Interface (TUI) commands | OpenRAG + + + + + + + +
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OpenRAG TUI Guide

+

The OpenRAG Terminal User Interface (TUI) provides a streamlined way to set up, configure, and monitor your OpenRAG deployment directly from the terminal, on any operating system.

+

OpenRAG TUI Interface

+

The TUI offers an easier way to use OpenRAG without sacrificing control. +Instead of starting OpenRAG using Docker commands and manually editing values in the .env file, the TUI walks you through the setup. It prompts for variables where required, creates a .env file for you, and then starts OpenRAG.

+

Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs.

+

Start the TUI​

+

To start the TUI, run the following commands from the directory where you installed OpenRAG. +For more information, see Install OpenRAG.

+
uv sync
uv run openrag
+

The TUI Welcome Screen offers basic and advanced setup options. +For more information on setup values during installation, see Install OpenRAG.

+ +

The TUI accepts mouse input or keyboard commands.

+
    +
  • Arrow keys: move between options
    • +
  • Tab/Shift+Tab: switch fields and buttons
    • +
  • Enter: select/confirm
    • +
  • Escape: back
    • +
  • Q: quit
    • +
  • Number keys (1-4): quick access to main screens
    • +
+

Container management​

+

The TUI can deploy, manage, and upgrade your OpenRAG containers.

+

Start container services​

+

Click Start Container Services to start the OpenRAG containers. +The TUI automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for CUDA, NVIDIA_SMI, and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses. +The TUI then pulls the images and deploys the containers with the following command.

+
docker compose up -d
+

If images are missing, the TUI runs docker compose pull, then runs docker compose up -d.

+

Start native services​

+

A "native" service in OpenRAG refers to a service run natively on your machine, and not within a container. +The docling-serve process is a native service in OpenRAG, because it's a document processing service that is run on your local machine, and controlled separately from the containers.

+

To start or stop docling-serve or any other native services, in the TUI main menu, click Start Native Services or Stop Native Services.

+

To view the status, port, or PID of a native service, in the TUI main menu, click Status.

+

Status​

+

The Status menu displays information on your container deployment. +Here you can check container health, find your service ports, view logs, and upgrade your containers.

+

To view streaming logs, select the container you want to view, and press l. +To copy your logs, click Copy to Clipboard.

+

To upgrade your containers, click Upgrade. +Upgrade runs docker compose pull and then docker compose up -d --force-recreate. +The first command pulls the latest images of OpenRAG. +The second command recreates the containers with your data persisted.

+

To reset your containers, click Reset. +Reset gives you a completely fresh start. +Reset deletes all of your data, including OpenSearch data, uploaded documents, and authentication. +Reset runs two commands. +It first stops and removes all containers, volumes, and local images.

+
docker compose down --volumes --remove-orphans --rmi local
+

When the first command is complete, OpenRAG removes any additional Docker objects with prune.

+
docker system prune -f
+

Diagnostics​

+

The Diagnostics menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security.

+ + \ No newline at end of file diff --git a/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg b/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg new file mode 100644 index 00000000..f50bef7d --- /dev/null +++ b/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg @@ -0,0 +1,237 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + OpenRAG TUI + + + + + + + + + + + + + + + + + + + + + + +██████╗ ██████╗ ███████╗███╗   ██╗██████╗  █████╗  ██████╗  +██╔═══██╗██╔══██╗██╔════╝████╗  ██║██╔══██╗██╔══██╗██╔════╝  +██║   ██║██████╔╝█████╗  ██╔██╗ ██║██████╔╝███████║██║  ███╗ +██║   ██║██╔═══╝ ██╔══╝  ██║╚██╗██║██╔══██╗██╔══██║██║   ██║ +╚██████╔╝██║     ███████╗██║ ╚████║██║  ██║██║  ██║╚██████╔╝ +╚═════╝ ╚═╝     ╚══════╝╚═╝  ╚═══╝╚═╝  ╚═╝╚═╝  ╚═╝╚═════╝ +Terminal User Interface for OpenRAG + +OAuth credentials detected — Advanced Setup recommended + + + + +▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔ + Advanced Setup  Monitor Services  +▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ + + + + + + + + + + + + + + q Quit  1 Basic Setup  2 Advanced Setup  3 Monitor Services  4 Diagnostics ▏^p palette + + + diff --git a/img/favicon.ico b/img/favicon.ico new file mode 100644 index 00000000..1225e208 Binary files /dev/null and b/img/favicon.ico differ diff --git a/img/logo-openrag-dark.svg b/img/logo-openrag-dark.svg new file mode 100644 index 00000000..351243f5 --- /dev/null +++ b/img/logo-openrag-dark.svg @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/img/logo-openrag-light.svg b/img/logo-openrag-light.svg new file mode 100644 index 00000000..6a43c60d --- /dev/null +++ b/img/logo-openrag-light.svg @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/img/opensearch-agent-flow.png b/img/opensearch-agent-flow.png new file mode 100644 index 00000000..d201aef9 Binary files /dev/null and b/img/opensearch-agent-flow.png differ diff --git a/index.html b/index.html new file mode 100644 index 00000000..0a2c3bc7 --- /dev/null +++ b/index.html @@ -0,0 +1,31 @@ + + + + + +What is OpenRAG? | OpenRAG + + + + + + + +
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What is OpenRAG?

OpenRAG is an open-source package for building agentic RAG systems. +It supports integration with a wide range of orchestration tools, vector databases, and LLM providers.

+

OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:

+
    +
  • +

    Langflow - Langflow is a powerful tool to build and deploy AI agents and MCP servers. It supports all major LLMs, vector databases and a growing library of AI tools.

    +
    • +
  • +

    OpenSearch - OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.

    +
    • +
  • +

    Docling - Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.

    +
    • +
+

OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing, with opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from popular sources like OneDrive, Google Drive, and AWS. And don't fear: every part of the stack is swappable. Write your own custom components in Python, try different language models, and customize your flows to build an agentic RAG system that solves problems.

+

Ready to get started? Install OpenRAG and then run the Quickstart to create a powerful RAG pipeline.

+ + \ No newline at end of file diff --git a/ingestion/index.html b/ingestion/index.html new file mode 100644 index 00000000..c6850de4 --- /dev/null +++ b/ingestion/index.html @@ -0,0 +1,39 @@ + + + + + +Docling Ingestion | OpenRAG + + + + + + + +
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Docling Ingestion

OpenRAG uses Docling for its document ingestion pipeline. +More specifically, OpenRAG uses Docling Serve, which starts a docling-serve process on your local machine and runs Docling ingestion through an API service.

+

Docling ingests documents from your local machine or OAuth connectors, splits them into chunks, and stores them as separate, structured documents in the OpenSearch documents index.

+

OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images.

+

Docling ingestion settings​

+

These settings configure the Docling ingestion parameters.

+

OpenRAG will warn you if docling-serve is not running. +To start or stop docling-serve or any other native services, in the TUI main menu, click Start Native Services or Stop Native Services.

+

Embedding model determines which AI model is used to create vector embeddings. The default is text-embedding-3-small.

+

Chunk size determines how large each text chunk is in number of characters. +Larger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context. +The default value of 1000 characters provides a good starting point that balances these considerations.

+

Chunk overlap controls the number of characters that overlap over chunk boundaries. +Use larger overlap values for documents where context is most important, and use smaller overlap values for simpler documents, or when optimization is most important. +The default value of 200 characters of overlap with a chunk size of 1000 (20% overlap) is suitable for general use cases. Decrease the overlap to 10% for a more efficient pipeline, or increase to 40% for more complex documents.

+

OCR enables or disabled OCR processing when extracting text from images and scanned documents. +OCR is disabled by default. This setting is best suited for processing text-based documents as quickly as possible with Docling's DocumentConverter. Images are ignored and not processed.

+

Enable OCR when you are processing documents containing images with text that requires extraction, or for scanned documents. Enabling OCR can slow ingestion performance.

+

If OpenRAG detects that the local machine is running on macOS, OpenRAG uses the ocrmac OCR engine. Other platforms use easyocr.

+

Picture descriptions adds image descriptions generated by the SmolVLM-256M-Instruct model to OCR processing. Enabling picture descriptions can slow ingestion performance.

+

Use OpenRAG default ingestion instead of Docling serve​

+

If you want to use OpenRAG's built-in pipeline instead of Docling serve, set DISABLE_INGEST_WITH_LANGFLOW=true in Environment variables.

+

The built-in pipeline still uses the Docling processor, but uses it directly without the Docling Serve API.

+

For more information, see processors.py in the OpenRAG repository.

+ + \ No newline at end of file diff --git a/install/index.html b/install/index.html new file mode 100644 index 00000000..cbb52d10 --- /dev/null +++ b/install/index.html @@ -0,0 +1,123 @@ + + + + + +Install OpenRAG | OpenRAG + + + + + + + +
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Install OpenRAG

OpenRAG can be installed in multiple ways:

+
    +
  • +

    Python wheel: Install the OpenRAG Python wheel and use the OpenRAG Terminal User Interface (TUI) to install, run, and configure your OpenRAG deployment without running Docker commands.

    +
    • +
  • +

    Docker Compose: Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies.

    +
    • +
+

Prerequisites​

+ +

Python wheel​

+

The Python wheel is currently available internally, but will be available on PyPI at launch. +The wheel installs the OpenRAG wheel, which includes the TUI for installing, running, and managing OpenRAG. +For more information on virtual environments, see uv.

+
    +
  1. +

    Create a new project with a virtual environment using uv.

    +
    uv init YOUR_PROJECT_NAME
    cd YOUR_PROJECT_NAME
    +
    2. +
  2. +

    Add the OpenRAG wheel to your project and install it in the virtual environment. +Replace PATH/TO/ and VERSION with your OpenRAG wheel location and version.

    +
    uv add PATH/TO/openrag-VERSION-py3-none-any.whl
    +
    3. +
  3. +

    Ensure all dependencies are installed and updated in your virtual environment.

    +
    uv sync
    +
    4. +
  4. +

    Start the OpenRAG TUI.

    +
    uv run openrag
    +

    The OpenRAG TUI opens.

    +
    5. +
  5. +

    To install OpenRAG with Basic Setup, click Basic Setup or press 1. Basic Setup does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS. For OAuth setup, see Advanced Setup. +The TUI prompts you for the required startup values. +Click Generate Passwords to autocomplete fields that contain Auto-generated Secure Password, or bring your own passwords.

    +
    Where do I find the required startup values?
    VariableWhere to FindDescription
    OPENSEARCH_PASSWORDAuto-generated secure passwordThe password for OpenSearch database access. Must be at least 8 characters and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character.
    OPENAI_API_KEYOpenAI PlatformAPI key from your OpenAI account.
    LANGFLOW_SUPERUSERUser generatedUsername for Langflow admin access. For more, see Langflow docs.
    LANGFLOW_SUPERUSER_PASSWORDAuto-generated secure passwordPassword for Langflow admin access. For more, see the Langflow docs.
    LANGFLOW_SECRET_KEYAuto-generated secure keySecret key for Langflow security. For more, see the Langflow docs.
    LANGFLOW_AUTO_LOGINAuto-generated or manualAuto-login configuration. For more, see the Langflow docs.
    LANGFLOW_NEW_USER_IS_ACTIVELangflowNew user activation setting. For more, see the Langflow docs.
    LANGFLOW_ENABLE_SUPERUSER_CLILangflow serverSuperuser CLI access setting. For more, see the Langflow docs.
    DOCUMENTS_PATHSet your local pathPath to your document storage directory.
    +

    To complete credentials, click Save Configuration.

    +
    6. +
  6. +

    To start OpenRAG with your credentials, click Start Container Services. +Startup pulls container images and starts them, so it can take some time. +The operation has completed when the Close button is available and the terminal displays:

    +
    Services started successfully
    Command completed successfully
    +
    7. +
  7. +

    To open the OpenRAG application, click Open App, press 6, or navigate to http://localhost:3000. +The application opens.

    +
    8. +
  8. +

    Select your language model and embedding model provider, and complete the required fields. +Your provider can only be selected once, and you must use the same provider for your language model and embedding model. +The language model can be changed, but the embeddings model cannot be changed. +To change your provider selection, you must restart OpenRAG and delete the config.yml file.

    +
    9. +
+
    +
  1. If you already entered a value for OPENAI_API_KEY in the TUI in Step 5, enable Get API key from environment variable.
    2. +
  2. Under Advanced settings, select your Embedding Model and Language Model.
    3. +
  3. To load 2 sample PDFs, enable Sample dataset. +This is recommended, but not required.
    4. +
  4. Click Complete.
    5. +
+
    +
  1. Continue with the Quickstart.
    2. +
+

Advanced Setup​

+

Advanced Setup includes the required values from Basic Setup, with additional settings for OAuth credentials. +If the OpenRAG TUI detects OAuth credentials, it enforces the Advanced Setup path.

+
    +
  1. Add your client and secret values for Google, Azure, or AWS OAuth. +These values can be found in your OAuth provider.
    2. +
  2. The OpenRAG TUI presents redirect URIs for your OAuth app. +These are the URLs your OAuth provider will redirect back to after user sign-in. +Register these redirect values with your OAuth provider as they are presented in the TUI.
    3. +
  3. To open the OpenRAG application, click Open App or press 6. +You will be presented with your provider's OAuth sign-in screen, and be redirected to the redirect URI after sign-in.
    4. +
+

Two additional variables are available for Advanced Setup:

+

The LANGFLOW_PUBLIC_URL controls where the Langflow web interface can be accessed. This is where users interact with their flows in a browser.

+

The WEBHOOK_BASE_URL controls where the endpoint for /connectors/CONNECTOR_TYPE/webhook will be available. +This connection enables real-time document synchronization with external services. +For example, for Google Drive file synchronization the webhook URL is /connectors/google_drive/webhook.

+ + \ No newline at end of file diff --git a/knowledge/index.html b/knowledge/index.html new file mode 100644 index 00000000..f3384322 --- /dev/null +++ b/knowledge/index.html @@ -0,0 +1,118 @@ + + + + + +OpenSearch Knowledge | OpenRAG + + + + + + + +
Skip to main content

OpenSearch Knowledge

OpenRAG uses OpenSearch for its vector-backed knowledge store. +OpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support.

+

Explore knowledge​

+

The Knowledge page lists the documents OpenRAG has ingested into the OpenSearch vector database's documents index.

+

To explore your current knowledge, click Knowledge. +Click on a document to display the chunks derived from splitting the default documents into the vector database.

+

Documents are processed with the default Knowledge Ingest flow, so if you want to split your documents differently, edit the Knowledge Ingest flow.

+

All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic. +To modify a flow, click Settings, and click Edit in Langflow. +Flows are edited in the same way as in the Langflow visual editor.

+

Ingest knowledge​

+

OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors.

+

Direct file ingestion​

+

The Knowledge Ingest flow uses Langflow's File component to split and embed files loaded from your local machine into the OpenSearch database.

+

The default path to your local folder is mounted from the ./documents folder in your OpenRAG project directory to the /app/documents/ directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the Documents Paths variable in either the TUI's Advanced Setup or in the .env used by Docker Compose.

+

To load and process a single file from the mapped location, click Add Knowledge, and then click Add File. +The file is loaded into your OpenSearch database, and appears in the Knowledge page.

+

To load and process a directory from the mapped location, click Add Knowledge, and then click Process Folder. +The files are loaded into your OpenSearch database, and appear in the Knowledge page.

+

Ingest files through OAuth connectors​

+

OpenRAG supports Google Drive, OneDrive, and AWS S3 as OAuth connectors for seamless document synchronization.

+

OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage.

+

Before users can connect their cloud storage accounts, you must configure OAuth credentials in OpenRAG. This requires registering OpenRAG as an OAuth application with a cloud provider and obtaining client ID and secret keys for each service you want to support.

+

To add an OAuth connector to OpenRAG, do the following. +This example uses Google OAuth. +If you wish to use another provider, add the secrets to another provider.

+
    +
  1. If OpenRAG is running, stop it with Status > Stop Services.
    2. +
  2. Click Advanced Setup.
    3. +
  3. Add the OAuth provider's client and secret key in the Advanced Setup menu.
    4. +
  4. Click Save Configuration. +The TUI generates a new .env file with your OAuth values.
    5. +
  5. Click Start Container Services.
    6. +
+

The OpenRAG frontend at http://localhost:3000 now redirects to an OAuth callback login page for your OAuth provider. +A successful authentication opens OpenRAG with the required scopes for your connected storage.

+

To add knowledge from an OAuth-connected storage provider, do the following:

+
    +
  1. Click Add Knowledge, and then select the storage provider, for example, Google Drive. +The Add Cloud Knowledge page opens.
    2. +
  2. To add files or folders from the connected storage, click Add Files. +Select the files or folders you want and click Select. +You can select multiples.
    3. +
  3. When your files are selected, click Ingest Files. +The ingestion process may take some time, depending on the size of your documents.
    4. +
  4. When ingestion is complete, your documents are available in the Knowledge screen.
    5. +
+

Sync cloud connectors​

+

Your connected data sources are found in the Settings page.

+

When you click Sync Now for a connected cloud service like Google Drive, OpenRAG scans your connected Google Drive account to find files that match your sync criteria. Sync criteria are controlled in Sync Settings on the same page. You can sync all files, or select a maximum number of files to sync.

+

For each file found, OpenRAG downloads, converts, and embeds the processed content into OpenSearch.

+

You can monitor the sync progress in the Tasks sidebar.

+

Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page.

+

Knowledge ingestion settings​

+

To configure the knowledge ingestion pipeline parameters, see Docling Ingestion.

+

Create knowledge filters​

+

OpenRAG includes a knowledge filter system for organizing and managing document collections. +Knowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of OpenRAG.

+

Knowledge filters help agents work more efficiently with large document collections by focusing their context within relevant documents sets.

+

To create a knowledge filter, do the following:

+
    +
  1. +

    Click All Knowledge, and then click Create New Filter. +The Create New Knowledge Filter pane appears.

    +
    2. +
  2. +

    Enter a Name and Description, and then click Create Filter. +A new filter is created with default settings that match everything.

    +
    3. +
  3. +

    To modify the default filter, click All Knowledge, and then click your new filter to edit it in the Knowledge Filter pane.

    +

    The following filter options are configurable.

    +
      +
    • Search Query: Enter text for semantic search, such as "financial reports from Q4".
      • +
    • Data Sources: Select specific data sources or folders to include.
      • +
    • Document Types: Filter by file type.
      • +
    • Owners: Filter by who uploaded the documents.
      • +
    • Sources: Filter by connector types, such as local upload or Google Drive.
      • +
    • Result Limit: Set maximum number of results. The default is 10.
      • +
    • Score Threshold: Set minimum relevance score. The default score is 0.
      • +
    +
    4. +
  4. +

    When you're done editing the filter, click Save Configuration.

    +
    5. +
  5. +

    To apply the filter to OpenRAG globally, click All Knowledge, and then select the filter to apply.

    +

    To apply the filter to a single chat session, in the Chat window, click @, and then select the filter to apply.

    +
    6. +
+

OpenRAG default configuration​

+

OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility.

+

The complete list of supported models is available at /src/services/models_service.py.

+

You can use custom embedding models by specifying them in your configuration.

+

If you use an unknown embedding model, OpenRAG will automatically fall back to 1536 dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from 1536.

+

The default embedding dimension is 1536 and the default model is text-embedding-3-small.

+

For models with known vector dimensions, see /src/config/settings.py.

+ + \ No newline at end of file diff --git a/quickstart/index.html b/quickstart/index.html new file mode 100644 index 00000000..6049f72d --- /dev/null +++ b/quickstart/index.html @@ -0,0 +1,130 @@ + + + + + +Quickstart | OpenRAG + + + + + + + +
Skip to main content

Quickstart

Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.

+

Prerequisites​

+
    +
  • +

    Install and start OpenRAG

    +
    • +
  • +

    Create a Langflow API key

    +
    Create a Langflow API key

    A Langflow API key is a user-specific token you can use with Langflow. +It is only used for sending requests to the Langflow server. +It does not access to OpenRAG.

    To create a Langflow API key, do the following:

      +
    1. +

      In Langflow, click your user icon, and then select Settings.

      +
      2. +
    2. +

      Click Langflow API Keys, and then click Add New.

      +
      3. +
    3. +

      Name your key, and then click Create API Key.

      +
      4. +
    4. +

      Copy the API key and store it securely.

      +
      5. +
    5. +

      To use your Langflow API key in a request, set a LANGFLOW_API_KEY environment variable in your terminal, and then include an x-api-key header or query parameter with your request. +For example:

      +
      # Set variable
      export LANGFLOW_API_KEY="sk..."

      # Send request
      curl --request POST \
      --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
      --header "Content-Type: application/json" \
      --header "x-api-key: $LANGFLOW_API_KEY" \
      --data '{
        "output_type": "chat",
        "input_type": "chat",
        "input_value": "Hello"
      }'
      +
      6. +
    +
    • +
+

Find your way around​

+
    +
  1. In OpenRAG, click Chat. +The chat is powered by the OpenRAG OpenSearch Agent. +For more information, see Langflow Agents.
    2. +
  2. Ask What documents are available to you? +The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care. +Knowledge is stored in OpenSearch. +For more information, see Knowledge.
    3. +
  3. To confirm the agent is correct, click Knowledge. +The Knowledge page lists the documents OpenRAG has ingested into the OpenSearch vector database. +Click on a document to display the chunks derived from splitting the default documents into the vector database.
    4. +
+

Add your own knowledge​

+
    +
  1. To add documents to your knowledge base, click Add Knowledge. +
      +
    • Select Add File to add a single file from your local machine (mapped with the Docker volume mount).
      • +
    • Select Process Folder to process an entire folder of documents from your local machine (mapped with the Docker volume mount).
      • +
    • Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see OAuth ingestion.
      • +
    +
    2. +
  2. Return to the Chat window and ask a question about your loaded data. +For example, with a manual about a PC tablet loaded, ask How do I connect this device to WiFI? +The agent responds with a message indicating it now has your knowledge as context for answering questions.
    3. +
  3. Click the Function Call: search_documents (tool_call) that is printed in the Playground. +These events log the agent's request to the tool and the tool's response, so you have direct visibility into your agent's functionality. +If you aren't getting the results you need, you can further tune the knowledge ingestion and agent behavior in the next section.
    4. +
+

Swap out the language model to modify agent behavior​

+

To modify the knowledge ingestion or Agent behavior, click Settings.

+

In this example, you'll try a different LLM to demonstrate how the Agent's response changes. +You can only change the Language model, and not the Model provider that you started with in OpenRAG. +If you're using Ollama, you can use any installed model.

+
    +
  1. +

    To edit the Agent's behavior, click Edit in Langflow. +You can more quickly access the Language Model and Agent Instructions fields in this page, but for illustration purposes, navigate to the Langflow visual builder.

    +
    2. +
  2. +

    OpenRAG warns you that you're entering Langflow. Click Proceed.

    +
    3. +
  3. +

    The OpenRAG OpenSearch Agent flow appears. +OpenRAG Open Search Agent Flow

    +
    4. +
  4. +

    In the Language Model component, under Model, select a different OpenAI model.

    +
    5. +
  5. +

    Save your flow with Command+S.

    +
    6. +
  6. +

    In OpenRAG, start a new conversation by clicking the in the Conversations tab.

    +
    7. +
  7. +

    Ask the same question as before to demonstrate how a different language model changes the results.

    +
    8. +
+

Integrate OpenRAG into your application​

+

To integrate OpenRAG into your application, use the Langflow API. +Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results.

+

Langflow provides code snippets to help you get started with the Langflow API.

+
    +
  1. +

    To navigate to the OpenRAG OpenSearch Agent flow, click Settings, and then click Edit in Langflow in the OpenRAG OpenSearch Agent flow.

    +
    2. +
  2. +

    Click Share, and then click API access.

    +

    The default code in the API access pane constructs a request with the Langflow server url, headers, and a payload of request data. The code snippets automatically include the LANGFLOW_SERVER_ADDRESS and FLOW_ID values for the flow. Replace these values if you're using the code for a different server or flow. The default Langflow server address is http://localhost:7860.

    +
    import requests
    import os
    import uuid

    api_key = 'LANGFLOW_API_KEY'
    url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID"  # The complete API endpoint URL for this flow

    # Request payload configuration
    payload = {
        "output_type": "chat",
        "input_type": "chat",
        "input_value": "hello world!"
    }
    payload["session_id"] = str(uuid.uuid4())

    headers = {"x-api-key": api_key}

    try:
        # Send API request
        response = requests.request("POST", url, json=payload, headers=headers)
        response.raise_for_status()  # Raise exception for bad status codes

        # Print response
        print(response.text)

    except requests.exceptions.RequestException as e:
        print(f"Error making API request: {e}")
    except ValueError as e:
        print(f"Error parsing response: {e}")
    +
    3. +
  3. +

    Copy the snippet, paste it in a script file, and then run the script to send the request. If you are using the curl snippet, you can run the command directly in your terminal.

    +
    4. +
+

If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more. +The following is an example of a response from running the Simple Agent template flow:

+
Result
{
  "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
  "outputs": [
    {
      "inputs": {
        "input_value": "hello world!"
      },
      "outputs": [
        {
          "results": {
            "message": {
              "text_key": "text",
              "data": {
                "timestamp": "2025-06-16 19:58:23 UTC",
                "sender": "Machine",
                "sender_name": "AI",
                "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
                "text": "Hello world! 🌍 How can I assist you today?",
                "files": [],
                "error": false,
                "edit": false,
                "properties": {
                  "text_color": "",
                  "background_color": "",
                  "edited": false,
                  "source": {
                    "id": "Agent-ZOknz",
                    "display_name": "Agent",
                    "source": "gpt-4o-mini"
                  },
                  "icon": "bot",
                  "allow_markdown": false,
                  "positive_feedback": null,
                  "state": "complete",
                  "targets": []
                },
                "category": "message",
                "content_blocks": [
                  {
                    "title": "Agent Steps",
                    "contents": [
                      {
                        "type": "text",
                        "duration": 2,
                        "header": {
                          "title": "Input",
                          "icon": "MessageSquare"
                        },
                        "text": "**Input**: hello world!"
                      },
                      {
                        "type": "text",
                        "duration": 226,
                        "header": {
                          "title": "Output",
                          "icon": "MessageSquare"
                        },
                        "text": "Hello world! 🌍 How can I assist you today?"
                      }
                    ],
                    "allow_markdown": true,
                    "media_url": null
                  }
                ],
                "id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",
                "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
                "duration": null
              },
              "default_value": "",
              "text": "Hello world! 🌍 How can I assist you today?",
              "sender": "Machine",
              "sender_name": "AI",
              "files": [],
              "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
              "timestamp": "2025-06-16T19:58:23+00:00",
              "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
              "error": false,
              "edit": false,
              "properties": {
                "text_color": "",
                "background_color": "",
                "edited": false,
                "source": {
                  "id": "Agent-ZOknz",
                  "display_name": "Agent",
                  "source": "gpt-4o-mini"
                },
                "icon": "bot",
                "allow_markdown": false,
                "positive_feedback": null,
                "state": "complete",
                "targets": []
              },
              "category": "message",
              "content_blocks": [
                {
                  "title": "Agent Steps",
                  "contents": [
                    {
                      "type": "text",
                      "duration": 2,
                      "header": {
                        "title": "Input",
                        "icon": "MessageSquare"
                      },
                      "text": "**Input**: hello world!"
                    },
                    {
                      "type": "text",
                      "duration": 226,
                      "header": {
                        "title": "Output",
                        "icon": "MessageSquare"
                      },
                      "text": "Hello world! 🌍 How can I assist you today?"
                    }
                  ],
                  "allow_markdown": true,
                  "media_url": null
                }
              ],
              "duration": null
            }
          },
          "artifacts": {
            "message": "Hello world! 🌍 How can I assist you today?",
            "sender": "Machine",
            "sender_name": "AI",
            "files": [],
            "type": "object"
          },
          "outputs": {
            "message": {
              "message": "Hello world! 🌍 How can I assist you today?",
              "type": "text"
            }
          },
          "logs": {
            "message": []
          },
          "messages": [
            {
              "message": "Hello world! 🌍 How can I assist you today?",
              "sender": "Machine",
              "sender_name": "AI",
              "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
              "stream_url": null,
              "component_id": "ChatOutput-aF5lw",
              "files": [],
              "type": "text"
            }
          ],
          "timedelta": null,
          "duration": null,
          "component_display_name": "Chat Output",
          "component_id": "ChatOutput-aF5lw",
          "used_frozen_result": false
        }
      ]
    }
  ]
}
+

To further explore the API, see:

+
+ + \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..40650423 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1 @@ +https://langflow-ai.github.io/openrag/agentsweekly0.5https://langflow-ai.github.io/openrag/configure/configurationweekly0.5https://langflow-ai.github.io/openrag/get-started/dockerweekly0.5https://langflow-ai.github.io/openrag/get-started/tuiweekly0.5https://langflow-ai.github.io/openrag/ingestionweekly0.5https://langflow-ai.github.io/openrag/installweekly0.5https://langflow-ai.github.io/openrag/knowledgeweekly0.5https://langflow-ai.github.io/openrag/quickstartweekly0.5https://langflow-ai.github.io/openrag/support/troubleshootweekly0.5https://langflow-ai.github.io/openrag/weekly0.5 \ No newline at end of file diff --git a/support/troubleshoot/index.html b/support/troubleshoot/index.html new file mode 100644 index 00000000..1cbd74f1 --- /dev/null +++ b/support/troubleshoot/index.html @@ -0,0 +1,49 @@ + + + + + +Troubleshoot | OpenRAG + + + + + + + +
Skip to main content

Troubleshoot

This page provides troubleshooting advice for issues you might encounter when using OpenRAG or contributing to OpenRAG.

+

OpenSearch fails to start​

+

Check that OPENSEARCH_PASSWORD is set and meets requirements. +The password must contain at least 8 characters, and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character that is strong.

+

Langflow connection issues​

+

Verify the LANGFLOW_SUPERUSER credentials are correct.

+

Memory errors​

+

Container out of memory errors​

+

Increase Docker memory allocation or use docker-compose-cpu.yml to deploy OpenRAG.

+

Podman on macOS memory issues​

+

If you're using Podman on macOS, you may need to increase VM memory on your Podman machine. +This example increases the machine size to 8 GB of RAM, which should be sufficient to run OpenRAG.

+
podman machine stop
podman machine rm
podman machine init --memory 8192   # 8 GB example
podman machine start
+

Port conflicts​

+

Ensure ports 3000, 7860, 8000, 9200, 5601 are available.

+

Langflow container already exists​

+

If you are running other versions of Langflow containers on your machine, you may encounter an issue where Docker or Podman thinks Langflow is already up.

+

Remove just the problem container, or clean up all containers and start fresh.

+

To reset your local containers and pull new images, do the following:

+
    +
  1. Stop your containers and completely remove them.
    2. +
+
# Stop all running containers
docker stop $(docker ps -q)

# Remove all containers (including stopped ones)
docker rm --force $(docker ps -aq)

# Remove all images
docker rmi --force $(docker images -q)

# Remove all volumes
docker volume prune --force

# Remove all networks (except default)
docker network prune --force

# Clean up any leftover data
docker system prune --all --force --volumes
+
    +
  1. +

    Restart OpenRAG and upgrade to get the latest images for your containers.

    +
    uv run openrag
    +
    2. +
  2. +

    In the OpenRAG TUI, click Status, and then click Upgrade. +When the Close button is active, the upgrade is complete. +Close the window and open the OpenRAG appplication.

    +
    3. +
+ + \ No newline at end of file