diff --git a/404.html b/404.html index ebc0764b..a19d68a0 100644 --- a/404.html +++ b/404.html @@ -4,7 +4,7 @@ OpenRAG - + diff --git a/agents/index.html b/agents/index.html index 29590226..d9b37785 100644 --- a/agents/index.html +++ b/agents/index.html @@ -4,16 +4,14 @@ Langflow Agents | OpenRAG - + -
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Langflow Agents

info

OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

-

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

+

Langflow Agents

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

Flows in Langflow are functional representations of application workflows, with multiple component nodes connected as single steps in a workflow.

In the OpenRAG OpenSearch Agent flow, components like the Langflow Agent component and OpenSearch component are connected to intelligently chat with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM.

OpenRAG Open Search Agent Flow

diff --git a/assets/js/0ba6a408.8ade4b0f.js b/assets/js/0ba6a408.8ade4b0f.js deleted file mode 100644 index ba36d2a4..00000000 --- a/assets/js/0ba6a408.8ade4b0f.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[571],{3227:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>p,contentTitle:()=>u,default:()=>f,frontMatter:()=>h,metadata:()=>i,toc:()=>g});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":"/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":"/agents"},"next":{"title":"Docling Ingestion","permalink":"/ingestion"}}');var o=s(4848),t=s(8453),r=s(9179),l=s(1470),c=s(9365),d=s(3782),a=s(9334);const h={title:"OpenSearch Knowledge",slug:"/knowledge"},u=void 0,p={},g=[...a.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:"Explore knowledge",id:"explore-knowledge",level:2},...d.RM,{value:"Create knowledge filters",id:"create-knowledge-filters",level:2},{value:"OpenRAG default configuration",id:"openrag-default-configuration",level:2}];function x(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.jsx)(a.Ay,{}),"\n",(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.\nThis is a specialized database for storing and retrieving embeddings, which helps your Agent efficiently find relevant information.\nOpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support."]}),"\n",(0,o.jsx)(n.h2,{id:"ingest-knowledge",children:"Ingest knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors.\nTo configure the knowledge ingestion pipeline parameters, see ",(0,o.jsx)(n.a,{href:"/ingestion",children:"Docling Ingestion"}),"."]}),"\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#setup",children:"Advanced Setup"})," menu 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#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.h2,{id:"explore-knowledge",children:"Explore knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.strong,{children:"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:"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.jsxs)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py",children:[(0,o.jsx)(n.code,{children:"models_service.py"})," in the OpenRAG repository"]}),"."]}),"\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.jsxs)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py",children:[(0,o.jsx)(n.code,{children:"settings.py"})," in the OpenRAG repository"]}),"."]})]})}function f(e={}){const{wrapper:n}={...(0,t.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(x,{...e})}):x(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 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"}),".\nOpenRAG's visual editor is based on the ",(0,i.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use case."]})}function c(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(l,{...e})}):l(e)}},9179:(e,n,s)=>{s.d(n,{A:()=>t});s(6540);var i=s(4827),o=s(4848);function t({name:e,...n}){const s=i[e];return s?(0,o.jsx)(s,{...n}):null}},9334:(e,n,s)=>{s.d(n,{Ay:()=>l,RM:()=>t});var i=s(4848),o=s(8453);const t=[];function r(e){const n={admonition:"admonition",p:"p",...(0,o.R)(),...e.components};return(0,i.jsx)(n.admonition,{type:"info",children:(0,i.jsx)(n.p,{children:"OpenRAG is is currently in public preview.\nDevelopment is ongoing, and the features and functionality are subject to change."})})}function l(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(r,{...e})}):r(e)}}}]); \ No newline at end of file diff --git a/assets/js/0ba6a408.c700ce3b.js b/assets/js/0ba6a408.c700ce3b.js new file mode 100644 index 00000000..05800059 --- /dev/null +++ b/assets/js/0ba6a408.c700ce3b.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[571],{3227:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>u,contentTitle:()=>h,default:()=>x,frontMatter:()=>a,metadata:()=>i,toc:()=>g});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":"/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":"/agents"},"next":{"title":"Docling Ingestion","permalink":"/ingestion"}}');var o=s(4848),r=s(8453),t=s(9179),l=s(1470),c=s(9365),d=s(3782);const a={title:"OpenSearch Knowledge",slug:"/knowledge"},h=void 0,u={},g=[{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:"Explore knowledge",id:"explore-knowledge",level:2},...d.RM,{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,r.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.\nThis is a specialized database for storing and retrieving embeddings, which helps your Agent efficiently find relevant information.\nOpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support."]}),"\n",(0,o.jsx)(n.h2,{id:"ingest-knowledge",children:"Ingest knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors.\nTo configure the knowledge ingestion pipeline parameters, see ",(0,o.jsx)(n.a,{href:"/ingestion",children:"Docling Ingestion"}),"."]}),"\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#setup",children:"Advanced Setup"})," menu 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)(t.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)(t.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#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)(t.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)(t.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)(t.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)(t.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.h2,{id:"explore-knowledge",children:"Explore knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.strong,{children:"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)(t.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:"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)(t.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click ",(0,o.jsx)(t.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)(t.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)(t.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)(t.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)(t.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)(t.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.jsxs)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py",children:[(0,o.jsx)(n.code,{children:"models_service.py"})," in the OpenRAG repository"]}),"."]}),"\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.jsxs)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py",children:[(0,o.jsx)(n.code,{children:"settings.py"})," in the OpenRAG repository"]}),"."]})]})}function x(e={}){const{wrapper:n}={...(0,r.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:()=>t});var i=s(4848),o=s(8453),r=s(9179);const t=[];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 provider-agnostic.\nTo modify a flow, click ",(0,i.jsx)(r.A,{name:"Settings2","aria-hidden":"true"})," ",(0,i.jsx)(n.strong,{children:"Settings"}),", and click ",(0,i.jsx)(n.strong,{children:"Edit in Langflow"}),".\nOpenRAG's visual editor is based on the ",(0,i.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use case."]})}function c(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(l,{...e})}):l(e)}},9179:(e,n,s)=>{s.d(n,{A:()=>r});s(6540);var i=s(4827),o=s(4848);function r({name:e,...n}){const s=i[e];return s?(0,o.jsx)(s,{...n}):null}}}]); \ No newline at end of file diff --git a/assets/js/21afe7ac.c654afca.js b/assets/js/21afe7ac.c654afca.js deleted file mode 100644 index 9eb14888..00000000 --- a/assets/js/21afe7ac.c654afca.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[961],{5689:(e,n,t)=>{t.d(n,{A:()=>s});const s=t.p+"assets/images/OpenRAG_TUI_2025-09-10T13_04_11_757637-9441c53ba39162a88ac6c11cbeaed0e0.svg"},6239:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>d,contentTitle:()=>c,default:()=>u,frontMatter:()=>a,metadata:()=>s,toc:()=>l});const s=JSON.parse('{"id":"get-started/tui","title":"Terminal User Interface (TUI) commands","description":"The OpenRAG Terminal User Interface (TUI) allows you to set up, configure, and monitor your OpenRAG deployment directly from the terminal, on any operating system.","source":"@site/docs/get-started/tui.mdx","sourceDirName":"get-started","slug":"/get-started/tui","permalink":"/get-started/tui","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/get-started/tui.mdx","tags":[],"version":"current","frontMatter":{"title":"Terminal User Interface (TUI) commands","slug":"/get-started/tui"},"sidebar":"tutorialSidebar","previous":{"title":"Quickstart","permalink":"/quickstart"},"next":{"title":"Langflow Agents","permalink":"/agents"}}');var r=t(4848),i=t(8453),o=t(9334);const a={title:"Terminal User Interface (TUI) commands",slug:"/get-started/tui"},c=void 0,d={},l=[...o.RM,{value:"Start the TUI",id:"start-the-tui",level:2},{value:"Navigation",id:"navigation",level:2},{value:"Container management",id:"container-management",level:2},{value:"Start container services",id:"start-container-services",level:3},{value:"Start native services",id:"start-native-services",level:3},{value:"Status",id:"status",level:3},{value:"Diagnostics",id:"diagnostics",level:2}];function h(e){const n={a:"a",code:"code",h2:"h2",h3:"h3",img:"img",li:"li",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,r.jsxs)(r.Fragment,{children:[(0,r.jsx)(o.Ay,{}),"\n",(0,r.jsx)(n.p,{children:"The OpenRAG Terminal User Interface (TUI) allows you to set up, configure, and monitor your OpenRAG deployment directly from the terminal, on any operating system."}),"\n",(0,r.jsx)(n.p,{children:(0,r.jsx)(n.img,{alt:"OpenRAG TUI Interface",src:t(5689).A+"",width:"1995",height:"1099"})}),"\n",(0,r.jsxs)(n.p,{children:["Instead of starting OpenRAG using Docker commands and manually editing values in the ",(0,r.jsx)(n.code,{children:".env"})," file, the TUI walks you through the setup. It prompts for variables where required, creates a ",(0,r.jsx)(n.code,{children:".env"})," file for you, and then starts OpenRAG."]}),"\n",(0,r.jsx)(n.p,{children:"Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs."}),"\n",(0,r.jsx)(n.h2,{id:"start-the-tui",children:"Start the TUI"}),"\n",(0,r.jsx)(n.p,{children:"To start the TUI, run the following commands from the directory where you installed OpenRAG."}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{className:"language-bash",children:"uv sync\nuv run openrag\n"})}),"\n",(0,r.jsxs)(n.p,{children:["The TUI Welcome Screen offers basic and advanced setup options.\nFor more information on setup values during installation, see ",(0,r.jsx)(n.a,{href:"/install",children:"Install OpenRAG"}),"."]}),"\n",(0,r.jsx)(n.h2,{id:"navigation",children:"Navigation"}),"\n",(0,r.jsx)(n.p,{children:"The TUI accepts mouse input or keyboard commands."}),"\n",(0,r.jsxs)(n.ul,{children:["\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Arrow keys"}),": move between options"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Tab"}),"/",(0,r.jsx)("kbd",{children:"Shift+Tab"}),": switch fields and buttons"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Enter"}),": select/confirm"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Escape"}),": back"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Q"}),": quit"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Number keys (1-4)"}),": quick access to main screens"]}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"container-management",children:"Container management"}),"\n",(0,r.jsx)(n.p,{children:"The TUI can deploy, manage, and upgrade your OpenRAG containers."}),"\n",(0,r.jsx)(n.h3,{id:"start-container-services",children:"Start container services"}),"\n",(0,r.jsxs)(n.p,{children:["Click ",(0,r.jsx)(n.strong,{children:"Start Container Services"})," to start the OpenRAG containers.\nThe TUI automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for ",(0,r.jsx)(n.code,{children:"CUDA"}),", ",(0,r.jsx)(n.code,{children:"NVIDIA_SMI"}),", and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses.\nThe TUI then pulls the images and deploys the containers with the following command."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{className:"language-bash",children:"docker compose up -d\n"})}),"\n",(0,r.jsxs)(n.p,{children:["If images are missing, the TUI runs ",(0,r.jsx)(n.code,{children:"docker compose pull"}),", then runs ",(0,r.jsx)(n.code,{children:"docker compose up -d"}),"."]}),"\n",(0,r.jsx)(n.h3,{id:"start-native-services",children:"Start native services"}),"\n",(0,r.jsxs)(n.p,{children:['A "native" service in OpenRAG refers to a service run natively on your machine, and not within a container.\nThe ',(0,r.jsx)(n.code,{children:"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."]}),"\n",(0,r.jsxs)(n.p,{children:["To start or stop ",(0,r.jsx)(n.code,{children:"docling-serve"})," or any other native services, in the TUI main menu, click ",(0,r.jsx)(n.strong,{children:"Start Native Services"})," or ",(0,r.jsx)(n.strong,{children:"Stop Native Services"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["To view the status, port, or PID of a native service, in the TUI main menu, click ",(0,r.jsx)(n.a,{href:"#status",children:"Status"}),"."]}),"\n",(0,r.jsx)(n.h3,{id:"status",children:"Status"}),"\n",(0,r.jsxs)(n.p,{children:["The ",(0,r.jsx)(n.strong,{children:"Status"})," menu displays information on your container deployment.\nHere you can check container health, find your service ports, view logs, and upgrade your containers."]}),"\n",(0,r.jsxs)(n.p,{children:["To view streaming logs, select the container you want to view, and press ",(0,r.jsx)("kbd",{children:"l"}),".\nTo copy your logs, click ",(0,r.jsx)(n.strong,{children:"Copy to Clipboard"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["To ",(0,r.jsx)(n.strong,{children:"upgrade"})," your containers, click ",(0,r.jsx)(n.strong,{children:"Upgrade"}),".\n",(0,r.jsx)(n.strong,{children:"Upgrade"})," runs ",(0,r.jsx)(n.code,{children:"docker compose pull"})," and then ",(0,r.jsx)(n.code,{children:"docker compose up -d --force-recreate"}),".\nThe first command pulls the latest images of OpenRAG.\nThe second command recreates the containers with your data persisted."]}),"\n",(0,r.jsxs)(n.p,{children:["To ",(0,r.jsx)(n.strong,{children:"reset"})," your containers, click ",(0,r.jsx)(n.strong,{children:"Reset"}),".\nReset gives you a completely fresh start.\nReset deletes all of your data, including OpenSearch data, uploaded documents, and authentication.\n",(0,r.jsx)(n.strong,{children:"Reset"})," runs two commands.\nIt first stops and removes all containers, volumes, and local images."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{children:"docker compose down --volumes --remove-orphans --rmi local\n"})}),"\n",(0,r.jsxs)(n.p,{children:["When the first command is complete, OpenRAG removes any additional Docker objects with ",(0,r.jsx)(n.code,{children:"prune"}),"."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{children:"docker system prune -f\n"})}),"\n",(0,r.jsx)(n.h2,{id:"diagnostics",children:"Diagnostics"}),"\n",(0,r.jsxs)(n.p,{children:["The ",(0,r.jsx)(n.strong,{children:"Diagnostics"})," menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security."]})]})}function u(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,r.jsx)(n,{...e,children:(0,r.jsx)(h,{...e})}):h(e)}},8453:(e,n,t)=>{t.d(n,{R:()=>o,x:()=>a});var s=t(6540);const r={},i=s.createContext(r);function o(e){const n=s.useContext(i);return s.useMemo(function(){return"function"==typeof e?e(n):{...n,...e}},[n,e])}function a(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:o(e.components),s.createElement(i.Provider,{value:n},e.children)}},9334:(e,n,t)=>{t.d(n,{Ay:()=>a,RM:()=>i});var s=t(4848),r=t(8453);const i=[];function o(e){const n={admonition:"admonition",p:"p",...(0,r.R)(),...e.components};return(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"OpenRAG is is currently in public preview.\nDevelopment is ongoing, and the features and functionality are subject to change."})})}function a(e={}){const{wrapper:n}={...(0,r.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(o,{...e})}):o(e)}}}]); 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It prompts for variables where required, creates a ",(0,r.jsx)(n.code,{children:".env"})," file for you, and then starts OpenRAG."]}),"\n",(0,r.jsx)(n.p,{children:"Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs."}),"\n",(0,r.jsx)(n.h2,{id:"start-the-tui",children:"Start the TUI"}),"\n",(0,r.jsx)(n.p,{children:"To start the TUI, run the following commands from the directory where you installed OpenRAG."}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{className:"language-bash",children:"uv sync\nuv run openrag\n"})}),"\n",(0,r.jsxs)(n.p,{children:["The TUI Welcome Screen offers basic and advanced setup options.\nFor more information on setup values during installation, see ",(0,r.jsx)(n.a,{href:"/install",children:"Install OpenRAG"}),"."]}),"\n",(0,r.jsx)(n.h2,{id:"navigation",children:"Navigation"}),"\n",(0,r.jsx)(n.p,{children:"The TUI accepts mouse input or keyboard commands."}),"\n",(0,r.jsxs)(n.ul,{children:["\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Arrow keys"}),": move between options"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Tab"}),"/",(0,r.jsx)("kbd",{children:"Shift+Tab"}),": switch fields and buttons"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Enter"}),": select/confirm"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Escape"}),": back"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Q"}),": quit"]}),"\n",(0,r.jsxs)(n.li,{children:[(0,r.jsx)("kbd",{children:"Number keys (1-4)"}),": quick access to main screens"]}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"container-management",children:"Container management"}),"\n",(0,r.jsx)(n.p,{children:"The TUI can deploy, manage, and upgrade your OpenRAG containers."}),"\n",(0,r.jsx)(n.h3,{id:"start-container-services",children:"Start container services"}),"\n",(0,r.jsxs)(n.p,{children:["Click ",(0,r.jsx)(n.strong,{children:"Start Container Services"})," to start the OpenRAG containers.\nThe TUI automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for ",(0,r.jsx)(n.code,{children:"CUDA"}),", ",(0,r.jsx)(n.code,{children:"NVIDIA_SMI"}),", and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses.\nThe TUI then pulls the images and deploys the containers with the following command."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{className:"language-bash",children:"docker compose up -d\n"})}),"\n",(0,r.jsxs)(n.p,{children:["If images are missing, the TUI runs ",(0,r.jsx)(n.code,{children:"docker compose pull"}),", then runs ",(0,r.jsx)(n.code,{children:"docker compose up -d"}),"."]}),"\n",(0,r.jsx)(n.h3,{id:"start-native-services",children:"Start native services"}),"\n",(0,r.jsxs)(n.p,{children:['A "native" service in OpenRAG refers to a service run natively on your machine, and not within a container.\nThe ',(0,r.jsx)(n.code,{children:"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."]}),"\n",(0,r.jsxs)(n.p,{children:["To start or stop ",(0,r.jsx)(n.code,{children:"docling-serve"})," or any other native services, in the TUI main menu, click ",(0,r.jsx)(n.strong,{children:"Start Native Services"})," or ",(0,r.jsx)(n.strong,{children:"Stop Native Services"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["To view the status, port, or PID of a native service, in the TUI main menu, click ",(0,r.jsx)(n.a,{href:"#status",children:"Status"}),"."]}),"\n",(0,r.jsx)(n.h3,{id:"status",children:"Status"}),"\n",(0,r.jsxs)(n.p,{children:["The ",(0,r.jsx)(n.strong,{children:"Status"})," menu displays information on your container deployment.\nHere you can check container health, find your service ports, view logs, and upgrade your containers."]}),"\n",(0,r.jsxs)(n.p,{children:["To view streaming logs, select the container you want to view, and press ",(0,r.jsx)("kbd",{children:"l"}),".\nTo copy your logs, click ",(0,r.jsx)(n.strong,{children:"Copy to Clipboard"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["To ",(0,r.jsx)(n.strong,{children:"upgrade"})," your containers, click ",(0,r.jsx)(n.strong,{children:"Upgrade"}),".\n",(0,r.jsx)(n.strong,{children:"Upgrade"})," runs ",(0,r.jsx)(n.code,{children:"docker compose pull"})," and then ",(0,r.jsx)(n.code,{children:"docker compose up -d --force-recreate"}),".\nThe first command pulls the latest images of OpenRAG.\nThe second command recreates the containers with your data persisted."]}),"\n",(0,r.jsxs)(n.p,{children:["To ",(0,r.jsx)(n.strong,{children:"reset"})," your containers, click ",(0,r.jsx)(n.strong,{children:"Reset"}),".\nReset gives you a completely fresh start.\nReset deletes all of your data, including OpenSearch data, uploaded documents, and authentication.\n",(0,r.jsx)(n.strong,{children:"Reset"})," runs two commands.\nIt first stops and removes all containers, volumes, and local images."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{children:"docker compose down --volumes --remove-orphans --rmi local\n"})}),"\n",(0,r.jsxs)(n.p,{children:["When the first command is complete, OpenRAG removes any additional Docker objects with ",(0,r.jsx)(n.code,{children:"prune"}),"."]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{children:"docker system prune -f\n"})}),"\n",(0,r.jsx)(n.h2,{id:"diagnostics",children:"Diagnostics"}),"\n",(0,r.jsxs)(n.p,{children:["The ",(0,r.jsx)(n.strong,{children:"Diagnostics"})," menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security."]})]})}function h(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,r.jsx)(n,{...e,children:(0,r.jsx)(l,{...e})}):l(e)}},8453:(e,n,t)=>{t.d(n,{R:()=>o,x:()=>a});var s=t(6540);const r={},i=s.createContext(r);function o(e){const n=s.useContext(i);return s.useMemo(function(){return"function"==typeof e?e(n):{...n,...e}},[n,e])}function a(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:o(e.components),s.createElement(i.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/33362219.d86f431d.js b/assets/js/33362219.d86f431d.js new file mode 100644 index 00000000..846003b2 --- /dev/null +++ b/assets/js/33362219.d86f431d.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[532],{3782:(e,n,t)=>{t.d(n,{Ay:()=>c,RM:()=>i});var o=t(4848),s=t(8453),r=t(9179);const i=[];function a(e){const n={a:"a",p:"p",strong:"strong",...(0,s.R)(),...e.components};return(0,o.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.\nTo modify a flow, click ",(0,o.jsx)(r.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"}),".\nOpenRAG's visual editor is based on the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use case."]})}function c(e={}){const{wrapper:n}={...(0,s.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(a,{...e})}):a(e)}},3982:(e,n,t)=>{t.d(n,{A:()=>o});const o=t.p+"assets/images/opensearch-agent-flow-a759d00657f2fdb62e64c84c643e8896.png"},8748:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>h,contentTitle:()=>l,default:()=>p,frontMatter:()=>c,metadata:()=>o,toc:()=>d});const o=JSON.parse('{"id":"core-components/agents","title":"Langflow Agents","description":"OpenRAG leverages Langflow\'s Agent component to power the OpenRAG OpenSearch Agent flow.","source":"@site/docs/core-components/agents.mdx","sourceDirName":"core-components","slug":"/agents","permalink":"/agents","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/core-components/agents.mdx","tags":[],"version":"current","frontMatter":{"title":"Langflow Agents","slug":"/agents"},"sidebar":"tutorialSidebar","previous":{"title":"Terminal User Interface (TUI)","permalink":"/get-started/tui"},"next":{"title":"OpenSearch Knowledge","permalink":"/knowledge"}}');var s=t(4848),r=t(8453),i=t(9179),a=(t(1470),t(9365),t(3782));const c={title:"Langflow Agents",slug:"/agents"},l=void 0,h={},d=[{value:"Use the OpenRAG OpenSearch Agent flow",id:"flow",level:2},...a.RM,{value:"Additional Langflow functionality",id:"additional-langflow-functionality",level:2}];function g(e){const n={a:"a",code:"code",h2:"h2",img:"img",li:"li",p:"p",strong:"strong",ul:"ul",...(0,r.R)(),...e.components},{Details:o}=n;return o||function(e,n){throw new Error("Expected "+(n?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}("Details",!0),(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(n.p,{children:"OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow."}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Flows"})," in Langflow are functional representations of application workflows, with multiple ",(0,s.jsx)(n.a,{href:"https://docs.langflow.org/concepts-components",children:"component"})," nodes connected as single steps in a workflow."]}),"\n",(0,s.jsxs)(n.p,{children:["In the OpenRAG OpenSearch Agent flow, components like the Langflow ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/agents",children:[(0,s.jsx)(n.strong,{children:"Agent"})," component"]})," and ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/bundles-elastic#opensearch",children:[(0,s.jsx)(n.strong,{children:"OpenSearch"})," component"]})," are connected to intelligently chat with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.img,{alt:"OpenRAG Open Search Agent Flow",src:t(3982).A+"",width:"4086",height:"2240"})}),"\n",(0,s.jsx)(n.p,{children:"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."}),"\n",(0,s.jsxs)(o,{closed:!0,children:[(0,s.jsx)("summary",{children:"How do agents work?"}),(0,s.jsx)(n.p,{children:"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."}),(0,s.jsx)(n.p,{children:"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."}),(0,s.jsx)(n.p,{children:"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."}),(0,s.jsx)(n.p,{children:"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."})]}),"\n",(0,s.jsx)(n.h2,{id:"flow",children:"Use the OpenRAG OpenSearch Agent flow"}),"\n",(0,s.jsxs)(n.p,{children:["If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG OpenSearch Agent chat flow.\nTo switch OpenRAG over to the ",(0,s.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"})," and view the OpenRAG OpenSearch Agentflow, click ",(0,s.jsx)(i.A,{name:"Settings2","aria-hidden":"true"})," ",(0,s.jsx)(n.strong,{children:"Settings"}),", and then click ",(0,s.jsx)(n.strong,{children:"Edit in Langflow"}),".\nThis flow contains eight components connected together to chat with your data:"]}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:["The ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/agents",children:[(0,s.jsx)(n.strong,{children:"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.\nThe ",(0,s.jsx)(n.strong,{children:"Agent"})," behaves according to the prompt in the ",(0,s.jsx)(n.strong,{children:"Agent Instructions"})," field."]}),"\n",(0,s.jsxs)(n.li,{children:["The ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/components-io",children:[(0,s.jsx)(n.strong,{children:"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."]}),"\n",(0,s.jsxs)(n.li,{children:["The ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/bundles-elastic#opensearch",children:[(0,s.jsx)(n.strong,{children:"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."]}),"\n",(0,s.jsxs)(n.li,{children:["The ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/components-models",children:[(0,s.jsx)(n.strong,{children:"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."]}),"\n",(0,s.jsxs)(n.li,{children:["The ",(0,s.jsxs)(n.a,{href:"https://docs.langflow.org/components-embedding-models",children:[(0,s.jsx)(n.strong,{children:"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. 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file diff --git a/assets/js/33362219.e124ffd1.js b/assets/js/33362219.e124ffd1.js deleted file mode 100644 index f4756f25..00000000 --- a/assets/js/33362219.e124ffd1.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[532],{3782:(e,n,t)=>{t.d(n,{Ay:()=>c,RM:()=>i});var o=t(4848),s=t(8453),r=t(9179);const i=[];function a(e){const n={a:"a",p:"p",strong:"strong",...(0,s.R)(),...e.components};return(0,o.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.\nTo modify a flow, click ",(0,o.jsx)(r.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"}),".\nOpenRAG's visual editor is based on the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use 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Use this Docker compose file for environments where GPU drivers aren't available."]}),"\n"]}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsx)(n.li,{children:(0,s.jsx)(n.a,{href:"https://www.python.org/downloads/release/python-3100/",children:"Python Version 3.10 to 3.13"})}),"\n",(0,s.jsx)(n.li,{children:(0,s.jsx)(n.a,{href:"https://docs.astral.sh/uv/getting-started/installation/",children:"uv"})}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.a,{href:"https://podman.io/docs/installation",children:"Podman"})," (recommended) or ",(0,s.jsx)(n.a,{href:"https://docs.docker.com/get-docker/",children:"Docker"})," installed"]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.a,{href:"https://docs.docker.com/compose/install/",children:"Docker Compose"})," installed. If you're using Podman, use ",(0,s.jsx)(n.a,{href:"https://docs.podman.io/en/latest/markdown/podman-compose.1.html",children:"podman-compose"})," or alias Docker compose commands to Podman commands."]}),"\n",(0,s.jsxs)(n.li,{children:["Create an ",(0,s.jsx)(n.a,{href:"https://platform.openai.com/api-keys",children:"OpenAI API key"}),". This key is ",(0,s.jsx)(n.strong,{children:"required"})," to start OpenRAG, but you can choose a different model provider during ",(0,s.jsx)(n.a,{href:"#application-onboarding",children:"Application Onboarding"}),"."]}),"\n",(0,s.jsx)(n.li,{children:"Optional: GPU support requires an NVIDIA GPU with CUDA support and compatible NVIDIA drivers installed on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment."}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"deploy-openrag-with-docker-compose",children:"Deploy OpenRAG with Docker Compose"}),"\n",(0,s.jsx)(n.p,{children:"To install OpenRAG with Docker Compose, do the following:"}),"\n",(0,s.jsxs)(n.ol,{children:["\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsx)(n.p,{children:"Clone the OpenRAG repository."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"git clone https://github.com/langflow-ai/openrag.git\ncd openrag\n"})}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsxs)(n.p,{children:["Copy the example ",(0,s.jsx)(n.code,{children:".env"})," file included in the repository root.\nThe example file includes all environment variables with comments to guide you in finding and setting their values."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"cp .env.example .env\n"})}),"\n",(0,s.jsxs)(n.p,{children:["Alternatively, create a new ",(0,s.jsx)(n.code,{children:".env"})," file in the repository root."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{children:"touch .env\n"})}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsxs)(n.p,{children:["Set environment variables. 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installation by confirming all services are running."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"docker compose ps\n"})}),"\n",(0,s.jsx)(n.p,{children:"You can now access the application at:"}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Frontend"}),": ",(0,s.jsx)(n.a,{href:"http://localhost:3000",children:"http://localhost:3000"})]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Backend API"}),": ",(0,s.jsx)(n.a,{href:"http://localhost:8000",children:"http://localhost:8000"})]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Langflow"}),": ",(0,s.jsx)(n.a,{href:"http://localhost:7860",children:"http://localhost:7860"})]}),"\n"]}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsxs)(n.p,{children:["Continue with ",(0,s.jsx)(n.a,{href:"#application-onboarding",children:"Application Onboarding"}),"."]}),"\n"]}),"\n"]}),"\n",(0,s.jsx)(a.Ay,{}),"\n",(0,s.jsx)(n.h2,{id:"container-management-commands",children:"Container management commands"}),"\n",(0,s.jsxs)(n.p,{children:["Manage your OpenRAG containers with the following commands.\nThese commands are also available in the TUI's ",(0,s.jsx)(n.a,{href:"/get-started/tui#status",children:"Status menu"}),"."]}),"\n",(0,s.jsx)(n.h3,{id:"upgrade-containers",children:"Upgrade containers"}),"\n",(0,s.jsx)(n.p,{children:"Upgrade your containers to the latest version while preserving your data."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"docker compose pull\ndocker compose up -d --force-recreate\n"})}),"\n",(0,s.jsx)(n.h3,{id:"rebuild-containers-destructive",children:"Rebuild containers (destructive)"}),"\n",(0,s.jsxs)(n.p,{children:["Reset state by rebuilding all of your containers.\nYour OpenSearch and Langflow databases will be lost.\nDocuments stored in the ",(0,s.jsx)(n.code,{children:"./documents"})," directory will persist, since the directory is mounted as a volume in the OpenRAG backend container."]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"docker compose up --build --force-recreate --remove-orphans\n"})}),"\n",(0,s.jsx)(n.h3,{id:"remove-all-containers-and-data-destructive",children:"Remove all containers and data (destructive)"}),"\n",(0,s.jsx)(n.p,{children:"Completely remove your OpenRAG installation and delete all data.\nThis deletes all of your data, including OpenSearch data, uploaded documents, and authentication."}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"docker compose down --volumes --remove-orphans --rmi local\ndocker system prune -f\n"})})]})}function u(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(h,{...e})}):h(e)}},8453:(e,n,r)=>{r.d(n,{R:()=>a,x:()=>l});var t=r(6540);const 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strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[717],{1260:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>h,contentTitle:()=>c,default:()=>g,frontMatter:()=>d,metadata:()=>s,toc:()=>u});const s=JSON.parse('{"id":"get-started/quickstart","title":"Quickstart","description":"Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.","source":"@site/docs/get-started/quickstart.mdx","sourceDirName":"get-started","slug":"/quickstart","permalink":"/quickstart","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/get-started/quickstart.mdx","tags":[],"version":"current","frontMatter":{"title":"Quickstart","slug":"/quickstart"},"sidebar":"tutorialSidebar","previous":{"title":"Deploy with Docker","permalink":"/get-started/docker"},"next":{"title":"Terminal User Interface (TUI)","permalink":"/get-started/tui"}}');var r=t(4848),o=t(8453),a=t(9179),i=t(1470),l=t(9365);const d={title:"Quickstart",slug:"/quickstart"},c=void 0,h={},u=[{value:"Prerequisites",id:"prerequisites",level:2},{value:"Find your way around",id:"find-your-way-around",level:2},{value:"Add your own knowledge",id:"add-your-own-knowledge",level:2},{value:"Swap out the language model to modify agent behavior",id:"change-components",level:2},{value:"Integrate OpenRAG into your application",id:"integrate-openrag-into-your-application",level:2}];function p(e){const n={a:"a",code:"code",h2:"h2",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,o.R)(),...e.components},{Details:s}=n;return s||function(e,n){throw new Error("Expected "+(n?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}("Details",!0),(0,r.jsxs)(r.Fragment,{children:[(0,r.jsx)(n.p,{children:"Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API."}),"\n",(0,r.jsx)(n.h2,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,r.jsxs)(n.ul,{children:["\n",(0,r.jsx)(n.li,{children:(0,r.jsx)(n.a,{href:"/install",children:"Install and start OpenRAG"})}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"find-your-way-around",children:"Find your way around"}),"\n",(0,r.jsxs)(n.ol,{children:["\n",(0,r.jsxs)(n.li,{children:["In OpenRAG, click ",(0,r.jsx)(a.A,{name:"MessageSquare","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Chat"}),".\nThe chat is powered by the OpenRAG OpenSearch Agent.\nFor more information, see ",(0,r.jsx)(n.a,{href:"/agents",children:"Langflow Agents"}),"."]}),"\n",(0,r.jsxs)(n.li,{children:["Ask ",(0,r.jsx)(n.code,{children:"What documents are available to you?"}),"\nThe 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.\nKnowledge is stored in OpenSearch.\nFor more information, see ",(0,r.jsx)(n.a,{href:"/knowledge",children:"Knowledge"}),"."]}),"\n",(0,r.jsxs)(n.li,{children:["To confirm the agent is correct, click ",(0,r.jsx)(a.A,{name:"Library","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Knowledge"}),".\nThe ",(0,r.jsx)(n.strong,{children:"Knowledge"})," page lists the documents OpenRAG has ingested into the OpenSearch vector database.\nClick on a document to display the chunks derived from splitting the default documents into the vector database."]}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"add-your-own-knowledge",children:"Add your own knowledge"}),"\n",(0,r.jsxs)(n.ol,{children:["\n",(0,r.jsxs)(n.li,{children:["To add documents to your knowledge base, click ",(0,r.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Add Knowledge"}),".","\n",(0,r.jsxs)(n.ul,{children:["\n",(0,r.jsxs)(n.li,{children:["Select ",(0,r.jsx)(n.strong,{children:"Add File"})," to add a single file from your local machine (mapped with the Docker volume mount)."]}),"\n",(0,r.jsxs)(n.li,{children:["Select ",(0,r.jsx)(n.strong,{children:"Process Folder"})," to process an entire folder of documents from your local machine (mapped with the Docker volume mount)."]}),"\n",(0,r.jsxs)(n.li,{children:["Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see ",(0,r.jsx)(n.a,{href:"/knowledge#oauth-ingestion",children:"OAuth ingestion"}),"."]}),"\n"]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["Return to the Chat window and ask a question about your loaded data.\nFor example, with a manual about a PC tablet loaded, ask ",(0,r.jsx)(n.code,{children:"How do I connect this device to WiFI?"}),"\nThe agent responds with a message indicating it now has your knowledge as context for answering questions."]}),"\n",(0,r.jsxs)(n.li,{children:["Click the ",(0,r.jsx)(a.A,{name:"Gear","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Function Call: search_documents (tool_call)"})," that is printed in the Playground.\nThese events log the agent's request to the tool and the tool's response, so you have direct visibility into your agent's functionality.\nIf you aren't getting the results you need, you can further tune the knowledge ingestion and agent behavior in the next section."]}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"change-components",children:"Swap out the language model to modify agent behavior"}),"\n",(0,r.jsxs)(n.p,{children:["To modify the knowledge ingestion or Agent behavior, click ",(0,r.jsx)(a.A,{name:"Settings2","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Settings"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["In this example, you'll try a different LLM to demonstrate how the Agent's response changes.\nYou can only change the ",(0,r.jsx)(n.strong,{children:"Language model"}),", and not the ",(0,r.jsx)(n.strong,{children:"Model provider"})," that you started with in OpenRAG.\nIf you're using Ollama, you can use any installed model."]}),"\n",(0,r.jsxs)(n.ol,{children:["\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["To edit the Agent's behavior, click ",(0,r.jsx)(n.strong,{children:"Edit in Langflow"}),".\nYou can more quickly access the ",(0,r.jsx)(n.strong,{children:"Language Model"})," and ",(0,r.jsx)(n.strong,{children:"Agent Instructions"})," fields in this page, but for illustration purposes, navigate to the Langflow visual builder."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["OpenRAG warns you that you're entering Langflow. Click ",(0,r.jsx)(n.strong,{children:"Proceed"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["The OpenRAG OpenSearch Agent flow appears.\n",(0,r.jsx)(n.img,{alt:"OpenRAG Open Search Agent Flow",src:t(3982).A+"",width:"4086",height:"2240"})]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["In the ",(0,r.jsx)(n.strong,{children:"Language Model"})," component, under ",(0,r.jsx)(n.strong,{children:"Model"}),", select a different OpenAI model."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["Save your flow with ",(0,r.jsx)("kbd",{children:"Command+S"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["In OpenRAG, start a new conversation by clicking the ",(0,r.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," in the ",(0,r.jsx)(n.strong,{children:"Conversations"})," tab."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsx)(n.p,{children:"Ask the same question as before to demonstrate how a different language model changes the results."}),"\n"]}),"\n"]}),"\n",(0,r.jsx)(n.h2,{id:"integrate-openrag-into-your-application",children:"Integrate OpenRAG into your application"}),"\n",(0,r.jsxs)(n.p,{children:["To integrate OpenRAG into your application, use the ",(0,r.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. Langflow provides code snippets to help you get started."]}),"\n",(0,r.jsxs)(n.ol,{children:["\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["Create a ",(0,r.jsx)(n.a,{href:"https://docs.langflow.org/api-keys-and-authentication",children:"Langflow API key"}),"."]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Create a Langflow API key"}),(0,r.jsxs)(n.p,{children:["A Langflow API key is a user-specific token you can use with Langflow.\nIt is ",(0,r.jsx)(n.strong,{children:"only"})," used for sending requests to the Langflow server.\nIt does ",(0,r.jsx)(n.strong,{children:"not"})," access to OpenRAG."]}),(0,r.jsx)(n.p,{children:"To create a Langflow API key, do the following:"}),(0,r.jsxs)(n.ol,{children:["\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["In Langflow, click your user icon, and then select ",(0,r.jsx)(n.strong,{children:"Settings"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["Click ",(0,r.jsx)(n.strong,{children:"Langflow API Keys"}),", and then click ",(0,r.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Add New"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["Name your key, and then click ",(0,r.jsx)(n.strong,{children:"Create API Key"}),"."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsx)(n.p,{children:"Copy the API key and store it securely."}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["To use your Langflow API key in a request, set a ",(0,r.jsx)(n.code,{children:"LANGFLOW_API_KEY"})," environment variable in your terminal, and then include an ",(0,r.jsx)(n.code,{children:"x-api-key"})," header or query parameter with your request.\nFor example:"]}),"\n",(0,r.jsx)(n.pre,{children:(0,r.jsx)(n.code,{className:"language-bash",children:'# Set variable\nexport LANGFLOW_API_KEY="sk..."\n\n# Send request\ncurl --request POST \\\n--url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \\\n--header "Content-Type: application/json" \\\n--header "x-api-key: $LANGFLOW_API_KEY" \\\n--data \'{\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "Hello"\n}\'\n'})}),"\n"]}),"\n"]})]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["To navigate to the OpenRAG OpenSearch Agent flow, click ",(0,r.jsx)(a.A,{name:"Settings2","aria-hidden":"true"})," ",(0,r.jsx)(n.strong,{children:"Settings"}),", and then click ",(0,r.jsx)(n.strong,{children:"Edit in Langflow"})," in the OpenRAG OpenSearch Agent flow."]}),"\n"]}),"\n",(0,r.jsxs)(n.li,{children:["\n",(0,r.jsxs)(n.p,{children:["Click ",(0,r.jsx)(n.strong,{children:"Share"}),", and then click ",(0,r.jsx)(n.strong,{children:"API access"}),"."]}),"\n",(0,r.jsxs)(n.p,{children:["The default code in the API access pane constructs a request with the Langflow server ",(0,r.jsx)(n.code,{children:"url"}),", ",(0,r.jsx)(n.code,{children:"headers"}),", and a ",(0,r.jsx)(n.code,{children:"payload"})," of request data. The code snippets automatically include the ",(0,r.jsx)(n.code,{children:"LANGFLOW_SERVER_ADDRESS"})," and ",(0,r.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,r.jsx)(n.a,{href:"http://localhost:7860",children:"http://localhost:7860"}),"."]}),"\n",(0,r.jsxs)(i.A,{children:[(0,r.jsx)(l.A,{value:"python",label:"Python",children:(0,r.jsx)(n.pre,{children:(0,r.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,r.jsx)(l.A,{value:"typescript",label:"TypeScript",children:(0,r.jsx)(n.pre,{children:(0,r.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,r.jsx)(l.A,{value:"curl",label:"curl",children:(0,r.jsx)(n.pre,{children:(0,r.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,r.jsxs)(n.li,{children:["\n",(0,r.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,r.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,r.jsx)(n.strong,{children:"Simple Agent"})," template flow:"]}),"\n",(0,r.jsxs)(s,{children:[(0,r.jsx)("summary",{children:"Result"}),(0,r.jsx)(n.pre,{children:(0,r.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,r.jsx)(n.p,{children:"To further explore the API, see:"}),"\n",(0,r.jsxs)(n.ul,{children:["\n",(0,r.jsxs)(n.li,{children:["The Langflow ",(0,r.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,r.jsx)(n.li,{children:(0,r.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,o.R)(),...e.components};return n?(0,r.jsx)(n,{...e,children:(0,r.jsx)(p,{...e})}):p(e)}},3982:(e,n,t)=>{t.d(n,{A:()=>s});const s=t.p+"assets/images/opensearch-agent-flow-a759d00657f2fdb62e64c84c643e8896.png"},9179:(e,n,t)=>{t.d(n,{A:()=>o});t(6540);var s=t(4827),r=t(4848);function o({name:e,...n}){const t=s[e];return t?(0,r.jsx)(t,{...n}):null}}}]); \ No newline at end of file diff --git a/assets/js/af920ffe.610d6519.js b/assets/js/af920ffe.610d6519.js deleted file mode 100644 index 7c38df0c..00000000 --- a/assets/js/af920ffe.610d6519.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[717],{1260:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>u,contentTitle:()=>h,default:()=>x,frontMatter:()=>c,metadata:()=>s,toc:()=>p});const s=JSON.parse('{"id":"get-started/quickstart","title":"Quickstart","description":"Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.","source":"@site/docs/get-started/quickstart.mdx","sourceDirName":"get-started","slug":"/quickstart","permalink":"/quickstart","draft":false,"unlisted":false,"editUrl":"https://github.com/openrag/openrag/tree/main/docs/docs/get-started/quickstart.mdx","tags":[],"version":"current","frontMatter":{"title":"Quickstart","slug":"/quickstart"},"sidebar":"tutorialSidebar","previous":{"title":"Deploy with Docker","permalink":"/get-started/docker"},"next":{"title":"Terminal User Interface (TUI)","permalink":"/get-started/tui"}}');var o=t(4848),r=t(8453),a=t(9179),i=t(1470),l=t(9365),d=t(9334);const c={title:"Quickstart",slug:"/quickstart"},h=void 0,u={},p=[...d.RM,{value:"Prerequisites",id:"prerequisites",level:2},{value:"Find your way around",id:"find-your-way-around",level:2},{value:"Add your own knowledge",id:"add-your-own-knowledge",level:2},{value:"Swap out the language model to modify agent behavior",id:"change-components",level:2},{value:"Integrate OpenRAG into your application",id:"integrate-openrag-into-your-application",level:2}];function g(e){const n={a:"a",code:"code",h2:"h2",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,r.R)(),...e.components},{Details:s}=n;return s||function(e,n){throw new Error("Expected "+(n?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}("Details",!0),(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(d.Ay,{}),"\n",(0,o.jsx)(n.p,{children:"Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API."}),"\n",(0,o.jsx)(n.h2,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsx)(n.li,{children:(0,o.jsx)(n.a,{href:"/install",children:"Install and start OpenRAG"})}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"find-your-way-around",children:"Find your way around"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["In OpenRAG, click ",(0,o.jsx)(a.A,{name:"MessageSquare","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Chat"}),".\nThe chat is powered by the OpenRAG OpenSearch Agent.\nFor more information, see ",(0,o.jsx)(n.a,{href:"/agents",children:"Langflow Agents"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["Ask ",(0,o.jsx)(n.code,{children:"What documents are available to you?"}),"\nThe 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.\nKnowledge is stored in OpenSearch.\nFor more information, see ",(0,o.jsx)(n.a,{href:"/knowledge",children:"Knowledge"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["To confirm the agent is correct, click ",(0,o.jsx)(a.A,{name:"Library","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Knowledge"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Knowledge"})," page lists the documents OpenRAG has ingested into the OpenSearch vector database.\nClick on a document to display the chunks derived from splitting the default documents into the vector database."]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"add-your-own-knowledge",children:"Add your own knowledge"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["To add documents to your knowledge base, click ",(0,o.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),".","\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:["Select ",(0,o.jsx)(n.strong,{children:"Add File"})," to add a single file from your local machine (mapped with the Docker volume mount)."]}),"\n",(0,o.jsxs)(n.li,{children:["Select ",(0,o.jsx)(n.strong,{children:"Process Folder"})," to process an entire folder of documents from your local machine (mapped with the Docker volume mount)."]}),"\n",(0,o.jsxs)(n.li,{children:["Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see ",(0,o.jsx)(n.a,{href:"/knowledge#oauth-ingestion",children:"OAuth ingestion"}),"."]}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["Return to the Chat window and ask a question about your loaded data.\nFor example, with a manual about a PC tablet loaded, ask ",(0,o.jsx)(n.code,{children:"How do I connect this device to WiFI?"}),"\nThe agent responds with a message indicating it now has your knowledge as context for answering questions."]}),"\n",(0,o.jsxs)(n.li,{children:["Click the ",(0,o.jsx)(a.A,{name:"Gear","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Function Call: search_documents (tool_call)"})," that is printed in the Playground.\nThese events log the agent's request to the tool and the tool's response, so you have direct visibility into your agent's functionality.\nIf you aren't getting the results you need, you can further tune the knowledge ingestion and agent behavior in the next section."]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"change-components",children:"Swap out the language model to modify agent behavior"}),"\n",(0,o.jsxs)(n.p,{children:["To modify the knowledge ingestion or Agent behavior, click ",(0,o.jsx)(a.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["In this example, you'll try a different LLM to demonstrate how the Agent's response changes.\nYou can only change the ",(0,o.jsx)(n.strong,{children:"Language model"}),", and not the ",(0,o.jsx)(n.strong,{children:"Model provider"})," that you started with in OpenRAG.\nIf you're using Ollama, you can use any installed model."]}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To edit the Agent's behavior, click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"}),".\nYou can more quickly access the ",(0,o.jsx)(n.strong,{children:"Language Model"})," and ",(0,o.jsx)(n.strong,{children:"Agent Instructions"})," fields in this page, but for illustration purposes, navigate to the Langflow visual builder."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["OpenRAG warns you that you're entering Langflow. 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:"4086",height:"2240"})]}),"\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. Langflow provides code snippets to help you get started."]}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Create a ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-keys-and-authentication",children:"Langflow API 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 ",(0,o.jsx)(n.strong,{children:"Create API Key"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Copy the API key and store it securely."}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To use your Langflow API key in a request, set a ",(0,o.jsx)(n.code,{children:"LANGFLOW_API_KEY"})," environment variable in your terminal, and then include an ",(0,o.jsx)(n.code,{children:"x-api-key"})," header or query parameter with your request.\nFor example:"]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:'# Set variable\nexport LANGFLOW_API_KEY="sk..."\n\n# Send request\ncurl --request POST \\\n--url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \\\n--header "Content-Type: application/json" \\\n--header "x-api-key: $LANGFLOW_API_KEY" \\\n--data \'{\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "Hello"\n}\'\n'})}),"\n"]}),"\n"]})]}),"\n"]}),"\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 x(e={}){const{wrapper:n}={...(0,r.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(g,{...e})}):g(e)}},3982:(e,n,t)=>{t.d(n,{A:()=>s});const s=t.p+"assets/images/opensearch-agent-flow-a759d00657f2fdb62e64c84c643e8896.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}},9334:(e,n,t)=>{t.d(n,{Ay:()=>i,RM:()=>r});var s=t(4848),o=t(8453);const r=[];function a(e){const n={admonition:"admonition",p:"p",...(0,o.R)(),...e.components};return(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"OpenRAG is is currently in public preview.\nDevelopment is ongoing, and the features and functionality are subject to change."})})}function i(e={}){const{wrapper:n}={...(0,o.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(a,{...e})}):a(e)}}}]); \ No newline at end of file diff --git a/assets/js/ca2c3c0c.295b1c55.js b/assets/js/ca2c3c0c.295b1c55.js new file mode 100644 index 00000000..bf77dcc8 --- /dev/null +++ b/assets/js/ca2c3c0c.295b1c55.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[919],{3782:(e,n,s)=>{s.d(n,{Ay:()=>l,RM:()=>r});var o=s(4848),t=s(8453),i=s(9179);const r=[];function c(e){const n={a:"a",p:"p",strong:"strong",...(0,t.R)(),...e.components};return(0,o.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.\nTo modify a flow, click ",(0,o.jsx)(i.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"}),".\nOpenRAG's visual editor is based on the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use case."]})}function l(e={}){const{wrapper:n}={...(0,t.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(c,{...e})}):c(e)}},7125:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>a,contentTitle:()=>l,default:()=>g,frontMatter:()=>c,metadata:()=>o,toc:()=>d});const o=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":"/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":"/knowledge"},"next":{"title":"Environment variables","permalink":"/reference/configuration"}}');var t=s(4848),i=s(8453),r=(s(9179),s(1470),s(9365),s(3782));const c={title:"Docling Ingestion",slug:"/ingestion"},l=void 0,a={},d=[{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},{value:"Knowledge ingestion flows",id:"knowledge-ingestion-flows",level:2},...r.RM,{value:"OpenSearch URL Ingestion flow",id:"url-flow",level:3}];function h(e){const n={a:"a",br:"br",code:"code",h2:"h2",h3:"h3",li:"li",p:"p",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsxs)(n.p,{children:["OpenRAG uses ",(0,t.jsx)(n.a,{href:"https://docling-project.github.io/docling/",children:"Docling"})," for its document ingestion pipeline.\nMore specifically, OpenRAG uses ",(0,t.jsx)(n.a,{href:"https://github.com/docling-project/docling-serve",children:"Docling Serve"}),", which starts a ",(0,t.jsx)(n.code,{children:"docling-serve"})," process on your local machine and runs Docling ingestion through an API service."]}),"\n",(0,t.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,t.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,t.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,t.jsx)(n.h2,{id:"docling-ingestion-settings",children:"Docling ingestion settings"}),"\n",(0,t.jsx)(n.p,{children:"These settings configure the Docling ingestion parameters."}),"\n",(0,t.jsxs)(n.p,{children:["OpenRAG will warn you if ",(0,t.jsx)(n.code,{children:"docling-serve"})," is not running.\nTo start or stop ",(0,t.jsx)(n.code,{children:"docling-serve"})," or any other native services, in the TUI main menu, click ",(0,t.jsx)(n.strong,{children:"Start Native Services"})," or ",(0,t.jsx)(n.strong,{children:"Stop Native Services"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Embedding model"})," determines which AI model is used to create vector embeddings. The default is ",(0,t.jsx)(n.code,{children:"text-embedding-3-small"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Chunk size"})," determines how large each text chunk is in number of characters.\nLarger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context.\nThe default value of ",(0,t.jsx)(n.code,{children:"1000"})," characters provides a good starting point that balances these considerations."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Chunk overlap"})," controls the number of characters that overlap over chunk boundaries.\nUse larger overlap values for documents where context is most important, and use smaller overlap values for simpler documents, or when optimization is most important.\nThe 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."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"OCR"})," enables or disabled OCR processing when extracting text from images and scanned documents.\nOCR is disabled by default. This setting is best suited for processing text-based documents as quickly as possible with Docling's ",(0,t.jsx)(n.a,{href:"https://docling-project.github.io/docling/reference/document_converter/",children:(0,t.jsx)(n.code,{children:"DocumentConverter"})}),". Images are ignored and not processed."]}),"\n",(0,t.jsx)(n.p,{children:"Enable OCR when you are processing documents containing images with text that requires extraction, or for scanned documents. Enabling OCR can slow ingestion performance."}),"\n",(0,t.jsxs)(n.p,{children:["If OpenRAG detects that the local machine is running on macOS, OpenRAG uses the ",(0,t.jsx)(n.a,{href:"https://www.piwheels.org/project/ocrmac/",children:"ocrmac"})," OCR engine. Other platforms use ",(0,t.jsx)(n.a,{href:"https://www.jaided.ai/easyocr/",children:"easyocr"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Picture descriptions"})," adds image descriptions generated by the ",(0,t.jsx)(n.a,{href:"https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct",children:"SmolVLM-256M-Instruct"})," model to OCR processing. Enabling picture descriptions can slow ingestion performance."]}),"\n",(0,t.jsx)(n.h2,{id:"use-openrag-default-ingestion-instead-of-docling-serve",children:"Use OpenRAG default ingestion instead of Docling serve"}),"\n",(0,t.jsxs)(n.p,{children:["If you want to use OpenRAG's built-in pipeline instead of Docling serve, set ",(0,t.jsx)(n.code,{children:"DISABLE_INGEST_WITH_LANGFLOW=true"})," in ",(0,t.jsx)(n.a,{href:"/reference/configuration#document-processing",children:"Environment variables"}),"."]}),"\n",(0,t.jsx)(n.p,{children:"The built-in pipeline still uses the Docling processor, but uses it directly without the Docling Serve API."}),"\n",(0,t.jsxs)(n.p,{children:["For more information, see ",(0,t.jsxs)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58",children:[(0,t.jsx)(n.code,{children:"processors.py"})," in the OpenRAG repository"]}),"."]}),"\n",(0,t.jsx)(n.h2,{id:"knowledge-ingestion-flows",children:"Knowledge ingestion flows"}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Flows"})," in Langflow are functional representations of application workflows, with multiple ",(0,t.jsx)(n.a,{href:"https://docs.langflow.org/concepts-components",children:"component"})," nodes connected as single steps in a workflow."]}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.strong,{children:"OpenSearch Ingestion"})," flow is the default knowledge ingestion flow in OpenRAG: when you ",(0,t.jsx)(n.strong,{children:"Add Knowledge"})," in OpenRAG, you run the OpenSearch Ingestion flow in the background. The flow ingests documents using ",(0,t.jsx)(n.strong,{children:"Docling Serve"})," to import and process documents."]}),"\n",(0,t.jsx)(n.p,{children:"This flow contains ten components connected together to process and store documents in your knowledge base."}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/bundles-docling",children:[(0,t.jsx)(n.strong,{children:"Docling Serve"})," component"]})," processes input documents by connecting to your instance of Docling Serve."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/components-docling",children:[(0,t.jsx)(n.strong,{children:"Export DoclingDocument"})," component"]})," exports the processed DoclingDocument to markdown format with image export mode set to placeholder. This conversion makes the structured document data into a standardized format for further processing."]}),"\n",(0,t.jsxs)(n.li,{children:["Three ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/components-processing#dataframe-operations",children:[(0,t.jsx)(n.strong,{children:"DataFrame Operations"})," components"]})," sequentially add metadata columns to the document data of ",(0,t.jsx)(n.code,{children:"filename"}),", ",(0,t.jsx)(n.code,{children:"file_size"}),", and ",(0,t.jsx)(n.code,{children:"mimetype"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/components-processing#split-text",children:[(0,t.jsx)(n.strong,{children:"Split Text"})," component"]})," splits the processed text into chunks with a chunk size of 1000 characters and an overlap of 200 characters."]}),"\n",(0,t.jsxs)(n.li,{children:["Four ",(0,t.jsx)(n.strong,{children:"Secret Input"})," components provide secure access to configuration variables: ",(0,t.jsx)(n.code,{children:"CONNECTOR_TYPE"}),", ",(0,t.jsx)(n.code,{children:"OWNER"}),", ",(0,t.jsx)(n.code,{children:"OWNER_EMAIL"}),", and ",(0,t.jsx)(n.code,{children:"OWNER_NAME"}),". These are runtime variables populated from OAuth login."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsx)(n.strong,{children:"Create Data"})," component combines the secret inputs into a structured data object that will be associated with the document embeddings."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/components-embedding-models",children:[(0,t.jsx)(n.strong,{children:"Embedding Model"})," component"]})," generates vector embeddings using OpenAI's ",(0,t.jsx)(n.code,{children:"text-embedding-3-small"})," model. The embedding model is selected at [Application onboarding] and cannot be changed."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/bundles-elastic#opensearch",children:[(0,t.jsx)(n.strong,{children:"OpenSearch"})," component"]})," stores the processed documents and their embeddings in the ",(0,t.jsx)(n.code,{children:"documents"})," index at ",(0,t.jsx)(n.code,{children:"https://opensearch:9200"}),". By default, the component is authenticated with a JWT token, but you can also select ",(0,t.jsx)(n.code,{children:"basic"})," auth mode, and enter your OpenSearch admin username and password."]}),"\n"]}),"\n",(0,t.jsx)(r.Ay,{}),"\n",(0,t.jsx)(n.h3,{id:"url-flow",children:"OpenSearch URL Ingestion flow"}),"\n",(0,t.jsxs)(n.p,{children:["An additional knowledge ingestion flow is included in OpenRAG, where it is used as an MCP tool by the ",(0,t.jsx)(n.a,{href:"/agents#flow",children:(0,t.jsx)(n.strong,{children:"Open Search Agent flow"})}),".\nThe agent calls this component to fetch web content, and the results are ingested into OpenSearch."]}),"\n",(0,t.jsxs)(n.p,{children:["For more on using MCP clients in Langflow, see ",(0,t.jsx)(n.a,{href:"https://docs.langflow.org/mcp-client",children:"MCP clients"}),".",(0,t.jsx)(n.br,{}),"\n","To connect additional MCP servers to the MCP client, see ",(0,t.jsx)(n.a,{href:"https://docs.langflow.org/mcp-tutorial",children:"Connect to MCP servers from your application"}),"."]})]})}function g(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,t.jsx)(n,{...e,children:(0,t.jsx)(h,{...e})}):h(e)}},9179:(e,n,s)=>{s.d(n,{A:()=>i});s(6540);var o=s(4827),t=s(4848);function i({name:e,...n}){const s=o[e];return s?(0,t.jsx)(s,{...n}):null}}}]); \ No newline at end of file diff --git a/assets/js/ca2c3c0c.956b2d03.js b/assets/js/ca2c3c0c.956b2d03.js deleted file mode 100644 index 916f9bdd..00000000 --- a/assets/js/ca2c3c0c.956b2d03.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[919],{3782:(e,n,s)=>{s.d(n,{Ay:()=>l,RM:()=>r});var o=s(4848),t=s(8453),i=s(9179);const r=[];function c(e){const n={a:"a",p:"p",strong:"strong",...(0,t.R)(),...e.components};return(0,o.jsxs)(n.p,{children:["All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.\nTo modify a flow, click ",(0,o.jsx)(i.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"}),".\nOpenRAG's visual editor is based on the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),", so you can edit your flows to match your specific use case."]})}function l(e={}){const{wrapper:n}={...(0,t.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(c,{...e})}):c(e)}},7125:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>d,contentTitle:()=>a,default:()=>g,frontMatter:()=>l,metadata:()=>o,toc:()=>h});const o=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":"/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":"/knowledge"},"next":{"title":"Environment variables","permalink":"/reference/configuration"}}');var t=s(4848),i=s(8453),r=(s(9179),s(1470),s(9365),s(3782)),c=s(9334);const l={title:"Docling Ingestion",slug:"/ingestion"},a=void 0,d={},h=[...c.RM,{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},{value:"Knowledge ingestion flows",id:"knowledge-ingestion-flows",level:2},...r.RM,{value:"OpenSearch URL Ingestion flow",id:"url-flow",level:3}];function p(e){const n={a:"a",br:"br",code:"code",h2:"h2",h3:"h3",li:"li",p:"p",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(c.Ay,{}),"\n",(0,t.jsxs)(n.p,{children:["OpenRAG uses ",(0,t.jsx)(n.a,{href:"https://docling-project.github.io/docling/",children:"Docling"})," for its document ingestion pipeline.\nMore specifically, OpenRAG uses ",(0,t.jsx)(n.a,{href:"https://github.com/docling-project/docling-serve",children:"Docling Serve"}),", which starts a ",(0,t.jsx)(n.code,{children:"docling-serve"})," process on your local machine and runs Docling ingestion through an API service."]}),"\n",(0,t.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,t.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,t.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,t.jsx)(n.h2,{id:"docling-ingestion-settings",children:"Docling ingestion settings"}),"\n",(0,t.jsx)(n.p,{children:"These settings configure the Docling ingestion parameters."}),"\n",(0,t.jsxs)(n.p,{children:["OpenRAG will warn you if ",(0,t.jsx)(n.code,{children:"docling-serve"})," is not running.\nTo start or stop ",(0,t.jsx)(n.code,{children:"docling-serve"})," or any other native services, in the TUI main menu, click ",(0,t.jsx)(n.strong,{children:"Start Native Services"})," or ",(0,t.jsx)(n.strong,{children:"Stop Native Services"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Embedding model"})," determines which AI model is used to create vector embeddings. The default is ",(0,t.jsx)(n.code,{children:"text-embedding-3-small"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Chunk size"})," determines how large each text chunk is in number of characters.\nLarger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context.\nThe default value of ",(0,t.jsx)(n.code,{children:"1000"})," characters provides a good starting point that balances these considerations."]}),"\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.strong,{children:"Chunk overlap"})," controls the number of characters that overlap over chunk boundaries.\nUse larger overlap values for documents where context is most important, and use smaller overlap values for simpler documents, or when optimization is most important.\nThe default value of 200 characters of overlap with a chunk size of 1000 (20% overlap) is suitable for general use cases. 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The flow ingests documents using ",(0,t.jsx)(n.strong,{children:"Docling Serve"})," to import and process documents."]}),"\n",(0,t.jsx)(n.p,{children:"This flow contains ten components connected together to process and store documents in your knowledge base."}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/bundles-docling",children:[(0,t.jsx)(n.strong,{children:"Docling Serve"})," component"]})," processes input documents by connecting to your instance of Docling Serve."]}),"\n",(0,t.jsxs)(n.li,{children:["The ",(0,t.jsxs)(n.a,{href:"https://docs.langflow.org/components-docling",children:[(0,t.jsx)(n.strong,{children:"Export DoclingDocument"})," component"]})," exports the processed DoclingDocument to markdown format with image export mode set to placeholder. 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e={354:0,869:0};f.f.j=(t,r)=>{var a=f.o(e,t)?e[t]:void 0;if(0!==a)if(a)r.push(a[2]);else if(/^(354|869)$/.test(t))e[t]=0;else{var o=new Promise((r,o)=>a=e[t]=[r,o]);r.push(a[2]=o);var n=f.p+f.u(t),c=new Error;f.l(n,r=>{if(f.o(e,t)&&(0!==(a=e[t])&&(e[t]=void 0),a)){var o=r&&("load"===r.type?"missing":r.type),n=r&&r.target&&r.target.src;c.message="Loading chunk "+t+" failed.\n("+o+": "+n+")",c.name="ChunkLoadError",c.type=o,c.request=n,a[1](c)}},"chunk-"+t,t)}},f.O.j=t=>0===e[t];var t=(t,r)=>{var a,o,n=r[0],c=r[1],i=r[2],d=0;if(n.some(t=>0!==e[t])){for(a in c)f.o(c,a)&&(f.m[a]=c[a]);if(i)var u=i(f)}for(t&&t(r);d Deploy with Docker | OpenRAG - + -

Deploy with Docker

info

OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

-

There are two different Docker Compose files. +

Deploy with Docker

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

  • diff --git a/get-started/tui/index.html b/get-started/tui/index.html index 0a5e7c71..f4a15ba1 100644 --- a/get-started/tui/index.html +++ b/get-started/tui/index.html @@ -4,16 +4,14 @@ Terminal User Interface (TUI) commands | OpenRAG - + -

    Terminal User Interface (TUI) commands

    info

    OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

    -

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

    +

    Terminal User Interface (TUI) commands

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

    OpenRAG TUI Interface

    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.

    diff --git a/index.html b/index.html index 35936cce..f41764fa 100644 --- a/index.html +++ b/index.html @@ -4,16 +4,14 @@ What is OpenRAG? | OpenRAG - + -

    What is OpenRAG?

    info

    OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

    -

    OpenRAG is an open-source package for building agentic RAG systems. +

    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:

      diff --git a/ingestion/index.html b/ingestion/index.html index 1e534792..4791e42f 100644 --- a/ingestion/index.html +++ b/ingestion/index.html @@ -4,16 +4,14 @@ Docling Ingestion | OpenRAG - + -

      Docling Ingestion

      info

      OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

      -

      OpenRAG uses Docling for its document ingestion pipeline. +

      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.

      diff --git a/install/index.html b/install/index.html index d8857a35..08920171 100644 --- a/install/index.html +++ b/install/index.html @@ -4,16 +4,14 @@ Install OpenRAG | OpenRAG - + -

      Install OpenRAG

      info

      OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

      -

      Install the OpenRAG Python wheel, and then run the OpenRAG Terminal User Interface(TUI) to start your OpenRAG deployment with a guided setup process.

      +

      Install OpenRAG

      Install the OpenRAG Python wheel, and then run the OpenRAG Terminal User Interface(TUI) to start your OpenRAG deployment with a guided setup process.

      If you prefer running Docker commands and manually editing .env files, see Deploy with Docker.

      Prerequisites​

        diff --git a/knowledge/index.html b/knowledge/index.html index b9caf9c4..4ea01c91 100644 --- a/knowledge/index.html +++ b/knowledge/index.html @@ -4,16 +4,14 @@ OpenSearch Knowledge | OpenRAG - + -

        OpenSearch Knowledge

        info

        OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

        -

        OpenRAG uses OpenSearch for its vector-backed knowledge store. +

        OpenSearch Knowledge

        OpenRAG uses OpenSearch for its vector-backed knowledge store. This is a specialized database for storing and retrieving embeddings, which helps your Agent efficiently find relevant information. OpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support.

        Ingest knowledge​

        diff --git a/quickstart/index.html b/quickstart/index.html index f83b9c6b..c9749b91 100644 --- a/quickstart/index.html +++ b/quickstart/index.html @@ -4,16 +4,14 @@ Quickstart | OpenRAG - + -

        Quickstart

        info

        OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

        -

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

        +

        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
          • diff --git a/reference/configuration/index.html b/reference/configuration/index.html index 7048195b..919872fb 100644 --- a/reference/configuration/index.html +++ b/reference/configuration/index.html @@ -4,7 +4,7 @@ Environment variables | OpenRAG - + diff --git a/support/troubleshoot/index.html b/support/troubleshoot/index.html index 60fbeee3..d91ac1ba 100644 --- a/support/troubleshoot/index.html +++ b/support/troubleshoot/index.html @@ -4,16 +4,14 @@ Troubleshoot | OpenRAG - + -

          Troubleshoot

          info

          OpenRAG is is currently in public preview. -Development is ongoing, and the features and functionality are subject to change.

          -

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

          +

          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 set in Environment variables 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.