From a63ef21a464e376c02a123a8863ec4cb2a749a97 Mon Sep 17 00:00:00 2001 From: zznate Date: Thu, 2 Oct 2025 17:02:19 +0000 Subject: [PATCH] deploy: a7bd2c2bc203b89869df74f6bff0a68cb0b3c03f --- 404.html | 6 +- agents/index.html | 51 +++--- ...tyles.c46b7464.css => styles.1847d619.css} | 2 +- assets/js/0ba6a408.143d35a8.js | 1 + assets/js/0ba6a408.4d6a2d2d.js | 1 - assets/js/21afe7ac.816d73dc.js | 1 - assets/js/21afe7ac.c654afca.js | 1 + assets/js/22dd74f7.7c8d1d11.js | 1 - assets/js/22dd74f7.a79a3853.js | 1 + assets/js/33362219.62ae0004.js | 1 - assets/js/33362219.8d5821a1.js | 1 + assets/js/749371cc.01a9e909.js | 1 + assets/js/749371cc.0a53eb7c.js | 1 - ...20ffe.1ba00f79.js => af920ffe.d761f3d0.js} | 2 +- assets/js/ca2c3c0c.39e376dd.js | 1 - assets/js/ca2c3c0c.5fc6b83f.js | 1 + assets/js/d0314b07.369feaa2.js | 1 - assets/js/d0314b07.ec4c264b.js | 1 + assets/js/e633a5ea.e372590c.js | 1 + assets/js/e633a5ea.ebd289b4.js | 1 - assets/js/eb5b356a.5fc785a5.js | 1 - assets/js/eb5b356a.641c3ecd.js | 1 + .../js/{main.c897035d.js => main.ea07e571.js} | 4 +- ...CENSE.txt => main.ea07e571.js.LICENSE.txt} | 0 ...n.a37ae89b.js => runtime~main.524c688b.js} | 2 +- configure/configuration/index.html | 8 +- get-started/docker/index.html | 67 ++++++-- get-started/tui/index.html | 18 +-- index.html | 13 +- ingestion/index.html | 8 +- install/index.html | 145 ++++++++++-------- knowledge/index.html | 31 ++-- quickstart/index.html | 65 ++++---- support/troubleshoot/index.html | 22 +-- 34 files changed, 272 insertions(+), 190 deletions(-) rename assets/css/{styles.c46b7464.css => styles.1847d619.css} (72%) create mode 100644 assets/js/0ba6a408.143d35a8.js delete mode 100644 assets/js/0ba6a408.4d6a2d2d.js delete mode 100644 assets/js/21afe7ac.816d73dc.js create mode 100644 assets/js/21afe7ac.c654afca.js delete mode 100644 assets/js/22dd74f7.7c8d1d11.js create mode 100644 assets/js/22dd74f7.a79a3853.js delete mode 100644 assets/js/33362219.62ae0004.js create mode 100644 assets/js/33362219.8d5821a1.js create mode 100644 assets/js/749371cc.01a9e909.js delete mode 100644 assets/js/749371cc.0a53eb7c.js rename assets/js/{af920ffe.1ba00f79.js => af920ffe.d761f3d0.js} (56%) delete mode 100644 assets/js/ca2c3c0c.39e376dd.js create mode 100644 assets/js/ca2c3c0c.5fc6b83f.js delete mode 100644 assets/js/d0314b07.369feaa2.js create mode 100644 assets/js/d0314b07.ec4c264b.js create mode 100644 assets/js/e633a5ea.e372590c.js delete mode 100644 assets/js/e633a5ea.ebd289b4.js delete mode 100644 assets/js/eb5b356a.5fc785a5.js create mode 100644 assets/js/eb5b356a.641c3ecd.js rename assets/js/{main.c897035d.js => main.ea07e571.js} (92%) rename assets/js/{main.c897035d.js.LICENSE.txt => main.ea07e571.js.LICENSE.txt} (100%) rename assets/js/{runtime~main.a37ae89b.js => runtime~main.524c688b.js} (85%) diff --git a/404.html b/404.html index 25b63316..5e4daa7d 100644 --- a/404.html +++ b/404.html @@ -3,9 +3,9 @@ -OpenRAG - - +OpenRAG + + diff --git a/agents/index.html b/agents/index.html index 6b94d1d5..c190df59 100644 --- a/agents/index.html +++ b/agents/index.html @@ -3,41 +3,56 @@ -Agents powered by Langflow | OpenRAG - - +Langflow Agents | OpenRAG + + -
Skip to main content

Agents powered by Langflow

info

OpenRAG is is currently in public preview. +

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.

-

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

+

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

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.

How do agents work?

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

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

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

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

Use the OpenRAG OpenSearch Agent flow​

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

+To switch OpenRAG over to the Langflow visual editor and view the OpenRAG OpenSearch Agentflow, click Settings, and then click Edit in Langflow. +This flow contains seven components connected together to chat with your data:

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

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

+OpenRAG's visual editor is based on the Langflow visual editor, so you can edit your flows to match your specific use case.

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

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

  • +OpenRAG warns you that this discards all custom settings. Click Restore to restore the flow.

    +

    Additional Langflow functionality​

    +

    Langflow includes features beyond Agents to help you integrate OpenRAG into your application, and all Langflow features are included in OpenRAG.

    +
      +
    • +

      Langflow can serve your flows as an MCP server, or consume other MCP servers as an MCP client. Get started with the MCP tutorial.

      +
      • +
    • +

      If you don't see the component you need, extend Langflow's functionality by creating custom Python components.

      +
      • +
    • +

      Langflow offers component bundles to integrate with many popular vector stores, AI/ML providers, and search APIs.

      +
      • +
  • © 2025 OpenRAG
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+"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[571],{3227:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>g,contentTitle:()=>u,default:()=>f,frontMatter:()=>h,metadata:()=>i,toc:()=>p});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,g={},p=[...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:"Knowledge ingestion settings",id:"knowledge-ingestion-settings",level:3},{value:"Create knowledge filters",id:"create-knowledge-filters",level:2},{value:"OpenRAG default configuration",id:"openrag-default-configuration",level:2}];function 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.jsx)(n.p,{children:"OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors."}),"\n",(0,o.jsx)(n.h3,{id:"direct-file-ingestion",children:"Direct file ingestion"}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow uses Langflow's ",(0,o.jsxs)(n.a,{href:"https://docs.langflow.org/components-data#file",children:[(0,o.jsx)(n.strong,{children:"File"})," component"]})," to split and embed files loaded from your local machine into the OpenSearch database."]}),"\n",(0,o.jsxs)(n.p,{children:["The default path to your local folder is mounted from the ",(0,o.jsx)(n.code,{children:"./documents"})," folder in your OpenRAG project directory to the ",(0,o.jsx)(n.code,{children:"/app/documents/"})," directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the ",(0,o.jsx)(n.strong,{children:"Documents Paths"})," variable in either the TUI's ",(0,o.jsx)(n.a,{href:"/install#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.h3,{id:"knowledge-ingestion-settings",children:"Knowledge ingestion settings"}),"\n",(0,o.jsxs)(n.p,{children:["To configure the knowledge ingestion pipeline parameters, see ",(0,o.jsx)(n.a,{href:"/ingestion",children:"Docling Ingestion"}),"."]}),"\n",(0,o.jsx)(n.h2,{id:"create-knowledge-filters",children:"Create knowledge filters"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG includes a knowledge filter system for organizing and managing document collections.\nKnowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of OpenRAG."}),"\n",(0,o.jsx)(n.p,{children:"Knowledge filters help agents work more efficiently with large document collections by focusing their context within relevant documents sets."}),"\n",(0,o.jsx)(n.p,{children:"To create a knowledge filter, do the following:"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create New Filter"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Create New Knowledge Filter"})," pane appears."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Enter a ",(0,o.jsx)(n.strong,{children:"Name"})," and ",(0,o.jsx)(n.strong,{children:"Description"}),", and then click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create Filter"}),".\nA new filter is created with default settings that match everything."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To modify the default filter, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click your new filter to edit it in the ",(0,o.jsx)(n.strong,{children:"Knowledge Filter"})," pane."]}),"\n",(0,o.jsx)(n.p,{children:"The following filter options are configurable."}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Search Query"}),': Enter text for semantic search, such as "financial reports from Q4".']}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Data Sources"}),": Select specific data sources or folders to include."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Document Types"}),": Filter by file type."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Owners"}),": Filter by who uploaded the documents."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Sources"}),": Filter by connector types, such as local upload or Google Drive."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Result Limit"}),": Set maximum number of results. The default is ",(0,o.jsx)(n.code,{children:"10"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Score Threshold"}),": Set minimum relevance score. The default score is ",(0,o.jsx)(n.code,{children:"0"}),"."]}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["When you're done editing the filter, click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Save Configuration"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To apply the filter to OpenRAG globally, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then select the filter to apply."]}),"\n",(0,o.jsxs)(n.p,{children:["To apply the filter to a single chat session, in the ",(0,o.jsx)(r.A,{name:"MessageSquare","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Chat"})," window, click ",(0,o.jsx)(n.strong,{children:"@"}),", and then select the filter to apply."]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"openrag-default-configuration",children:"OpenRAG default configuration"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility."}),"\n",(0,o.jsxs)(n.p,{children:["The complete list of supported models is available at ",(0,o.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/0ba6a408.4d6a2d2d.js b/assets/js/0ba6a408.4d6a2d2d.js deleted file mode 100644 index 24f073c4..00000000 --- a/assets/js/0ba6a408.4d6a2d2d.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:()=>g,contentTitle:()=>u,default:()=>f,frontMatter:()=>h,metadata:()=>i,toc:()=>p});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,g={},p=[...a.RM,{value:"Explore knowledge",id:"explore-knowledge",level:2},...d.RM,{value:"Ingest knowledge",id:"ingest-knowledge",level:2},{value:"Direct file ingestion",id:"direct-file-ingestion",level:3},{value:"Ingest files through OAuth connectors",id:"oauth-ingestion",level:3},{value:"Sync cloud connectors",id:"sync-cloud-connectors",level:3},{value:"Knowledge ingestion settings",id:"knowledge-ingestion-settings",level:3},{value:"Create knowledge filters",id:"create-knowledge-filters",level:2},{value:"OpenRAG default configuration",id:"openrag-default-configuration",level:2}];function 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.\nOpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support."]}),"\n",(0,o.jsx)(n.h2,{id:"explore-knowledge",children:"Explore knowledge"}),"\n",(0,o.jsxs)(n.p,{children:["The Knowledge page lists the documents OpenRAG has ingested into the OpenSearch vector database's ",(0,o.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,o.jsxs)(n.p,{children:["To explore your current knowledge, click ",(0,o.jsx)(r.A,{name:"Library","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Knowledge"}),".\nClick on a document to display the chunks derived from splitting the default documents into the vector database."]}),"\n",(0,o.jsxs)(n.p,{children:["Documents are processed with the default ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow, so if you want to split your documents differently, edit the ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow."]}),"\n",(0,o.jsx)(d.Ay,{}),"\n",(0,o.jsx)(n.h2,{id:"ingest-knowledge",children:"Ingest knowledge"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors."}),"\n",(0,o.jsx)(n.h3,{id:"direct-file-ingestion",children:"Direct file ingestion"}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.strong,{children:"Knowledge Ingest"})," flow uses Langflow's ",(0,o.jsxs)(n.a,{href:"https://docs.langflow.org/components-data#file",children:[(0,o.jsx)(n.strong,{children:"File"})," component"]})," to split and embed files loaded from your local machine into the OpenSearch database."]}),"\n",(0,o.jsxs)(n.p,{children:["The default path to your local folder is mounted from the ",(0,o.jsx)(n.code,{children:"./documents"})," folder in your OpenRAG project directory to the ",(0,o.jsx)(n.code,{children:"/app/documents/"})," directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the ",(0,o.jsx)(n.strong,{children:"Documents Paths"})," variable in either the TUI's ",(0,o.jsx)(n.a,{href:"/install#advanced-setup",children:"Advanced Setup"})," or in the ",(0,o.jsx)(n.code,{children:".env"})," used by Docker Compose."]}),"\n",(0,o.jsxs)(n.p,{children:["To load and process a single file from the mapped location, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then click ",(0,o.jsx)(n.strong,{children:"Add File"}),".\nThe file is loaded into your OpenSearch database, and appears in the Knowledge page."]}),"\n",(0,o.jsxs)(n.p,{children:["To load and process a directory from the mapped location, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then click ",(0,o.jsx)(n.strong,{children:"Process Folder"}),".\nThe files are loaded into your OpenSearch database, and appear in the Knowledge page."]}),"\n",(0,o.jsx)(n.h3,{id:"oauth-ingestion",children:"Ingest files through OAuth connectors"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG supports Google Drive, OneDrive, and AWS S3 as OAuth connectors for seamless document synchronization."}),"\n",(0,o.jsx)(n.p,{children:"OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage."}),"\n",(0,o.jsx)(n.p,{children:"Before users can connect their cloud storage accounts, you must configure OAuth credentials in OpenRAG. This requires registering OpenRAG as an OAuth application with a cloud provider and obtaining client ID and secret keys for each service you want to support."}),"\n",(0,o.jsx)(n.p,{children:"To add an OAuth connector to OpenRAG, do the following.\nThis example uses Google OAuth.\nIf you wish to use another provider, add the secrets to another provider."}),"\n",(0,o.jsxs)(l.A,{groupId:"Installation type",children:[(0,o.jsx)(c.A,{value:"TUI",label:"TUI",default:!0,children:(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["If OpenRAG is running, stop it with ",(0,o.jsx)(n.strong,{children:"Status"})," > ",(0,o.jsx)(n.strong,{children:"Stop Services"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Advanced Setup"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:["Add the OAuth provider's client and secret key in the ",(0,o.jsx)(n.a,{href:"/install#advanced-setup",children:"Advanced Setup"})," menu."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Save Configuration"}),".\nThe TUI generates a new ",(0,o.jsx)(n.code,{children:".env"})," file with your OAuth values."]}),"\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(n.strong,{children:"Start Container Services"}),"."]}),"\n"]})}),(0,o.jsxs)(c.A,{value:".env",label:".env",children:[(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsx)(n.li,{children:"Stop the Docker deployment."}),"\n",(0,o.jsxs)(n.li,{children:["Add the OAuth provider's client and secret key in the ",(0,o.jsx)(n.code,{children:".env"})," file for Docker Compose."]}),"\n"]}),(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:"GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'\nGOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'\n"})}),(0,o.jsxs)(n.ol,{start:"3",children:["\n",(0,o.jsxs)(n.li,{children:["Save your ",(0,o.jsx)(n.code,{children:".env"}),". file."]}),"\n",(0,o.jsx)(n.li,{children:"Start the Docker deployment."}),"\n"]})]})]}),"\n",(0,o.jsxs)(n.p,{children:["The OpenRAG frontend at ",(0,o.jsx)(n.code,{children:"http://localhost:3000"})," now redirects to an OAuth callback login page for your OAuth provider.\nA successful authentication opens OpenRAG with the required scopes for your connected storage."]}),"\n",(0,o.jsx)(n.p,{children:"To add knowledge from an OAuth-connected storage provider, do the following:"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["Click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Knowledge"}),", and then select the storage provider, for example, ",(0,o.jsx)(n.strong,{children:"Google Drive"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Add Cloud Knowledge"})," page opens."]}),"\n",(0,o.jsxs)(n.li,{children:["To add files or folders from the connected storage, click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Add Files"}),".\nSelect the files or folders you want and click ",(0,o.jsx)(n.strong,{children:"Select"}),".\nYou can select multiples."]}),"\n",(0,o.jsxs)(n.li,{children:["When your files are selected, click ",(0,o.jsx)(n.strong,{children:"Ingest Files"}),".\nThe ingestion process may take some time, depending on the size of your documents."]}),"\n",(0,o.jsx)(n.li,{children:"When ingestion is complete, your documents are available in the Knowledge screen."}),"\n"]}),"\n",(0,o.jsx)(n.h3,{id:"sync-cloud-connectors",children:"Sync cloud connectors"}),"\n",(0,o.jsxs)(n.p,{children:["Your connected data sources are found in the ",(0,o.jsx)(r.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"})," page."]}),"\n",(0,o.jsxs)(n.p,{children:["When you click ",(0,o.jsx)(n.strong,{children:"Sync Now"})," for a connected cloud service like Google Drive, OpenRAG scans your connected Google Drive account to find files that match your sync criteria. Sync criteria are controlled in ",(0,o.jsx)(n.strong,{children:"Sync Settings"})," on the same page. You can sync all files, or select a maximum number of files to sync."]}),"\n",(0,o.jsx)(n.p,{children:"For each file found, OpenRAG downloads, converts, and embeds the processed content into OpenSearch."}),"\n",(0,o.jsxs)(n.p,{children:["You can monitor the sync progress in the ",(0,o.jsx)(r.A,{name:"Bell","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Tasks"})," sidebar."]}),"\n",(0,o.jsx)(n.p,{children:"Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page."}),"\n",(0,o.jsx)(n.h3,{id:"knowledge-ingestion-settings",children:"Knowledge ingestion settings"}),"\n",(0,o.jsxs)(n.p,{children:["To configure the knowledge ingestion pipeline parameters, see ",(0,o.jsx)(n.a,{href:"/ingestion",children:"Docling Ingestion"}),"."]}),"\n",(0,o.jsx)(n.h2,{id:"create-knowledge-filters",children:"Create knowledge filters"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG includes a knowledge filter system for organizing and managing document collections.\nKnowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of OpenRAG."}),"\n",(0,o.jsx)(n.p,{children:"Knowledge filters help agents work more efficiently with large document collections by focusing their context within relevant documents sets."}),"\n",(0,o.jsx)(n.p,{children:"To create a knowledge filter, do the following:"}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click ",(0,o.jsx)(r.A,{name:"Plus","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create New Filter"}),".\nThe ",(0,o.jsx)(n.strong,{children:"Create New Knowledge Filter"})," pane appears."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Enter a ",(0,o.jsx)(n.strong,{children:"Name"})," and ",(0,o.jsx)(n.strong,{children:"Description"}),", and then click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Create Filter"}),".\nA new filter is created with default settings that match everything."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To modify the default filter, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then click your new filter to edit it in the ",(0,o.jsx)(n.strong,{children:"Knowledge Filter"})," pane."]}),"\n",(0,o.jsx)(n.p,{children:"The following filter options are configurable."}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Search Query"}),': Enter text for semantic search, such as "financial reports from Q4".']}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Data Sources"}),": Select specific data sources or folders to include."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Document Types"}),": Filter by file type."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Owners"}),": Filter by who uploaded the documents."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Sources"}),": Filter by connector types, such as local upload or Google Drive."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Result Limit"}),": Set maximum number of results. The default is ",(0,o.jsx)(n.code,{children:"10"}),"."]}),"\n",(0,o.jsxs)(n.li,{children:[(0,o.jsx)(n.strong,{children:"Score Threshold"}),": Set minimum relevance score. The default score is ",(0,o.jsx)(n.code,{children:"0"}),"."]}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["When you're done editing the filter, click ",(0,o.jsx)(r.A,{name:"Save","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Save Configuration"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To apply the filter to OpenRAG globally, click ",(0,o.jsx)(r.A,{name:"Funnel","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"All Knowledge"}),", and then select the filter to apply."]}),"\n",(0,o.jsxs)(n.p,{children:["To apply the filter to a single chat session, in the ",(0,o.jsx)(r.A,{name:"MessageSquare","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Chat"})," window, click ",(0,o.jsx)(n.strong,{children:"@"}),", and then select the filter to apply."]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"openrag-default-configuration",children:"OpenRAG default configuration"}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility."}),"\n",(0,o.jsxs)(n.p,{children:["The complete list of supported models is available at ",(0,o.jsx)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py",children:"/src/services/models_service.py"}),"."]}),"\n",(0,o.jsx)(n.p,{children:"You can use custom embedding models by specifying them in your configuration."}),"\n",(0,o.jsxs)(n.p,{children:["If you use an unknown embedding model, OpenRAG will automatically fall back to ",(0,o.jsx)(n.code,{children:"1536"})," dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from ",(0,o.jsx)(n.code,{children:"1536"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["The default embedding dimension is ",(0,o.jsx)(n.code,{children:"1536"})," and the default model is ",(0,o.jsx)(n.code,{children:"text-embedding-3-small"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["For models with known vector dimensions, see ",(0,o.jsx)(n.a,{href:"https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py",children:"/src/config/settings.py"}),"."]})]})}function 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"}),".\nFlows are edited in the same way as in the ",(0,i.jsx)(n.a,{href:"https://docs.langflow.org/concepts-overview",children:"Langflow visual editor"}),"."]})}function 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/21afe7ac.816d73dc.js b/assets/js/21afe7ac.816d73dc.js deleted file mode 100644 index e73377f4..00000000 --- a/assets/js/21afe7ac.816d73dc.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) provides a streamlined way 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":"Docker Deployment","permalink":"/get-started/docker"},"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) provides a streamlined way 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:["The TUI offers an easier way to use OpenRAG without sacrificing control.\nInstead 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.jsxs)(n.p,{children:["To start the TUI, run the following commands from the directory where you installed OpenRAG.\nFor more information, see ",(0,r.jsx)(n.a,{href:"/install",children:"Install 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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Click ",(0,s.jsx)(n.strong,{children:"Restore"})," to restore the flow."]})]})}function u(e={}){const{wrapper:n}={...(0,r.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(g,{...e})}):g(e)}},9179:(e,n,t)=>{t.d(n,{A:()=>r});t(6540);var o=t(4827),s=t(4848);function r({name:e,...n}){const t=o[e];return t?(0,s.jsx)(t,{...n}):null}},9334:(e,n,t)=>{t.d(n,{Ay:()=>a,RM:()=>r});var o=t(4848),s=t(8453);const r=[];function i(e){const n={admonition:"admonition",p:"p",...(0,s.R)(),...e.components};return(0,o.jsx)(n.admonition,{type:"info",children:(0,o.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,s.R)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(i,{...e})}):i(e)}}}]); \ No newline at end of file diff --git a/assets/js/33362219.8d5821a1.js b/assets/js/33362219.8d5821a1.js new file mode 100644 index 00000000..3ee6f6d1 --- /dev/null +++ b/assets/js/33362219.8d5821a1.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[532],{3782:(e,n,t)=>{t.d(n,{Ay:()=>c,RM:()=>a});var o=t(4848),s=t(8453),r=t(9179);const a=[];function i(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)(i,{...e})}):i(e)}},3982:(e,n,t)=>{t.d(n,{A:()=>o});const o=t.p+"assets/images/opensearch-agent-flow-a3c183a9f2d5ec513819aba1f1b49c39.png"},8748:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>d,contentTitle:()=>h,default:()=>u,frontMatter:()=>l,metadata:()=>o,toc:()=>p});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),a=t(9179),i=(t(1470),t(9365),t(3782)),c=t(9334);const l={title:"Langflow Agents",slug:"/agents"},h=void 0,d={},p=[...c.RM,{value:"Use the OpenRAG OpenSearch Agent flow",id:"use-the-openrag-opensearch-agent-flow",level:2},...i.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)(c.Ay,{}),"\n",(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:"4108",height:"2250"})}),"\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. 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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:"use-the-openrag-opensearch-agent-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)(a.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 seven 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. 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\ No newline at end of file diff --git a/assets/js/749371cc.01a9e909.js b/assets/js/749371cc.01a9e909.js new file mode 100644 index 00000000..70168a74 --- /dev/null +++ b/assets/js/749371cc.01a9e909.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkopenrag_docs=self.webpackChunkopenrag_docs||[]).push([[272],{887:(e,n,r)=>{r.d(n,{Ay:()=>c,RM:()=>a});var t=r(4848),s=r(8453),o=r(1470),l=r(9365);const a=[{value:"Application onboarding",id:"application-onboarding",level:2}];function i(e){const n={a:"a",admonition:"admonition",code:"code",em:"em",h2:"h2",li:"li",ol:"ol",p:"p",strong:"strong",...(0,s.R)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.h2,{id:"application-onboarding",children:"Application onboarding"}),"\n",(0,t.jsxs)(n.p,{children:["The first time you start OpenRAG, whether using the TUI or a ",(0,t.jsx)(n.code,{children:".env"})," file, you must complete application onboarding."]}),"\n",(0,t.jsxs)(n.p,{children:["Values input during onboarding can be changed later in the OpenRAG ",(0,t.jsx)(n.strong,{children:"Settings"})," page, except for the language model and embedding model ",(0,t.jsx)(n.em,{children:"provider"}),".\n",(0,t.jsx)(n.strong,{children:"Your provider can only be selected once, and you must use the same provider for your language model and embedding model."}),"\nThe language model can be changed, but the embeddings model cannot be changed.\nTo change your provider selection, you must completely reinstall OpenRAG."]}),"\n",(0,t.jsxs)(o.A,{groupId:"Provider",children:[(0,t.jsx)(l.A,{value:"OpenAI",label:"OpenAI",default:!0,children:(0,t.jsxs)(n.ol,{children:["\n",(0,t.jsxs)(n.li,{children:["Enable ",(0,t.jsx)(n.strong,{children:"Get API key from environment variable"})," to automatically enter your key from the TUI-generated ",(0,t.jsx)(n.code,{children:".env"})," file."]}),"\n",(0,t.jsxs)(n.li,{children:["Under ",(0,t.jsx)(n.strong,{children:"Advanced settings"}),", select your ",(0,t.jsx)(n.strong,{children:"Embedding Model"})," and ",(0,t.jsx)(n.strong,{children:"Language Model"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["To load 2 sample PDFs, enable ",(0,t.jsx)(n.strong,{children:"Sample dataset"}),".\nThis is recommended, but not required."]}),"\n",(0,t.jsxs)(n.li,{children:["Click ",(0,t.jsx)(n.strong,{children:"Complete"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["Continue with the ",(0,t.jsx)(n.a,{href:"/quickstart",children:"Quickstart"}),"."]}),"\n"]})}),(0,t.jsx)(l.A,{value:"IBM watsonx.ai",label:"IBM watsonx.ai",children:(0,t.jsxs)(n.ol,{children:["\n",(0,t.jsxs)(n.li,{children:["Complete the fields for ",(0,t.jsx)(n.strong,{children:"watsonx.ai API Endpoint"}),", ",(0,t.jsx)(n.strong,{children:"IBM API key"}),", and ",(0,t.jsx)(n.strong,{children:"IBM Project ID"}),".\nThese values are found in your IBM watsonx deployment."]}),"\n",(0,t.jsxs)(n.li,{children:["Under ",(0,t.jsx)(n.strong,{children:"Advanced settings"}),", select your ",(0,t.jsx)(n.strong,{children:"Embedding Model"})," and ",(0,t.jsx)(n.strong,{children:"Language Model"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["To load 2 sample PDFs, enable ",(0,t.jsx)(n.strong,{children:"Sample dataset"}),".\nThis is recommended, but not required."]}),"\n",(0,t.jsxs)(n.li,{children:["Click ",(0,t.jsx)(n.strong,{children:"Complete"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:["Continue with the ",(0,t.jsx)(n.a,{href:"/quickstart",children:"Quickstart"}),"."]}),"\n"]})}),(0,t.jsxs)(l.A,{value:"Ollama",label:"Ollama",children:[(0,t.jsx)(n.admonition,{type:"tip",children:(0,t.jsxs)(n.p,{children:["Ollama is not included with OpenRAG. 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interface."})]})]})]}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsx)(n.p,{children:"Verify 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"]}),"\n",(0,s.jsxs)(n.p,{children:["Continue with the ",(0,s.jsx)(n.a,{href:"/quickstart",children:"Quickstart"}),"."]}),"\n",(0,s.jsx)(n.h2,{id:"rebuild-all-docker-containers",children:"Rebuild all Docker containers"}),"\n",(0,s.jsxs)(n.p,{children:["If you need to reset state and rebuild all of your containers, run the following command.\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"})})]})}function p(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:()=>i,x:()=>l});var t=r(6540);const s={},o=t.createContext(s);function i(e){const n=t.useContext(o);return t.useMemo(function(){return"function"==typeof e?e(n):{...n,...e}},[n,e])}function l(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:i(e.components),t.createElement(o.Provider,{value:n},e.children)}},9334:(e,n,r)=>{r.d(n,{Ay:()=>l,RM:()=>o});var t=r(4848),s=r(8453);const o=[];function i(e){const n={admonition:"admonition",p:"p",...(0,s.R)(),...e.components};return(0,t.jsx)(n.admonition,{type:"info",children:(0,t.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,s.R)(),...e.components};return n?(0,t.jsx)(n,{...e,children:(0,t.jsx)(i,{...e})}):i(e)}}}]); \ No newline at end of file diff --git a/assets/js/af920ffe.1ba00f79.js b/assets/js/af920ffe.d761f3d0.js similarity index 56% rename from assets/js/af920ffe.1ba00f79.js rename to assets/js/af920ffe.d761f3d0.js index 0975688a..8a3dbf91 100644 --- a/assets/js/af920ffe.1ba00f79.js +++ b/assets/js/af920ffe.d761f3d0.js @@ -1 +1 @@ -"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":"Installation","permalink":"/install"},"next":{"title":"Docker Deployment","permalink":"/get-started/docker"}}');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.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.a,{href:"/install",children:"Install and start OpenRAG"})}),"\n"]}),"\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"]}),"\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:"4108",height:"2250"})]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In the ",(0,o.jsx)(n.strong,{children:"Language Model"})," component, under ",(0,o.jsx)(n.strong,{children:"Model"}),", select a different OpenAI model."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Save your flow with ",(0,o.jsx)("kbd",{children:"Command+S"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In OpenRAG, start a new conversation by clicking the ",(0,o.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," in the ",(0,o.jsx)(n.strong,{children:"Conversations"})," tab."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Ask the same question as before to demonstrate how a different language model changes the results."}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"integrate-openrag-into-your-application",children:"Integrate OpenRAG into your application"}),"\n",(0,o.jsxs)(n.p,{children:["To integrate OpenRAG into your application, use the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-reference-api-examples",children:"Langflow API"}),".\nMake requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results."]}),"\n",(0,o.jsx)(n.p,{children:"Langflow provides code snippets to help you get started with the Langflow API."}),"\n",(0,o.jsxs)(n.ol,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["To navigate to the OpenRAG OpenSearch Agent flow, click ",(0,o.jsx)(a.A,{name:"Settings2","aria-hidden":"true"})," ",(0,o.jsx)(n.strong,{children:"Settings"}),", and then click ",(0,o.jsx)(n.strong,{children:"Edit in Langflow"})," in the OpenRAG OpenSearch Agent flow."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Click ",(0,o.jsx)(n.strong,{children:"Share"}),", and then click ",(0,o.jsx)(n.strong,{children:"API access"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["The default code in the API access pane constructs a request with the Langflow server ",(0,o.jsx)(n.code,{children:"url"}),", ",(0,o.jsx)(n.code,{children:"headers"}),", and a ",(0,o.jsx)(n.code,{children:"payload"})," of request data. The code snippets automatically include the ",(0,o.jsx)(n.code,{children:"LANGFLOW_SERVER_ADDRESS"})," and ",(0,o.jsx)(n.code,{children:"FLOW_ID"})," values for the flow. Replace these values if you're using the code for a different server or flow. The default Langflow server address is ",(0,o.jsx)(n.a,{href:"http://localhost:7860",children:"http://localhost:7860"}),"."]}),"\n",(0,o.jsxs)(i.A,{children:[(0,o.jsx)(l.A,{value:"python",label:"Python",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-python",children:'import requests\nimport os\nimport uuid\n\napi_key = \'LANGFLOW_API_KEY\'\nurl = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow\n\n# Request payload configuration\npayload = {\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "hello world!"\n}\npayload["session_id"] = str(uuid.uuid4())\n\nheaders = {"x-api-key": api_key}\n\ntry:\n # Send API request\n response = requests.request("POST", url, json=payload, headers=headers)\n response.raise_for_status() # Raise exception for bad status codes\n\n # Print response\n print(response.text)\n\nexcept requests.exceptions.RequestException as e:\n print(f"Error making API request: {e}")\nexcept ValueError as e:\n print(f"Error parsing response: {e}")\n'})})}),(0,o.jsx)(l.A,{value:"typescript",label:"TypeScript",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-typescript",children:'const crypto = require(\'crypto\');\nconst apiKey = \'LANGFLOW_API_KEY\';\nconst payload = {\n "output_type": "chat",\n "input_type": "chat",\n "input_value": "hello world!"\n};\npayload.session_id = crypto.randomUUID();\n\nconst options = {\n method: \'POST\',\n headers: {\n \'Content-Type\': \'application/json\',\n "x-api-key": apiKey\n },\n body: JSON.stringify(payload)\n};\n\nfetch(\'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID\', options)\n .then(response => response.json())\n .then(response => console.warn(response))\n .catch(err => console.error(err));\n'})})}),(0,o.jsx)(l.A,{value:"curl",label:"curl",children:(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:'curl --request POST \\\n --url \'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false\' \\\n --header \'Content-Type: application/json\' \\\n --header "x-api-key: LANGFLOW_API_KEY" \\ \n --data \'{\n\t\t "output_type": "chat",\n\t\t "input_type": "chat",\n\t\t "input_value": "hello world!",\n\t\t }\'\n'})})})]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Copy the snippet, paste it in a script file, and then run the script to send the request. If you are using the curl snippet, you can run the command directly in your terminal."}),"\n"]}),"\n"]}),"\n",(0,o.jsxs)(n.p,{children:["If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.\nThe following is an example of a response from running the ",(0,o.jsx)(n.strong,{children:"Simple Agent"})," template flow:"]}),"\n",(0,o.jsxs)(s,{children:[(0,o.jsx)("summary",{children:"Result"}),(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-json",children:'{\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "outputs": [\n {\n "inputs": {\n "input_value": "hello world!"\n },\n "outputs": [\n {\n "results": {\n "message": {\n "text_key": "text",\n "data": {\n "timestamp": "2025-06-16 19:58:23 UTC",\n "sender": "Machine",\n "sender_name": "AI",\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "text": "Hello world! \ud83c\udf0d How can I assist you today?",\n "files": [],\n "error": false,\n "edit": false,\n "properties": {\n "text_color": "",\n "background_color": "",\n "edited": false,\n "source": {\n "id": "Agent-ZOknz",\n "display_name": "Agent",\n "source": "gpt-4o-mini"\n },\n "icon": "bot",\n "allow_markdown": false,\n "positive_feedback": null,\n "state": "complete",\n "targets": []\n },\n "category": "message",\n "content_blocks": [\n {\n "title": "Agent Steps",\n "contents": [\n {\n "type": "text",\n "duration": 2,\n "header": {\n "title": "Input",\n "icon": "MessageSquare"\n },\n "text": "**Input**: hello world!"\n },\n {\n "type": "text",\n "duration": 226,\n "header": {\n "title": "Output",\n "icon": "MessageSquare"\n },\n "text": "Hello world! \ud83c\udf0d How can I assist you today?"\n }\n ],\n "allow_markdown": true,\n "media_url": null\n }\n ],\n "id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",\n "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "duration": null\n },\n "default_value": "",\n "text": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "files": [],\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "timestamp": "2025-06-16T19:58:23+00:00",\n "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "error": false,\n "edit": false,\n "properties": {\n "text_color": "",\n "background_color": "",\n "edited": false,\n "source": {\n "id": "Agent-ZOknz",\n "display_name": "Agent",\n "source": "gpt-4o-mini"\n },\n "icon": "bot",\n "allow_markdown": false,\n "positive_feedback": null,\n "state": "complete",\n "targets": []\n },\n "category": "message",\n "content_blocks": [\n {\n "title": "Agent Steps",\n "contents": [\n {\n "type": "text",\n "duration": 2,\n "header": {\n "title": "Input",\n "icon": "MessageSquare"\n },\n "text": "**Input**: hello world!"\n },\n {\n "type": "text",\n "duration": 226,\n "header": {\n "title": "Output",\n "icon": "MessageSquare"\n },\n "text": "Hello world! \ud83c\udf0d How can I assist you today?"\n }\n ],\n "allow_markdown": true,\n "media_url": null\n }\n ],\n "duration": null\n }\n },\n "artifacts": {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "files": [],\n "type": "object"\n },\n "outputs": {\n "message": {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "type": "text"\n }\n },\n "logs": {\n "message": []\n },\n "messages": [\n {\n "message": "Hello world! \ud83c\udf0d How can I assist you today?",\n "sender": "Machine",\n "sender_name": "AI",\n "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",\n "stream_url": null,\n "component_id": "ChatOutput-aF5lw",\n "files": [],\n "type": "text"\n }\n ],\n "timedelta": null,\n "duration": null,\n "component_display_name": "Chat Output",\n "component_id": "ChatOutput-aF5lw",\n "used_frozen_result": false\n }\n ]\n }\n ]\n}\n'})})]}),"\n",(0,o.jsx)(n.p,{children:"To further explore the API, see:"}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:["The Langflow ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/quickstart#extract-data-from-the-response",children:"Quickstart"})," extends this example with extracting fields from the response."]}),"\n",(0,o.jsx)(n.li,{children:(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-reference-api-examples",children:"Get started with the Langflow API"})}),"\n"]})]})}function 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-a3c183a9f2d5ec513819aba1f1b49c39.png"},9179:(e,n,t)=>{t.d(n,{A:()=>r});t(6540);var s=t(4827),o=t(4848);function r({name:e,...n}){const t=s[e];return t?(0,o.jsx)(t,{...n}):null}},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 +"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:"4108",height:"2250"})]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In the ",(0,o.jsx)(n.strong,{children:"Language Model"})," component, under ",(0,o.jsx)(n.strong,{children:"Model"}),", select a different OpenAI model."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["Save your flow with ",(0,o.jsx)("kbd",{children:"Command+S"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:["In OpenRAG, start a new conversation by clicking the ",(0,o.jsx)(a.A,{name:"Plus","aria-hidden":"true"})," in the ",(0,o.jsx)(n.strong,{children:"Conversations"})," tab."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsx)(n.p,{children:"Ask the same question as before to demonstrate how a different language model changes the results."}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h2,{id:"integrate-openrag-into-your-application",children:"Integrate OpenRAG into your application"}),"\n",(0,o.jsxs)(n.p,{children:["To integrate OpenRAG into your application, use the ",(0,o.jsx)(n.a,{href:"https://docs.langflow.org/api-reference-api-examples",children:"Langflow API"}),".\nMake requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results. 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 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n={a:"a",code:"code",h2:"h2",p:"p",strong:"strong",...(0,t.R)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(r.Ay,{}),"\n",(0,o.jsxs)(n.p,{children:["OpenRAG uses ",(0,o.jsx)(n.a,{href:"https://docling-project.github.io/docling/",children:"Docling"})," for its document ingestion pipeline.\nMore specifically, OpenRAG uses ",(0,o.jsx)(n.a,{href:"https://github.com/docling-project/docling-serve",children:"Docling Serve"}),", which starts a ",(0,o.jsx)(n.code,{children:"docling-serve"})," process on your local machine and runs Docling ingestion through an API service."]}),"\n",(0,o.jsxs)(n.p,{children:["Docling ingests documents from your local machine or OAuth connectors, splits them into chunks, and stores them as separate, structured documents in the OpenSearch ",(0,o.jsx)(n.code,{children:"documents"})," index."]}),"\n",(0,o.jsx)(n.p,{children:"OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of 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r||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,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(i.Ay,{}),"\n",(0,t.jsx)(n.p,{children:"OpenRAG can be installed in multiple ways:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.a,{href:"#install-python-wheel",children:(0,t.jsx)(n.strong,{children:"Python wheel"})}),": Install the OpenRAG Python wheel and use the ",(0,t.jsx)(n.a,{href:"/get-started/tui",children:"OpenRAG Terminal User Interface (TUI)"})," to install, run, and configure your OpenRAG deployment without running Docker commands."]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:["\n",(0,t.jsxs)(n.p,{children:[(0,t.jsx)(n.a,{href:"get-started/docker",children:(0,t.jsx)(n.strong,{children:"Docker Compose"})}),": Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and 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For more, see the ",(0,t.jsx)(n.a,{href:"https://docs.langflow.org/api-keys-and-authentication#langflow-new-user-is-active",children:"Langflow docs"}),"."]})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:(0,t.jsx)(n.code,{children:"LANGFLOW_ENABLE_SUPERUSER_CLI"})}),(0,t.jsx)(n.td,{children:"Langflow server"}),(0,t.jsxs)(n.td,{children:["Superuser CLI access setting. 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nl(t,e.child,null,n),(e=ql(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function Vl(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ka(e.return,t,n)}function Wl(e,t,n,r,a){var o=e.memoizedState;null===o?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(o.isBackwards=t,o.rendering=null,o.renderingStartTime=0,o.last=r,o.tail=n,o.tailMode=a)}function Ql(e,t,n){var r=t.pendingProps,a=r.revealOrder,o=r.tail;if(Tl(e,t,r.children,n),2&(r=cl.current))r=1&r|2,t.flags|=128;else{if(null!==e&&128&e.flags)e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&Vl(e,n,t);else if(19===e.tag)Vl(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break 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0!==(e.lanes&t)||!(null===(e=e.dependencies)||!Ea(e))}function Xl(e,t,n){if(null!==e)if(e.memoizedProps!==t.pendingProps)_l=!0;else{if(!(Yl(e,n)||128&t.flags))return _l=!1,function(e,t,n){switch(t.tag){case 3:V(t,t.stateNode.containerInfo),va(0,La,e.memoizedState.cache),pa();break;case 27:case 5:Q(t);break;case 4:V(t,t.stateNode.containerInfo);break;case 10:va(0,t.type,t.memoizedProps.value);break;case 13:var r=t.memoizedState;if(null!==r)return null!==r.dehydrated?(il(t),t.flags|=128,null):0!==(n&t.child.childLanes)?Ul(e,t,n):(il(t),null!==(e=Kl(e,t,n))?e.sibling:null);il(t);break;case 19:var a=!!(128&e.flags);if((r=0!==(n&t.childLanes))||(xa(e,t,n,!1),r=0!==(n&t.childLanes)),a){if(r)return Ql(e,t,n);t.flags|=128}if(null!==(a=t.memoizedState)&&(a.rendering=null,a.tail=null,a.lastEffect=null),$(cl,cl.current),r)break;return null;case 22:case 23:return t.lanes=0,Ll(e,t,n);case 24:va(0,La,e.memoizedState.cache)}return Kl(e,t,n)}(e,t,n);_l=!!(131072&e.flags)}else _l=!1,oa&&1048576&t.flags&&ea(t,Wr,t.index);switch(t.lanes=0,t.tag){case 16:e:{e=t.pendingProps;var r=t.elementType,a=r._init;if(r=a(r._payload),t.type=r,"function"!=typeof r){if(null!=r){if((a=r.$$typeof)===S){t.tag=11,t=jl(null,t,r,e,n);break e}if(a===C){t.tag=14,t=Pl(null,t,r,e,n);break e}}throw t=L(r)||r,Error(i(306,t,""))}Mr(r)?(e=gl(r,e),t.tag=1,t=Ml(null,t,r,e,n)):(t.tag=0,t=Dl(null,t,r,e,n))}return t;case 0:return Dl(e,t,t.type,t.pendingProps,n);case 1:return Ml(e,t,r=t.type,a=gl(r,t.pendingProps),n);case 3:e:{if(V(t,t.stateNode.containerInfo),null===e)throw Error(i(387));r=t.pendingProps;var o=t.memoizedState;a=o.element,ro(e,t),co(t,r,null,n);var l=t.memoizedState;if(r=l.cache,va(0,La,r),r!==o.cache&&Sa(t,[La],n,!0),uo(),r=l.element,o.isDehydrated){if(o={element:r,isDehydrated:!1,cache:l.cache},t.updateQueue.baseState=o,t.memoizedState=o,256&t.flags){t=Il(e,t,r,n);break e}if(r!==a){ha(a=Er(Error(i(424)),t)),t=Il(e,t,r,n);break 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a=n;if(e.nodeName.toLowerCase()!==t.toLowerCase()){if(!r&&("INPUT"!==e.nodeName||"hidden"!==e.type))break}else if(r){if(!e[ze])switch(t){case"meta":if(!e.hasAttribute("itemprop"))break;return e;case"link":if("stylesheet"===(o=e.getAttribute("rel"))&&e.hasAttribute("data-precedence"))break;if(o!==a.rel||e.getAttribute("href")!==(null==a.href||""===a.href?null:a.href)||e.getAttribute("crossorigin")!==(null==a.crossOrigin?null:a.crossOrigin)||e.getAttribute("title")!==(null==a.title?null:a.title))break;return e;case"style":if(e.hasAttribute("data-precedence"))break;return e;case"script":if(((o=e.getAttribute("src"))!==(null==a.src?null:a.src)||e.getAttribute("type")!==(null==a.type?null:a.type)||e.getAttribute("crossorigin")!==(null==a.crossOrigin?null:a.crossOrigin))&&o&&e.hasAttribute("async")&&!e.hasAttribute("itemprop"))break;return e;default:return e}}else{if("input"!==t||"hidden"!==e.type)return e;var o=null==a.name?null:""+a.name;if("hidden"===a.type&&e.getAttribute("name")===o)return e}if(null===(e=yd(e.nextSibling)))break}return null}(r,t.type,t.pendingProps,la))?(t.stateNode=r,ra=t,aa=yd(r.firstChild),la=!1,a=!0):a=!1),a||ua(t)),Q(t),a=t.type,o=t.pendingProps,l=null!==e?e.memoizedProps:null,r=o.children,id(a,o)?r=null:null!==l&&id(a,l)&&(t.flags|=32),null!==t.memoizedState&&(a=Lo(e,t,Do,null,null,n),Qd._currentValue=a),Rl(e,t),Tl(e,t,r,n),t.child;case 6:return null===e&&oa&&((e=n=aa)&&(null!==(n=function(e,t,n){if(""===t)return null;for(;3!==e.nodeType;){if((1!==e.nodeType||"INPUT"!==e.nodeName||"hidden"!==e.type)&&!n)return null;if(null===(e=yd(e.nextSibling)))return null}return e}(n,t.pendingProps,la))?(t.stateNode=n,ra=t,aa=null,e=!0):e=!1),e||ua(t)),null;case 13:return Ul(e,t,n);case 4:return V(t,t.stateNode.containerInfo),r=t.pendingProps,null===e?t.child=nl(t,null,r,n):Tl(e,t,r,n),t.child;case 11:return jl(e,t,t.type,t.pendingProps,n);case 7:return Tl(e,t,t.pendingProps,n),t.child;case 8:case 12:return Tl(e,t,t.pendingProps.children,n),t.child;case 10:return r=t.pendingProps,va(0,t.type,r.value),Tl(e,t,r.children,n),t.child;case 9:return a=t.type._context,r=t.pendingProps.children,Ca(t),r=r(a=Aa(a)),t.flags|=1,Tl(e,t,r,n),t.child;case 14:return Pl(e,t,t.type,t.pendingProps,n);case 15:return Nl(e,t,t.type,t.pendingProps,n);case 19:return Ql(e,t,n);case 31:return r=t.pendingProps,n=t.mode,r={mode:r.mode,children:r.children},null===e?((n=Hl(r,n)).ref=t.ref,t.child=n,n.return=t,t=n):((n=Ir(e.child,r)).ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return Ll(e,t,n);case 24:return Ca(t),r=Aa(La),null===e?(null===(a=Ua())&&(a=ru,o=Oa(),a.pooledCache=o,o.refCount++,null!==o&&(a.pooledCacheLanes|=n),a=o),t.memoizedState={parent:r,cache:a},no(t),va(0,La,a)):(0!==(e.lanes&n)&&(ro(e,t),co(t,null,null,n),uo()),a=e.memoizedState,o=t.memoizedState,a.parent!==r?(a={parent:r,cache:r},t.memoizedState=a,0===t.lanes&&(t.memoizedState=t.updateQueue.baseState=a),va(0,La,r)):(r=o.cache,va(0,La,r),r!==a.cache&&Sa(t,[La],n,!0))),Tl(e,t,t.pendingProps.children,n),t.child;case 29:throw t.pendingProps}throw Error(i(156,t.tag))}function Zl(e){e.flags|=4}function Jl(e,t){if("stylesheet"!==t.type||4&t.state.loading)e.flags&=-16777217;else if(e.flags|=16777216,!$d(t)){if(null!==(t=al.current)&&((4194048&ou)===ou?null!==ol:(62914560&ou)!==ou&&!(536870912&ou)||t!==ol))throw Za=Qa,Va;e.flags|=8192}}function es(e,t){null!==t&&(e.flags|=4),16384&e.flags&&(t=22!==e.tag?xe():536870912,e.lanes|=t,yu|=t)}function ts(e,t){if(!oa)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function ns(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=65011712&a.subtreeFlags,r|=65011712&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function rs(e,t,n){var r=t.pendingProps;switch(na(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:case 1:return ns(t),null;case 3:return n=t.stateNode,r=null,null!==e&&(r=e.memoizedState.cache),t.memoizedState.cache!==r&&(t.flags|=2048),wa(La),W(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),null!==e&&null!==e.child||(fa(t)?Zl(t):null===e||e.memoizedState.isDehydrated&&!(256&t.flags)||(t.flags|=1024,ma())),ns(t),null;case 26:return n=t.memoizedState,null===e?(Zl(t),null!==n?(ns(t),Jl(t,n)):(ns(t),t.flags&=-16777217)):n?n!==e.memoizedState?(Zl(t),ns(t),Jl(t,n)):(ns(t),t.flags&=-16777217):(e.memoizedProps!==r&&Zl(t),ns(t),t.flags&=-16777217),null;case 27:K(t),n=H.current;var a=t.type;if(null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zl(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return ns(t),null}e=U.current,fa(t)?ca(t):(e=wd(a,r,n),t.stateNode=e,Zl(t))}return ns(t),null;case 5:if(K(t),n=t.type,null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zl(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return ns(t),null}if(e=U.current,fa(t))ca(t);else{switch(a=rd(H.current),e){case 1:e=a.createElementNS("http://www.w3.org/2000/svg",n);break;case 2:e=a.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;default:switch(n){case"svg":e=a.createElementNS("http://www.w3.org/2000/svg",n);break;case"math":e=a.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;case"script":(e=a.createElement("div")).innerHTML=" - - +Configuration | OpenRAG + + -

Configuration

info

OpenRAG is is currently in public preview. +

Configuration

info

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

OpenRAG supports multiple configuration methods with the following priority:

    diff --git a/get-started/docker/index.html b/get-started/docker/index.html index cfaf12ee..16d322c0 100644 --- a/get-started/docker/index.html +++ b/get-started/docker/index.html @@ -3,15 +3,15 @@ -Docker deployment | OpenRAG - - +Deploy with Docker | OpenRAG + + -

    Docker deployment

    info

    OpenRAG is is currently in public preview. +

    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. They deploy the same applications and containers, but to different environments.

    @@ -23,24 +23,34 @@ They deploy the same applications and containers, but to different environments.

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

    -

    To install OpenRAG with Docker Compose:

    +

    Prerequisites​

    +
      +
    • Python Version 3.10 to 3.13
      • +
    • uv
      • +
    • Podman (recommended) or Docker installed
      • +
    • Docker Compose installed. If you're using Podman, use podman-compose or alias Docker compose commands to Podman commands.
      • +
    • Create an OpenAI API key. This key is required to start OpenRAG, but you can choose a different model provider during Application Onboarding.
      • +
    • 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.
      • +
    +

    Deploy OpenRAG with Docker Compose​

    +

    To install OpenRAG with Docker Compose, do the following:

    1. Clone the OpenRAG repository.

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

      Copy the example .env file that is included in the repository root. +

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

      cp .env.example .env

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

      touch .env
    3. -

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

      +

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

      OPENSEARCH_PASSWORD=your_secure_password
      OPENAI_API_KEY=your_openai_api_key

      LANGFLOW_SUPERUSER=admin
      LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
      LANGFLOW_SECRET_KEY=your_secret_key
      -

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

      +

      For more information on configuring OpenRAG with environment variables, see Environment variables.

    4. Deploy OpenRAG with Docker Compose based on your deployment type.

      @@ -61,13 +71,48 @@ For additional configuration values, including config.yaml, see Langflow: http://localhost:7860
      5. +
    5. +

      Continue with Application Onboarding.

      +
    -

    Continue with the Quickstart.

    +

    Application onboarding​

    +

    The first time you start OpenRAG, whether using the TUI or a .env file, you must complete application onboarding.

    +

    Values input during onboarding can be changed later in the OpenRAG Settings page, except for the language model and embedding model provider. +Your provider can only be selected once, and you must use the same provider for your language model and embedding model. +The language model can be changed, but the embeddings model cannot be changed. +To change your provider selection, you must completely reinstall OpenRAG.

    +
      +
    1. Enable Get API key from environment variable to automatically enter your key from the TUI-generated .env file.
      2. +
    2. Under Advanced settings, select your Embedding Model and Language Model.
      3. +
    3. To load 2 sample PDFs, enable Sample dataset. +This is recommended, but not required.
      4. +
    4. Click Complete.
      5. +
    5. Continue with the Quickstart.
      6. +

    Rebuild all Docker containers​

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

    -
    docker compose up --build --force-recreate --remove-orphans
    • +
      docker compose up --build --force-recreate --remove-orphans
    • © 2025 OpenRAG
    diff --git a/get-started/tui/index.html b/get-started/tui/index.html index 9c90b99a..855403d8 100644 --- a/get-started/tui/index.html +++ b/get-started/tui/index.html @@ -3,24 +3,22 @@ -Terminal User Interface (TUI) commands | OpenRAG - - +Terminal User Interface (TUI) commands | OpenRAG + + -

    Terminal User Interface (TUI) commands

    info

    OpenRAG is is currently in public preview. +

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

    +

    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

    -

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

    +

    Instead of starting OpenRAG using Docker commands and manually editing values in the .env file, the TUI walks you through the setup. It prompts for variables where required, creates a .env file for you, and then starts OpenRAG.

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

    Start the TUI​

    -

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

    +

    To start the TUI, run the following commands from the directory where you installed OpenRAG.

    uv sync
    uv run openrag

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

    @@ -65,7 +63,7 @@ It first stops and removes all containers, volumes, and local images.

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

    docker system prune -f

    Diagnostics​

    -

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

    • +

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

    • © 2025 OpenRAG
    diff --git a/index.html b/index.html index 3e82c60b..32606bfd 100644 --- a/index.html +++ b/index.html @@ -3,15 +3,15 @@ -What is OpenRAG? | OpenRAG - - +What is OpenRAG? | OpenRAG + + -

    What is OpenRAG?

    info

    OpenRAG is is currently in public preview. +

    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. It supports integration with a wide range of orchestration tools, vector databases, and LLM providers.

    @@ -27,8 +27,9 @@ It supports integration with a wide range of orchestration tools, vector databas

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

    -

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

    -

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

    • +

      OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing, with opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from popular sources like OneDrive, Google Drive, and AWS.

      +

      What's more, every part of the stack is swappable. Write your own custom components in Python, try different language models, and customize your flows to build an agentic RAG system.

      +

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

    • © 2025 OpenRAG
    diff --git a/ingestion/index.html b/ingestion/index.html index a5db2c2e..91f7a5af 100644 --- a/ingestion/index.html +++ b/ingestion/index.html @@ -3,9 +3,9 @@ -Docling Ingestion | OpenRAG - - +Docling Ingestion | OpenRAG + + @@ -36,7 +36,7 @@ OCR is disabled by default. This setting is best suited for processing text-base

    Use OpenRAG default ingestion instead of Docling serve​

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

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

    -

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

  • © 2025 OpenRAG
diff --git a/install/index.html b/install/index.html index 2464defa..8ec06093 100644 --- a/install/index.html +++ b/install/index.html @@ -3,46 +3,43 @@ -Install OpenRAG | OpenRAG - - +Install OpenRAG | OpenRAG + + -

Install OpenRAG

info

OpenRAG is is currently in public preview. +

Install OpenRAG

info

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

-

OpenRAG can be installed in multiple ways:

-
    -
  • -

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

    -
    • -
  • -

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

    -
    • -
+

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

+

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

Prerequisites​

-

Python wheel​

-

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

+

Install the OpenRAG Python wheel​

+
important

The .whl file is currently available as an internal download during public preview, and will be published to PyPI in a future release.

+

The OpenRAG wheel installs the Terminal User Interface (TUI) for running and managing OpenRAG.

  1. -

    Create a new project with a virtual environment using uv.

    +

    Create a new project with a virtual environment using uv. +This creates and activates a virtual environment for your project.

    uv init YOUR_PROJECT_NAME
    cd YOUR_PROJECT_NAME
    +

    The terminal prompt won't change like it would when using venv, but the uv commands will use the project's virtual environment. +For more information on virtual environments, see the uv documentation.

  2. -

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

    +

    Add the local OpenRAG wheel to your project's virtual environment.

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

    Replace PATH/TO/ and VERSION with the path and version of your downloaded OpenRAG .whl file.

    +

    For example, if your .whl file is in the ~/Downloads directory, the command is uv add ~/Downloads/openrag-0.1.8-py3-none-any.whl.

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

    @@ -51,46 +48,93 @@ Replace PATH/TO/ and VERSION with your OpenRAG wheel l

  4. Start the OpenRAG TUI.

    uv run openrag
    -

    The OpenRAG TUI opens.

  5. -

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

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

    To complete credentials, click Save Configuration.

    +

    Continue with Setup OpenRAG with the TUI.

    +
    6. +
+

Set up OpenRAG with the TUI​

+

Basic Setup completes or auto-generates most of the required values to start OpenRAG. +Basic Setup does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS. +For OAuth setup, use Advanced Setup.

+

If the TUI detects OAuth credentials, it enforces the Advanced Setup path. +If the TUI detects a .env file in the OpenRAG root directory, it will source any variables from the .env file.

+
    +
  1. To install OpenRAG with Basic Setup, click Basic Setup or press 1.
    2. +
  2. Click Generate Passwords to generate passwords for OpenSearch and Langflow.
    3. +
  3. Paste your OpenAI API key in the OpenAI API key field.
    4. +
  4. Click Save Configuration.
    5. +
  5. To start OpenRAG, click Start Container Services. +Startup pulls container images and runs them, so it can take some time. +When startup is complete, the TUI displays the following: +
    Services started successfully
    Command completed successfully
    +
    6. +
  6. To open the OpenRAG application, click Open App.
    7. +
  7. Continue with Application Onboarding.
    8. +
+

Application onboarding​

+

The first time you start OpenRAG, whether using the TUI or a .env file, you must complete application onboarding.

+

Values input during onboarding can be changed later in the OpenRAG Settings page, except for the language model and embedding model provider. Your provider can only be selected once, and you must use the same provider for your language model and embedding model. The language model can be changed, but the embeddings model cannot be changed. -To change your provider selection, you must restart OpenRAG and delete the config.yml file.

- - -
    -
  1. If you already entered a value for OPENAI_API_KEY in the TUI in Step 5, enable Get API key from environment variable.
    2. +To change your provider selection, you must completely reinstall OpenRAG.

    +
      +
    1. Enable Get API key from environment variable to automatically enter your key from the TUI-generated .env file.
    2. Under Advanced settings, select your Embedding Model and Language Model.
    3. To load 2 sample PDFs, enable Sample dataset. This is recommended, but not required.
    4. Click Complete.
      5. -
    -
    1. Continue with the Quickstart.
      2. -
    -

    Advanced Setup​

    -

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

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

    Two additional variables are available for Advanced Setup:

    -

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

    -

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

  • © 2025 OpenRAG
diff --git a/knowledge/index.html b/knowledge/index.html index fcce2929..e05db1d5 100644 --- a/knowledge/index.html +++ b/knowledge/index.html @@ -3,9 +3,9 @@ -OpenSearch Knowledge | OpenRAG - - +OpenSearch Knowledge | OpenRAG + + @@ -14,20 +14,13 @@

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. +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.

-

Explore knowledge​

-

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

-

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

-

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

-

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

Ingest knowledge​

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

Direct file ingestion​

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

-

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

+

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

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

To load and process a directory from the mapped location, click Add Knowledge, and then click Process Folder. @@ -42,7 +35,7 @@ If you wish to use another provider, add the secrets to another provider.

  1. If OpenRAG is running, stop it with Status > Stop Services.
  2. Click Advanced Setup.
    3. -
  3. Add the OAuth provider's client and secret key in the Advanced Setup menu.
    4. +
  4. Add the OAuth provider's client and secret key in the Advanced Setup menu.
  5. Click Save Configuration. The TUI generates a new .env file with your OAuth values.
  6. Click Start Container Services.
    7. @@ -72,6 +65,14 @@ The ingestion process may take some time, depending on the size of your document

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

    You can monitor the sync progress in the Tasks sidebar.

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

    +

    Explore knowledge​

    +

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

    +

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

    +

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

    +

    All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic. +To modify a flow, click Settings, and click Edit in Langflow. +OpenRAG's visual editor is based on the Langflow visual editor, so you can edit your flows to match your specific use case.

    Knowledge ingestion settings​

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

    Create knowledge filters​

    @@ -111,11 +112,11 @@ A new filter is created with default settings that match everything.

OpenRAG default configuration​

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

-

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

+

The complete list of supported models is available at models_service.py in the OpenRAG repository.

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

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

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

-

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

  • © 2025 OpenRAG
diff --git a/quickstart/index.html b/quickstart/index.html index bddcdd2d..8a8016ee 100644 --- a/quickstart/index.html +++ b/quickstart/index.html @@ -3,46 +3,20 @@ -Quickstart | OpenRAG - - +Quickstart | OpenRAG + + -

Quickstart

info

OpenRAG is is currently in public preview. +

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.

Prerequisites​

    -
  • -

    Install and start OpenRAG

    -
    • -
  • -

    Create a Langflow API key

    -
    Create a Langflow API key

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

    To create a Langflow API key, do the following:

      -
    1. -

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

      -
      2. -
    2. -

      Click Langflow API Keys, and then click Add New.

      -
      3. -
    3. -

      Name your key, and then click Create API Key.

      -
      4. -
    4. -

      Copy the API key and store it securely.

      -
      5. -
    5. -

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

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

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

Find your way around​

    @@ -105,10 +79,33 @@ You can more quickly access the Language Model and Agen

Integrate OpenRAG into your application​

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

-

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

+Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results. Langflow provides code snippets to help you get started.

  1. +

    Create a Langflow API key.

    +
    Create a Langflow API key

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

    To create a Langflow API key, do the following:

      +
    1. +

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

      +
      2. +
    2. +

      Click Langflow API Keys, and then click Add New.

      +
      3. +
    3. +

      Name your key, and then click Create API Key.

      +
      4. +
    4. +

      Copy the API key and store it securely.

      +
      5. +
    5. +

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

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

      # Send request
      curl --request POST \
      --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
      --header "Content-Type: application/json" \
      --header "x-api-key: $LANGFLOW_API_KEY" \
      --data '{
        "output_type": "chat",
        "input_type": "chat",
        "input_value": "Hello"
      }'
      +
      6. +
    +
    2. +

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

  3. @@ -127,7 +124,7 @@ The following is an example of a response from running the Simple Agent<
  • © 2025 OpenRAG
diff --git a/support/troubleshoot/index.html b/support/troubleshoot/index.html index 787751ed..06e7643c 100644 --- a/support/troubleshoot/index.html +++ b/support/troubleshoot/index.html @@ -3,9 +3,9 @@ -Troubleshoot | OpenRAG - - +Troubleshoot | OpenRAG + + @@ -15,10 +15,10 @@ 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.

OpenSearch fails to start​

-

Check that OPENSEARCH_PASSWORD is set and meets requirements. +

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.

Langflow connection issues​

-

Verify the LANGFLOW_SUPERUSER credentials are correct.

+

Verify the LANGFLOW_SUPERUSER credentials set in Environment variables are correct.

Memory errors​

Container out of memory errors​

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

@@ -33,20 +33,20 @@ This example increases the machine size to 8 GB of RAM, which should be sufficie

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

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

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

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

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

# Remove all volumes
docker volume prune --force

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

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

    Stop your containers and completely remove them.

    +
    # Stop all running containers
    docker stop $(docker ps -q)

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

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

    # Remove all volumes
    docker volume prune --force

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

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

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

    -
    uv run openrag
    +
    uv sync
    uv run openrag

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

    4. -