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class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Core components</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Docling Ingestion</span></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Docling Ingestion</h1></header><p>OpenRAG uses <a href="https://docling-project.github.io/docling/" target="_blank" rel="noopener noreferrer">Docling</a> for its document ingestion pipeline.
More specifically, OpenRAG uses <a href="https://github.com/docling-project/docling-serve" target="_blank" rel="noopener noreferrer">Docling Serve</a>, which starts a <code>docling serve</code> process on your local machine and runs Docling ingestion through an API service.</p>
<p>Docling ingests documents from your local machine or OAuth connectors, splits them into chunks, and stores them as separate, structured documents in the OpenSearch <code>documents</code> index.</p>
<p>OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images.</p>
<h2 class="anchor anchorWithStickyNavbar_LWe7" id="docling-ingestion-settings">Docling ingestion settings<a href="#docling-ingestion-settings" class="hash-link" aria-label="Direct link to Docling ingestion settings" title="Direct link to Docling ingestion settings">​</a></h2>
<p>These settings configure the Docling ingestion parameters.</p>
<p>OpenRAG will warn you if <code>docling serve</code> is not running.
To start or stop <code>docling serve</code> or any other native services, in the TUI main menu, click <strong>Start Native Services</strong> or <strong>Stop Native Services</strong>.</p>
<p><strong>Embedding model</strong> determines which AI model is used to create vector embeddings. The default is <code>text-embedding-3-small</code>.</p>
<p><strong>Chunk size</strong> determines how large each text chunk is in number of characters.
Larger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context.
The default value of <code>1000</code> characters provides a good starting point that balances these considerations.</p>
<p><strong>Chunk overlap</strong> controls the number of characters that overlap over chunk boundaries.
Use larger overlap values for documents where context is most important, and use smaller overlap values for simpler documents, or when optimization is most important.
The default value of 200 characters of overlap with a chunk size of 1000 (20% overlap) is suitable for general use cases. Decrease the overlap to 10% for a more efficient pipeline, or increase to 40% for more complex documents.</p>
<p><strong>OCR</strong> enables or disabled OCR processing when extracting text from images and scanned documents.
OCR is disabled by default. This setting is best suited for processing text-based documents as quickly as possible with Docling&#x27;s <a href="https://docling-project.github.io/docling/reference/document_converter/" target="_blank" rel="noopener noreferrer"><code>DocumentConverter</code></a>. Images are ignored and not processed.</p>
<p>Enable OCR when you are processing documents containing images with text that requires extraction, or for scanned documents. Enabling OCR can slow ingestion performance.</p>
<p>If OpenRAG detects that the local machine is running on macOS, OpenRAG uses the <a href="https://www.piwheels.org/project/ocrmac/" target="_blank" rel="noopener noreferrer">ocrmac</a> OCR engine. Other platforms use <a href="https://www.jaided.ai/easyocr/" target="_blank" rel="noopener noreferrer">easyocr</a>.</p>
<p><strong>Picture descriptions</strong> adds image descriptions generated by the <a href="https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct" target="_blank" rel="noopener noreferrer">SmolVLM-256M-Instruct</a> model to OCR processing. Enabling picture descriptions can slow ingestion performance.</p>
<h2 class="anchor anchorWithStickyNavbar_LWe7" id="use-openrag-default-ingestion-instead-of-docling-serve">Use OpenRAG default ingestion instead of Docling serve<a href="#use-openrag-default-ingestion-instead-of-docling-serve" class="hash-link" aria-label="Direct link to Use OpenRAG default ingestion instead of Docling serve" title="Direct link to Use OpenRAG default ingestion instead of Docling serve">​</a></h2>
<p>If you want to use OpenRAG&#x27;s built-in pipeline instead of Docling serve, set <code>DISABLE_INGEST_WITH_LANGFLOW=true</code> in <a href="/reference/configuration#document-processing">Environment variables</a>.</p>
<p>The built-in pipeline still uses the Docling processor, but uses it directly without the Docling Serve API.</p>
<p>For more information, see <a href="https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58" target="_blank" rel="noopener noreferrer"><code>processors.py</code> in the OpenRAG repository</a>.</p>
<h2 class="anchor anchorWithStickyNavbar_LWe7" id="knowledge-ingestion-flows">Knowledge ingestion flows<a href="#knowledge-ingestion-flows" class="hash-link" aria-label="Direct link to Knowledge ingestion flows" title="Direct link to Knowledge ingestion flows">​</a></h2>
<p><a href="https://docs.langflow.org/concepts-overview" target="_blank" rel="noopener noreferrer">Flows</a> in Langflow are functional representations of application workflows, with multiple <a href="https://docs.langflow.org/concepts-components" target="_blank" rel="noopener noreferrer">component</a> nodes connected as single steps in a workflow.</p>
<p>The <strong>OpenSearch Ingestion</strong> flow is the default knowledge ingestion flow in OpenRAG: when you <strong>Add Knowledge</strong> in OpenRAG, you run the OpenSearch Ingestion flow in the background. The flow ingests documents using <strong>Docling Serve</strong> to import and process documents.</p>
<p>This flow contains ten components connected together to process and store documents in your knowledge base.</p>
<ul>
<li>The <a href="https://docs.langflow.org/bundles-docling" target="_blank" rel="noopener noreferrer"><strong>Docling Serve</strong> component</a> processes input documents by connecting to your instance of Docling Serve.</li>
<li>The <a href="https://docs.langflow.org/components-docling" target="_blank" rel="noopener noreferrer"><strong>Export DoclingDocument</strong> component</a> exports the processed DoclingDocument to markdown format with image export mode set to placeholder. This conversion makes the structured document data into a standardized format for further processing.</li>
<li>Three <a href="https://docs.langflow.org/components-processing#dataframe-operations" target="_blank" rel="noopener noreferrer"><strong>DataFrame Operations</strong> components</a> sequentially add metadata columns to the document data of <code>filename</code>, <code>file_size</code>, and <code>mimetype</code>.</li>
<li>The <a href="https://docs.langflow.org/components-processing#split-text" target="_blank" rel="noopener noreferrer"><strong>Split Text</strong> component</a> splits the processed text into chunks with a chunk size of 1000 characters and an overlap of 200 characters.</li>
<li>Four <strong>Secret Input</strong> components provide secure access to configuration variables: <code>CONNECTOR_TYPE</code>, <code>OWNER</code>, <code>OWNER_EMAIL</code>, and <code>OWNER_NAME</code>. These are runtime variables populated from OAuth login.</li>
<li>The <strong>Create Data</strong> component combines the secret inputs into a structured data object that will be associated with the document embeddings.</li>
<li>The <a href="https://docs.langflow.org/components-embedding-models" target="_blank" rel="noopener noreferrer"><strong>Embedding Model</strong> component</a> generates vector embeddings using OpenAI&#x27;s <code>text-embedding-3-small</code> model. The embedding model is selected at [Application onboarding] and cannot be changed.</li>
<li>The <a href="https://docs.langflow.org/bundles-elastic#opensearch" target="_blank" rel="noopener noreferrer"><strong>OpenSearch</strong> component</a> stores the processed documents and their embeddings in the <code>documents</code> index at <code>https://opensearch:9200</code>. By default, the component is authenticated with a JWT token, but you can also select <code>basic</code> auth mode, and enter your OpenSearch admin username and password.</li>
</ul>
<p>All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.
To modify a flow, click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-settings2 lucide-settings-2" aria-hidden="true"><path d="M14 17H5"></path><path d="M19 7h-9"></path><circle cx="17" cy="17" r="3"></circle><circle cx="7" cy="7" r="3"></circle></svg> <strong>Settings</strong>, and click <strong>Edit in Langflow</strong>.
OpenRAG&#x27;s visual editor is based on the <a href="https://docs.langflow.org/concepts-overview" target="_blank" rel="noopener noreferrer">Langflow visual editor</a>, so you can edit your flows to match your specific use case.</p>
<h3 class="anchor anchorWithStickyNavbar_LWe7" id="url-flow">OpenSearch URL Ingestion flow<a href="#url-flow" class="hash-link" aria-label="Direct link to OpenSearch URL Ingestion flow" title="Direct link to OpenSearch URL Ingestion flow">​</a></h3>
<p>An additional knowledge ingestion flow is included in OpenRAG, where it is used as an MCP tool by the <a href="/agents#flow"><strong>Open Search Agent flow</strong></a>.
The agent calls this component to fetch web content, and the results are ingested into OpenSearch.</p>
<p>For more on using MCP clients in Langflow, see <a href="https://docs.langflow.org/mcp-client" target="_blank" rel="noopener noreferrer">MCP clients</a>.<br>
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