Merge pull request #154 from langflow-ai/docs-core-component-docling-ingestion

docs: core component - docling ingestion

docs/docs/core-components/ingestion.mdx

@@ -0,0 +1,50 @@
+---
+title: Docling Ingestion
+slug: /ingestion
+---
+
+import Icon from "@site/src/components/icon/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialModifyFlows from '@site/docs/_partial-modify-flows.mdx';
+
+OpenRAG uses [Docling](https://docling-project.github.io/docling/) for its document ingestion pipeline.
+More specifically, OpenRAG uses [Docling Serve](https://github.com/docling-project/docling-serve), which starts a `docling-serve` process on your local machine and runs Docling ingestion through an API service.
+
+Docling ingests documents from your local machine or OAuth connectors, splits them into chunks, and stores them as separate, structured documents in the OpenSearch `documents` index.
+
+OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images.
+
+## Docling ingestion settings
+
+These settings configure the Docling ingestion parameters.
+
+OpenRAG will warn you if `docling-serve` is not running.
+To start or stop `docling-serve` or any other native services, in the TUI main menu, click **Start Native Services** or **Stop Native Services**.
+
+**Embedding model** determines which AI model is used to create vector embeddings. The default is `text-embedding-3-small`.
+
+**Chunk size** determines how large each text chunk is in number of characters.
+Larger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context. 
+The default value of `1000` characters provides a good starting point that balances these considerations.
+
+**Chunk overlap** controls the number of characters that overlap over chunk boundaries.
+Use larger overlap values for documents where context is most important, and use smaller overlap values for simpler documents, or when optimization is most important.
+The default value of 200 characters of overlap with a chunk size of 1000 (20% overlap) is suitable for general use cases. Decrease the overlap to 10% for a more efficient pipeline, or increase to 40% for more complex documents.
+
+**OCR** enables or disabled OCR processing when extracting text from images and scanned documents.
+OCR is disabled by default. This setting is best suited for processing text-based documents as quickly as possible with Docling's [`DocumentConverter`](https://docling-project.github.io/docling/reference/document_converter/). Images are ignored and not processed.
+
+Enable OCR when you are processing documents containing images with text that requires extraction, or for scanned documents. Enabling OCR can slow ingestion performance.
+
+If OpenRAG detects that the local machine is running on macOS, OpenRAG uses the [ocrmac](https://www.piwheels.org/project/ocrmac/) OCR engine. Other platforms use [easyocr](https://www.jaided.ai/easyocr/).
+
+**Picture descriptions** adds image descriptions generated by the [SmolVLM-256M-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) model to OCR processing. Enabling picture descriptions can slow ingestion performance.
+
+## Use OpenRAG default ingestion instead of Docling serve
+
+If you want to use OpenRAG's built-in pipeline instead of Docling serve, set `DISABLE_INGEST_WITH_LANGFLOW=true` in [Environment variables](/configure/configuration#ingestion-configuration).
+
+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](https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58).

docs/docs/core-components/knowledge.mdx

@@ -97,6 +97,10 @@ You can monitor the sync progress in the <Icon name="Bell" aria-hidden="true"/>
 
 Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page.
 
+### Knowledge ingestion settings
+
+To configure the knowledge ingestion pipeline parameters, see [Docling Ingestion](/ingestion).
+
 ## Create knowledge filters
 
 OpenRAG includes a knowledge filter system for organizing and managing document collections.

docs/sidebars.js

@@ -60,6 +60,11 @@ const sidebars = {
           type: "doc",
           id: "core-components/knowledge",
           label: "OpenSearch Knowledge"
+        },
+        {
+          type: "doc",
+          id: "core-components/ingestion",
+          label: "Docling Ingestion"
         }
       ],
     },