knowledge-ingestion-flows

docs/docs/core-components/ingestion.mdx

@@ -50,4 +50,29 @@ If you want to use OpenRAG's built-in pipeline instead of Docling serve, set `DI
 
 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).
+For more information, see [`processors.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58).
+
+## Knowledge ingestion flows
+
+[Flows](https://docs.langflow.org/concepts-overview) in Langflow are functional representations of application workflows, with multiple [component](https://docs.langflow.org/concepts-components) nodes connected as single steps in a workflow.
+
+The **OpenSearch Ingestion** flow is the default knowledge ingestion flow in OpenRAG: when you **Add Knowledge** in OpenRAG, you run the OpenSearch Ingestion flow in the background. The flow ingests documents using **Docling Serve** to import and process documents.
+
+This flow contains ten components connected together to process and store documents in your knowledge base:
+
+* The [**Docling Serve** component](https://docs.langflow.org/bundles-docling) processes input documents by connecting to your instance of Docling Serve.
+* The [**Export DoclingDocument** component](https://docs.langflow.org/components-docling) 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.
+* Three [**DataFrame Operations** components](https://docs.langflow.org/components-processing#dataframe-operations) sequentially add metadata columns to the document data of `filename`, `file_size`, and `mimetype`.
+* The [**Split Text** component](https://docs.langflow.org/components-processing#split-text) splits the processed text into chunks with a chunk size of 1000 characters and an overlap of 200 characters.
+* Four **Secret Input** components provide secure access to configuration variables: `CONNECTOR_TYPE`, `OWNER`, `OWNER_EMAIL`, and `OWNER_NAME`. These are runtime variables populated from OAuth login.
+* The **Create Data** component combines the secret inputs into a structured data object that will be associated with the document embeddings.
+* The [**Embedding Model** component](https://docs.langflow.org/components-embedding-models) generates vector embeddings using OpenAI's `text-embedding-3-small` model. The embedding model is selected at [Application onboarding] and cannot be changed.
+* The [**OpenSearch** component](https://docs.langflow.org/bundles-elastic#opensearch) stores the processed documents and their embeddings in the `documents` index at `https://opensearch:9200`.
+
+
+<PartialModifyFlows />
+
+### OpenSearch URL Ingestion flow
+
+An additional knowledge ingestion flow is included in OpenRAG.
+The **OpenSearch URL Ingestion flow**

docs/docs/core-components/knowledge.mdx

@@ -18,6 +18,7 @@ OpenSearch provides powerful hybrid search capabilities with enterprise-grade se
 ## Ingest knowledge
 
 OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors.
+To configure the knowledge ingestion pipeline parameters, see [Docling Ingestion](/ingestion).
 
 ### Direct file ingestion
 
@@ -101,10 +102,6 @@ Documents are processed with the default **Knowledge Ingest** flow, so if you wa
 
 <PartialModifyFlows />
 
-### 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.