Merge branch 'main' into tui-copy-flows

docs/docs/core-components/agents.mdx

@@ -34,11 +34,11 @@ In an agentic context, tools are functions that the agent can run to perform tas
 
 </details>
 
-## Use the OpenRAG OpenSearch Agent flow
+## Use the OpenRAG OpenSearch Agent flow {#flow}
 
 If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG OpenSearch Agent chat flow.
 To switch OpenRAG over to the [Langflow visual editor](https://docs.langflow.org/concepts-overview) and view the OpenRAG OpenSearch Agentflow, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow**.
-This flow contains seven components connected together to chat with your data:
+This flow contains eight components connected together to chat with your data:
 
 * The [**Agent** component](https://docs.langflow.org/agents) 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.
@@ -49,6 +49,7 @@ The **Agent** behaves according to the prompt in the **Agent Instructions** fiel
 * The [**Text Input** component](https://docs.langflow.org/components-io) is populated with the global variable `OPENRAG-QUERY-FILTER`. 
 This filter is the [Knowledge filter](/knowledge#create-knowledge-filters), and filters which knowledge sources to search through.
 * The **Agent** component's Output port is connected to the [**Chat Output** component](https://docs.langflow.org/components-io), which returns the final response to the user or application.
+* An [**MCP Tools** component](https://docs.langflow.org/mcp-client) is connected to the Agent's **Tools** port. This component calls the [OpenSearch URL Ingestion flow](/ingestion#url-flow), which Langflow uses as an MCP server to fetch content from URLs and store in OpenSearch.
 
 <PartialModifyFlows />
 

docs/docs/core-components/ingestion.mdx

@@ -50,4 +50,31 @@ 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`. By default, the component is authenticated with a JWT token, but you can also select `basic` auth mode, and enter your OpenSearch admin username and password.
+
+<PartialModifyFlows />
+
+### OpenSearch URL Ingestion flow {#url-flow}
+
+An additional knowledge ingestion flow is included in OpenRAG, where it is used as an MCP tool by the [**Open Search Agent flow**](/agents#flow).
+The agent calls this component to fetch web content, and the results are ingested into OpenSearch.
+
+For more on using MCP clients in Langflow, see [MCP clients](https://docs.langflow.org/mcp-client).\
+To connect additional MCP servers to the MCP client, see [Connect to MCP servers from your application](https://docs.langflow.org/mcp-tutorial).

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.