diff --git a/flows/ingestion_flow.json b/flows/ingestion_flow.json index b525bd6a..20802a82 100644 --- a/flows/ingestion_flow.json +++ b/flows/ingestion_flow.json @@ -4149,12 +4149,39 @@ }, "selected": false, "type": "genericNode" + }, + { + "data": { + "id": "note-DCu9M", + "node": { + "description": "## README\n\nThis flow transforms raw documents into searchable knowledge stored in an OpenSearch vector database.\nThis [knowledge](https://docs.openr.ag/knowledge) serves as context that your [agents](https://docs.openr.ag/agents) draw upon to answer questions and perform tasks.\n\n* Data sources: This flow ingests data from OAuth connectors or can load from your local machine. For more, see [Ingest Knowledge](https://docs.openr.ag/knowledge#ingest-knowledge).\n* Docling ingestion: The [**Docling Serve** component](https://docs.openr.ag/ingestion) processes input documents by connecting to your instance of Docling serve. For more, see [Docling Ingestion](https://docs.openr.ag/ingestion).\n* Processing: The flow adds metadata through three [**DataFrame Operations** components](https://docs.langflow.org/components-processing#dataframe-operations) that add `filename`, `file_size`, and `mimetype` columns.\nThe **Split Text** component then splits the processed text into uniform, easily searchable chunks.\n* Embedding generation:The [**Embedding Model** component](https://docs.langflow.org/components-embedding-models) generates vector embeddings with the model you selected at [Application onboarding](https://docs.openr.ag/install#application-onboarding), and the [**OpenSearch** component](https://docs.langflow.org/bundles-elastic#opensearch) stores the processed documents and their embeddings in the documents index.\n* Metadata and ownership: The **Secret Input** components provide user context that is stored as metadata in OpenSearch. These fields are populated from OAuth configuration values, and enable multi-tenant document isolation in OpenSearch, so each user's documents remain private and traceable.\n\nFor more information, see the [OpenRAG docs](https://docs.openr.ag/ingestion#knowledge-ingestion-flows).\n", + "display_name": "", + "documentation": "", + "template": {} + }, + "type": "note" + }, + "dragging": false, + "height": 439, + "id": "note-DCu9M", + "measured": { + "height": 439, + "width": 1000 + }, + "position": { + "x": -538.3997974029603, + "y": 1984.9915833571447 + }, + "resizing": true, + "selected": false, + "type": "noteNode", + "width": 1000 } ], "viewport": { - "x": -418.8241631881149, - "y": -563.2891507884635, - "zoom": 0.6194861362488232 + "x": 502.99612621025017, + "y": -497.01724621189965, + "zoom": 0.5248650535598084 } }, "description": "Load your data for chat context with Retrieval Augmented Generation.", diff --git a/flows/openrag_agent.json b/flows/openrag_agent.json index aad7be61..ddaee850 100644 --- a/flows/openrag_agent.json +++ b/flows/openrag_agent.json @@ -191,6 +191,7 @@ } }, "id": "xy-edge__MCP-7EY21{œdataTypeœ:œMCPœ,œidœ:œMCP-7EY21œ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-crjWf{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}", + "selected": false, "source": "MCP-7EY21", "sourceHandle": "{œdataTypeœ:œMCPœ,œidœ:œMCP-7EY21œ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}", "target": "Agent-crjWf", @@ -448,8 +449,8 @@ "width": 192 }, "position": { - "x": 1264.0651279011304, - "y": 1192.017532447814 + "x": 1338.2905337667046, + "y": 1206.5715335979264 }, "selected": false, "type": "genericNode" @@ -755,7 +756,7 @@ ], "frozen": false, "icon": "OpenSearch", - "last_updated": "2025-10-06T15:23:50.339Z", + "last_updated": "2025-10-09T17:03:19.845Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -1375,8 +1376,8 @@ "width": 320 }, "position": { - "x": 1202.1762389080463, - "y": 395.8072555285192 + "x": 1183.2560374129, + "y": 320.1264495479339 }, "selected": false, "type": "genericNode" @@ -1409,7 +1410,7 @@ ], "frozen": false, "icon": "binary", - "last_updated": "2025-10-06T15:23:50.341Z", + "last_updated": "2025-10-09T17:03:19.846Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -1734,7 +1735,7 @@ ], "frozen": false, "icon": "bot", - "last_updated": "2025-10-06T15:23:50.396Z", + "last_updated": "2025-10-09T17:03:19.888Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -2241,8 +2242,8 @@ "width": 320 }, "position": { - "x": 745.3341059713564, - "y": 95.0152511387621 + "x": 722.0477041311764, + "y": 119.75705309395346 }, "selected": false, "type": "genericNode" @@ -2273,7 +2274,7 @@ ], "frozen": false, "icon": "brain-circuit", - "last_updated": "2025-10-06T15:23:50.343Z", + "last_updated": "2025-10-09T17:03:19.855Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -2571,8 +2572,8 @@ "width": 320 }, "position": { - "x": 1206.0291133693556, - "y": -185.39565741253472 + "x": 1188.5643119892204, + "y": -237.60813653856357 }, "selected": false, "type": "genericNode" @@ -2601,7 +2602,7 @@ "frozen": false, "icon": "Mcp", "key": "mcp_lf-starter_project", - "last_updated": "2025-10-06T15:23:56.578Z", + "last_updated": "2025-10-09T17:03:19.855Z", "legacy": false, "mcpServerName": "lf-starter_project", "metadata": { @@ -2819,17 +2820,46 @@ "width": 320 }, "position": { - "x": 675.7137923419156, - "y": 878.6218422334763 + "x": 733.9297969423658, + "y": 862.6124409683524 }, "selected": false, "type": "genericNode" + }, + { + "data": { + "id": "note-Wg9xF", + "node": { + "description": "## README\n\nThis flow generates answers for OpenRAG's chat, informed by the context stored in OpenSearch.\nIn this flow, the [**Langflow Agent** component](https://docs.langflow.org/agents) uses the connected [**Language Model** component](https://docs.langflow.org/components-models) to select the correct tool to complete requests.\n* If the Agent determines your request requires external knowledge, it will embed your query with the [**Embedding Model** component](https://docs.langflow.org/components-embedding-models) and query your [OpenSearch knowledge](https://docs.openr.ag/knowledge).\n\n* If the Agent determines your request requires a web search, it selects the [**MCP Tools** component](https://docs.langflow.org/mcp-client#mcp-tools-parameters) to fetch web content with the [OpenSearch URL ingestion flow](https://docs.openr.ag/ingestion#url-flow).\n\nUsing the retrieved data, the Agent generates a response with the connected [**Language Model** component](https://docs.langflow.org/components-models) and sends it to the [**Chat Output** component](https://docs.langflow.org/components-io).\n\nFor more information, see the [OpenRAG docs](https://docs.openr.ag/agents).", + "display_name": "", + "documentation": "", + "template": { + "backgroundColor": "amber" + } + }, + "type": "note" + }, + "dragging": false, + "height": 469, + "id": "note-Wg9xF", + "measured": { + "height": 469, + "width": 644 + }, + "position": { + "x": 19.942791510714386, + "y": 259.5061905471592 + }, + "resizing": false, + "selected": false, + "type": "noteNode", + "width": 644 } ], "viewport": { - "x": -237.0727605845459, - "y": 154.6885920024542, - "zoom": 0.602433700773958 + "x": 15.987785397208654, + "y": 201.25966590756093, + "zoom": 0.6870962766086257 } }, "description": "OpenRAG OpenSearch Agent",