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Merge branch 'main' of github.com:langflow-ai/openrag into chat-input-files

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This commit is contained in:
Mike Fortman 2025-10-24 15:21:08 -05:00
commit eb3060e9d3
13 changed files with 200 additions and 2833 deletions
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13
docs/docusaurus.config.js
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66
docs/package-lock.json generated
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1
docs/package.json
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44
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33
flows/ingestion_flow.json
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@ -4149,12 +4149,39 @@
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{
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"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",
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"description": "Load your data for chat context with Retrieval Augmented Generation.",

66
flows/openrag_agent.json
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@ -191,6 +191,7 @@
}
},
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"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).",
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2810
flows/openrag_ingest_docling.json
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