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Merge pull request #676 from langflow-ai/issue-529-docs-opensearch-desc

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Docs: Don't describe OpenSearch as only a vector db
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April I. Murphy 2025-12-17 07:47:59 -08:00 • committed by GitHub
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4
docs/docs/get-started/quickstart.mdx
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@ -89,7 +89,7 @@ For example: `What documents are available to you?`
The agent responds with a summary of OpenRAG's default documents.
3. To verify the agent's response, click <Icon name="Library" aria-hidden="true"/> **Knowledge** to view the documents stored in the OpenRAG OpenSearch vector database.
3. To verify the agent's response, click <Icon name="Library" aria-hidden="true"/> **Knowledge** to view the documents stored in the OpenRAG OpenSearch database.
You can click a document to view the chunks of the document as they are stored in the database.
4. Click **Add Knowledge** to add your own documents to your OpenRAG knowledge base.
@ -106,7 +106,7 @@ You can click a document to view the chunks of the document as they are stored i
* Click <Icon name="Gear" aria-hidden="true"/> **Function Call: search_documents (tool_call)** to view the log of tool calls made by the agent. This is helpful for troubleshooting because it shows you how the agent used particular tools.
* Click <Icon name="Library" aria-hidden="true"/> **Knowledge** to confirm that the documents are present in the OpenRAG OpenSearch vector database, and then click each document to see how the document was chunked.
* Click <Icon name="Library" aria-hidden="true"/> **Knowledge** to confirm that the documents are present in the OpenRAG OpenSearch database, and then click each document to see how the document was chunked.
If a document was chunked improperly, you might need to tweak the ingestion or modify and reupload the document.
* Click <Icon name="Settings2" aria-hidden="true"/> **Settings** to modify the knowledge ingestion settings.

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docs/docs/get-started/what-is-openrag.mdx
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@ -4,11 +4,11 @@ slug: /
hide_table_of_contents: true
---
OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, vector databases, and LLM providers.
OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, databases, and LLM providers.
OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:
* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, vector databases, and a growing library of AI tools.
* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, popular vector databases, and a growing library of AI tools.
OpenRAG uses several built-in flows, and it provides full access to all Langflow features through the embedded Langflow visual editor.
@ -17,7 +17,7 @@ OpenRAG connects and amplifies three popular, proven open-source projects into o
* [OpenSearch](https://docs.opensearch.org/latest/): OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
It provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support.
OpenRAG uses OpenSearch as the underlying vector database for storing and retrieving your documents and associated vector data (embeddings). You can ingest documents from a variety of sources, including your local filesystem and OAuth authenticated connectors to popular cloud storage services.
OpenRAG uses OpenSearch as the underlying database for storing and retrieving your documents and associated vector data (embeddings). You can ingest documents from a variety of sources, including your local filesystem and OAuth authenticated connectors to popular cloud storage services.
* [Docling](https://docling-project.github.io/docling/): Docling simplifies document processing, supports many file formats and advanced PDF parsing, and provides seamless integrations with the generative AI ecosystem.
@ -59,7 +59,7 @@ flowchart TD
* **OpenRAG backend**: The central orchestration service that coordinates all other components.
* **Langflow**: This container runs a Langflow instance. It provides the embedded Langflow visual editor for editing and creating flow, and it connects to the **OpenSearch** container for vector storage and retrieval.
* **Langflow**: This container runs a Langflow instance. It provides the embedded Langflow visual editor for editing and creating flow, and it connects to the **OpenSearch** container for document storage and retrieval.
* **Docling Serve**: This is a local document processing service managed by the **OpenRAG backend**.