From 810daa9f4ce2bbf166b32059dcfdc330b3e4f7b7 Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Wed, 1 Oct 2025 09:36:26 -0400
Subject: [PATCH] knowledge-page

---
 docs/docs/core-components/knowledge.mdx | 27 +++++++++++++------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/docs/docs/core-components/knowledge.mdx b/docs/docs/core-components/knowledge.mdx
index 5d23db96..872a5f67 100644
--- a/docs/docs/core-components/knowledge.mdx
+++ b/docs/docs/core-components/knowledge.mdx
@@ -9,19 +9,9 @@ import TabItem from '@theme/TabItem';
 import PartialModifyFlows from '@site/docs/_partial-modify-flows.mdx';
 
 OpenRAG uses [OpenSearch](https://docs.opensearch.org/latest/) for its vector-backed knowledge store.
+This is a specialized database for storing and retrieving embeddings, which helps your Agent efficiently find relevant information.
 OpenSearch provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support.
 
-## Explore knowledge
-
-The Knowledge page lists the documents OpenRAG has ingested into the OpenSearch vector database's `documents` index.
-
-To explore your current knowledge, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
-Click on a document to display the chunks derived from splitting the default documents into the vector database.
-
-Documents are processed with the default **Knowledge Ingest** flow, so if you want to split your documents differently, edit the **Knowledge Ingest** flow.
-
-<PartialModifyFlows />
-
 ## Ingest knowledge
 
 OpenRAG supports knowledge ingestion through direct file uploads and OAuth connectors.
@@ -97,6 +87,17 @@ You can monitor the sync progress in the <Icon name="Bell" aria-hidden="true"/>
 
 Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page.
 
+## Explore knowledge
+
+The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database's `documents` index.
+
+To explore your current knowledge, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
+Click on a document to display the chunks derived from splitting the default documents into the vector database.
+
+Documents are processed with the default **Knowledge Ingest** flow, so if you want to split your documents differently, edit the **Knowledge Ingest** flow.
+
+<PartialModifyFlows />
+
 ## Create knowledge filters
 
 OpenRAG includes a knowledge filter system for organizing and managing document collections.
@@ -132,7 +133,7 @@ A new filter is created with default settings that match everything.
 
 OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility.
 
-The complete list of supported models is available at [/src/services/models_service.py](https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py).
+The complete list of supported models is available at [`models_service.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py).
 
 You can use custom embedding models by specifying them in your configuration.
 
@@ -140,4 +141,4 @@ If you use an unknown embedding model, OpenRAG will automatically fall back to `
 
 The default embedding dimension is `1536` and the default model is `text-embedding-3-small`.
 
-For models with known vector dimensions, see [/src/config/settings.py](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py).
\ No newline at end of file
+For models with known vector dimensions, see [`settings.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py).
\ No newline at end of file