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knowledge-page

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Mendon Kissling 2025-10-01 09:36:26 -04:00
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docs/docs/core-components/knowledge.mdx
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@ -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).
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).