From 362721595161059e446fcbed8832098bc69789ee Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Wed, 29 Oct 2025 10:03:14 -0400
Subject: [PATCH] knowledge-filters
---
docs/docs/core-components/agents.mdx | 2 +-
docs/docs/core-components/ingestion.mdx | 2 +-
docs/docs/core-components/knowledge.mdx | 44 +++++++++++++------------
3 files changed, 25 insertions(+), 23 deletions(-)
diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx
index a7c5ef24..88102a60 100644
--- a/docs/docs/core-components/agents.mdx
+++ b/docs/docs/core-components/agents.mdx
@@ -52,7 +52,7 @@ This filter is the [Knowledge filter](/knowledge#create-knowledge-filters), and
For an example of changing out the agent's language model in OpenRAG, see the [Quickstart](/quickstart#change-components).
-To restore the flow to its initial state, in OpenRAG, click **Settings**, and then click **Restore Flow**.
+To restore the flow to its initial state, in OpenRAG, click **Settings**, and then click **Restore Flow**.
OpenRAG warns you that this discards all custom settings. Click **Restore** to restore the flow.
## Additional Langflow functionality
diff --git a/docs/docs/core-components/ingestion.mdx b/docs/docs/core-components/ingestion.mdx
index 585ea6e3..f72e9ab0 100644
--- a/docs/docs/core-components/ingestion.mdx
+++ b/docs/docs/core-components/ingestion.mdx
@@ -15,7 +15,7 @@ Docling ingests documents from your local machine or OAuth connectors, splits th
OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images.
-To modify OpenRAG's ingestion settings, including the Docling settings and ingestion flows, click **Settings**.
+To modify OpenRAG's ingestion settings, including the Docling settings and ingestion flows, click 2" aria-hidden="true"/> **Settings**.
## Knowledge ingestion settings
diff --git a/docs/docs/core-components/knowledge.mdx b/docs/docs/core-components/knowledge.mdx
index cae39659..045918f1 100644
--- a/docs/docs/core-components/knowledge.mdx
+++ b/docs/docs/core-components/knowledge.mdx
@@ -31,10 +31,10 @@ The **Knowledge Ingest** flow uses Langflow's [**File** component](https://docs.
The default path to your local folder is mounted from the `./documents` folder in your OpenRAG project directory to the `/app/documents/` directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the **Documents Paths** variable in either the TUI's [Advanced Setup](/install#setup) menu or in the `.env` used by Docker Compose.
-To load and process a single file from the mapped location, click **Add Knowledge**, and then click **Add File**.
+To load and process a single file from the mapped location, click **Add Knowledge**, and then click **File**.
The file is loaded into your OpenSearch database, and appears in the Knowledge page.
-To load and process a directory from the mapped location, click **Add Knowledge**, and then click **Process Folder**.
+To load and process a directory from the mapped location, click **Add Knowledge**, and then click **Folder**.
The files are loaded into your OpenSearch database, and appear in the Knowledge page.
### Ingest files through OAuth connectors {#oauth-ingestion}
@@ -61,11 +61,11 @@ If you wish to use another provider, add the secrets to another provider.
1. Stop the Docker deployment.
2. Add the OAuth provider's client and secret key in the `.env` file for Docker Compose.
- ```bash
- GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'
- GOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'
- ```
- 3. Save your `.env`. file.
+ ```bash
+ GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'
+ GOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'
+ ```
+ 3. Save your `.env` file.
4. Start the Docker deployment.
@@ -75,11 +75,11 @@ A successful authentication opens OpenRAG with the required scopes for your conn
To add knowledge from an OAuth-connected storage provider, do the following:
-1. Click **Add Knowledge**, and then select the storage provider, for example, **Google Drive**.
+1. Click **Add Knowledge**, and then select the storage provider, for example, **Google Drive**.
The **Add Cloud Knowledge** page opens.
-2. To add files or folders from the connected storage, click **Add Files**.
+2. To add files or folders from the connected storage, click **Add Files**.
Select the files or folders you want and click **Select**.
-You can select multiples.
+You can select multiple files.
3. When your files are selected, click **Ingest Files**.
The ingestion process may take some time, depending on the size of your documents.
4. When ingestion is complete, your documents are available in the Knowledge screen.
@@ -104,11 +104,11 @@ Knowledge filters help agents work more efficiently with large document collecti
To create a knowledge filter, do the following:
-1. Click **All Knowledge**, and then click **Create New Filter**.
- The **Create New Knowledge Filter** pane appears.
-2. Enter a **Name** and **Description**, and then click **Create Filter**.
-A new filter is created with default settings that match everything.
-3. To modify the default filter, click **All Knowledge**, and then click your new filter to edit it in the **Knowledge Filter** pane.
+1. Click **Knowledge**, and then click **Knowledge Filters**.
+ The **Knowledge Filter** pane appears.
+2. Enter a **Name** and **Description**, and then click **Create Filter**.
+A new filter is created with default settings that match all documents.
+3. To modify the filter, click **Knowledge**, and then click your new filter to edit it in the **Knowledge Filter** pane.
The following filter options are configurable.
@@ -116,15 +116,17 @@ A new filter is created with default settings that match everything.
* **Data Sources**: Select specific data sources or folders to include.
* **Document Types**: Filter by file type.
* **Owners**: Filter by who uploaded the documents.
- * **Sources**: Filter by connector types, such as local upload or Google Drive.
- * **Result Limit**: Set maximum number of results. The default is `10`.
+ * **Connectors**: Filter by connector types, such as local upload or Google Drive.
+ * **Response Limit**: Set maximum number of results. The default is `10`.
* **Score Threshold**: Set minimum relevance score. The default score is `0`.
-4. When you're done editing the filter, click **Save Configuration**.
+4. When you're done editing the filter, click **Update Filter**.
-5. To apply the filter to OpenRAG globally, click **All Knowledge**, and then select the filter to apply.
+5. To apply the filter to OpenRAG globally, click **Knowledge**, and then select the filter to apply. One filter can be enabled at a time.
- To apply the filter to a single chat session, in the **Chat** window, click **@**, and then select the filter to apply.
+ To apply the filter to a single chat session, in the **Chat** window, click , and then select the filter to apply.
+
+ To delete the filter, in the **Knowledge Filter** pane, click **Delete Filter**.
## OpenRAG default configuration
@@ -132,7 +134,7 @@ OpenRAG automatically detects and configures the correct vector dimensions for e
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.
+You can use custom embe*dding models by specifying them in your configuration.
If you use an unknown embedding model, OpenRAG will automatically fall back to `1536` dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from `1536`.