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more detail about nudges flow

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April M 2026-01-16 13:26:14 -08:00
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docs/docs/core-components/agents.mdx
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@ -20,11 +20,18 @@ You can customize these flows and create your own flows using OpenRAG's embedded
All OpenRAG flows are designed to be modular, performant, and provider-agnostic.
To modify a flow in OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
From here, you can manage model provider configurations and edit commonly used parameters, such as the **Language model** and **Agent Instructions**.
To further explore and edit a flow, click **Edit in Langflow** to launch the embedded [Langflow visual editor](https://docs.langflow.org/concepts-overview) where you can fully [customize the flow](https://docs.langflow.org/concepts-flows) to suit your use case.
To view and modify a flow in OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
From here, you can manage OAuth connectors, model providers, and common parameters for the **Agent** and **Knowledge Ingestion** flows.
For example, the following steps explain how to view and edit the built-in **Agent** flow, which is the **OpenRAG OpenSearch Agent** flow used for the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**:
To further explore and edit flows, click **Edit in Langflow** to launch the embedded [Langflow visual editor](https://docs.langflow.org/concepts-overview) where you can fully [customize the flow](https://docs.langflow.org/concepts-flows) to suit your use case.
:::tip
After you click **Edit in Langflow**, you can access and edit all of OpenRAG's built-in flows from the Langflow editor's [**Projects** page](https://docs.langflow.org/concepts-flows#projects).
If you edit any flows other than the **Agent** or **Knowledge Ingestion** flows, it is recommended that you [export the flows](https://docs.langflow.org/concepts-flows-import) before editing so you can revert them to their original state if needed.
:::
For example, the following steps explain how to edit the built-in **Agent** flow, which is the **OpenRAG OpenSearch Agent** flow used for the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**:
1. In OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then find the **Agent** section.
@ -49,9 +56,12 @@ This ensures that the chat doesn't persist any context from the previous convers
### Revert a built-in flow to its original configuration {#revert-a-built-in-flow-to-its-original-configuration}
After you edit a built-in flow, you can click **Restore flow** on the **Settings** page to revert the flow to its original state when you first installed OpenRAG.
After you edit the **Agent** or **Knowledge Ingestion** built-in flows, you can click **Restore flow** on the **Settings** page to revert either flow to its original state when you first installed OpenRAG.
This is a destructive action that discards all customizations to the flow.
This option isn't available for other built-in flows such as the **Nudges** flow.
To restore these flows to their original state, you must reimport the flow from a backup (if you exported one before editing), or [reset](/manage-services#reset-containers) or [reinstall](/reinstall) OpenRAG.
## Build custom flows and use other Langflow functionality
In addition to OpenRAG's built-in flows, all Langflow features are available through OpenRAG, including the ability to [create your own flows](https://docs.langflow.org/concepts-flows) and popular extensibility features such as the following:

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docs/docs/core-components/chat.mdx
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@ -11,7 +11,7 @@ import PartialTempKnowledge from '@site/docs/_partial-temp-knowledge.mdx';
After you [upload documents to your knowledge base](/ingestion), you can use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat** feature to interact with your knowledge through natural language queries.
The OpenRAG **Chat** uses an LLM-powered agent to understand your queries, retrieve relevant information from your knowledge base, and generate context-aware responses.
The OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat** uses an LLM-powered agent to understand your queries, retrieve relevant information from your knowledge base, and generate context-aware responses.
The agent can also fetch information from URLs and new documents that you provide during the chat session.
To limit the knowledge available to the agent, use [filters](/knowledge-filters).
@ -24,7 +24,7 @@ Try chatting, uploading documents, and modifying chat settings in the [quickstar
## OpenRAG OpenSearch Agent flow {#flow}
When you use the OpenRAG **Chat**, the **OpenRAG OpenSearch Agent** flow runs in the background to retrieve relevant information from your knowledge base and generate a response.
When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, the **OpenRAG OpenSearch Agent** flow runs in the background to retrieve relevant information from your knowledge base and generate a response.
If you [inspect the flow in Langflow](/agents#inspect-and-modify-flows), you'll see that it is comprised of eight components that work together to ingest chat messages, retrieve relevant information from your knowledge base, and then generate responses.
When you inspect this flow, you can edit the components to customize the agent's behavior.
@ -32,7 +32,7 @@ When you inspect this flow, you can edit the components to customize the agent's
![OpenRAG Open Search Agent Flow](/img/opensearch-agent-flow.png)
* [**Chat Input** component](https://docs.langflow.org/chat-input-and-output#chat-input): This component starts the flow when it receives a chat message. It is connected to the **Agent** component's **Input** port.
When you use the OpenRAG **Chat**, your chat messages are passed to the **Chat Input** component, which then sends them to the **Agent** component for processing.
When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, your chat messages are passed to the **Chat Input** component, which then sends them to the **Agent** component for processing.
* [**Agent** component](https://docs.langflow.org/components-agents): This component orchestrates the entire flow by processing chat messages, searching the knowledge base, and organizing the retrieved information into a cohesive response.
The agent's general behavior is defined by the prompt in the **Agent Instructions** field and the model connected to the **Language Model** port.
@ -73,12 +73,18 @@ If no knowledge filter is set, then the `OPENRAG-QUERY-FILTER` variable is empty
## Nudges {#nudges}
When you use the OpenRAG **Chat**, the **OpenRAG OpenSearch Nudges** flow runs in the background to pull additional context from your knowledge base and chat history.
When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, the **OpenRAG OpenSearch Nudges** flow runs in the background to pull additional context from your knowledge base and chat history.
Nudges appear as prompts in the chat.
Click a nudge to accept it and provide the nudge's context to the OpenRAG **Chat** agent (the **OpenRAG OpenSearch Agent** flow).
Nudges appear as prompts in the chat, and they are based on the contents of your OpenRAG OpenSearch knowledge base.
Click a nudge to accept it and start a chat based on the nudge.
Like OpenRAG's other built-in flows, you can [inspect the flow in Langflow](/agents#inspect-and-modify-flows), and you can customize it if you want to change the nudge behavior.
However, this flow is specifically designed to work with the OpenRAG chat and knowledge base.
Major changes to this flow might break the nudge functionality or produce irrelevant nudges.
The **Nudges** flow consists of **Embedding model**, **Language model**, **OpenSearch**, **Input/Output*, and other components that browse your knowledge base, identify key themes and possible insights, and then produce prompts based on the findings.
For example, if your knowledge base contains documents about cybersecurity, possible nudges might include `Explain zero trust architecture principles` or `How to identify a social engineering attack`.
## Upload documents to the chat