From b00e6e9c98779ff331d81f4381793defd13c3cc9 Mon Sep 17 00:00:00 2001 From: April M <36110273+aimurphy@users.noreply.github.com> Date: Tue, 25 Nov 2025 10:49:03 -0800 Subject: [PATCH] langflow functionality --- docs/docs/core-components/agents.mdx | 85 ++++++++++++++++--------- docs/docs/core-components/ingestion.mdx | 20 +++--- docs/docs/core-components/knowledge.mdx | 1 - 3 files changed, 65 insertions(+), 41 deletions(-) diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx index c27de881..6702fff4 100644 --- a/docs/docs/core-components/agents.mdx +++ b/docs/docs/core-components/agents.mdx @@ -7,45 +7,56 @@ import Icon from "@site/src/components/icon/icon"; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow. +OpenRAG includes a built-in [Langflow](https://docs.langflow.org/) instance for creating and managing application workflows called [_flows_](https://docs.langflow.org/concepts-overview). +In a flow, the individual workflow steps are represented by [_components_](https://docs.langflow.org/concepts-components) that are connected together to form a complete process. -[Flows](https://docs.langflow.org/concepts-overview) in Langflow are functional representations of application workflows, with multiple [component](https://docs.langflow.org/concepts-components) nodes connected as single steps in a workflow. +OpenRAG includes several built-in flows: -In the OpenRAG OpenSearch Agent flow, components like the Langflow [**Agent** component](https://docs.langflow.org/agents) and [**OpenSearch** component](https://docs.langflow.org/bundles-elastic#opensearch) are connected to intelligently chat with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM. +* The [**OpenRAG OpenSearch Agent** flow](/agents#flow) powers the **Chat** feature in OpenRAG. +* The [**OpenSearch Ingestion** and **OpenSearch URL Ingestion** flows](/ingestion#knowledge-ingestion-flows) process documents and web content for storage in your OpenSearch knowledge bases. + +You can customize the built-in flows or create your own flows using OpenRAG's embedded Langflow visual editor. + +## About the OpenRAG Chat flow (OpenRAG OpenSearch Agent flow) {#flow} + +When you **Chat** with your knowledge in OpenRAG, the **OpenRAG OpenSearch Agent** flow runs in the background. + +If you [inspect the flow in Langflow](#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. ![OpenRAG Open Search Agent Flow](/img/opensearch-agent-flow.png) -The Agent component shines here in its ability to make decisions on not only what query should be sent, but when a query is necessary to solve the problem at hand. - -
-How do agents work? - -Agents extend Large Language Models (LLMs) by integrating tools, which are functions that provide additional context and enable autonomous task execution. These integrations make agents more specialized and powerful than standalone LLMs. - -Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action. For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code. - -Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response. The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API. - -In an agentic context, tools are functions that the agent can run to perform tasks or access external resources. A function is wrapped as a Tool object with a common interface that the agent understands. Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization. The Tool object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request. - -
- -## Use the OpenRAG Chat (OpenRAG OpenSearch Agent flow) {#flow} - -If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG OpenSearch Agent chat flow. -To switch OpenRAG over to the [Langflow visual editor](https://docs.langflow.org/concepts-overview) and view the OpenRAG OpenSearch Agentflow, click