audit LF docs links

docs/docs/_partial-ingestion-flow.mdx

@@ -10,8 +10,8 @@ The **OpenSearch Ingestion** flow is comprised of several components that work t
 
 * [**Docling Serve** component](https://docs.langflow.org/bundles-docling#docling-serve): Ingests files and processes them by connecting to OpenRAG's local Docling Serve service. The output is `DoclingDocument` data that contains the extracted text and metadata from the documents.
 * [**Export DoclingDocument** component](https://docs.langflow.org/bundles-docling#export-doclingdocument): Exports processed `DoclingDocument` data to Markdown format with image placeholders. This conversion standardizes the document data in preparation for further processing.
-* [**DataFrame Operations** component](https://docs.langflow.org/components-processing#dataframe-operations): Three of these components run sequentially to add metadata to the document data: `filename`, `file_size`, and `mimetype`.
-* [**Split Text** component](https://docs.langflow.org/components-processing#split-text): Splits the processed text into chunks, based on the configured [chunk size and overlap settings](/knowledge#knowledge-ingestion-settings).
+* [**DataFrame Operations** component](https://docs.langflow.org/dataframe-operations): Three of these components run sequentially to add metadata to the document data: `filename`, `file_size`, and `mimetype`.
+* [**Split Text** component](https://docs.langflow.org/split-text): Splits the processed text into chunks, based on the configured [chunk size and overlap settings](/knowledge#knowledge-ingestion-settings).
 * **Secret Input** component: If needed, four of these components securely fetch the [OAuth authentication](/knowledge#auth) configuration variables: `CONNECTOR_TYPE`, `OWNER`, `OWNER_EMAIL`, and `OWNER_NAME`.
 * **Create Data** component: Combines the authentication credentials from the **Secret Input** components into a structured data object that is associated with the document embeddings.
 * [**Embedding Model** component](https://docs.langflow.org/components-embedding-models): Generates vector embeddings using your selected [embedding model](/knowledge#set-the-embedding-model-and-dimensions).

docs/docs/_partial-integrate-chat.mdx

@@ -109,6 +109,6 @@ This key doesn't grant access to OpenRAG; it is only for authenticating with the
 If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.
 
 In production, you won't pass the raw response to the user in its entirety.
-Instead, you extract and reformat relevant fields for different use cases, as demonstrated in the [Langflow quickstart](https://docs.langflow.org/quickstart#extract-data-from-the-response).
+Instead, you extract and reformat relevant fields for different use cases, as demonstrated in the [Langflow quickstart](https://docs.langflow.org/get-started-quickstart#extract-data-from-the-response).
 For example, you could pass the chat output text to a front-end user-facing application, and store specific fields in logs and backend data stores for monitoring, chat history, or analytics.
 You could also pass the output from one flow as input to another flow.

docs/docs/core-components/chat.mdx

@@ -31,10 +31,10 @@ 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/components-io): This component starts the flow when it receives a chat message. It is connected to the **Agent** component's **Input** port.
+* [**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.
 
-* [**Agent** component](https://docs.langflow.org/agents): This component orchestrates the entire flow by processing chat messages, searching the knowledge base, and organizing the retrieved information into a cohesive response.
+* [**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.
 One or more specialized tools can be attached to the **Tools** port to extend the agent's capabilities. In this case, there are two tools: **MCP Tools** and **OpenSearch**.
 
@@ -66,10 +66,10 @@ This flow fetches content from URLs, and then stores the content in your OpenRAG
 
    It is critical that the embedding model used here matches the embedding model used when you [upload documents to your knowledge base](/ingestion). Mismatched models and dimensions can degrade the quality of similarity search results causing the agent to retrieve irrelevant documents from your knowledge base.
 
-* [**Text Input** component](https://docs.langflow.org/components-io): Connected to the **OpenSearch** component's **Search Filters** port, this component is populated with a Langflow global variable named `OPENRAG-QUERY-FILTER`. If a global or chat-level [knowledge filter](/knowledge-filters) is set, then the variable contains the filter expression, which limits the documents that the agent can access in the knowledge base.
+* [**Text Input** component](https://docs.langflow.org/text-input-and-output#text-input): Connected to the **OpenSearch** component's **Search Filters** port, this component is populated with a Langflow global variable named `OPENRAG-QUERY-FILTER`. If a global or chat-level [knowledge filter](/knowledge-filters) is set, then the variable contains the filter expression, which limits the documents that the agent can access in the knowledge base.
 If no knowledge filter is set, then the `OPENRAG-QUERY-FILTER` variable is empty, and the agent can access all documents in the knowledge base.
 
-* [**Chat Output** component](https://docs.langflow.org/components-io): Connected to the **Agent** component's **Output** port, this component returns the agent's generated response as a chat message.
+* [**Chat Output** component](https://docs.langflow.org/chat-input-and-output#chat-output): Connected to the **Agent** component's **Output** port, this component returns the agent's generated response as a chat message.
 
 ## Nudges {#nudges}