Add mailersend image

NolanTrem · NolanTrem · commit fc21da8df6ad · 2025-06-10T11:03:12.000-07:00
diff --git a/docs/README.md b/docs/README.md
@@ -26,8 +26,3 @@ The most advanced AI retrieval system. Agentic Retrieval-Augmented Generation (R
 - [Installation](./self-hosting/getting-started/installation/)
 - [Configuration](./self-hosting/configuration/)
 - [Deployment](./self-hosting/deployment/)
-
-## 🔗 Quick Links
-- [GitHub Repository](https://github.com/SciPhi-AI/R2R)
-- [Community Discord](https://discord.gg/p6KqD2kjtB)
-- [Support](mailto:founders@sciphi.ai)
diff --git a/docs/cookbooks/application.md b/docs/cookbooks/application.md
@@ -1,11 +1,3 @@
----
-title: Application
-subtitle: Learn how to set up and use the R2R Application for managing your instance.
-icon: display
-slug: cookbooks/application
----
-
-
 R2R offers an [open-source React+Next.js application](https://github.com/SciPhi-AI/R2R-Application) designed to give developers an administrative portal for their R2R deployment, and users an application to communicate with out of the box.
 
 ## Setup
diff --git a/docs/cookbooks/email.md b/docs/cookbooks/email.md
@@ -21,7 +21,8 @@ provider, and generate an API key.
 ## Creating a Template
 Once you have registered for an account with your email provider, you will want to create an email template. Providers will have pre-made templates, or you can build these from scratch.
 
-<img src="../images/cookbooks/email/mailersend.png" alt="A Mailersend welcome template." />
+![A Mailersend welcome template](./images/email/mailersend.png)
+
 
 Once you save a template, you will want to make note of the template id. These will go into the configuration files.
 
diff --git a/docs/cookbooks/evals.md b/docs/cookbooks/evals.md
@@ -1,4 +1,3 @@
-## Overview
 This guide demonstrates how to evaluate your R2R RAG outputs using the Ragas evaluation framework.
 
 In this tutorial, you will:
diff --git a/docs/cookbooks/graphs.md b/docs/cookbooks/graphs.md
@@ -1,5 +1,3 @@
-## Overview
-
 R2R allows you to build and analyze knowledge graphs from your documents through a collection-based architecture. The system extracts entities and relationships from documents, enabling richer search capabilities that understand connections between information.
 
 The process works in several key stages:
diff --git a/docs/cookbooks/images/email/mailersend.png b/docs/cookbooks/images/email/mailersend.png
diff --git a/docs/cookbooks/ingestion.md b/docs/cookbooks/ingestion.md
@@ -1,12 +1,3 @@
----
-title: Ingestion
-subtitle: Learn how to ingest, update, and delete documents with R2R
-icon: file-arrow-up
-slug: cookbooks/ingestion
----
-
-## Introduction
-
 R2R provides a powerful and flexible ingestion to process and manage various types of documents. It supports a wide range of file formats—text, documents, PDFs, images, audio, and even video—and transforms them into searchable, analyzable content. The ingestion process includes parsing, chunking, embedding, and optionally extracting entities and relationships for knowledge graph construction.
 
 This cookbook will guide you through:
diff --git a/docs/cookbooks/local.md b/docs/cookbooks/local.md
@@ -1,27 +1,8 @@
----
-title: Local LLMs
-subtitle: Run R2R with Local LLMs
-icon: computer
-slug: cookbooks/local-llms
----
-
-## Overview
-
 There are many amazing LLMs and embedding models that can be run locally. R2R fully supports using these models, giving you full control over your data and infrastructure.
 
 Running models locally can be ideal for sensitive data handling, reducing API costs, or situations where internet connectivity is limited. While cloud-based LLMs often provide cutting-edge performance,
 local models offer a compelling balance of capability, privacy, and cost-effectiveness for many use cases.
 
-
-<Warning>
-
-Local LLM features are currently restricted to:
-- Self-hosted instances
-- Enterprise tier cloud accounts
-
-Contact our sales team for Enterprise pricing and features.
-</Warning>
-
 <Steps>
 ### Serving Local Models
 
diff --git a/docs/cookbooks/logging.md b/docs/cookbooks/logging.md
@@ -1,11 +1,3 @@
----
-title: Logging
-subtitle: Configuring Advanced Logging with R2R
-icon: scroll
-slug: cookbooks/logging
----
-
-## Overview
 Users deploying R2R into production settings benefit from robust, persistant logging. R2R supports this via [Victorialogs](https://docs.victoriametrics.com/victorialogs), open source user-friendly database for logs from [VictoriaMetrics](https://docs.victoriametrics.com).
 
 Victorialogs ships by default with the [full version of R2R](/self-hosting/installation/full) and hosts a UI to view your logs at http://localhost:9428/select/vmui.
diff --git a/docs/cookbooks/maintenance.md b/docs/cookbooks/maintenance.md
@@ -1,11 +1,3 @@
-<Warning>
-User management features are currently restricted to:
-- Self-hosted instances
-- Enterprise tier cloud accounts
-
-Contact our sales team for Enterprise pricing and features.
-</Warning>
-
 This guide covers essential maintenance tasks for R2R deployments, with a focus on vector index management and system updates.
 Understanding when and how to build vector indices, as well as keeping your R2R installation current, is crucial for maintaining
 optimal performance at scale.
diff --git a/docs/cookbooks/mcp.md b/docs/cookbooks/mcp.md
@@ -1,5 +1,3 @@
-## Overview
-
 The R2R Retrieval System is a Model Context Protocol (MCP) server that enhances Claude with retrieval and search capabilities. This server enables Claude to search through your knowledge base, perform vector searches, graph searches, web searches, and document searches, making it a powerful tool for retrieving relevant information.
 
 ## Features
diff --git a/docs/cookbooks/orchestration.md b/docs/cookbooks/orchestration.md
@@ -1,11 +1,3 @@
-<Warning>
-User management features are currently restricted to:
-- Self-hosted instances
-- Enterprise tier cloud accounts
-
-Contact our sales team for Enterprise pricing and features.
-</Warning>
-
 R2R uses [Hatchet](https://docs.hatchet.run/home) for orchestrating complex workflows, particularly for ingestion and knowledge graph construction processes.
 
 Hatchet is a distributed, fault-tolerant task queue that solves scaling problems like concurrency, fairness, and rate limiting. It allows R2R to distribute functions between workers with minimal configuration.
diff --git a/docs/cookbooks/structured-output.md b/docs/cookbooks/structured-output.md
@@ -1,4 +1,3 @@
-## Overview
 Structured outputs allow users to ensure that the retrieval response generated by the LLM follows a user-defined structure. This provides reliable type-safety, making it easier to generate high-quality, production-ready applications.
 
 R2R supports passing Pydantic models via our Python SDK.

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-## Overview`
`2`	`1`	`This guide demonstrates how to evaluate your R2R RAG outputs using the Ragas evaluation framework.`
`3`	`2`
`4`	`3`	`In this tutorial, you will:`
Original file line number	Diff line number	Diff line change
`@@ -1,5 +1,3 @@`
`1`		`-## Overview`
`2`		`-`
`3`	`1`	`R2R allows you to build and analyze knowledge graphs from your documents through a collection-based architecture. The system extracts entities and relationships from documents, enabling richer search capabilities that understand connections between information.`
`4`	`2`
`5`	`3`	`The process works in several key stages:`