Merge branch 'main' into patch-1

Author: imilinovic

.github/CODEOWNERS

@@ -1 +1 @@
-* @hal-eisen-MG
+* @katarinasupe @gitbuda

.github/pull_request_template.md

@@ -17,12 +17,15 @@ PR this doc page is related to:
 Closes:
 (paste the link to the issue it closes)
 
-
 ### Checklist:
 
+- [ ] Make sure all relevant tech details are documented
+    - [ ] Update reference pages (e.g. [clauses](https://memgraph.com/docs/querying/clauses), [functions](https://memgraph.com/docs/querying/functions), [flags](https://memgraph.com/docs/database-management/configuration#list-of-configuration-flags), [experimental](https://memgraph.com/docs/database-management/experimental-features), [monitoring](https://memgraph.com/docs/database-management/monitoring))
+    - [ ] Search for the feature you are working on (mentions) and make updates if needed
+    - [ ] Provide a basic example of usage
+    - [ ] In case your feature is an Enterprise one, list it under [ME page](https://memgraph.com/docs/database-management/enabling-memgraph-enterprise) and mark its page with Enterprise ([example](https://memgraph.com/docs/database-management/authentication-and-authorization/role-based-access-control)).
 - [ ] Check all content with Grammarly
 - [ ] Perform a self-review of my code
-- [ ] Make corresponding changes to the rest of the documentation (consult with the DX team)
 - [ ] The build passes locally
 - [ ] My changes generate no new warnings or errors
 - [ ] Add a corresponding label

components/code-snippet/CodeSnippet.tsx

@@ -29,11 +29,11 @@ export default function CodeSnippet(props: { code: string; page: string, os: str
 
   const copyToClipboard = () => {
     navigator.clipboard.writeText(props.code);
-    global.window.analytics.track("Docker Run Copied", {
-      source: "docs",
-      page: props.page,
-      os: props.os
-    });
+    // global.window.analytics.track("Docker Run Copied", {
+    //   source: "docs",
+    //   page: props.page,
+    //   os: props.os
+    // });
     document.getElementById(idCopy)?.classList.add("hidden");
     document.getElementById(idCopied)?.classList.remove("hidden");
     setTimeout(() => {

components/link-click/LinkClick.tsx

@@ -2937,13 +2937,13 @@ module.exports = withNextra({
         source: '/memgraph/2.1.0/getting-started',
         destination: '/getting-started',
         permanent: true
-      },      
+      },
       //END 404 links redirects
       {
         source: '/configuration/exposing-system-metrics',
         destination: '/configuration/monitoring-server',
         permanent: true
-      },       
+      },
       {
         source: '/configuration/ldap-security',
         destination: '/configuration/auth-module',
@@ -2958,12 +2958,12 @@ module.exports = withNextra({
         source: '/advanced-algorithms/available-algorithms/schema',
         destination: '/querying/functions#schema',
         permanent: true
-      },       
+      },
       {
         source: '/advanced-algorithms/available-algorithms/convert',
         destination: '/querying/functions#conversion-functions',
         permanent: true
-      },      
+      },
       {
         source: '/querying/functions#schema',
         destination: '/querying/schema',
@@ -2975,8 +2975,8 @@ module.exports = withNextra({
         permanent: true
       },
       {
-        source: '/configuration/experimental-replication',
-        destination: '/configuration/replication/experimental-replication',
+        source: '/configuration/system-replication',
+        destination: '/configuration/replication/system-replication',
         permanent: true
       },
       {
@@ -3046,8 +3046,8 @@ module.exports = withNextra({
         permanent: true
       },
       {
-        source: '/configuration/replication/experimental-replication',
-        destination: '/clustering/replication/experimental-replication',
+        source: '/configuration/replication/system-replication',
+        destination: '/clustering/replication/system-replication',
         permanent: true
       },
       {

next.config.js

@@ -9,12 +9,6 @@ function MyApp({ Component, pageProps }: AppProps) {
 
   return (
     <>
-        <Script strategy="lazyOnload" id="segment">{`
-!function(){var analytics=window.analytics=window.analytics||[];if(!analytics.initialize)if(analytics.invoked)window.console&&console.error&&console.error("Segment snippet included twice.");else{analytics.invoked=!0;analytics.methods=["trackSubmit","trackClick","trackLink","trackForm","pageview","identify","reset","group","track","ready","alias","debug","page","once","off","on","addSourceMiddleware","addIntegrationMiddleware","setAnonymousId","addDestinationMiddleware"];analytics.factory=function(e){return function(){var t=Array.prototype.slice.call(arguments);t.unshift(e);analytics.push(t);return analytics}};for(var e=0;e<analytics.methods.length;e++){var key=analytics.methods[e];analytics[key]=analytics.factory(key)}analytics.load=function(key,e){var t=document.createElement("script");t.type="text/javascript";t.async=!0;t.src="https://cdn.segment.com/analytics.js/v1/" + key + "/analytics.min.js";var n=document.getElementsByTagName("script")[0];n.parentNode.insertBefore(t,n);analytics._loadOptions=e};analytics._writeKey="CqNYSAW7IN6mk3psB5M7tb4FS6BD9fJx";analytics.SNIPPET_VERSION="4.13.2";
-analytics.load("CqNYSAW7IN6mk3psB5M7tb4FS6BD9fJx");
-analytics.page();
-}}();
-`}</Script>
         <Script strategy="lazyOnload" id="google-tag-manager">{`
 (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
 new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],

pages/_app.tsx

@@ -2,6 +2,7 @@
   "index": "Home",
   "getting-started": "Getting started",
   "client-libraries": "Client libraries",
+  "ai-ecosystem": "AI ecosystem",
   "fundamentals": "Fundamentals",
   "data-migration": "Data migration",
   "querying": "Querying",

pages/_meta.json

@@ -48,10 +48,18 @@ its expectation, which is Rank(v).
 | **Edge weights**    | unweighted |
 | **Parallelism**     | sequential |
 
+<Callout type="info">
+This algorithm is currently running in a sequential manner, but can be parallelized.
+If you have a use case for parallelizing this algorithm, please contact us over
+**[Discord](https://discord.gg/memgraph).**
+</Callout>
+
 ## Procedures
 
 Online PageRank should be used by executing the procedures in the following way:
-1. Parameters and the context is set using the `set()` procedure.
+1. With the `set()` procedure, the PageRank values are calculated on the graph for the first time. This function
+is also important as it sets the streaming context for this algorithm, so further updates of the graph
+can result in faster execution.
 
 2. To make the incremental flow, set the proper trigger using the `update()` function:
 
@@ -63,7 +71,9 @@ Online PageRank should be used by executing the procedures in the following way:
   SET node.rank = rank;
   ```
 
-3. Use the `get()` procedure to return the resulting values stored in the cache.
+3. Use the `get()` procedure to return the resulting values stored in the cache. If the user hasn't previously run
+`set()`, the procedure will also do the `set()` functionality first in order to initialize the streaming context of 
+this algorithm.
 4. Finally, the `reset()` function resets the context and enables you to start new runs.
 
 <Callout type="info">
@@ -72,12 +82,15 @@ You can execute this algorithm on [graph projections, subgraphs or portions of t
 
 ### `set()`
 
-The procedure calculates PageRank for the nodes in the graph. 
+The procedure calculates PageRank for the nodes in the graph. The procedure is currently running in a 
+sequential manner, but can be parallelized 
+(the [non-streaming version of pagerank](/advanced-algorithms/available-algorithms/pagerank) offers 
+parallelism, but is not applicable as it doesn't set the streaming context for the algorithm).
 
 {<h4> Input: </h4>}
 
 - `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
-- `walks_per_node: integer (default=10)` ➡ Number of sampled walks per node.
+- `walks_per_node: integer (default=10)` ➡ Number of sampled walks per node. Note that increasing the number of walks per node will make the execution run for more time.
 - `walk_stop_epsilon: double (default=0.1)` ➡ The probability of stopping when deriving
   the random walk. On average, it will create walks of length `1 /
   walk_stop_epsilon`.
@@ -93,7 +106,7 @@ The procedure calculates PageRank for the nodes in the graph.
 Use the following query to calculate PageRank: 
 
 ```cypher
-CALL pagerank_online.set(100, 0.2)
+CALL pagerank_online.set()
 YIELD node, rank;
 ```
 

pages/advanced-algorithms/available-algorithms/pagerank_online.mdx

@@ -0,0 +1,41 @@
+---
+title: Memgraph's AI Ecosystem
+description: Explore key features, such as community detection, node embeddings, and graph neural networks, alongside integrations with popular AI libraries like LangChain and LlamaIndex, to create powerful, data-driven GenAI solutions.
+---
+
+import { Card, Cards } from 'nextra/components'
+import { Callout } from 'nextra/components'
+
+# Memgraph's AI Ecosystem
+
+To learn about Memgraph's key features to build AI apps, explore the following
+pages:
+
+- [GraphRAG](/ai-ecosystem/graph-rag)
+- [Machine learning](/ai-ecosystem/machine-learning)
+
+
+AI spans multiple areas like machine learning (ML), natural language processing
+(NLP), and knowledge representation and reasoning (KRR), often overlapping to
+create advanced systems. A key example is **Generative AI (GenAI)**, which generates
+new content like text or images. Large Language Models (LLMs) power many GenAI
+apps, but getting them to work with your custom data can be challenging.
+
+Fine-tuning LLMs to incorporate custom data is often complex, slow, and costly.
+Plus, frequent updates make it inefficient. 
+
+**Retrieval-Augmented Generation (RAG)** solves this by enhancing LLMs with
+external data sources, enabling dynamic, scalable knowledge updates. Traditional
+RAG is based on vector structure with vector databases, and it has proven to be
+a great solution in many use cases. Still, it often falls short when retrieving
+crucial knowledge from complex datasets. That is where GraphRAG excels.
+
+**GraphRAG** improves on this by using knowledge graphs and graph features (e.g.,
+community detection, neighborhood analysis) for more accurate retrieval and
+data-rich insights. This hybrid approach provides better context and performance
+for GenAI applications. 
+
+Memgraph has been a popular choice in AI, especially for cases that utilize
+[machine learning](/ai-ecosystem/machine-learning). It also proves to be a great
+choice to build a [GraphRAG](/ai-ecosystem/graph-rag). 
+

pages/ai-ecosystem.mdx

@@ -0,0 +1,4 @@
+{
+    "graph-rag": "GraphRAG",
+    "machine-learning": "Machine learning"
+}