Merge branch 'memgraph-2-21' into update-algorithm-pages

Author: katarinasupe

.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

.github/workflows/create_issue_on_release.yml

@@ -0,0 +1,31 @@
+name: Create Issue on Release PR Merge
+
+on:
+  pull_request:
+    types: [closed]
+    
+jobs:
+  create_issue:
+    if: github.event.pull_request.merged == true && contains(github.event.pull_request.base.ref, 'main') # Only when merged to main
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check for Specific Label
+        id: label_check
+        run: |
+          LABEL_FOUND=$(echo "${{ github.event.pull_request.labels[*].name }}" | grep -c "release-pr")
+          echo "label_found=$LABEL_FOUND" >> $GITHUB_ENV
+
+      - name: Create GitHub Issue
+        if: env.label_found == '1'
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const { data: issue } = await github.issues.create({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              title: `Update hardcoded Memgraph versions under Getting Started`,
+              body: `This issue was automatically created after PR #${{ github.event.pull_request.number }}. https://www.notion.so/memgraph/WIP-Documentation-branching-and-release-strategy-814417bb95ae45848071f535013d3d83?pvs=4#10b6b158b981801c9f0cc43f9f122462`
+              assignees: ["katarinasupe"]
+            });
+            console.log(`Created issue: ${issue.html_url}`);

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

@@ -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

@@ -101,6 +101,11 @@ CALL leiden_community_detection.get()
 YIELD node, community_id, communities;
 ```
 
+<Callout type="info">
+The algorithm throws an exception if no communities are detected. This can happen if in the first iteration 
+all nodes merge into a single community or if each node forms its own. If this occurs, try adjusting the algorithm's `gamma` parameter.
+</Callout>
+
 ### `get_subgraph()`
 
 Computes graph communities over a subgraph using the Leiden method.

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

@@ -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">
@@ -72,13 +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. 
 If subgraph is not specified, the algorithm is computed on the entire graph by default.
-
 - `walks_per_node: integer (default=10)` ➡ Number of sampled walks per node.
 - `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 /
@@ -95,7 +107,7 @@ If subgraph is not specified, the algorithm is computed on the entire graph by d
 Use the following query to calculate PageRank: 
 
 ```cypher
-CALL pagerank_online.set(100, 0.2)
+CALL pagerank_online.set()
 YIELD node, rank;
 ```
 
@@ -135,7 +147,6 @@ changes in the graph.
 
 - `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. 
 If subgraph is not specified, the algorithm is computed on the entire graph by default.
-
 - `created_vertices` ➡ Nodes that were created in the last transaction.
 - `created_edges` ➡ Relationships created in a period from the last transaction.
 - `deleted_vertices` ➡ Nodes deleted from the last transaction.

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

@@ -381,8 +381,8 @@ procedure uses bfs.
 
  | Name 	             | Type   | Default	| Description 	                                                |
  |-	                   |-	      |-	      |-	                                                            |
- | minLevel            | Int 	  | 0	      | The minimum number of hops in the traversal. Set to `0` if the start node should be included in the subgraph, or `1` otherwise. 	|
- | maxLevel            | Int   	| -1   	  | The maximum number of hops in the traversal. 	|
+ | minHops            | Int 	  | 0	      | The minimum number of hops in the traversal. Set to `0` if the start node should be included in the subgraph, or `1` otherwise. 	|
+ | maxHops            | Int   	| -1   	  | The maximum number of hops in the traversal. 	|
  | relationshipFilter  | List 	| [ ]    	| List of relationships which the subgraph formation will follow. Relationships
   can be filtered using the notation described below.	|
  | labelFilter         | List 	| [ ]	    | List of labels which will define filtering. Labels
@@ -489,8 +489,8 @@ MATCH (w:Wolf), (d:Dog), (c:Cat), (m:Mouse), (h:Human)
 CALL path.subgraph_all(d, {
       relationshipFilter: ["CATCHES>","<HATES"],
       labelFilter: [">Mouse", ">Human"],
-      minLevel: 0,
-      maxLevel: 4
+      minHops: 0,
+      maxHops: 4
 })
 YIELD nodes, rels
 RETURN nodes, rels;
@@ -534,8 +534,8 @@ MATCH (w:Wolf), (d:Dog), (c:Cat), (m:Mouse), (h:Human)
 CALL path.subgraph_all(d, {
       relationshipFilter: ["<"],
       labelFilter: ["-Human"],
-      minLevel: 0,
-      maxLevel: 4
+      minHops: 0,
+      maxHops: 4
 })
 YIELD nodes, rels
 RETURN nodes, rels;
@@ -583,8 +583,8 @@ relationship and label filters, and ensures each node is visited only once.
 
  | Name 	             | Type   | Default	| Description 	                                                |
  |-	                   |-	      |-	      |-	                                                            |
- | minLevel            | Int 	  | 0	      | The minimum number of hops in the traversal. Set to `0` if the start node should be included in the subgraph, or `1` otherwise. 	|
- | maxLevel            | Int   	| -1   	  | The maximum number of hops in the traversal. 	|
+ | minHops            | Int 	  | 0	      | The minimum number of hops in the traversal. Set to `0` if the start node should be included in the subgraph, or `1` otherwise. 	|
+ | maxHops            | Int   	| -1   	  | The maximum number of hops in the traversal. 	|
  | relationshipFilter  | List 	| [ ]    	| List of relationships which the subgraph formation will follow. Explained in detail below.	|
  | labelFilter         | List 	| [ ]	    | List of labels which will define filtering. Explained in detail below. |
  | filterStartNode     | Bool 	| False	  | Whether the labelFilter applies to the start nodes. 	|
@@ -683,8 +683,8 @@ MATCH (w:Wolf), (d:Dog), (c:Cat), (m:Mouse), (h:Human)
 CALL path.subgraph_nodes(d, {
       relationshipFilter: ["CATCHES>","<HATES"],
       labelFilter: [">Mouse", ">Human"],
-      minLevel: 0,
-      maxLevel: 4
+      minHops: 0,
+      maxHops: 4
 })
 YIELD nodes
 RETURN nodes;
@@ -729,8 +729,8 @@ MATCH (w:Wolf), (d:Dog), (c:Cat), (m:Mouse), (h:Human)
 CALL path.subgraph_nodes(d, {
       relationshipFilter: ["<"],
       labelFilter: ["-Human"],
-      minLevel: 0,
-      maxLevel: 4
+      minHops: 0,
+      maxHops: 4
 })
 YIELD nodes
 RETURN nodes
@@ -763,4 +763,4 @@ that depend on the internal database `id` values.
  |     "type": "node"         |
  | }                          |
  +----------------------------+
- ```
+ ```

pages/advanced-algorithms/available-algorithms/path.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). 
+