neo4j-aura-graph-analytics-skill

Name: neo4j-aura-graph-analytics-skill
Author: neo4j-contrib

311installs33forks

How to install neo4j-aura-graph-analytics-skill?

neo4j-contrib/neo4j-skills/neo4j-aura-graph-analytics-skill

$npx -y skills add neo4j-contrib/neo4j-skills --skill neo4j-aura-graph-analytics-skill

Installs into the current project.

Summary

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions,

Files · 1

View on GitHub

SKILL.md

1	## When to Use
2	- Running GDS algorithms in Aura Graph Analytics GDS Sessions
3	- Creating `GdsSessions` or using `AuraGraphDataScience`
4	- Remote projecting connected Neo4j data with `gds.graph.project.remote(...)`
5	- Using AuraDB Cypher API projection with `{ memory: ... }` or `{ sessionId: ... }`
6	- Processing graph data from non-Neo4j sources (Pandas, Spark, CSV)
7	- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
8	- Full isolation from the live database during analytics
9
10	## When NOT to Use
11	- Aura Pro with embedded GDS plugin → `neo4j-gds-skill`
12	- Self-managed Neo4j with embedded GDS plugin → `neo4j-gds-skill`
13	- Writing Cypher queries → `neo4j-cypher-skill`
14	- Snowflake Graph Analytics → `neo4j-snowflake-graph-analytics-skill`
15
16	---
17
18	## Deployment Decision Table
19
20	\| Deployment \| Use \|
21	\|---\|---\|
22	\| Aura Free \| ❌ AGA not available \|
23	\| Aura Pro \| `neo4j-gds-skill` (embedded plugin) \|
24	\| AuraDB + Python client sessions \| this skill \|
25	\| AuraDB + Cypher API \| this skill for AGA-specific projection/session notes; `neo4j-cypher-skill` for query authoring \|
26	\| Self-managed Neo4j + AGA session \| this skill \|
27	\| Self-managed Neo4j + embedded plugin \| `neo4j-gds-skill` \|
28	\| Non-Neo4j data (Pandas, Spark) \| this skill (standalone mode) \|
29
30	---
31
32	## Defaults
33
34	- `graphdatascience >= 1.15` required; `>= 1.18` for Spark
35	- Prefer v2 endpoints: `gds.v2.graph.project(...)`, `gds.v2.page_rank.`, `gds.v2.graph.node_properties.`
36	- Use snake_case parameters end-to-end; never mix v2 with camelCase params
37	- Use v1 if v2 endpoint missing/incompatible; label fallback
38	- Call `gds.v2.verify_session_connectivity()` after session creation
39	- Connected sessions: call `gds.v2.verify_db_connectivity()` when source DB access required
40	- Estimate memory before large sessions
41	- Set TTL; default 1h idle, max 7d
42	- Close session when done: `gds.delete()` or `sessions.delete(name)` stops billing
43	- Use `AuraAPICredentials.from_env()` — never hardcode credentials
44
45	---
46
47	## Installation
48
49	```bash
50	pip install "graphdatascience>=1.15"
51	```
52
53	---
54
55	## Key Patterns
56
57	### Step 1 — Authenticate
58
59	```python
60	import os
61	from graphdatascience.session import AuraAPICredentials, GdsSessions
62
63	sessions = GdsSessions(api_credentials=AuraAPICredentials.from_env())
64	# Reads: AURA_CLIENT_ID, AURA_CLIENT_SECRET, AURA_PROJECT_ID (optional)
65	# Create API credentials in Aura Console → Account → API credentials
66	```
67
68	If member of multiple projects: set `AURA_PROJECT_ID` or pass `project_id=`.
69
70	### Step 2 — Estimate Memory
71
72	```python
73	from graphdatascience.session import AlgorithmCategory, SessionMemory
74
75	memory = sessions.estimate(
76	node_count=1_000_000,
77	relationship_count=5_000_000,
78	algorithm_categories=[
79	AlgorithmCategory.CENTRALITY,
80	AlgorithmCategory.NODE_EMBEDDING,
81	AlgorithmCategory.COMMUNITY_DETECTION,
82	],
83	)
84	# Returns SessionMemory tier, e.g. SessionMemory.m_8GB
85	# Fixed tiers: m_2GB … m_256GB — see references/limitations.md
86	```
87
88	### Step 3 — Create Session
89
90	Mode A — AuraDB connected:
91	```python
92	from graphdatascience.session import DbmsConnectionInfo, SessionMemory, CloudLocation
93	from datetime import timedelta
94
95	db_connection = DbmsConnectionInfo(
96	username=os.environ["NEO4J_USERNAME"],
97	password=os.environ["NEO4J_PASSWORD"],
98	aura_instance_id=os.environ["AURA_INSTANCEID"], # from Aura Console URL
99	)
100
101	gds = sessions.get_or_create(
102	session_name="my-analysis",
103	memory=memory,
104	db_connection=db_connection,
105	ttl=timedelta(hours=2),
106	)
107	gds.v2.verify_session_connectivity()
108	gds.v2.verify_db_connectivity()
109	```
110
111	Mode B — Self-managed Neo4j:
112	```python
113	db_connection = DbmsConnectionInfo(
114	uri=os.environ["NEO4J_URI"], # e.g. "bolt://my-server:7687"
115	username=os.environ["NEO4J_USERNAME"],
116	password=os.environ["NEO4J_PASSWORD"],
117	)
118	gds = sessions.get_or_create(
119	session_name="my-analysis-sm",
120	memory=SessionMemory.m_8GB,
121	db_connection=db_connection,
122	ttl=timedelta(hours=2),
123	cloud_location=CloudLocation("gcp", "europe-west1"),
124	)
125	gds.v2.verify_session_connectivity()
126	gds.v2.verify_db_connectivity()
127	```
128
129	**Mode C — Standalone (no

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neo4j-aura-graph-analytics-skill

311installs33forks

View on GitHub

How to install neo4j-aura-graph-analytics-skill?

neo4j-contrib/neo4j-skills/neo4j-aura-graph-analytics-skill

$npx -y skills add neo4j-contrib/neo4j-skills --skill neo4j-aura-graph-analytics-skill

Installs into the current project.

Summary

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions,

Files · 1

View on GitHub

SKILL.md

1	## When to Use
2	- Running GDS algorithms in Aura Graph Analytics GDS Sessions
3	- Creating `GdsSessions` or using `AuraGraphDataScience`
4	- Remote projecting connected Neo4j data with `gds.graph.project.remote(...)`
5	- Using AuraDB Cypher API projection with `{ memory: ... }` or `{ sessionId: ... }`
6	- Processing graph data from non-Neo4j sources (Pandas, Spark, CSV)
7	- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
8	- Full isolation from the live database during analytics
9
10	## When NOT to Use
11	- Aura Pro with embedded GDS plugin → `neo4j-gds-skill`
12	- Self-managed Neo4j with embedded GDS plugin → `neo4j-gds-skill`
13	- Writing Cypher queries → `neo4j-cypher-skill`
14	- Snowflake Graph Analytics → `neo4j-snowflake-graph-analytics-skill`
15
16	---
17
18	## Deployment Decision Table
19
20	\| Deployment \| Use \|
21	\|---\|---\|
22	\| Aura Free \| ❌ AGA not available \|
23	\| Aura Pro \| `neo4j-gds-skill` (embedded plugin) \|
24	\| AuraDB + Python client sessions \| this skill \|
25	\| AuraDB + Cypher API \| this skill for AGA-specific projection/session notes; `neo4j-cypher-skill` for query authoring \|
26	\| Self-managed Neo4j + AGA session \| this skill \|
27	\| Self-managed Neo4j + embedded plugin \| `neo4j-gds-skill` \|
28	\| Non-Neo4j data (Pandas, Spark) \| this skill (standalone mode) \|
29
30	---
31
32	## Defaults
33
34	- `graphdatascience >= 1.15` required; `>= 1.18` for Spark
35	- Prefer v2 endpoints: `gds.v2.graph.project(...)`, `gds.v2.page_rank.`, `gds.v2.graph.node_properties.`
36	- Use snake_case parameters end-to-end; never mix v2 with camelCase params
37	- Use v1 if v2 endpoint missing/incompatible; label fallback
38	- Call `gds.v2.verify_session_connectivity()` after session creation
39	- Connected sessions: call `gds.v2.verify_db_connectivity()` when source DB access required
40	- Estimate memory before large sessions
41	- Set TTL; default 1h idle, max 7d
42	- Close session when done: `gds.delete()` or `sessions.delete(name)` stops billing
43	- Use `AuraAPICredentials.from_env()` — never hardcode credentials
44
45	---
46
47	## Installation
48
49	```bash
50	pip install "graphdatascience>=1.15"
51	```
52
53	---
54
55	## Key Patterns
56
57	### Step 1 — Authenticate
58
59	```python
60	import os
61	from graphdatascience.session import AuraAPICredentials, GdsSessions
62
63	sessions = GdsSessions(api_credentials=AuraAPICredentials.from_env())
64	# Reads: AURA_CLIENT_ID, AURA_CLIENT_SECRET, AURA_PROJECT_ID (optional)
65	# Create API credentials in Aura Console → Account → API credentials
66	```
67
68	If member of multiple projects: set `AURA_PROJECT_ID` or pass `project_id=`.
69
70	### Step 2 — Estimate Memory
71
72	```python
73	from graphdatascience.session import AlgorithmCategory, SessionMemory
74
75	memory = sessions.estimate(
76	node_count=1_000_000,
77	relationship_count=5_000_000,
78	algorithm_categories=[
79	AlgorithmCategory.CENTRALITY,
80	AlgorithmCategory.NODE_EMBEDDING,
81	AlgorithmCategory.COMMUNITY_DETECTION,
82	],
83	)
84	# Returns SessionMemory tier, e.g. SessionMemory.m_8GB
85	# Fixed tiers: m_2GB … m_256GB — see references/limitations.md
86	```
87
88	### Step 3 — Create Session
89
90	Mode A — AuraDB connected:
91	```python
92	from graphdatascience.session import DbmsConnectionInfo, SessionMemory, CloudLocation
93	from datetime import timedelta
94
95	db_connection = DbmsConnectionInfo(
96	username=os.environ["NEO4J_USERNAME"],
97	password=os.environ["NEO4J_PASSWORD"],
98	aura_instance_id=os.environ["AURA_INSTANCEID"], # from Aura Console URL
99	)
100
101	gds = sessions.get_or_create(
102	session_name="my-analysis",
103	memory=memory,
104	db_connection=db_connection,
105	ttl=timedelta(hours=2),
106	)
107	gds.v2.verify_session_connectivity()
108	gds.v2.verify_db_connectivity()
109	```
110
111	Mode B — Self-managed Neo4j:
112	```python
113	db_connection = DbmsConnectionInfo(
114	uri=os.environ["NEO4J_URI"], # e.g. "bolt://my-server:7687"
115	username=os.environ["NEO4J_USERNAME"],
116	password=os.environ["NEO4J_PASSWORD"],
117	)
118	gds = sessions.get_or_create(
119	session_name="my-analysis-sm",
120	memory=SessionMemory.m_8GB,
121	db_connection=db_connection,
122	ttl=timedelta(hours=2),
123	cloud_location=CloudLocation("gcp", "europe-west1"),
124	)
125	gds.v2.verify_session_connectivity()
126	gds.v2.verify_db_connectivity()
127	```
128
129	**Mode C — Standalone (no

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