Neo4j Knowledge Graph

Neo4j Knowledge Graph

Overview

Use this skill to turn tabular or semi-structured data into a Neo4j knowledge graph, then choose the right path: legacy py2neo compatibility, modern official-driver Cypher, or GraphRAG/vector search.

Workflow

1. Frame the graph task

   • Identify the source data: CSV, Excel, pandas DataFrame, database export, API data, or existing Neo4j graph.
   • Ask what the user wants to do: import, model, query, migrate, visualize, or add GraphRAG.
   • Confirm Neo4j target: local Neo4j, Aura, legacy Neo4j 3.x, Neo4j 5+, or unknown.

2. Profile data before modeling

   • For CSV/Excel files, run scripts/profile_table.py <path> when local files are available.
   • Inspect columns, sample values, blank counts, likely identifiers, and repeated values.
   • Do not infer graph labels from one row only.

3. Model the graph

   • Use nouns for labels: Invoice, Person, Company, Product, Location.
   • Use verbs or role phrases for relationships: ISSUED_BY, PAID_TO, HAS_PARTICIPANT.
   • Choose stable IDs before writing Cypher.
   • Put frequently queried identifiers under uniqueness constraints.
   • Keep relationship properties for roles, timestamps, source rows, and confidence.

4. Generate safe Cypher

   • Prefer parameterized MERGE + UNWIND for imports.
   • Create constraints before loading data.
   • Avoid destructive commands unless the user explicitly asks and the target is confirmed.
   • For modern projects, prefer the official neo4j Python driver.
   • Use py2neo only when maintaining legacy Neo4j 3.x / py2neo v3 code.

5. Add GraphRAG only when useful

   • Use vector search when users need semantic retrieval, fuzzy matching, natural-language search, or RAG.
   • Build a clear search_text from graph facts.
   • Create a Neo4j vector index on the embedding property.
   • Use deterministic embeddings only for demos/tests; use neo4j-graphrag or a real embedding provider for production.

6. Verify

   • Dry-run on a small sample before loading the full dataset.
   • Report node/relationship counts, constraints, and index status.
   • Show representative Cypher queries users can run in Neo4j Browser.

Resource Guide

• Read references/modeling.md for spreadsheet-to-graph modeling patterns.
• Read references/cypher-and-graphrag.md before writing import Cypher, vector index Cypher, or GraphRAG examples.
• Run scripts/profile_table.py --help for table profiling options.

Output Shape

For a substantial task, return:

• graph model summary
• column-to-label/relationship mapping
• constraints and indexes
• import or migration steps
• verification queries
• risks and assumptions

For code changes, include tests or a dry-run path that does not require a live Neo4j instance.

Source: MazzaWill/neo4j-python-pandas-py2neo-v3 — distributed by TomeVault.