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neo4j-vector-index-skill

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, semantic or structural nearest-neighbor lookup, or hybrid search (vector + fulltext, multiple vector sources, or graph-derived scores). Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext-only/keyword-only search — use neo4j-cypher-skill. Does NOT compute GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.

74

Quality

92%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Evaluation results

100%

24%

Building a Semantic Search Ingestion Pipeline for a Knowledge Base

Vector embedding ingestion pipeline

Criteria
Without context
With context

Index before ingest

100%

100%

CYPHER 25 prefix

0%

100%

IF NOT EXISTS

100%

100%

Correct dimensions

100%

100%

Cosine similarity

100%

100%

ONLINE polling

100%

100%

Poll interval 5s

0%

100%

UNWIND batch pattern

83%

100%

Batch size 500

0%

100%

Dimension assertion

100%

100%

No literal API key

100%

100%

Same model ingest/query

100%

100%

100%

67%

Version-Aware Semantic Search with Property Filtering

Version-aware vector search with in-index filtering

Criteria
Without context
With context

Preflight version query

87%

100%

SEARCH clause for 2026.01+

0%

100%

queryNodes for 2025.x

100%

100%

CYPHER 25 prefix

0%

100%

WITH filterable properties

20%

100%

Literal index name

0%

100%

In-index AND predicates only

25%

100%

Post-filter over-fetch

30%

100%

IF NOT EXISTS

0%

100%

Similarity function explicit

100%

100%

SEARCH preferred label

0%

100%

Scalar types note

37%

100%

100%

51%

Hybrid Semantic + Lexical Search for a Product Catalog

Hybrid vector and fulltext search with WRRF

Criteria
Without context
With context

UNION ALL structure

0%

100%

Same columns per branch

0%

100%

Rank-based combination

83%

100%

sourceK > finalK

75%

100%

Sum contributions

37%

100%

Stable tie-break

0%

100%

Per-source ranking

62%

100%

CYPHER 25 prefix

0%

100%

Params include sourceK and finalK

66%

100%

sourceWeights in params

50%

100%

Extensibility noted

87%

100%

No raw score comparison

100%

100%

Repository
neo4j-contrib/neo4j-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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