Neo4j Vector Index Skill

# neo4j-vector-index-skill

Skill for creating and querying vector indexes in Neo4j for semantic or structural similarity search.

**Covers:**
- Creating vector indexes: `CREATE VECTOR INDEX` with dimensions and similarity function
- Waiting for index `ONLINE` status; `SHOW VECTOR INDEXES`
- Embedding ingestion: Python batch loop with `UNWIND`, `db.create.setNodeVectorProperty`
- In-Cypher embedding with `ai.text.embed()` [2025.12] — replaces deprecated `genai.vector.encode()`
- Batch embedding procedure `ai.text.embedBatch()` for large datasets
- Vector search: `SEARCH` clause [2026.01+] and `db.index.vector.queryNodes()` procedure fallback
- Combining vector search with graph traversal (hybrid retrieval)
- Hybrid search, including semantic + lexical + structural sources
- Vector indexes over embeddings already written by GDS algorithms
- Chunking strategy before ingestion (fixed-size, sentence, semantic)
- Similarity function guidance: cosine vs euclidean — match your model's training loss
- Common errors: wrong dimensions, index not ONLINE, provider null returns

**Version / compatibility:**
- `SEARCH` clause requires Neo4j 2026.01+; `db.index.vector.queryNodes` available 5.x+
- `ai.text.embed()` requires Neo4j 2025.12+ and CYPHER 25; `genai.vector.encode()` is deprecated
- Vector type is native in CYPHER 25; stored as `LIST<FLOAT>` in older versions

**Not covered:**
- Full `ai.text.*` plugin reference (completion, chat, structured output) → `neo4j-genai-plugin-skill`
- GraphRAG pipelines with `neo4j-graphrag` → `neo4j-graphrag-skill`
- Fulltext-only / keyword-only search → `neo4j-cypher-skill`
- Computing GDS node embedding algorithms (FastRP, GraphSAGE) → `neo4j-gds-skill`

**Install:**
```bash
npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-vector-index-skill
```

Or paste this link into your coding assistant:
https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-vector-index-skill


# Hybrid Search

Hybrid search is useful when one retrieval signal is not enough:
- Semantic vector search finds paraphrases; misses exact names, acronyms, codes, and domain terms.
- Lexical fulltext search finds exact words; misses related concepts that do not share words.
- Structural search uses graph topology, paths, communities, or GDS node embeddings; captures relationships text does not contain.

Combining ranked sources improves recall and can boost results that are supported by more than one signal. The common pattern is vector + fulltext, but the same query shape works for any two or more ranked/scored sources: several vector indexes, title/body fulltext indexes, GDS-written structural embeddings, graph-derived candidate scores, or external retrieval scores.

Use when the user asks for custom Cypher hybrid search, WRRF/RRF, vector + fulltext, semantic + lexical + structural search, multiple vector indexes, or combining two+ ranked/scored retrieval sources.

## When NOT to Use
- `neo4j-graphrag` package `HybridRetriever` / `HybridCypherRetriever` -> use `neo4j-graphrag-skill`
- Fulltext-only / keyword-only search -> use `neo4j-cypher-skill`
- Single vector search -> use main `neo4j-vector-index-skill`

## Rules
- Run each source independently.
- Rank each source by `score DESC, stable_id ASC`.
- Do not compare raw scores from different sources.
- Compute `contribution = sourceWeight / (rrfConstant + sourceRank)`.
- Sum contributions per node.
- Order final rows by `wrrf DESC, stable_id ASC`.
- Use `sourceK > finalK`; combine before final limiting.
- Use stable unique property for tie breaks. If no stable key exists, add one before production use.
- Keep `LIMIT $sourceK` inside `SEARCH`; Cypher rejects a `LET` alias there.
- For structural vector sources, compute/write GDS embeddings first, then create a vector index over that property.

## Index Setup

Vector index:
```cypher
CYPHER 25
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS {
  indexConfig: {
    `