neo4j-vector-index-skill

## When to Use
- Creating a vector index (`CREATE VECTOR INDEX`) on nodes or relationships
- Running vector similarity / nearest-neighbor search
- Storing embeddings on graph nodes during ingestion
- Indexing/querying embeddings already written by GDS algorithms
- Choosing similarity function, dimensions, HNSW params, or quantization
- Using `SEARCH` clause (2026.01+) or `db.index.vector.queryNodes()` (2025.x)
- Batch-updating embeddings after model change
- Combining vector results with immediate graph neighborhood (full retrieval_query pipelines → `neo4j-graphrag-skill`)
- Hybrid search that combines vector results with fulltext or other ranked sources

## When NOT to Use
- **GraphRAG pipelines** (VectorCypherRetriever, HybridCypherRetriever, retrieval_query) → `neo4j-graphrag-skill`
- **Fulltext-only / keyword-only search** (FULLTEXT INDEX, `db.index.fulltext.queryNodes`) → `neo4j-cypher-skill`
- **Computing GDS graph embeddings** (FastRP, Node2Vec, GraphSAGE) → `neo4j-gds-skill`
- **Index admin** (list all indexes, drop range/text/lookup indexes) → `neo4j-cypher-skill`

---

## Pre-flight — Determine Version

Drives syntax choice:
```cypher
CALL dbms.components() YIELD versions RETURN versions[0] AS neo4j_version
```

| Version | Use |
|---|---|
| `2026.01` or higher | `SEARCH` clause (in-index filtering, preferred) |
| `2025.x` | `db.index.vector.queryNodes()` procedure (**deprecated 2026.04** — use `SEARCH` when on 2026.x) |

---

## Step 1 — Create Vector Index

Node index (single label):
```cypher
CYPHER 25
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS {
  indexConfig: {
    `vector.dimensions`: 1536,
    `vector.similarity_function`: 'cosine',
    `vector.quantization.enabled`: true,
    `vector.hnsw.m`: 16,
    `vector.hnsw.ef_construction`: 100
  }
}
```

Node index **with filterable properties** [2026.01+] — `WITH` declares which properties can be used in `SEARCH ... WHERE`:
```cypher
CYPHER 25
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
WITH [c.source, c.lang, c.published_year]  // stored as metadata; filterable in SEARCH WHERE
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' } }
```

Multi-label index with filterable properties [2026.01+]:
```cypher
CYPHER 25
CREATE VECTOR INDEX doc_embedding IF NOT EXISTS
FOR (n:Document|Article) ON n.embedding
WITH [n.author, n.published_year, n.lang]
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' } }
```

Relationship index:
```cypher
CYPHER 25
CREATE VECTOR INDEX rel_embedding IF NOT EXISTS
FOR ()-[r:HAS_CHUNK]-() ON (r.embedding)
OPTIONS { indexConfig: { `vector.dimensions`: 768, `vector.similarity_function`: 'cosine' } }
```

**`WITH` property types** — only scalar types allowed: `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME`, `DURATION`. Not allowed: `LIST`, `POINT`, or the vector property itself.

**Index config reference:**

| Parameter | Type | Default | Notes |
|---|---|---|---|
| `vector.dimensions` | INTEGER 1–4096 | none | Required; must match embedding model exactly |
| `vector.similarity_function` | STRING | `'cosine'` | `'cosine'` or `'euclidean'` |
| `vector.quantization.enabled` | BOOLEAN | `true` | Reduces storage; slight accuracy tradeoff; needs vector-2.0+ (5.18+) |
| `vector.hnsw.m` | INTEGER 1–512 | `16` | HNSW graph connections; higher = better recall, more memory |
| `vector.hnsw.ef_construction` | INTEGER 1–3200 | `100` | Build-time candidates; higher = better recall, slower build |

**Similarity function choice:**

| Use case | Function |
|---|---|
| Normalized embeddings (OpenAI, Cohere, Voyage, Google) | `'cosine'` |
| Unnormalized / raw distance matters | `'euclidean'` |

---

## Step 2 — Wait for Index ONLINE

Index builds asynchronously — do NOT query until ONLINE:
```cypher
SHOW VECTOR INDEXES YIELD name, state, populationPercent
WHERE name = 'chunk_embedding'
RETURN name, state, populationPercent
```

Poll every 5s until `state = 'ONLINE'` and `populationPercent = 100.0`. If `state = 'FAILED'` → stop, check logs.

Shell poll (cypher-shell):
```bash
until cypher-shell -u neo4j -p "$NEO4J_PASSWORD" \
  "SHOW VECTOR INDEXES YIELD name, state WHERE name='chunk_embedding' RETURN state" \
  | grep -q ONLINE; do
  sleep 5
done
```

---

## Step 3 — Ingest Embeddings

Batch UNWIND pattern (use for > 100 nodes — never one-node-per-transaction):
```python
from neo4j import GraphDatabase

driver = GraphDatabase.driver(uri, auth=(user, password))

def embed_batch(texts: list[str]) -> list[list[float]]:
    response = openai_client.embeddings.create(
        model="text-embedding-3-small", input=texts
    )
    return [r.embedding for r in response.data]

def store_embeddings(records: list[dict], batch_size: int = 500):
    expected_dim = 1536  # must match vector.dimensions
    texts = [r["text"] for r in records]
    embeddings = embed_batch(texts)
    for emb in embeddings:
        assert len(emb) == expected_dim, f"Dim mismatch: {len(emb)} != {expected_dim}"
    rows = [{"id": r["id"], "embedding": emb}
            for r, emb in zip(records, embeddings)]
    for i in range(0, len(rows), batch_size):
        driver.execute_query(
            "UNWIND $rows AS row MATCH (c:Chunk {id: row.id}) SET c.embedding = row.embedding",
            rows=rows[i:i+batch_size]
        )
```

❌ Never create index after embeddings are already stored — always create index first.
✅ Create index → poll ONLINE → ingest embeddings.

---

## Step 4 — Run Vector Search

### SEARCH clause (2026.01+, preferred)

```cypher
CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    LIMIT 10
  ) SCORE AS score
RETURN c.text, score
ORDER BY score DESC
```

With in-index filter [2026.01+] — properties must be declared in `WITH` at index creation:
```cypher
// Index must have b