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:

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 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 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 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 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:

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):

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):

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 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:

// Index must have been created with: WITH [c.source, c.lang, c.published_year]
CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    WHERE c.source = $source AND c.lang = 'en' AND c.published_year >= 2024
    LIMIT 10
  ) SCORE AS score
RETURN c.text, c.source, score
ORDER BY score DESC

Filtering strategy — choose one:

| Strategy | When to use | Tradeoff | |---|---|---| | In-index WHERE [2026.01+] | Filters on pre-declared WITH properties; known at index design time | Fast, consistent latency; properties must be declared upfront | | Post-filter (MATCH + procedure) | Arbitrary Cypher predicates, graph traversal, OR/NOT | Full flexibility; may over-fetch then discard | | Pre-filter (MATCH first, then SEARCH) | Small known candidate set; exact nearest-neighbor within subset | Deterministic; slow on large candidate sets |

In-index WHERE hard limits [2026.01+]:

• Property must be listed in WITH [...] at index creation — undeclared properties silently fall back to post-filtering
• AND predicates only — no OR, NOT, list ops, string ops
• Scalar types only: INTEGER, FLOAT, STRING, BOOLEAN, temporal types — not VECTOR/LIST/POINT

Post-filter pattern (2025.x or arbitrary predicates)

CYPHER 25
CALL db.index.vector.queryNodes('chunk_embedding', 50, $queryEmbedding)
YIELD node AS c, score
WHERE c.source = $source    // post-filter: fetch more, then filter
RETURN c.text, score
ORDER BY score DESC LIMIT 10

Relationship index procedure:

CYPHER 25
CALL db.index.vector.queryRelationships('rel_embedding', 5, $queryEmbedding)
YIELD relationship AS r, score
RETURN r.text, score

SEARCH clause hard limits (all versions):

• Index name cannot be a parameter ($indexName not allowed — use literal string)
• Binding variable must come from the enclosing MATCH pattern
• Query vector cannot reference the binding variable

---

Step 5 — Combine with Graph Traversal (simple cases)

Vector search as entry point, then graph hop:

CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    LIMIT 10
  ) SCORE AS score
MATCH (c)(org:Organization)
RETURN c.text, a.title, score, collect(DISTINCT org.name) AS organizations
ORDER BY score DESC

For full retrieval_query pipelines, HybridCypherRetriever, or neo4j-graphrag library → delegate to neo4j-graphrag-skill.

---

Step 6 — Hybrid Search

Use hybrid search when one signal misses useful candidates: semantic vectors miss exact terms, lexical fulltext misses paraphrases, structural graph signals find topology not present in text. The common pattern is vector + fulltext, but the same approach works for several vector indexes, GDS-written embeddings, graph traversal scores, or any two+ ranked/scored sources. Load [references/hybrid-search.md](references/hybrid-search.md) and apply its query shape.

Rules:

• Run each source independently; rank each by score DESC, stable_id ASC.
• Combine by rank, not raw scores; fulltext and vector scores are not comparable.
• Every UNION ALL branch returns same columns: matched node + contribution.
• Use sourceK > finalK; combine before final limiting.
• Sum contributions per node; order final rows by wrrf DESC, stable_id ASC.
• Add more sources with extra UNION ALL branches and new sourceWeights keys.

---

Embedding Provider Quick-Reference

| Provider / Model | Dimensions | Similarity | Notes | |---|---|---|---| | OpenAI text-embedding-3-small | 1536 | cosine | Default; reducible to 256–1536 via dimensions= param | | OpenAI text-embedding-3-large | 3072 | cosine | Reducible to 256–3072 | | OpenAI text-embedding-ada-002 | 1536 | cosine | Legacy; prefer 3-small | | Cohere embed-v3 (English) | 1024 | cosine | Use input_type='search_document' at ingest, 'search_query' at query | | Voyage voyage-3-large | 1024 | cosine | High quality; needs voyage-ai package | | Google text-embedding-004 | 768 | cosine | Via Vertex AI | | Ollama nomic-embed-text | 768 | cosine | Local dev/testing | | Ollama mxbai-embed-large | 1024 | cosine | Local; production-quality |

vector.dimensions must exactly match model output — no auto-truncation.

---

Vector Functions

Ad-hoc similarity (not for kNN search — use index for that):

MATCH (a:Chunk {id: $id1}), (b:Chunk {id: $id2})
RETURN vector.similarity.cosine(a.embedding, b.embedding) AS sim
// vector.similarity.euclidean(a, b) — same signature, 0–1 range

// vector_distance (2025.10+) — metrics: EUCLIDEAN, EUCLIDEAN_SQUARED, MANHATTAN, COSINE, DOT, HAMMING
// Returns distance (lower = more similar, inverse of similarity)
RETURN vector_distance(a.embedding, b.embedding, 'COSINE') AS dist

// vector_dimension_count (2025.10+)
RETURN vector_dimension_count(n.embedding) AS dims

// vector_norm (2025.20+) — metrics: EUCLIDEAN, MANHATTAN
RETURN vector_norm(n.embedding, 'EUCLIDEAN') AS norm

Convert LIST to typed VECTOR:

// vector(value, dimension, coordinateType)
// coordinateType: FLOAT64, FLOAT32, INTEGER8/16/32/64
WITH vector([1.0, 2.0, 3.0], 3, 'FLOAT32') AS v
RETURN vector_dimension_count(v)

---

Index Management

// Show all vector indexes with config
SHOW VECTOR INDEXES YIELD name, state, populationPercent,
  labelsOrTypes, properties, indexConfig
RETURN name, state, populationPercent, labelsOrTypes, properties, indexConfig;

// Drop (node data unchanged — only index structure removed)
DROP INDEX chunk_embedding IF EXISTS;

// No ALTER VECTOR INDEX — to change dimensions or similarity function:
// 1. DROP INDEX old_index IF EXISTS
// 2. CREATE VECTOR INDEX new_index ... with new OPTIONS
// 3. Re-generate all embeddings with new model
// 4. Poll until ONLINE

---

Common Errors

| Error | Cause | Fix | |---|---|---| | IllegalArgumentException: Index dimension mismatch | Stored embedding dim ≠ vector.dimensions | Fix embed generation; drop + recreate index with correct dim | | Search returns incomplete results | Index still POPULATING | Poll until state = 'ONLINE' | | Unknown procedure db.index.vector.queryNodes | Neo4j = 2026.01 (preferred); procedure fallback only on 2025.x (deprecated 2026.04)

• [ ] SEARCH WHERE uses AND-only predicates with scalar types
• [ ] Batch UNWIND pattern used for > 100 nodes
• [ ] If model changes: drop index → recreate with new dimensions → re-generate all embeddings

---

In-Cypher Embedding Generation — ai.text.embed() [2025.12]

Generate embeddings at query time without external Python code. Use ai.text.embed() — the current API since [2025.12]:

// Syntax (requires CYPHER 25)
CYPHER 25
// ai.text.embed(resource :: STRING, provider :: STRING, configuration :: MAP) :: VECTOR

Provider strings are lowercase ('openai', 'vertexai', 'bedrock-titan', 'azure-openai'). Full provider config → neo4j-genai-plugin-skill.

Full query pattern — embed at query time, search immediately (procedure fallback for 2025.x):

CYPHER 25
WITH ai.text.embed(
    "What are good open source projects",
    "openai",
    { token: $openaiKey, model: 'text-embedding-3-small' }) AS userEmbedding
CALL db.index.vector.queryNodes('chunk_embedding', 6, userEmbedding)  // deprecated 2026.04
YIELD node AS c, score
RETURN c.text, score
ORDER BY score DESC

With SEARCH clause (2026.01+):

CYPHER 25
WITH ai.text.embed("my query", "openai", { token: $openaiKey, model: 'text-embedding-3-small' }) AS userEmbedding
MATCH (c:Chunk)
  SEARCH c IN (VECTOR INDEX chunk_embedding FOR userEmbedding LIMIT 6) SCORE AS score
RETURN c.text, score
ORDER BY score DESC

❌ Never pass API key as literal string in production — use $param or apoc.static.get(). ✅ Use $openaiKey parameter; inject via driver params dict.

Rule: Use same model at ingest time and query time — embeddings from different models are not comparable.

Deprecated (still works but do not use in new code):

• genai.vector.encode() [deprecated] → use ai.text.embed() [2025.12]
• genai.vector.encodeBatch() [deprecated] → use CALL ai.text.embedBatch() [2025.12]
• genai.vector.listEncodingProviders() [deprecated] → use CALL ai.text.embed.providers() [2025.12]

For full ai.text.* reference (completion, structured output, chat, tokenization) → neo4j-genai-plugin-skill.

---

Cypher-Based Embedding Ingestion — db.create.setNodeVectorProperty

Set vector property via Cypher (e.g. during LOAD CSV or MERGE pipeline):

LOAD CSV WITH HEADERS FROM 'https://example.com/data.csv' AS row
MERGE (q:Question {text: row.question})
WITH q, row
CALL db.create.setNodeVectorProperty(q, 'embedding', apoc.convert.fromJsonList(row.question_embedding))

Use when embedding is already in CSV/JSON form as a string — apoc.convert.fromJsonList() converts "[0.1,0.2,...]" to LIST. For Python-generated embeddings, use the Python UNWIND batch pattern (Step 3) instead.

---

Similarity Function — Extended Guidance

Existing table (Step 1) gives the basic rule. Additional guidance from course patterns:

Choose based on training loss function:

• Check embedding model docs — models trained with cosine loss → use 'cosine'
• Models trained with L2/Euclidean loss → use 'euclidean'
• When docs are silent: default to 'cosine' (all major hosted APIs use it)

Common pitfall — wrong similarity function:

❌ Created index with 'euclidean' but model outputs L2-normalized vectors
   → scores are mathematically correct but rankings differ from expected cosine order
   → no error thrown; wrong results silently returned
✅ Verify: run vector.similarity.cosine(a.embedding, b.embedding) manually on known
   similar pairs — score should be > 0.9 for near-duplicate text

Sanity check query after index creation:

MATCH (c:Chunk) WITH c LIMIT 2
WITH collect(c) AS nodes
RETURN vector.similarity.cosine(nodes[0].embedding, nodes[1].embedding) AS cosine_check,
       vector.si

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [neo4j-contrib](https://github.com/neo4j-contrib)
- **Source:** [neo4j-contrib/neo4j-skills](https://github.com/neo4j-contrib/neo4j-skills)
- **License:** MIT
- **Homepage:** https://neo4j.com/llms.txt

Install and usage instructions live in the source repository linked above.