Vector Search

Search through high-dimensional embeddings and connect the results to your graph data

TuringDB has a built-in vector index that lets you run k-nearest-neighbor searches over embedding vectors. You bring your own embeddings, from any model or provider, and TuringDB handles the indexing, storage, and fast retrieval.

Each vector is associated with a numerical ID. That ID can be a node property, an edge property, or a foreign key referencing data in an external system. This keeps the index lightweight and flexible: the vector store doesn’t need to know what your data looks like.

Vector indexes live at the TuringDB root level, independent of graphs and versioning. A single vector index can serve searches across multiple graphs and commits.

Create a vector index

A vector index is defined by a name, a dimension, and a distance metric.

Syntax:

CREATE VECTOR INDEX <name> WITH DIMENSION <dim> METRIC <metric>

Parameter	Description
<name>	Identifier for the vector index
<dim>	Dimension of the embedding vectors (positive integer)
<metric>	Distance metric: EUCLID (Euclidean distance) or COSINE (cosine similarity)

Cypher

CREATE VECTOR INDEX doc_embeddings WITH DIMENSION 768 METRIC COSINE

Python SDK

client.query("CREATE VECTOR INDEX doc_embeddings WITH DIMENSION 768 METRIC COSINE")

Load embeddings

Once the index exists, load your pre-computed embeddings from a file. Each row in the file maps a numerical ID to a vector.

The file path is relative to your TuringDB data directory (~/.turing/data by default).

The TuringDB data directory defaults to ~/.turing/data. You can change it at startup with the -turing-dir flag.

Syntax:

LOAD VECTOR FROM "<filepath>" IN <index_name>

Parameter	Description
<filepath>	Path to the embeddings file, relative to the TuringDB data directory
<index_name>	Name of the target vector index

Cypher

LOAD VECTOR FROM "document_vectors.csv" IN doc_embeddings

Python SDK

client.query('LOAD VECTOR FROM "document_vectors.csv" IN doc_embeddings')

Search

VECTOR SEARCH finds the k nearest neighbors of a query vector and yields their IDs. It is a read statement, so you can chain it with MATCH to pull back the actual graph data.

Syntax:

VECTOR SEARCH IN <index_name> FOR <k> (<vector>) YIELD <variable>

Parameter	Description
<index_name>	Name of the vector index to search
<k>	Number of nearest neighbors to return (positive integer)
<vector>	Query vector as a list literal of float values
<variable>	Variable name to hold the result IDs

Standalone search

Cypher

VECTOR SEARCH IN doc_embeddings FOR 5 (0.12, 0.45, 0.78, 0.33) YIELD ids
RETURN ids

Python SDK

df = client.query("""
VECTOR SEARCH IN doc_embeddings FOR 5 (0.12, 0.45, 0.78, 0.33) YIELD ids
RETURN ids
""")
print(df)

Combining with MATCH

This is where it gets interesting. Chain VECTOR SEARCH with a MATCH clause to join the nearest-neighbor IDs back to your graph:

Cypher

VECTOR SEARCH IN doc_embeddings FOR 10 (0.12, 0.45, 0.78, 0.33) YIELD ids
MATCH (d:Document) WHERE d.id = ids
RETURN d.title, d.summary

Python SDK

df = client.query("""
VECTOR SEARCH IN doc_embeddings FOR 10 (0.12, 0.45, 0.78, 0.33) YIELD ids
MATCH (d:Document) WHERE d.id = ids
RETURN d.title, d.summary
""")
print(df)

The ids variable works exactly like a variable introduced by CALL ... YIELD, so any subsequent MATCH clause can reference it.

Manage indexes

List all vector indexes

SHOW VECTOR INDEXES

Delete a vector index

DELETE VECTOR INDEX doc_embeddings

This removes the index and frees the associated resources.

Complete workflow

Cypher

// 1. Create a vector index for product embeddings
CREATE VECTOR INDEX product_vectors WITH DIMENSION 384 METRIC COSINE

// 2. Load embeddings generated by your model
LOAD VECTOR FROM "product_embeddings.csv" IN product_vectors

// 3. Find the 10 most similar products to a query embedding
VECTOR SEARCH IN product_vectors FOR 10 (0.15, 0.82, 0.44, 0.91) YIELD ids
MATCH (p:Product) WHERE p.id = ids
RETURN p.name, p.price, p.category

// 4. Inspect existing indexes
SHOW VECTOR INDEXES

// 5. Clean up
DELETE VECTOR INDEX product_vectors

Python SDK

from turingdb import TuringDB

client = TuringDB(host="http://localhost:6666")

# 1. Create a vector index for product embeddings
client.query("CREATE VECTOR INDEX product_vectors WITH DIMENSION 384 METRIC COSINE")

# 2. Load embeddings generated by your model
client.query('LOAD VECTOR FROM "product_embeddings.csv" IN product_vectors')

# 3. Find the 10 most similar products to a query embedding
df = client.query("""
VECTOR SEARCH IN product_vectors FOR 10 (0.15, 0.82, 0.44, 0.91) YIELD ids
MATCH (p:Product) WHERE p.id = ids
RETURN p.name, p.price, p.category
""")
print(df)

# 4. Inspect existing indexes
client.query("SHOW VECTOR INDEXES")

# 5. Clean up
client.query("DELETE VECTOR INDEX product_vectors")