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) |
CREATE VECTOR INDEX doc_embeddings WITH DIMENSION 768 METRIC COSINE
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.
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 |
LOAD VECTOR FROM "document_vectors.csv" IN doc_embeddings
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
VECTOR SEARCH IN doc_embeddings FOR 5 [0.12, 0.45, 0.78, 0.33] YIELD ids
RETURN ids
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:
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
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)
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
Delete a vector index
DELETE VECTOR INDEX doc_embeddings
Complete workflow
// 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
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")
