Advanced algorithmsAvailable algorithmsembeddings

embeddings

The embeddings module provides tools for calculating sentence embeddings on node strings using pytorch.

Source code
TraitValue
Module typealgorithm
ImplementationPython
Parallelismparallel

Procedures

compute()

The procedure computes the sentence embeddings on the string properties of nodes. Embeddings are created as a property of the nodes in the graph.

Input:

  • input_nodes: List[Vertex] (OPTIONAL) ➡ The list of nodes to compute the embeddings for. If not provided, the embeddings are computed for all nodes in the graph.
  • embedding_property: string ➡ The name of the property to store the embeddings in. This property is embedding by default.
  • excluded_properties: List[string] ➡ The list of properties to exclude from the embeddings computation. This list is empty by default.
  • model_name: string ➡ The name of the model to use for the embeddings computation, buy default this module uses the all-MiniLM-L6-v2 model provided by the sentence-transformers library.
  • batch_size: int ➡ The batch size to use for the embeddings computation. This is set to 2000 by default.
  • chunk_size: int ➡ The number of batches per “chunk”. This is used when computing embeddings across multiple GPUs, as this has to be done by spawning multiple processes. Each spawned process computes the embeddings for a single chunk. This is set to 48 by default.
  • device: string|int|List[string|int] ➡ The device to use for the embeddings computation. This can be any of the following:
    • "cpu" - Use CPU for computation.
    • "cuda" or "all" - Use all available CUDA devices for computation.
    • "cuda:id" - Use a specific CUDA device for computation.
    • id - Use a specific device for computation.
    • [id1, id2, ...] - Use a list of device ids for computation.
    • ["cuda:id1", "cuda:id2", ...] - Use a list of CUDA devices for computation. by default, the first device (0) is used.

Output:

  • success: bool ➡ Whether the embeddings computation was successful.

Usage:

To compute the embeddings across the entire graph with the default parameters, use the following query:

CALL embeddings.compute()
YIELD success;

To compute the embeddings for a specific list of nodes, use the following query:

MATCH (n)
WITH n ORDER BY id(n)
LIMIT 5
WITH collect(n) AS subset
CALL embeddings.compute(subset)
YIELD success;

To run the computation on specific device(s), use the following query:

CALL embeddings.compute(
    NULL,
    "embedding",
    NULL,
    "all-MiniLM-L6-v2",
    2000,
    48,
    "cuda:1"
)
YIELD success;

Example

Create the following graph:

CREATE (n:Node {id: 1, Title: "Stilton", Description: "A stinky cheese from the UK"}),
(n:Node {id: 2, Title: "Roquefort", Description: "A blue cheese from France"}),
(n:Node {id: 3, Title: "Cheddar", Description: "A yellow cheese from the UK"}),
(n:Node {id: 4, Title: "Gouda", Description: "A Dutch cheese"}),
(n:Node {id: 5, Title: "Parmesan", Description: "An Italian cheese"}),
(n:Node {id: 6, Title: "Red Leicester", Description: "The best cheese in the world"});

Run the following query to compute the embeddings:

CALL embeddings.compute()
YIELD success;
 
MATCH (n) 
WHERE n.embedding IS NOT NULL 
RETURN n.Title, n.embedding;

Results:

+---------+
| success |
+---------+
| true    |
+---------+
+----------------------------------------------------------------------+----------------------------------------------------------------------+
| n.Title                                                              | n.embedding                                                          |
+----------------------------------------------------------------------+----------------------------------------------------------------------+
| "Stilton"                                                            | [-0.0485366, -0.021823, 0.0159757, 0.0376443, 0.00594089, -0.0044... |
| "Roquefort"                                                          | [-0.0252884, 0.0250485, -0.0249728, 0.0571037, 0.0386177, 0.03863... |
| "Cheddar"                                                            | [-0.0129724, -0.00756301, -0.00379329, 0.0037531, -0.0134941, 0.0... |
| "Gouda"                                                              | [0.0128716, 0.025435, -0.0288951, 0.0177759, -0.0624398, 0.043577... |
| "Parmesan"                                                           | [-0.0755439, 0.00906182, -0.010977, 0.0208911, -0.0527448, 0.0085... |
| "Red Leicester"                                                      | [-0.0244318, -0.0280038, -0.0373183, 0.0284436, -0.0277753, 0.066... |
+----------------------------------------------------------------------+----------------------------------------------------------------------+
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