v1.6.4

[1.6.4] - 2024-08-01

Release of pyTigerGraph version 1.6.4

Fixed:

• Fixed a bug in deleteToken() that prevented deletion of tokens on SSL-enabled connections on databases greater than version 3.5.

v1.6.3

[1.6.3] - 2024-07-25

Release of pyTigerGraph version 1.6.3

Fixed:

• Fixed a bug in refreshToken() that resulted in generating a new token for database versions > 3.5.

v1.6.2

[1.6.2] - 2024-06--6

Release of pyTigerGraph version 1.6.2

Fixed:

• Fixed a featurizer error when failing to access the algorithm GitHub.
• Fixed error parsing logic when running loading jobs through gsql()
• Fixed an error with the DELETE REST operations (Issue #223)
• Fixed an error with getQueryDescription()

v1.6

[1.6] - 2024-04-30

Release of pyTigerGraph version 1.6.

Added:

• Added a new submodule for interacting with TigerGraph CoPilot, a framework for integrating Generative AI with TigerGraph.

v1.5.2

[1.5.2] - 2024-02-15

Release of pyTigerGraph version 1.5.2.

Added:

• Initial support for InquiryAI component of TigerGraph CoPilot.

Fixed:

• Error when getting a token with a secret in TigerGraph versions greater than 3.5

v1.5.1

[1.5.1] - 2023-12-12

Release of pyTigerGraph version 1.5.1.

Added:

• Support to use the connection's username and password in getToken()

Fixed:

• Errors when upserting MAP attributes
• Object-oriented schema error when a vertex's primary ID is not an attribute
• Object-oriented schema error when adding an undirected edge

v1.5

[1.5] - 2023-09-25

Release of pyTigerGraph version 1.5.

Added:

• Object-oriented schema definition and modifcation. Define graph schemas in native Python, without knowing GSQL.
• gsql() handles some common error cases and raises an exception if they occur.

Changed:

• Dataloaders that experience a parsing error due to missing/dirty data handle the error more gracefully.
• Removed the use of pyTigerDriver for GSQL operations.
• Various bug fixes.

v1.4.2

[1.4.2] - 2023-09-01

Release of pyTigerGraph version 1.4.2.

Fixed:

• Fixed behavior of not being able to use upsertVertexDataframe() when MAP types were in a column.

v1.4.1

[1.4.1] - 2023-06-05

Release of pyTigerGraph version 1.4.1.

Fixed:

• Consistent batch sizes in the EdgeLoader and EdgeNeighborLoader
• Handle empty MAP attributes in dataloaders correctly
• Type error in customizeHeader() when passing integer parameters
• Built-in trainer eval metrics collection

v1.4.0

[1.4] - 2023-05-16

Release of pyTigerGraph version 1.4.

Note: if you are using the Graph Data Science dataloaders, continue to use the latest 1.3.x version until you have upgraded your ML Workbench installation.
There is a incompatibility between v1.3 and v1.4 of pyTigerGraph and the corresponding ML Workbench versions.

Added:

• Additional Query Management Support
  • showQuery() returns the GSQL of a given query.
  • getQueryMetadata() returns the metadata details about a query, such as input parameters and what is returned in PRINT statements.
  • getRunningQueries() shows the statistics of queries currently running on the graph.
  • abortQuery() aborts a selected query by ID or all queries on the graph.
• Additional System Management Support
  • ping() is a public API to check the health of the TigerGraph server.
  • getSystemMetrics() monitors system metrics such as CPU and RAM usage.
  • getQueryPerformance() returns real-time query performance statistics over a given period.
  • getServiceStatus() returns the status of TigerGraph services specified in the request.
  • rebuildGraph() rebuilds the graph immediately.
• Built in Graph ML models and Trainer
  • Various GraphSAGE models for vertex classification and regression, as well as link prediction
  • NodePiece MLP model for vertex classification.
  • General purpose trainer to enable training of Graph ML models in a concise fashion.
• Transforms
  • PyGTemporalTransform to create a sequence of subgraphs for a given batch of data, in a temporal manner.
  • NodePieceMLPTransform to transform a batch produced by a NodePiece dataloader into a batch that can be fed into a PyTorch multilayer perceptron.
• Additional Dataloader Support
  • SSL Support: two-way SSL encryption via Kerberos
  • Collaborative dataloaders: use dataloaders on multiple machines to pull batches from the same Kafka queue. Helpful for data distributed model training.
  • Datetime support in dataloaders: Output DATETIME attributes from the database using the dataloaders. Exports as UNIX epoch timestamps.
  • Optional distributed_query parameter in dataloaders if running on distributed database clusters. If set to True, installs the dataloader using the DISTRIBUTED keyword in the query heading. Useful for distributed database clusters.
  • stop() function in dataloaders: Kill the query producing batches for the dataloader immediately. Helpful for stopping the production of batches sent to Kafka after breaking out of a training loop.

Changed:

• Dataloader factory produces multiple dataloaders if filter_by is a list of different filters.
• Improved the scalability of the NodePiece dataloader.