$ show memgraph/gqlalchemy
GQLAlchemy is a Python library and Object Graph Mapper (OGM) for the Memgraph graph database, with secondary support for Neo4j. It provides a fluent Cypher query builder, declarative node/relationship models (Pydantic v2-based), and full lifecycle management of graph database instances (Docker/binary), streams (Kafka, Pulsar), triggers, and high-availability routing. The library also includes a rich set of transformations for bidirectional conversion between Memgraph/Neo4j and popular graph machine learning frameworks (NetworkX, DGL, PyG, TF-GNN), as well as tabular data import (CSV, Parquet, ORC, Feather) from local filesystem, S3, and Azure Blob Storage. While primarily a software library for building graph applications, it has strong data research capabilities through its graph ML translators and ETL importers, making it a hybrid tool suited for both production graph apps and data science pipelines. The codebase is well-tested (unit and integration tests), has comprehensive CI/CD, and follows a modular architecture with abstract base classes for extensibility. However, some modules are large (e.g., monolithic query builders), and optional dependencies (PyTorch, DGL, TF-GNN, PyArrow) increase maintenance surface, with a medium level of technical drift due to some deprecated Pydantic v1 patterns and skipped tests.
The LLM identified 3 files worth highlighting for reuse:
Provides a set of reusable, self-contained utility functions and classes for converting Python values to Cypher-compatible strings. Includes `to_cypher_value`, `to_cypher_properties`, `to_cypher_labels`, and the `CypherVariable` class for generating unique Cypher variable names. These functions are used across the entire codebase but are completely isolated (no internal imports beyond standard library) and can be extracted to any Cypher-generating project.
Implements an extensible, file-system-agnostic data ingestion pipeline for tabular data (CSV, Parquet, ORC, Feather). Defines abstract `FileSystemHandler` and `DataLoader` classes with concrete implementations for local filesystem, S3, and Azure Blob. The `TableToGraphImporter` orchestrates reading batches via PyArrow and generating Cypher queries with configurable mappings (indices, one-to-many, many-to-many). This pattern is cleanly abstracted and can be reused for any tabular-to-graph ETL task with minimal adaptation.
Implements bidirectional conversion between Memgraph and NetworkX graphs, handling node and edge properties, labels, and custom settings (e.g., NaN handling, index creation). The `NxTranslator` class follows a consistent `Translator` base interface with `get_instance()` and `to_cypher_queries()` methods. It demonstrates a clean conversion pattern that isolates the mapping logic, supports configuration via `NetworkXCypherConfig`, and can be extracted for any NetworkX-to-Cypher conversion use case.