nsarathy/Coffy

AI Summary: Contribute to the Coffy project by adding comprehensive tests for the newly implemented ORM in the `coffy.sql` module. This involves following the project's contribution guidelines, writing tests in the specified test file (`coffy/tests/test_sql1`), and ensuring no test duplication. The goal is to thoroughly test the ORM's functionality and identify any potential bugs.

AI Summary: Implement a new method, `count_relationships_by_type(type)`, in a Python graph database library (Coffy). This method should count the number of relationships of a specified type. The implementation should include comprehensive unit tests and updated documentation.

AI Summary: Implement a sorting function `.sort(field, reverse=False)` for the Coffy NoSQL query builder. This function should sort query results by a specified field in ascending or descending order, handle missing fields gracefully, manage mixed data types, and include comprehensive unit tests.

AI Summary: Implement a `.columns` property for the `SQLDict` class in the Coffy Python library. This property should return an ordered list of column names from the first row of a query result, or an empty list if the result is empty. Unit tests must be added to cover both scenarios (populated and empty results). The docstring for `SQLDict` should also be updated to reflect this new functionality.

AI Summary: Enhance the Coffy SQL module's `SQLDict.__repr__` method to include a footer displaying the number of rows and columns in the query result. This involves modifying the `SQLDict` class and adding corresponding snapshot tests to ensure correctness.

AI Summary: Develop a command-line interface (CLI) for Coffy, a lightweight embedded database engine in Python. This involves creating CLIs for Coffy's SQL, NoSQL, and Graph modules, building a solid and extensible foundation, and ensuring a user-friendly experience. Maintainer access is a possibility for significant contributions.

AI Summary: Update the Coffy GraphDB documentation to include examples of OR and NOT logic within the `find_nodes` method, ensuring consistency with existing test cases. This involves adding code snippets to `Documentation/GRAPH_DOCS.md` illustrating these logical operations and potentially adding minimal assertions to `tests/test_graph.py` for clarity.

AI Summary: Implement a new `distinct(field)` method in Coffy's NoSQL engine to retrieve a sorted list of unique values for a specified field from the database. This involves modifying the `coffy/nosql/engine.py` file to handle unique value extraction, sorting, and error conditions (missing fields, mixed data types). Comprehensive unit tests must also be added to `tests/test_nosql.py` to cover various scenarios, including duplicates, missing fields, mixed data types, and empty results.

AI Summary: Implement a new `remove_field` method in a Python NoSQL database engine. This method should remove a specified field (allowing dot notation for nested fields) from documents matching a given query. The implementation needs to handle cases where the field is missing and return the number of documents modified. Unit tests are required to verify functionality.

AI Summary: Implement a new method, `remove_relationships_by_type`, in a Python graph database library (Coffy) to efficiently remove all relationships of a specified type. This involves adding the method to `graphdb_nx.py`, iterating through edges to remove matching ones, returning the count of removed edges, and adding corresponding unit tests in `test_graph.py` to ensure correctness.

AI Summary: Implement a new `between(a, b)` method in Coffy's NoSQL query builder. This method should filter results based on a numerical field falling within a specified inclusive range (a to b), internally using `.gte(a).lte(b)`. The implementation must handle reversed input ranges and include comprehensive unit tests.

AI Summary: Create one or more markdown files (GRAPH_EXAMPLES.md, NOSQL_EXAMPLES.md, SQL_EXAMPLES.md) containing at least two advanced usage examples for each Coffy module (graph, NoSQL, SQL). Examples should demonstrate chained logic, merges/lookups, complex queries/transforms, and avoid duplicating existing basic examples.

AI Summary: Write comprehensive unit tests for the `coffy.sql` module, covering initialization (`init()`), query execution (`query()`), and data handling (`SQLDict`), including edge cases and error handling. Tests should cover both in-memory and file-backed database scenarios, various SQL statement types (DDL, DML, SELECT), and data export functionalities.

AI Summary: Add comprehensive unit tests to the Coffy Python library's `GraphDB` module for several currently untested methods, including `degree`, `neighbors`, `nodes`, `relationships`, `to_dict`, `load`, and `_match_conditions`. Tests should cover edge cases and unusual inputs, using the `unittest` framework and temporary files where appropriate. The goal is to improve the overall test coverage and robustness of the `GraphDB` functionality.

AI Summary: Write unit tests for under-tested methods in Coffy's NoSQL module, specifically `lookup()` and `merge()` used together, `DocList.to_json()`, `CollectionManager.import_()`, and `QueryBuilder.replace()`. Tests should cover edge cases and follow existing style guidelines. All tests must pass.

AI Summary: Implement three aggregation methods (`count_nodes`, `count_relationships`, `avg_degree`) in a Python graph database library, ensuring correct functionality for both directed and undirected graphs, adding unit tests, and updating documentation.

AI Summary: Implement pagination (limit and offset) for graph query results in the Coffy Python library. This involves adding optional `limit` and `offset` parameters to several graph query methods (`find_nodes`, `find_relationships`, etc.), updating documentation, and adding unit tests to ensure correct functionality.

AI Summary: Implement pagination (limit and offset) functionality for the NoSQL query builder in the Coffy Python library. This involves adding `.limit()` and `.offset()` methods to the query builder, modifying the `.run()` method to apply slicing based on these parameters, updating documentation, and adding comprehensive unit tests.