feat(python-sdk): add in-memory config evaluation managers - #2

The config fixture attempts to load test_config.json. Due to the invalid JSON structure in test_config.json (missing brace for a feature variable, see comment in test_config.json), this line will raise a json.JSONDecodeError, causing all tests relying on this fixture to fail.

The test_config.json file contains invalid JSON. Specifically, there is a missing opening curly brace { for a dictionary within the features array, making the entry for the "link" variable malformed. This will cause parsing errors when the configuration is loaded.

          {
            "key": "link",
            "type": "string"
          },

The is_valid_config_data method incorrectly considers a configuration valid if it contains an "error" key with a truthy value. A configuration with an "error" key typically indicates an invalid state, not a valid one. The logic should likely check for the absence of an error.

            not data.get("error")
            and data.get("account_id")
            and (data.get("project") or {}).get("id")

In match_rules_by_field, the line segments = self.get_items_by_ids(experience.get("audiences") or [], "segments") appears to be a logical error. It attempts to retrieve segments using the audiences field of the experience object. Audiences and segments are distinct entities, and experience objects typically have an audiences field, but not a segments field that would contain segment IDs. This will likely result in incorrect segment matching.

The run_features method silently drops potential errors returned by _data_manager.get_bucketing. If get_bucketing returns a BucketingError or RuleError enum member (as it's designed to do), these errors are not propagated or handled, leading to features not being evaluated without any clear indication of why. This can make debugging difficult.

The config parameter is passed to the __init__ method but is immediately deleted using del config. This indicates that the parameter is not used within the method. It's better practice to prefix unused parameters with an underscore (e.g., _config) to clearly signal intent and avoid unnecessary operations like del.

In the cast_type function, when kind is "json", if json.loads(str(value)) fails due to an invalid JSON string, the function falls back to returning str(value). This can lead to ambiguous behavior where a caller might expect a JSON object (dict/list) or an error, but instead receives an unparsed string. It would be clearer to either raise an exception or return None to explicitly indicate a failure to cast to JSON.

The config parameter is passed to the __init__ method but is immediately deleted using del config. This indicates that the parameter is not used within the method. It's better practice to prefix unused parameters with an underscore (e.g., _config) to clearly signal intent and avoid unnecessary operations like del.

The methods _retrieve_bucketing and _get_bucketing_by_field (line 355) can return either a dictionary (representing a bucketing result) or a BucketingError/RuleError enum member. This inconsistency in return types can make error handling more complex for callers. Consider raising exceptions for error conditions or returning None consistently and letting the caller interpret the absence of a result as an error.

The config object is passed to the constructors of DataManager, SegmentsManager, ExperienceManager, and FeatureManager. However, these managers then immediately discard or ignore the config parameter (e.g., using del config or by only extracting sub-parts of it and not using the full object). This creates an inconsistent API where config is required in the signature but not fully utilized, which can be misleading. Consider refactoring the constructors to only accept the specific configuration parts they need, or to use a common base class that handles the config extraction if it's a shared pattern.