semantica/docs/reference/utils.md

title, description, icon

title	description	icon
Utils Module	Shared utilities for logging, validation, error handling, progress tracking, and common operations.	wrench

semantica.utils provides shared infrastructure used throughout Semantica:

• Structured logging: setup_logging(), get_logger(), log_execution_time decorator
• Validation helpers: validate_entity() and validate_config() return (bool, Optional[str]) without raising
• Progress tracking: ProgressTracker class and track_progress() iterable wrapper with ETA
• Typed exceptions: SemanticaError, ValidationError, ProcessingError, ConfigurationError, QualityError

Most users won't call utils directly: it's the shared foundation for all modules.

Exported Classes

Name	Type	Role
setup_logging	function	Configure the semantica root logger: accepts level, file, console, rotation kwargs
get_logger	function	Get a named logger instance (semantica.<name>)
log_execution_time	decorator	Wraps a function: logs name, execution time, and success/failure
log_performance	function	Log pre-collected performance metrics: log_performance(func_name, execution_time, **metrics)
validate_entity	function	Validate entity dict: returns (bool, Optional[str]); does not raise
validate_config	function	Validate config dict: returns (bool, Optional[str]); does not raise
ProgressTracker	class	Class-based progress tracker with ETA and step callbacks
track_progress	function	Wrap any iterable with a live progress bar
clean_text	function	Normalize whitespace and strip zero-width control characters
hash_data	function	Deterministic SHA-256 hash of a str, bytes, or dict
SemanticaError	exception	Base exception for all Semantica errors
ValidationError	exception	Raised when input fails validation; has .field, .value, .message
ProcessingError	exception	Raised during extraction, graph build, or pipeline step; has .stage
ConfigurationError	exception	Raised for configuration validation failures
QualityError	exception	Raised when data quality falls below threshold

What You Get

Structured logging with `@log_execution_time` decorator and quality metrics via environment variables. `validate_entity` and `validate_config` with a typed `ValidationError` carrying field and value context. `track_progress` wraps any iterable: auto-detects console vs Jupyter for the right renderer. `clean_text`, `hash_data`, `safe_filename`, and nested dict utilities used throughout the framework. `SemanticaError` → `ValidationError`, `ProcessingError`: typed exceptions for targeted recovery. `read_json_file` raises `FileNotFoundError` or `json.JSONDecodeError` on failure: no boilerplate try/except around JSON I/O.

Logging

```python from semantica.utils import setup_logging, get_logger

setup_logging(level="INFO")   # "DEBUG" | "INFO" | "WARNING" | "ERROR"
logger = get_logger(__name__)
```

```python from semantica.utils import log_execution_time

@log_execution_time
def expensive_step(data):
    ...
# Logs: "expensive_step completed in 2.34s"
```

```bash export SEMANTICA_LOG_LEVEL=DEBUG export SEMANTICA_LOG_FORMAT=json # "json" | "text" export SEMANTICA_DISABLE_PROGRESS=true ```

Validation

from semantica.utils import validate_entity, validate_config, ValidationError

# validate_entity returns (is_valid, error_message)
is_valid, error = validate_entity({"id": "1", "type": "PERSON", "text": "Alice"})
if not is_valid:
    raise ValidationError(error)

# validate_config returns (is_valid, error_message)
is_valid, error = validate_config(config, required_keys=["model", "provider"])
if not is_valid:
    print(f"Invalid config: {error}")

Function	Description	Returns
validate_entity(data)	Check entity dict has required fields (id, text, type) and correct types	Tuple[bool, Optional[str]]
validate_config(cfg, required_keys=None)	Check configuration dict; optionally enforce required keys	Tuple[bool, Optional[str]]

Progress Tracking

from semantica.utils import track_progress

# Wraps any iterable: auto-detects console vs Jupyter
for item in track_progress(items, desc="Processing documents"):
    process(item)

Supports:

• Console: tqdm progress bar with ETA
• Jupyter: notebook-compatible widget (auto-detected)
• File: write progress to a log file

Helper Functions

from semantica.utils import clean_text, hash_data, safe_filename

# Normalize whitespace and strip control characters
clean = clean_text("  Hello   World  ")     # -> "Hello World"

# Deterministic SHA-256 hash of a string, bytes, or dict
uid   = hash_data({"key": "value"})         # -> hex digest string

# Sanitize a string for use as a filename
fname = safe_filename("My File?.txt")       # -> "My_File.txt"

Nested Dict Utilities

Helper functions for deep configuration access: used extensively inside Config and ConfigManager:

from semantica.utils import get_nested_value, set_nested_value, merge_dicts

config = {
    "processing": {"batch_size": 32, "max_workers": 4},
    "llm":        {"provider": "groq", "model": "llama-3.3-70b-versatile"},
}

# Dot-notation read: returns default if key path is absent
batch = get_nested_value(config, "processing.batch_size", default=16)
# -> 32

# Dot-notation write
set_nested_value(config, "processing.batch_size", 64)

# Deep merge: nested keys are merged recursively (deep=True by default)
base      = {"a": {"x": 1, "y": 2}, "b": 3}
overrides = {"a": {"y": 99, "z": 4}, "c": 5}
merged    = merge_dicts(base, overrides)
# -> {"a": {"x": 1, "y": 99, "z": 4}, "b": 3, "c": 5}

Exception Hierarchy

from semantica.utils import SemanticaError, ValidationError, ProcessingError

try:
    run_pipeline(data)
except ValidationError as e:
    # Input data did not pass schema validation
    logger.error("Validation failed: %s", e.message)
except ProcessingError as e:
    # Failure during extraction or graph construction
    logger.error("Processing failed at stage %s: %s", e.stage, e)
except SemanticaError as e:
    # Catch-all for all Semantica framework errors
    logger.error("Framework error: %s", e)

Exception	When Raised	Key Attributes
SemanticaError	Base class: all framework errors inherit from this	.message, .context, .error_code
ValidationError	Input data failed schema or type validation	.field, .value, .constraint
ProcessingError	Failure during extraction, graph build, or pipeline step	.stage, .input_data, .output_data
ConfigurationError	Configuration key missing or wrong type	.config_key, .config_value, .expected_type
QualityError	Data quality score fell below threshold	.quality_score, .threshold, .metrics

File Utilities

from semantica.utils import read_json_file

# Read and parse a JSON file: raises FileNotFoundError or json.JSONDecodeError on failure
config = read_json_file("config.json")

Tips and Common Pitfalls

**Call `setup_logging(level="INFO")` once at application startup.** Without it, Semantica falls back to Python's root logger, which may be silent or misconfigured. Call it before importing other Semantica modules to capture initialization messages. **`@log_execution_time` is the performance decorator.** Apply it to any function to automatically log its name, execution time, and success/failure. `log_performance` is a lower-level function for logging metrics you've already collected: it is not a decorator. **`hash_data()` is deterministic across runs.** Given the same input dict (any JSON-serializable object), `hash_data()` always returns the same SHA-256 hex string: suitable as a cache key or idempotency token in pipeline steps. **Catch `SemanticaError` as the broadest exception net.** All framework errors inherit from `SemanticaError`, so `except SemanticaError` catches validation failures, processing errors, and everything in between. Use specific subclasses for targeted recovery logic. **`track_progress` auto-detects Jupyter.** In a terminal it renders a tqdm progress bar; in a Jupyter notebook it renders an interactive widget. You don't need to check the environment: the same call works in both. Framework orchestration that uses Utils internally. Uses ProgressTracker for per-step tracking.