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Concepts API

The Concepts API allows you to manage and execute Vadalog concepts within projects. This is one of the core APIs for working with logical reasoning and data analysis.

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Save Concept

Save or update a concept with Vadalog logic and optional Python scripts. 
import prometheux_chain as px

concept_def = """
company("Apple", "Redwood City, CA").
company("Google", "Mountain View, CA").
company("Microsoft", "Redmond, WA").

location(Location) <- company(_,Location).

@output("location").
"""

result = px.save_concept(
    project_id="my_project_id",
    definition=concept_def
)
Function Signature
def save_concept(project_id, definition, python_scripts=None, scope="user",
                 description=None, concept_type="logic", concept_name=None,
                 binds=None, output_predicate="", existing_name=None,
                 position=None, group="group_id", compute=None)
Parameters
ParameterTypeRequiredDescription
project_idstrYesThe project identifier
definitionstrYesThe Vadalog rules, SQL query, or Python logic defining the concept
python_scriptsdictNoDictionary of Python scripts to inject
scopestrNoThe scope of the concept. Defaults to "user"
descriptionstrNoHuman-readable description of the concept
concept_typestrNo"logic" or "sql". Defaults to "logic"
concept_namestrConditionalRequired for SQL concepts

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Python Script Injection Requirements

When using Python script injection with save_concept, there are specific requirements:

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1. Main Function Requirement

Every Python script must contain a main() function: 
def main():
    # Your script logic here
    return results

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2. Tabular Output Format

The main() function must return results as a list of dictionaries: 
def main():
    results = [{"Column1": value1, "Column2": value2}]
    return results

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3. Concept Head Alignment

Output column names must match the parameters in the concept head: 
# Python script returns:
results = [{"Numbers": [1, 2, 3], "Total": 6, "Average": 2.0}]

# Vadalog concept head must match:
test_python(Numbers, Total, Average) <- python:run("python_script").

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4. Standalone Concept Rule

Python script concepts must be standalone: 
# Correct - standalone Python script concept
@output("my_python_concept").
my_python_concept(Col1, Col2) <- python:run("my_script").

# Incorrect - cannot combine with other logic
@output("mixed_concept").
mixed_concept(Col1, Col2) <- python:run("my_script"), other_predicate(Col1).

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Run Concept

Execute a concept with specified parameters. 
import prometheux_chain as px

results = px.run_concept(
    project_id="my_project_id",
    concept_name="location",
    params={"filter_city": "CA"},
    persist_outputs=True
)
Function Signature
def run_concept(project_id, concept_name, params=None, scope="user",
                force_rerun=True, persist_outputs=False, compute=None)
Parameters
ParameterTypeRequiredDescription
project_idstrYesThe project identifier
concept_namestrYesThe name of the concept to run
paramsdictNoParameters to pass to the concept execution
scopestrNoThe scope of the project. Defaults to "user"
force_rerunboolNoForce rerun even if results exist. Defaults to True
persist_outputsboolNoPersist the outputs. Defaults to False

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Fetch Results

Paginated fetch of stored results for an output predicate. 
import prometheux_chain as px

rows = px.fetch_results(
    project_id="my_project_id",
    output_predicate="location",
    page=1,
    page_size=10,
    scope="user",
)

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List Concepts

List all concepts in a project. 
import prometheux_chain as px

concepts = px.list_concepts(project_id="my_project_id")
print(f"Available concepts: {concepts}")

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Cleanup Concepts

Delete concepts from a project. 
import prometheux_chain as px

# Clean up all concepts in a project
result = px.cleanup_concepts(project_id="my_project_id")

# Clean up specific concepts by name
result = px.cleanup_concepts(
    project_id="my_project_id",
    concept_names=["ordered_bom", "product_analysis"],
)