AI Context Serializer #

The to_context function converts Kerykeion data models into precise, plain-text descriptions optimized for Large Language Models (LLMs).

Usage #

The primary function is to_context(). It takes a Kerykeion model and returns a formatted string.

from kerykeion import AstrologicalSubjectFactory, to_context

# 1. Create Model
subject = AstrologicalSubjectFactory.from_birth_data("Alice", 1990, 1, 1, 12, 0, "London", "GB")

# 2. Serialize for AI
ai_context = to_context(subject)
print(ai_context)

Output Preview:

Chart for Alice
Birth data: 1990-01-01 12:00, London, GB
...
Celestial Points:
  - Sun at 10.81° in Capricorn (Earth) in Tenth House, speed 1.02°/day...
  ...

Supported Models #

You can pass almost any Kerykeion object to to_context:

AstrologicalSubjectModel (Basic chart)
SingleChartDataModel (Chart with calculated aspects)
DualChartDataModel (Synastry/Transits with inter-aspects)
CompositeSubjectModel
PlanetaryReturnModel

AI Integration Example #

Inject the serialized context directly into your LLM system prompt:

prompt = f"""
You are an expert astrologer. Analyze the following chart data:

{to_context(subject)}

Focus on career potential.
"""

Helper Functions #

For finer control, use these individual converters:

Function	Input Model	Description
`kerykeion_point_to_context`	`KerykeionPointModel`	Describes a single planet/point.
`lunar_phase_to_context`	`LunarPhaseModel`	Describes the lunar phase.
`aspect_to_context`	`AspectModel`	Describes an aspect between two points.
`point_in_house_to_context`	`PointInHouseModel`	Describes a point in another’s house.
`astrological_subject_to_context`	`AstrologicalSubjectModel`	Full subject description.
`single_chart_data_to_context`	`SingleChartDataModel`	Natal chart with aspects.
`dual_chart_data_to_context`	`DualChartDataModel`	Synastry/Transit with inter-aspects.
`element_distribution_to_context`	`ElementDistributionModel`	Element breakdown.
`quality_distribution_to_context`	`QualityDistributionModel`	Quality breakdown.
`house_comparison_to_context`	`HouseComparisonModel`	House overlay analysis.
`transit_moment_to_context`	`TransitMomentModel`	Single transit moment snapshot.
`transits_time_range_to_context`	`TransitsTimeRangeModel`	Time-range transit data.

from kerykeion.context_serializer import kerykeion_point_to_context

sun_context = kerykeion_point_to_context(subject.sun)
print(sun_context)
# "Sun at 10.81° in Capricorn in First House, quality: Cardinal, element: Earth..."