anukriti-pgx-core

Deterministic pharmacogenomics infrastructure

Authoritative, CPIC-pinned, no LLMs, no randomness. Same inputs → same outputs.

Platform and docs

This repo is the library at the core of the three-repo Anukriti platform (anukriti-pgx-core + anukriti + anukriti-swarm).

• 🧭 PLATFORM.md — canonical three-repo map. Start here if you're new to the platform.
• 📚 anukriti_docs — 14-module progressive learning course covering what the platform is, how it works, why deterministic, and the tech choices behind it.
• 🧾 PROJECT_CONTEXT.md — the "why" doc for this library: D1–D11 locked founder decisions, forward-work items, regression contract.
• 🏛️ docs/adr/ — platform-wide Architecture Decision Records with "Revisit when" triggers.

What this is

The deterministic core extracted from anukriti (PGx product) and anukriti-swarm (research platform), now shippable as a standalone library and (optionally) HTTP service.

Three stacked layers:

    VCF variants (rsid → ref,alt,gt)
            │
            ▼  Layer 1: calling/       ← VCFCaller.call(gene, variants)
    Star alleles (*1, *17, *2/*17 …)
            │
            ▼  Layer 2: phenotype/     ← PhenotypeEngine.infer(gene, a1, a2)  ← PHASE 1
    Phenotype (PM / IM / NM / RM / UM)
            │
            ▼  Layer 3: recommendations/  ← RecommendationLookup.lookup(...)
    CPIC recommendation

Each layer is independently usable. A consumer that already has star alleles (e.g. a swarm agent) uses Layer 2 directly. A consumer with VCF input (e.g. a clinical pipeline) uses Layer 1, which calls into Layer 2 internally.

Why this exists

Before the extraction, the same phenotype logic lived in two places:

• anukriti-swarm/rules/phenotype_rules.py — star-allele level, 3 genes
• anukriti/src/*_caller.py (16 files) — VCF level, 16 genes, inconsistent signatures

That meant two truth sources for the same CPIC tables, and no way for a third party to consume just the deterministic core without pulling in a FastAPI product or a multi-agent research framework.

This package is the deterministic core. It has:

• Zero runtime dependencies (Phase 1)
• No LLMs, no randomness, no I/O side effects
• CPIC table versions pinned by filename (e.g. CYP2C19_named_diplotypes_v2022.1.json)
• An explicit upgrade rhythm: CPIC updates → new file → new test pin → review → bump

Phase status

Install

pip install anukriti-pgx-core==0.9.0

# Or locally while iterating:
pip install -e /path/to/anukriti-pgx-core

Quick use (Layer 2 — what Swarm consumes)

from anukriti_pgx_core import PhenotypeEngine

engine = PhenotypeEngine()

result = engine.infer("CYP2C19", "*1", "*17")
# -> PhenotypeInference(
#        gene="CYP2C19",
#        diplotype="*1/*17",
#        activity_score=2.5,
#        phenotype="Rapid Metabolizer",           # per CPIC 2022 Table 2
#        confidence=1.0,
#        rule_version="cpic_activity_score_v2",
#        source="CPIC named-diplotype table (CYP2C19)",
#        cpic_table_version="CYP2C19_named_diplotypes_v2022.1",
#        ...
#    )

engine.supported_genes()
# -> ["CYP2C19", "CYP2D6"]

engine.cpic_table_version("CYP2C19")
# -> "CYP2C19_named_diplotypes_v2022.1"

Authoritative sources (pinned)

Updating a pinned table = adding a new file + bumping the pin + adding/updating the regression case. Silent in-place edits are not allowed.

Tests

cd anukriti-pgx-core
pip install -e ".[dev]"
pytest

# Or standalone:
python -m tests.test_pinned_star_alleles

Integration with existing Anukriti projects

From anukriti-swarm

Swarm's rules/phenotype_rules.py is a thin re-export shim that delegates here. All existing swarm code (agents/pharmacogene/base.py, core/verification/safety.py, core/runtime/runtime.py) continues to work unchanged. Migration to direct anukriti_pgx_core imports is opt-in per call site.

From anukriti (FastAPI product)

Phase 2 will migrate the 16 gene callers in anukriti/src/ into anukriti_pgx_core.calling. Until then, anukriti continues to use its existing caller modules unchanged.

License

Apache 2.0. See LICENSE.