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TCM Templates

Traditional Chinese Medicine text analysis.

Overview

TCM (Traditional Chinese Medicine) templates are designed for extracting knowledge from TCM texts, including herbal medicine, formulas, and syndrome differentiation.

Templates

herb_property

Type: model

Purpose: Extract herb properties and characteristics

Best for: - Materia medica texts - Herb databases - Prescription references

Fields:

Field Type Description
name str Herb name (Chinese/pinyin)
properties list[str] Nature (hot, warm, cool, cold, neutral)
flavors list[str] Five flavors (sour, bitter, sweet, pungent, salty)
meridians list[str] Entered meridians
functions list[str] Main functions
indications list[str] Clinical indications 
he parse herb_text.md -t tcm/herb_property -l zh

ka = Template.create("tcm/herb_property", "zh")
herb = ka.parse(huang_qi_text)

print(f"Name: {herb.data.name}")
print(f"Nature: {', '.join(herb.data.properties)}")
print(f"Functions: {', '.join(herb.data.functions)}")

formula_composition

Type: graph

Purpose: Extract herbal formula compositions

Best for: - Formula collections (方剂学) - Prescription texts - Classic texts (伤寒论, 金匮要略)

Entities:

  • Herbs (药物)
  • Formulas (方剂)
  • Conditions (病症)

Relations:

  • contains — Formula contains herb
  • treats — Formula treats condition
  • modifies — Modified formula relationship
he parse formula_book.md -t tcm/formula_composition -l zh

ka = Template.create("tcm/formula_composition", "zh")
result = ka.parse(formula_text)

print(f"Entities: {len(result.nodes)}")
print(f"Relations: {len(result.edges)}")

# List herbs in formula
for edge in result.edges:
    if edge.type == "contains":
        print(f"  - {edge.target}")

herb_relation

Type: graph

Purpose: Extract herb-to-herb relationships

Best for: - Herb pairing references (药对) - Compatibility texts (十八反, 十九畏) - Combination guides

Relations:

  • pairs_with — Common pairing
  • synergizes_with — Synergistic effect
  • incompatible_with — Contraindicated combination
he parse herb_pairs.md -t tcm/herb_relation -l zh

ka = Template.create("tcm/herb_relation", "zh")
result = ka.parse(herb_text)

print(f"Herbs: {len(result.nodes)}")
for edge in result.edges:
    print(f"{edge.source} {edge.type} {edge.target}")

meridian_graph

Type: graph

Purpose: Extract meridian pathway information

Best for: - Acupuncture texts - Meridian studies - Point location references

Entities:

  • Meridians (经脉)
  • Acupoints (穴位)
  • Organs (脏腑)

Relations:

  • connects_to — Meridian connections
  • belongs_to — Point to meridian
  • influences — Meridian-organ relationship
he parse acupuncture.md -t tcm/meridian_graph -l zh

ka = Template.create("tcm/meridian_graph", "zh")
result = ka.parse(acupuncture_text)

print(f"Meridians: {len(result.nodes)}")
print(f"Connections: {len(result.edges)}")

# Visualize meridian pathways
result.build_index()
result.show()

syndrome_reasoning

Type: graph

Purpose: Extract syndrome differentiation logic

Best for: - Diagnostics texts (中医诊断学) - Case studies - Syndrome differentiation guides

Entities:

  • Symptoms (症状)
  • Syndromes (证型)
  • Patterns (病机)

Relations:

  • indicates — Symptom indicates syndrome
  • differentiates_from — Differential diagnosis
  • leads_to — Pattern progression
he parse diagnostics.md -t tcm/syndrome_reasoning -l zh

ka = Template.create("tcm/syndrome_reasoning", "zh")
result = ka.parse(diagnostics_text)

print(f"Symptoms: {len(result.nodes)}")
print(f"Indications: {len(result.edges)}")

# Explore syndrome relationships
result.build_index()
result.show()

Use Cases

Herb Database Building

from hyperextract import Template

ka = Template.create("tcm/herb_property", "zh")
herb_db = {}

for herb_file in herb_files:
    herb = ka.parse(herb_file.read_text())
    herb_db[herb.data.name] = herb.data

# Query by function
for name, data in herb_db.items():
    if "补气" in data.functions:
        print(f"{name}: Qi tonic herb")

Formula Analysis

ka = Template.create("tcm/formula_composition", "zh")
formula = ka.parse(si_jun_zi_tang_text)

# List herbs
print("Formula composition:")
for relation in formula.data.relations:
    if relation.type == "contains":
        print(f"  - {relation.target}")

Syndrome Study

ka = Template.create("tcm/syndrome_reasoning", "zh")
syndromes = ka.parse(diagnostics_text)

# Find what symptom indicates what syndrome
symptom = "舌淡苔白"
related = [
    r for r in syndromes.data.relations
    if r.source == symptom and r.type == "indicates"
]

for r in related:
    print(f"{symptom} indicates {r.target}")

Tips

  1. Use Chinese language (-l zh) — Better extraction from TCM texts
  2. herb_property for materia medica — Build herb databases
  3. formula_composition for方剂学 — Study classic formulas
  4. meridian_graph for acupuncture — Map meridian pathways

See Also