Searching OMOP concepts

results = client.search.basic("diabetes mellitus", page_size=20)
for concept in results["concepts"]:
    print(f"{concept['concept_id']}: {concept['concept_name']}")

results = client.search.basic(
    "heart attack",
    vocabulary_ids=["SNOMED", "ICD10CM"],
)

results = client.search.basic(
    "aspirin",
    domain_ids=["Drug"],
    page_size=10,
)

results = client.search.basic(
    "aspirin",
    concept_class_ids=["Clinical Drug", "Ingredient"],
    page_size=10,
)

results = client.search.basic(
    "diabetes",
    standard_concept="S",  # "S" = Standard, "C" = Classification
    vocabulary_ids=["SNOMED"],
)

results = client.search.basic(
    "heart attack",
    include_synonyms=True,
    vocabulary_ids=["SNOMED"],
)

results = client.search.basic(
    "myocardial infarction",
    vocabulary_ids=["SNOMED"],
    domain_ids=["Condition"],
    standard_concept="S",
    include_synonyms=True,
    min_score=0.5,
    page_size=20,
)

result = client.search.autocomplete("diab", page_size=10)
for suggestion in result["suggestions"]:
    print(suggestion["suggestion"])

# Get first page
results = client.search.basic("diabetes", page=1, page_size=50)

# Access pagination metadata
pagination = results["meta"]["pagination"]
print(f"Total: {pagination['total_items']}")
print(f"Pages: {pagination['total_pages']}")
print(f"Has next: {pagination['has_next']}")

# Get next page
if pagination["has_next"]:
    results = client.search.basic("diabetes", page=2, page_size=50)

count = 0
for concept in client.search.basic_iter("diabetes", page_size=100):
    print(concept["concept_name"])
    count += 1
    if count >= 500:  # Limit for demo
        break

results = client.search.advanced(
    "diabetes",
    vocabulary_ids=["SNOMED", "ICD10CM"],
    domain_ids=["Condition"],
    standard_concepts_only=True,
    page_size=50,
)

# Access results
for concept in results["data"]:
    print(f"{concept['concept_id']}: {concept['concept_name']}")

# Access pagination
print(f"Total: {results['meta']['pagination']['total_items']}")

results = client.search.advanced(
    "diabetes",
    relationship_filters=[
        {"relationship_id": "Is a", "concept_id": 4116142}  # Has parent concept
    ],
    standard_concepts_only=True,
)

# Basic semantic search
results = client.search.semantic("heart attack", page_size=10)

# With filters
results = client.search.semantic(
    "diabetes mellitus",
    vocabulary_ids=["SNOMED"],
    domain_ids=["Condition"],
    threshold=0.5,  # Higher = more similar
    page_size=20,
)

# Access results with similarity scores
for concept in results["data"]["results"]:
    print(f"{concept['concept_name']}: {concept['similarity_score']:.2f}")

# Iterate through all results
for concept in client.search.semantic_iter("diabetes", page_size=50):
    print(concept["concept_name"])

# Collect first N results
import itertools
top_100 = list(itertools.islice(
    client.search.semantic_iter("diabetes"), 100
))

# With filters
for concept in client.search.semantic_iter(
    "heart failure",
    vocabulary_ids=["SNOMED"],
    threshold=0.4,
):
    print(f"{concept['concept_id']}: {concept['concept_name']}")

# Async semantic search
results = await client.search.semantic("heart attack", page_size=10)

# Async iterator
async for concept in client.search.semantic_iter("diabetes"):
    print(concept["concept_name"])

results = client.search.bulk_basic([
    {"search_id": "q1", "query": "diabetes mellitus"},
    {"search_id": "q2", "query": "hypertension"},
    {"search_id": "q3", "query": "aspirin"},
], defaults={"vocabulary_ids": ["SNOMED"], "page_size": 5})

for item in results["results"]:
    print(f"{item['search_id']}: {len(item['results'])} results ({item['status']})")

# Defaults apply to all searches; per-search values override
results = client.search.bulk_basic(
    [
        {"search_id": "q1", "query": "diabetes"},
        {"search_id": "q2", "query": "heart failure"},
        {"search_id": "q3", "query": "metformin", "domain_ids": ["Drug"]},  # overrides default
    ],
    defaults={
        "vocabulary_ids": ["SNOMED"],
        "domain_ids": ["Condition"],
        "page_size": 10,
    },
)

results = client.search.bulk_semantic([
    {"search_id": "s1", "query": "heart failure treatment options"},
    {"search_id": "s2", "query": "type 2 diabetes medication"},
    {"search_id": "s3", "query": "elevated blood pressure"},
], defaults={"threshold": 0.5, "page_size": 10})

for item in results["results"]:
    print(f"{item['search_id']}: {item.get('result_count', 0)} results")
    for concept in item["results"]:
        print(f"  {concept['concept_name']} ({concept['similarity_score']:.2f})")

# Both bulk methods have async variants
results = await client.search.bulk_basic([...])
results = await client.search.bulk_semantic([...])

# By concept ID
similar = client.search.similar(concept_id=4329847)  # MI

# By concept name
similar = client.search.similar(concept_name="Type 2 diabetes mellitus")

# By natural language query
similar = client.search.similar(query="elevated blood sugar")

# With all options
similar = client.search.similar(
    concept_id=4329847,
    algorithm="semantic",  # or "lexical", "hybrid"
    similarity_threshold=0.7,
    page_size=50,
    vocabulary_ids=["SNOMED"],
    domain_ids=["Condition"],
    include_scores=True,
    include_explanations=True,
)

# Access results
for concept in similar["similar_concepts"]:
    print(f"{concept['concept_name']}: {concept['similarity_score']:.2f}")

# Async version
similar = await client.search.similar(concept_id=4329847)