Enrich

resp = mc.api.enrich(
    [{"record_id": "1", "linkedin_url": "https://www.linkedin.com/in/example"}],
    match_condition_fields=["linkedin_url"],
    return_fields=["full_name", "professional_emails", "phones"],
)

for r in resp.results:
    print(r.record_id, r.is_match, r.minerva_pid, r.match_score)
    print(r.personal_information)     # full_name, gender, dob, age, ...
    print(r.professional_emails)      # [{email_rank, email_address}, ...]

resp.to_df()                          # one row per record, nested fields flattened

resp.to_dicts()        # list[dict]
resp.to_df()           # pandas DataFrame   (needs [pandas])
resp.to_csv("out.csv")
resp.to_table()        # pretty terminal table   (needs [table])

from pydantic import BaseModel

class Lead(BaseModel):
    record_id: str
    full_name: str | None = None
    estimated_income_range: str | None = None
    crm_id: str | None = None          # not a Minerva field → stays None, warns once

leads = mc.api.enrich(records).map_to(Lead)      # -> list[Lead]
# pass strict=True to validate instead (raises on mismatch)