可定制 NLP 工具包 spaCy 与 LLM 集成技术解析

# 基于规则的匹配系统示例matcher = Matcher(nlp.vocab)pattern = [{"LOWER": "patients"}, {"POS": {"IN": ["NUM"]}},            {"LOWER": "received"}, {"POS": "NOUN"}]matcher.add("TreatmentGroup", [pattern])

class TrialSummaryTask:    def parse_responses(self, docs, responses):        for doc, response in zip(docs, responses):            # 将LLM自由文本输出转换为结构化实体            matcher.add("Patient_Group", patterns)            doc.ents = filter_spans(matches)