AI写作助手如何提升知识工作者效率_04

class CrossModalAttention(nn.Module):
    def __init__(self, text_dim, image_dim, hidden_dim):
        super().__init__()
        self.text_proj = nn.Linear(text_dim, hidden_dim)
        self.image_proj = nn.Linear(image_dim, hidden_dim)
        self.attention = nn.MultiheadAttention(hidden_dim, 8)

    def forward(self, text_emb, image_emb, mask=None):
        text_proj = self.text_proj(text_emb).transpose(0, 1)
        image_proj = self.image_proj(image_emb).transpose(0, 1)
        attn_output, _ = self.attention(
            text_proj, image_proj, image_proj, attn_mask=mask
        )
        return attn_output.transpose(0, 1)

@prefix med: <http://example.org/medicine#> .
med:Diabetes a owl:Class .
med:Type2Diabetes rdfs:subClassOf med:Diabetes .
med:hasSymptom a owl:ObjectProperty ;
    domain med:Disease ;
    range med:Symptom .
med:Type2Diabetes med:hasSymptom med:Polyuria .

from rdflib import Graph, URIRef, Literal
from rdflib.namespace import RDF

class KnowledgeGraphEnhancer:
    def __init__(self, kg_path):
        self.graph = Graph()
        self.graph.parse(kg_path, format="turtle")
        self.med_namespace = "http://example.org/medicine#"

    def get_related_concepts(self, topic, limit=5):
        query = f"""
        PREFIX med: <{self.med_namespace}>
        SELECT ?concept ?relation
        WHERE {{
            med:{topic} ?relation ?concept .
            ?concept a med:Concept .
        }}
        LIMIT {limit}
        """
        results = self.graph.query(query)
        return [(str(row.concept), str(row.relation)) for row in results]

    def generate_knowledge_prompt(self, topic):
        concepts = self.get_related_concepts(topic)
        prompt = f"基于领域知识图谱，以下是与{topic}相关的核心概念：\n"""\n"""
        for concept, relation in concepts:
            rel_name = relation.split('#')[-1]
            prompt += f"- {rel_name}: {concept.split('#')[-1]}\n"
        return prompt

from ai_writer.academic import LiteratureReviewAssistant

assistant = LiteratureReviewAssistant()
review = assistant.generate_review(
    topic="生成式AI在医疗诊断中的应用",
    time_span=(2022, 2024),
    limit=50,
    visualization=True
)
review.save("literature_review.md")

# 智能写作助手核心框架
class AIWritingAssistant:
    def __init__(self, model_name="gpt-4-turbo", domain="general"):
        self.model = self._load_model(model_name)
        self.kg_enhancer = KnowledgeGraphEnhancer(f"{domain}_kg.ttl")
        self.reference_manager = ReferenceManager()

    def _load_model(self, model_name):
        """加载基础模型"""
        if model_name.startswith("gpt-"):
            return OpenAI(model_name=model_name)
        elif model_name == "claude":
            return Anthropic(model_name="claude-3-opus-20240229")
        else:
            raise ValueError(f"不支持的模型: {model_name}")

    def generate_document(self, topic, structure=None, style="academic"):
        """生成完整文档"""
        # 1. 知识增强提示构建
        knowledge_prompt = self.kg_enhancer.generate_knowledge_prompt(topic)

        # 2. 文档结构生成
        if not structure:
            structure = self._generate_structure(topic, style)

        # 3. 内容填充与优化
        document = []
        for section in structure:
            section_prompt = f"{knowledge_prompt}\n请撰写'{section}'部分，风格{style}"
            content = self.model.completions.create(
                prompt=section_prompt,
                max_tokens=1000
            ).choices[0].text
            document.append(f"## {section}\n{content}")

        # 4. 引用格式处理
        final_doc = self.reference_manager.format_citations("\n".join(document))
        return final_doc

# 使用示例
assistant = AIWritingAssistant(domain="medicine")
document = assistant.generate_document(
    topic="AI辅助糖尿病诊断研究进展",
    style="academic"
)
with open("diabetes_research.md", "w") as f:
    f.write(document)