feat: Phase 2.5 - Skill Execution Pipeline + Chat Workflow Enhancement - Stage A: MarketApp->Skill ID mapping, real skill execution engine, SkillVersion snapshot fix - Stage B: AgentConfig dynamic injection, multi-turn context persistence, Function Calling loop (5 rounds) - Stage C: KnowledgeDocumentChunk model, BM25 text retrieval, smart KB context injection - D: PipelineScheduler LLM-based skill pattern extraction replacing mock

backend/python/app/models.py

@@ -202,6 +202,28 @@ class KnowledgeDocument(Base):
     updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
 
 
+
+
+class KnowledgeDocumentChunk(Base):
+    """知识库文档分段表 — Stage C.1"""
+    __tablename__ = "knowledge_document_chunks"
+
+    id = Column(String(36), primary_key=True, default=gen_uuid)
+    document_id = Column(String(36), ForeignKey("knowledge_documents.id"), nullable=False)
+    knowledge_base_id = Column(String(36), ForeignKey("knowledge_bases.id"), nullable=False)
+    chunk_index = Column(Integer, nullable=False, default=0, comment="分段序号")
+    content = Column(Text, nullable=False, comment="分段文本内容")
+    start_char = Column(Integer, default=0, comment="起始字符位置")
+    end_char = Column(Integer, default=0, comment="结束字符位置")
+    chunk_metadata = Column(JSON, default=None, comment="分段元数据(标题、关键词等)")
+    token_count = Column(Integer, default=0, comment="分段token数估算")
+    created_at = Column(DateTime, default=datetime.now)
+
+    __table_args__ = (
+        Index("idx_kdc_doc_id", "document_id"),
+        Index("idx_kdc_kb_id", "knowledge_base_id"),
+    )
+
 class KnowledgeGraphNode(Base):
     __tablename__ = "knowledge_graph_nodes"