feat(knowledge): 任务队列、独立 worker 与 pgvector 向量索引

知识处理改为数据库任务队列，独立 worker 做切块与向量化，失败可重试。

backend/app/knowledge/enhanced_pdf_kb.py

@@ -0,0 +1,81 @@
+"""
+Copyright 2024-2026 ChatterMate
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from typing import Any, Dict, Iterator, List, Optional
+from agno.knowledge.pdf import PDFKnowledgeBase
+from agno.document import Document
+from app.core.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class EnhancedPDFKnowledgeBase(PDFKnowledgeBase):
+    """Enhanced PDF knowledge base that supports metadata filters"""
+    
+    def load(
+        self,
+        recreate: bool = False,
+        upsert: bool = True,
+        skip_existing: bool = True,
+        filters: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Load the PDF contents to the vector db with metadata filters
+        
+        Args:
+            recreate (bool, optional): Whether to recreate the collection. Defaults to False.
+            upsert (bool, optional): Whether to upsert documents. Defaults to True.
+            skip_existing (bool, optional): Whether to skip existing documents. Defaults to True.
+            filters (Optional[Dict[str, Any]], optional): Metadata filters to apply to the documents. Defaults to None.
+        """
+        if self.vector_db is None:
+            logger.warning("No vector db provided")
+            return
+
+        if recreate:
+            self.vector_db.drop()
+            self.vector_db.create()
+        elif not self.vector_db.exists():
+            self.vector_db.create()
+
+        logger.info(f"Loading PDF knowledge base: {self.path}")
+
+        # Read documents from PDF
+        if self.reader is None:
+            logger.warning("No reader provided")
+            return
+
+        # Get documents and embed them
+        for document_list in self.document_lists:
+            # Embed documents if embedder is available
+            if self.vector_db and hasattr(self.vector_db, 'embedder'):
+                for document in document_list:
+                    if document.embedding is None:
+                        try:
+                            document.embed(embedder=self.vector_db.embedder)
+                        except Exception as e:
+                            logger.error(f"Error embedding document {document.id}: {str(e)}")
+            
+            # Upsert documents with filters
+            if upsert and self.vector_db:
+                try:
+                    logger.debug(f"Upserting {len(document_list)} documents with filters: {filters}")
+                    self.vector_db.upsert(documents=document_list, filters=filters)
+                except Exception as e:
+                    logger.error(f"Error upserting documents: {str(e)}")
+        
+        logger.info(f"Completed loading PDF knowledge base: {self.path}")
+