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/embedder.py

@@ -0,0 +1,60 @@
+"""
+Copyright 2024-2026 ReplyPier
+
+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.
+
+The local embedding model, loaded once per process.
+
+agno's ``FastEmbedEmbedder`` builds a new ``TextEmbedding`` (an ONNX session)
+inside every ``get_embedding`` call — about 1.5 s of model loading around a
+7 ms embedding. One row per page hid that; one row per chunk would not.
+"""
+
+import threading
+from dataclasses import dataclass
+from typing import Dict, List
+
+import numpy as np
+from agno.embedder.fastembed import FastEmbedEmbedder
+from fastembed import TextEmbedding
+
+from app.core.config import settings
+
+_models: Dict[str, TextEmbedding] = {}
+_lock = threading.Lock()
+
+
+def _model(model_id: str) -> TextEmbedding:
+    model = _models.get(model_id)
+    if model is None:
+        with _lock:
+            model = _models.get(model_id)
+            if model is None:
+                model = _models[model_id] = TextEmbedding(model_name=model_id)
+    return model
+
+
+@dataclass
+class CachedFastEmbedEmbedder(FastEmbedEmbedder):
+    """``FastEmbedEmbedder`` that shares one loaded model per model id."""
+
+    id: str = settings.FASTEMBED_MODEL
+
+    def get_embedding(self, text: str) -> List[float]:
+        embedding = list(_model(self.id).embed(text))[0]
+        return embedding.tolist() if isinstance(embedding, np.ndarray) else list(embedding)
+
+
+def get_embedder() -> CachedFastEmbedEmbedder:
+    """The embedder every knowledge path should use for the configured model."""
+    return CachedFastEmbedEmbedder()