修复本地embedding模型的运行bug，添加了 batch_encode 的方法

src/models/embedding.py

@@ -2,28 +2,12 @@ import os
 import json
 import requests
 from FlagEmbedding import FlagModel
+from zhipuai import ZhipuAI
 
 from src.config import EMBED_MODEL_INFO
 from src.utils import hashstr, logger
 
 
-class LocalEmbeddingModel(FlagModel):
-    def __init__(self, config, **kwargs):
-        info = EMBED_MODEL_INFO[config.embed_model]
-        model_name_or_path = config.model_local_paths.get(info["name"], info.get("default_path"))
-        logger.info(f"Loading embedding model {info['name']} from {model_name_or_path}")
-
-        super().__init__(model_name_or_path,
-                query_instruction_for_retrieval=info.get("query_instruction", None),
-                use_fp16=False, **kwargs)
-
-        logger.info(f"Embedding model {info['name']} loaded")
-
-
-
-from zhipuai import ZhipuAI
-
-
 class RemoteEmbeddingModel:
     embed_state = {}
 
@@ -51,6 +35,44 @@ class RemoteEmbeddingModel:
 
         return data
 
+class LocalEmbeddingModel(FlagModel, RemoteEmbeddingModel):
+    def __init__(self, config, **kwargs):
+        info = EMBED_MODEL_INFO[config.embed_model]
+        model_name_or_path = config.model_local_paths.get(info["name"], info.get("default_path"))
+        logger.info(f"Loading embedding model {info['name']} from {model_name_or_path}")
+
+        super().__init__(model_name_or_path,
+                query_instruction_for_retrieval=info.get("query_instruction", None),
+                use_fp16=False, **kwargs)
+
+        logger.info(f"Embedding model {info['name']} loaded")
+
+
+    def batch_encode(self, messages, batch_size=20):
+        logger.info(f"Batch encoding {len(messages)} messages")
+        data = []
+
+        if len(messages) > batch_size:
+            task_id = hashstr(messages)
+            self.embed_state[task_id] = {
+                'status': 'in-progress',
+                'total': len(messages),
+                'progress': 0
+            }
+
+        for i in range(0, len(messages), batch_size):
+            group_msg = messages[i:i+batch_size]
+            logger.info(f"Encoding {i} to {i+batch_size} with {len(messages)} messages")
+            response = self.encode_queries(group_msg)
+            data.extend(response)
+
+        if len(messages) > batch_size:
+            self.embed_state[task_id]['progress'] = len(messages)
+            self.embed_state[task_id]['status'] = 'completed'
+
+        return data
+
+
 class ZhipuEmbedding(RemoteEmbeddingModel):
 
     def __init__(self, config) -> None: