From a90bee1b9530b4340bfba5aaf0ed1241e57b99e8 Mon Sep 17 00:00:00 2001
From: martsforever <martsforever@aliyun.com>
Date: Sun, 7 Sep 2025 01:06:23 +0800
Subject: [PATCH] =?UTF-8?q?feat:=20=E4=BD=BF=E7=94=A8=E7=99=BE=E7=82=BC?=
 =?UTF-8?q?=E7=9A=84=E5=B5=8C=E5=85=A5=E6=A8=A1=E5=9E=8B=E6=9C=8D=E5=8A=A1?=
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---
 app/config/ai_configs.py  | 5 +++++
 app/utils/llm_utils.py    | 2 +-
 app/utils/milvus_utils.py | 5 ++++-
 3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/app/config/ai_configs.py b/app/config/ai_configs.py
index ccb34c4..13a1306 100644
--- a/app/config/ai_configs.py
+++ b/app/config/ai_configs.py
@@ -71,4 +71,9 @@ ai_configs = {
     'url': 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions',
     'key': env.llm_key_bailian,
   },
+  'bailian-embedding': {
+    'model': 'text-embedding-v4',
+    'url': 'https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings',
+    'key': env.llm_key_bailian,
+  },
 }
diff --git a/app/utils/llm_utils.py b/app/utils/llm_utils.py
index c0d28b1..a0ea91c 100644
--- a/app/utils/llm_utils.py
+++ b/app/utils/llm_utils.py
@@ -37,7 +37,7 @@ def create_llama_index_llm(platform_code='huoshan-doubao', temperature=0.5):
   )
 
 
-def create_embeddings(platform_code="huoshan-embedding-240715"):
+def create_embeddings(platform_code="bailian-embedding"):
   """
   创建自定义嵌入模型实例
 
diff --git a/app/utils/milvus_utils.py b/app/utils/milvus_utils.py
index 9aa0be5..1a96f34 100644
--- a/app/utils/milvus_utils.py
+++ b/app/utils/milvus_utils.py
@@ -1,3 +1,4 @@
+import asyncio
 from typing import List, Optional
 
 from llama_index.core import Document, VectorStoreIndex
@@ -45,7 +46,9 @@ class MilvusService:
     )
     from llama_index.core.node_parser import HierarchicalNodeParser
     parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
-    nodes = await parser.aget_nodes_from_documents(documents)
+    # nodes = await parser.aget_nodes_from_documents(documents)
+    # 使用同步方法并包装在 asyncio.to_thread 中
+    nodes = await asyncio.to_thread(parser.get_nodes_from_documents,documents)
     await vector_index.ainsert_nodes(nodes)
     return vector_index