From 93e5c9db389f3b2a6587ee8e58953134fbdcf2d0 Mon Sep 17 00:00:00 2001
From: wxg0103 <727495428@qq.com>
Date: Wed, 27 Aug 2025 11:14:05 +0800
Subject: [PATCH] feat: enhance model provider with additional LLM and
 embedding models

---
 .../wenxin_model_provider.py                  | 21 +++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/apps/models_provider/impl/wenxin_model_provider/wenxin_model_provider.py b/apps/models_provider/impl/wenxin_model_provider/wenxin_model_provider.py
index ab84934d4..7f751ee70 100644
--- a/apps/models_provider/impl/wenxin_model_provider/wenxin_model_provider.py
+++ b/apps/models_provider/impl/wenxin_model_provider/wenxin_model_provider.py
@@ -31,19 +31,28 @@ model_info_list = [ModelInfo('ERNIE-Bot-4',
                              ModelTypeConst.LLM, win_xin_llm_model_credential, QianfanChatModel),
                    ModelInfo('qianfan-chinese-llama-2-13b',
                              '',
-                             ModelTypeConst.LLM, win_xin_llm_model_credential, QianfanChatModel)
+                             ModelTypeConst.LLM, win_xin_llm_model_credential, QianfanChatModel),
+                   ModelInfo('ernie-4.5-turbo-32k', '', ModelTypeConst.LLM, win_xin_llm_model_credential,
+                             QianfanChatModel),
+                   ModelInfo('ernie-speed-8k', '', ModelTypeConst.LLM, win_xin_llm_model_credential, QianfanChatModel),
+                   ModelInfo('ernie-4.5-0.3b', '', ModelTypeConst.LLM, win_xin_llm_model_credential, QianfanChatModel)
 
                    ]
-embedding_model_info = ModelInfo('Embedding-V1',
-                                 _('Embedding-V1 is a text representation model based on Baidu Wenxin large model technology. It can convert text into a vector form represented by numerical values and can be used in text retrieval, information recommendation, knowledge mining and other scenarios. Embedding-V1 provides the Embeddings interface, which can generate corresponding vector representations based on input content. You can call this interface to input text into the model and obtain the corresponding vector representation for subsequent text processing and analysis.'),
-                                 ModelTypeConst.EMBEDDING, qianfan_embedding_credential, QianfanEmbeddings)
+embedding_model_info_list = [ModelInfo('Embedding-V1',
+                                       _('Embedding-V1 is a text representation model based on Baidu Wenxin large model technology. It can convert text into a vector form represented by numerical values and can be used in text retrieval, information recommendation, knowledge mining and other scenarios. Embedding-V1 provides the Embeddings interface, which can generate corresponding vector representations based on input content. You can call this interface to input text into the model and obtain the corresponding vector representation for subsequent text processing and analysis.'),
+                                       ModelTypeConst.EMBEDDING, qianfan_embedding_credential, QianfanEmbeddings),
+                             ModelInfo('tao-8k', '', ModelTypeConst.EMBEDDING, qianfan_embedding_credential,
+                                       QianfanEmbeddings),
+                             ModelInfo('bge-large-zh', '', ModelTypeConst.EMBEDDING, qianfan_embedding_credential,
+                                       QianfanEmbeddings)
+                             ]
 model_info_manage = ModelInfoManage.builder().append_model_info_list(model_info_list).append_default_model_info(
     ModelInfo('ERNIE-Bot-4',
               _('ERNIE-Bot-4 is a large language model independently developed by Baidu. It covers massive Chinese data and has stronger capabilities in dialogue Q&A, content creation and generation.'),
               ModelTypeConst.LLM,
               win_xin_llm_model_credential,
-              QianfanChatModel)).append_model_info(embedding_model_info).append_default_model_info(
-    embedding_model_info).build()
+              QianfanChatModel)).append_model_info_list(embedding_model_info_list).append_default_model_info(
+    embedding_model_info_list[0]).build()
 
 
 class WenxinModelProvider(IModelProvider):