diff --git a/dsRagAnything/Config/Config.py b/dsRagAnything/Config/Config.py
index e8735d6d..e3ad3fef 100644
--- a/dsRagAnything/Config/Config.py
+++ b/dsRagAnything/Config/Config.py
@@ -16,18 +16,18 @@ GLM_API_KEY = "sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl"
 GLM_MODEL_NAME = "THUDM/GLM-4.1V-9B-Thinking"
 
 # 阿里云API信息【YLT】
-#ALY_LLM_API_KEY = "sk-f6da0c787eff4b0389e4ad03a35a911f"
+# ALY_LLM_API_KEY = "sk-f6da0c787eff4b0389e4ad03a35a911f"
 # 阿里云API信息【绘智科技】
 ALY_LLM_API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
 ALY_LLM_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-ALY_LLM_MODEL_NAME = "qwen-plus"
-
-
+# 通义千问
+# ALY_LLM_MODEL_NAME = "qwen-plus"
+# Kimi K2大模型
+ALY_LLM_MODEL_NAME = "Moonshot-Kimi-K2-Instruct"
 
 # 智谱的API KEY【吴缤申请个人版免费】
 ZHIPU_API_KEY = "78dc1dfe37e04f29bd4ca9a49858a969.gn7TIZTfzpY35nx9"
 
 # GPTNB的API KEY
 GPTNB_API_KEY = "sk-amQHwiEzPIZIB2KuF5A10dC23a0e4b02B48a7a2b6aFa0662"
-GPTNB_BASE_URL="https://goapi.gptnb.ai"
-
+GPTNB_BASE_URL = "https://goapi.gptnb.ai"
diff --git a/dsRagAnything/Config/__pycache__/Config.cpython-310.pyc b/dsRagAnything/Config/__pycache__/Config.cpython-310.pyc
index 15276ce7..5099f04f 100644
Binary files a/dsRagAnything/Config/__pycache__/Config.cpython-310.pyc and b/dsRagAnything/Config/__pycache__/Config.cpython-310.pyc differ
diff --git a/dsRagAnything/Doc/GeoGebra5经典版指令汇编201903061.pdf b/dsRagAnything/Doc/GeoGebra5经典版指令汇编201903061.pdf
new file mode 100644
index 00000000..795c3748
Binary files /dev/null and b/dsRagAnything/Doc/GeoGebra5经典版指令汇编201903061.pdf differ
diff --git a/dsRagAnything/Test.py b/dsRagAnything/Test.py
index 70b6eb37..37fbeb58 100644
--- a/dsRagAnything/Test.py
+++ b/dsRagAnything/Test.py
@@ -3,10 +3,13 @@ from raganything import RAGAnything, RAGAnythingConfig
 from lightrag.llm.openai import openai_complete_if_cache, openai_embed
 from lightrag.utils import EmbeddingFunc
 
+import Config.Config
+
+
 async def main():
     # 设置 API 配置
-    api_key = "your-api-key"
-    base_url = "your-base-url"  # 可选
+    api_key = Config.Config.ALY_LLM_API_KEY
+    base_url = Config.Config.ALY_LLM_BASE_URL
 
     # 创建 RAGAnything 配置
     config = RAGAnythingConfig(
@@ -21,7 +24,7 @@ async def main():
     # 定义 LLM 模型函数
     def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
         return openai_complete_if_cache(
-            "gpt-4o-mini",
+            Config.Config.ALY_LLM_MODEL_NAME,
             prompt,
             system_prompt=system_prompt,
             history_messages=history_messages,
@@ -32,24 +35,24 @@ async def main():
 
     # 定义视觉模型函数用于图像处理
     def vision_model_func(
-        prompt, system_prompt=None, history_messages=[], image_data=None, messages=None, **kwargs
+            prompt, system_prompt=None, history_messages=[], image_data=None, messages=None, **kwargs
     ):
         # 如果提供了messages格式（用于多模态VLM增强查询），直接使用
         if messages:
             return openai_complete_if_cache(
-                "gpt-4o",
+                Config.Config.GLM_MODEL_NAME,
                 "",
                 system_prompt=None,
                 history_messages=[],
                 messages=messages,
-                api_key=api_key,
-                base_url=base_url,
+                api_key=Config.Config.GLM_API_KEY,
+                base_url=Config.Config.GLM_BASE_URL,
                 **kwargs,
             )
         # 传统单图片格式
         elif image_data:
             return openai_complete_if_cache(
-                "gpt-4o",
+                Config.Config.GLM_MODEL_NAME,
                 "",
                 system_prompt=None,
                 history_messages=[],
@@ -72,8 +75,8 @@ async def main():
                     if image_data
                     else {"role": "user", "content": prompt},
                 ],
-                api_key=api_key,
-                base_url=base_url,
+                api_key=Config.Config.GLM_API_KEY,
+                base_url=Config.Config.GLM_BASE_URL,
                 **kwargs,
             )
         # 纯文本格式
@@ -82,13 +85,13 @@ async def main():
 
     # 定义嵌入函数
     embedding_func = EmbeddingFunc(
-        embedding_dim=3072,
-        max_token_size=8192,
+        embedding_dim=Config.Config.EMBED_DIM,
+        max_token_size=Config.Config.EMBED_MAX_TOKEN_SIZE,
         func=lambda texts: openai_embed(
             texts,
-            model="text-embedding-3-large",
-            api_key=api_key,
-            base_url=base_url,
+            model=Config.Config.EMBED_MODEL_NAME,
+            api_key=Config.Config.EMBED_API_KEY,
+            base_url=Config.Config.EMBED_BASE_URL,
         ),
     )
 
@@ -102,7 +105,7 @@ async def main():
 
     # 处理文档
     await rag.process_document_complete(
-        file_path="path/to/your/document.pdf",
+        file_path="./Doc/GeoGebra5经典版指令汇编201903061.pdf",
         output_dir="./output",
         parse_method="auto"
     )
@@ -115,19 +118,6 @@ async def main():
     )
     print("文本查询结果:", text_result)
 
-    # 多模态查询 - 包含具体多模态内容的查询
-    multimodal_result = await rag.aquery_with_multimodal(
-        "分析这个性能数据并解释与现有文档内容的关系",
-        multimodal_content=[{
-            "type": "table",
-            "table_data": """系统,准确率,F1分数
-                            RAGAnything,95.2%,0.94
-                            基准方法,87.3%,0.85""",
-            "table_caption": "性能对比结果"
-        }],
-        mode="hybrid"
-    )
-    print("多模态查询结果:", multimodal_result)
 
 if __name__ == "__main__":
-    asyncio.run(main())
\ No newline at end of file
+    asyncio.run(main())