'commit'

dsRagAnything/.idea/misc.xml

@@ -4,4 +4,7 @@
     <option name="sdkName" value="D:\anaconda3\envs\raganything" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="D:\anaconda3\envs\raganything" project-jdk-type="Python SDK" />
+  <component name="PythonCompatibilityInspectionAdvertiser">
+    <option name="version" value="3" />
+  </component>
 </project>

dsRagAnything/Doc/文档.txt

@@ -41,3 +41,11 @@ python examples/text_format_test.py --check-reportlab --file dummy
 
 # MinerU
 https://github.com/opendatalab/MinerU/blob/master/README_zh-CN.md
+
+# 硅基流动的视觉模型
+https://cloud.siliconflow.cn/sft-b86b3myzge/models?tags=%E8%A7%86%E8%A7%89
+# 免费的模型
+
+# 调用地址
+https://api.siliconflow.cn/v1/chat/completions
+model:GLM-4.1V-9B-Thinking

dsRagAnything/Tools/T2_End_to_End.py

@@ -6,17 +6,17 @@ from lightrag.utils import EmbeddingFunc
 async def main():
     # Initialize RAGAnything
     rag = RAGAnything(
-        working_dir="./rag_storage",
         llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
-            "gpt-4o-mini",
+            "deepseek-chat",
             prompt,
             system_prompt=system_prompt,
             history_messages=history_messages,
-            api_key="your-api-key",
+            api_key="sk-44ae895eeb614aa1a9c6460579e322f1",
+            base_url="https://api.deepseek.com"
             **kwargs,
         ),
         vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
-            "gpt-4o",
+            "GLM-4.1V-9B-Thinking",
             "",
             system_prompt=None,
             history_messages=[],
@@ -27,14 +27,16 @@ async def main():
                     {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
                 ]} if image_data else {"role": "user", "content": prompt}
             ],
-            api_key="your-api-key",
+            api_key="sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl",
+            base_url='https://api.siliconflow.cn/v1/chat/completions'
             **kwargs,
         ) if image_data else openai_complete_if_cache(
-            "gpt-4o-mini",
+            "GLM-4.1V-9B-Thinking",
             prompt,
             system_prompt=system_prompt,
             history_messages=history_messages,
-            api_key="your-api-key",
+            api_key="sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl",
+            base_url='https://api.siliconflow.cn/v1/chat/completions'
             **kwargs,
         ),
         embedding_func=EmbeddingFunc(
@@ -42,16 +44,16 @@ async def main():
             max_token_size=8192,
             func=lambda texts: openai_embed(
                 texts,
-                model="text-embedding-3-large",
-                api_key=api_key,
-                base_url=base_url,
+                model="BAAI/bge-m3",
+                api_key="sk-pbqibyjwhrgmnlsmdygplahextfaclgnedetybccknxojlyl",
+                base_url="https://api.siliconflow.cn/v1/embeddings",
             ),
         ),
     )
 
     # Process a document
     await rag.process_document_complete(
-        file_path="path/to/your/document.pdf",
+        file_path="../Txt/黄琬乔2023蓝桥杯省赛准考证.pdf",
         output_dir="./output",
         parse_method="auto"
     )
@@ -59,22 +61,22 @@ async def main():
     # Query the processed content
     # Pure text query - for basic knowledge base search
     text_result = await rag.aquery(
-        "What are the main findings shown in the figures and tables?",
+        "这篇文档中说了什么内容?",
         mode="hybrid"
     )
     print("Text query result:", text_result)
 
-    # Multimodal query with specific multimodal content
-    multimodal_result = await rag.aquery_with_multimodal(
-    "Explain this formula and its relevance to the document content",
-    multimodal_content=[{
-        "type": "equation",
-        "latex": "P(d|q) = \\frac{P(q|d) \\cdot P(d)}{P(q)}",
-        "equation_caption": "Document relevance probability"
-    }],
-    mode="hybrid"
-)
-    print("Multimodal query result:", multimodal_result)
+#     # Multimodal query with specific multimodal content
+#     multimodal_result = await rag.aquery_with_multimodal(
+#     "Explain this formula and its relevance to the document content",
+#     multimodal_content=[{
+#         "type": "equation",
+#         "latex": "P(d|q) = \\frac{P(q|d) \\cdot P(d)}{P(q)}",
+#         "equation_caption": "Document relevance probability"
+#     }],
+#     mode="hybrid"
+# )
+#     print("Multimodal query result:", multimodal_result)
 
 if __name__ == "__main__":
     asyncio.run(main())

dsRagAnything/raganything/mineru_parser.py

@@ -112,7 +112,7 @@ class MineruParser:
             cmd.extend(["-d", device])
 
         try:
-            print("转换命令:" + cmd)
+            #print("转换命令:" + cmd)
             result = subprocess.run(
                 cmd,
                 capture_output=True,