v2.0，还原models_server等原方法代码，更改groundcontrol拉起方式

scripts/ai_agent/config/rag_config.json

@@ -6,7 +6,7 @@
         "collection_name":"drone_docs",
         "model_config_llamacpp":
         {
-            "n_ctx":512,
+            "n_ctx":40960,
             "n_threads":4,
             "n_gpu_layers":36,
             "n_seq_max":256,

scripts/ai_agent/models/models_client.py

@@ -213,6 +213,39 @@ class Models_Client:
         返回:
             包含推理结果的字典
         """
+        # # 处理图像
+        # image_data = []
+
+        # if images:
+        #     # for img in images:
+        #     try:
+        #         # image_data.append(PIL_image_to_base64(image=img))
+        #         image_data= images
+        #     except Exception as e:
+        #         return {"error": f"处理图像base64对象失败: {str(e)}"}
+        
+        # elif image_paths:
+        #     for path in image_paths:
+        #         try:
+        #             image_data.append(PIL_image_to_base64(image_path=path))
+        #         except Exception as e:
+        #             return {"error": f"处理图像 {path} 失败: {str(e)}"}
+        
+        
+        
+        # if not image_data:
+        #     return {"error": "未提供有效的图像数据"}
+        
+        # payload = {
+        #     "user_prompt": prompt,
+        #     "image_data": image_data,
+        #     "max_tokens": max_tokens,
+        #     "temperature": temperature,
+        #     "top_p": top_p,
+        #     "system_prompt": system_prompt,
+        #     "stop": stop
+        # }
+        
         try:
             # 将 Pydantic 模型转换为 JSON 字符串
             if hasattr(request, 'model_dump_json'):

scripts/ai_agent/models/models_server.py

@@ -818,7 +818,6 @@ def test_inference(inference_type=0):
 
 
 if __name__ == "__main__":
-    main()
+    # main()
     # test_inference(2)
-    # logger.info("work finish")
+    logger.info("work finish")
-

scripts/ai_agent/tools/data_process/image_process/image_process.py

@@ -85,7 +85,90 @@ def PIL_image_to_base64( image_path: Optional[str] = None, image: Optional[PIL_I
     image.save(buffered, format="JPEG", quality=90)
     return base64.b64encode(buffered.getvalue()).decode('utf-8')
 
-# Removed ROS specific functions (ros_image2pil_image, ros_image2dict) as they depend on rospy/cv_bridge
+# def ros_image2pil_image(self, ros_image_msg:Sensor_Image,supported_image_formats):
+#     """回调函数：将ROS Image转为PIL Image并处理"""
+#     try:
+#         bridge_cv = CvBridge()
+#         # 1. 将ROS Image消息转为OpenCV格式（默认BGR8编码）
+#         # 若图像编码不同（如rgb8），需指定格式：bridge.imgmsg_to_cv2(ros_image_msg, "rgb8")
+#         # 尝试转换为OpenCV格式
+#         if ros_image_msg.encoding in supported_image_formats:
+#             cv_image = bridge_cv.imgmsg_to_cv2(ros_image_msg, desired_encoding=ros_image_msg.encoding)
+#         else:
+#             # 尝试默认转换
+#             cv_image = bridge_cv.imgmsg_to_cv2(ros_image_msg)
+#             logger.warning(f"转换不支持的图像编码: {ros_image_msg.encoding}")
+
+
+#         # 2. OpenCV默认是BGR格式，转为PIL需要的RGB格式
+#         rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
+
+#         # 3. 转为PIL Image格式
+#         pil_image = PIL_Image.fromarray(rgb_image)
+
+#         # 此处可添加对PIL Image的处理（如显示、保存等）
+#         rospy.loginfo(f"转换成功：PIL Image尺寸 {pil_image.size}")
+#         return pil_image
+#         # 示例：显示图像（需要PIL的显示支持）
+#         # pil_image.show()
+
+#     except CvBridgeError as e:
+#         rospy.logerr(f"转换失败：{e}")
+#     except Exception as e:
+#         rospy.logerr(f"处理错误：{e}")
+    
+
+# def ros_image2dict(self, image_msg: Sensor_Image,
+#                       supported_image_formats=['bgr8', 'rgb8', 'mono8'],
+#                       max_dim:int =2000) :
+#     """
+#     将ROS图像消息转换为字典格式，便于在上下文中存储
+    
+#     参数:
+#         image_msg: ROS的Image消息
+        
+#     返回:
+#         包含图像信息的字典，或None(转换失败时)
+#     """
+#     try:
+#         # 尝试转换为OpenCV格式
+#         if image_msg.encoding in self.supported_image_formats:
+#             cv_image = self.bridge.imgmsg_to_cv2(image_msg, desired_encoding=image_msg.encoding)
+#         else:
+#             # 尝试默认转换
+#             cv_image = self.bridge.imgmsg_to_cv2(image_msg)
+#             logger.warning(f"转换不支持的图像编码: {image_msg.encoding}")
+            
+#         # 图像预处理：调整大小以减少数据量
+#         h, w = cv_image.shape[:2]
+#         if max(h, w) > max_dim:
+#             scale = max_dim / max(h, w)
+#             cv_image = cv2.resize(
+#                 cv_image, 
+#                 (int(w * scale), int(h * scale)),
+#                 interpolation=cv2.INTER_AREA
+#             )
+            
+#         # 转换为JPEG并编码为base64
+#         encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 80]
+#         _, buffer = cv2.imencode('.jpg', cv_image, encode_param)
+#         img_base64 = base64.b64encode(buffer).decode('utf-8')
+        
+#         return {
+#             "type": "image",
+#             "format": "jpg",
+#             "data": img_base64,
+#             "width": cv_image.shape[1],
+#             "height": cv_image.shape[0],
+#             "original_encoding": image_msg.encoding,
+#             "timestamp": image_msg.header.stamp.to_sec()
+#         }
+        
+#     except CvBridgeError as e:
+#         logger.error(f"CV桥接错误: {str(e)}")
+#     except Exception as e:
+#         logger.error(f"图像处理错误: {str(e)}")
+#     return None
 
 def PIL_image_to_base64_sizelmt(image_path:Optional[str]=None,image:Optional[PIL_Image.Image] = None, 
                                 need_size_lmt:bool= False, max_size:tuple=(800, 800), quality:float=90):

scripts/ai_agent/tools/memory_mag/rag/rag.py

@@ -101,16 +101,15 @@ def set_embeddings(embedding_model_path,
                    model_config:Optional[dict[str,Any]]):
 
     if "llamacpp_embeddings" == embedding_type:
-        # 使用 .get() 方法提供默认值，确保即使配置文件中缺少某些字段也能正常工作
         embeddings = set_embeddings_llamacpp(
             model_path=embedding_model_path,
-            n_ctx=model_config.get("n_ctx", 512),
+            n_ctx=model_config["n_ctx"],
-            n_threads=model_config.get("n_threads", 4),
+            n_threads=model_config["n_threads"],
-            n_gpu_layers=model_config.get("n_gpu_layers", 0),  # RTX 5090建议设20，充分利用GPU
+            n_gpu_layers=model_config["n_gpu_layers"],  # RTX 5090建议设20，充分利用GPU
-            n_seq_max=model_config.get("n_seq_max", 128),
+            n_seq_max=model_config["n_seq_max"],
-            n_threads_batch=model_config.get("n_threads_batch", 4),
+            n_threads_batch = model_config["n_threads_batch"],
-            flash_attn=model_config.get("flash_attn", True),
+            flash_attn = model_config["flash_attn"],
-            verbose=model_config.get("verbose", False)
+            verbose=model_config["verbose"]
         )
     elif "huggingFace_embeddings" == embedding_type:
         embeddings = set_embeddings_huggingFace(
@@ -157,49 +156,19 @@ def load_vector_database(embeddings,
                          collection_name:str):
     """加载已存在的向量数据库"""
     try:
-        if not os.path.exists(path=persist_directory):
+        if os.path.exists(path=persist_directory):
-            logger.warning(f"向量数据库目录不存在: {persist_directory}")
-            return None
-        
-        # 先检查集合是否存在
-        try:
-            import chromadb
-            client = chromadb.PersistentClient(path=persist_directory)
-            collections = client.list_collections()
-            collection_names = [col.name for col in collections]
-            
-            if collection_name not in collection_names:
-                logger.warning(f"集合 '{collection_name}' 不存在于数据库中。")
-                logger.info(f"可用的集合: {collection_names}")
-                # 尝试查找有数据的集合
-                for col in collections:
-                    if col.count() > 0:
-                        logger.info(f"发现非空集合: {col.name} (count: {col.count()})")
-                return None
-            
-            # 检查集合是否有数据
-            target_collection = client.get_collection(name=collection_name)
-            doc_count = target_collection.count()
-            if doc_count == 0:
-                logger.warning(f"集合 '{collection_name}' 存在但为空 (count: 0)")
-                logger.warning(f"⚠️  该集合没有数据，RAG 检索将返回空结果")
-                # 仍然返回数据库对象，允许后续操作（如添加数据）
-            else:
-                logger.info(f"✅ 集合 '{collection_name}' 包含 {doc_count} 条文档")
-        except Exception as check_error:
-            logger.warning(f"检查集合时出错: {check_error}，继续尝试加载...")
-        
-        # 加载向量数据库
             vector_db = Chroma(
                 persist_directory=persist_directory,
                 embedding_function=embeddings,
                 collection_name=collection_name
             )
-        logger.info(f"✅ 成功加载向量数据库 from {persist_directory}, 集合: {collection_name}")
+            print(f"已加载向量数据库 from {persist_directory}")
-        return vector_db
 
+            return vector_db
+        else:
+            return None
     except Exception as e:
-        logger.error(f"vector_database加载失败: {str(e)}", exc_info=True)
+        logger.error(f"vector_database加载失败: {str(e)}")
         return None
 
 #设置文本分割器   
@@ -237,7 +206,7 @@ def set_document_loaders(self):
 def retrieve_relevant_info(vectorstore,
                            query: str,
                            k: int = 3, 
-                           score_threshold: float = None
+                           score_threshold: float = 0.2
                            ) -> List[Dict[str, Any]]:
     """
     检索与查询相关的信息
@@ -245,8 +214,7 @@ def retrieve_relevant_info(vectorstore,
     参数:
         query: 查询文本
         k: 最多返回的结果数量
-        score_threshold: 相关性分数阈值。如果为 None，则使用自适应阈值（前 k 个结果中最高分数的 1.5 倍）
+        score_threshold: 相关性分数阈值
-                        对于 L2 距离，分数越小越相似，阈值应该是一个较大的值
         
     返回:
         包含相关文档内容和分数的列表
@@ -255,41 +223,21 @@ def retrieve_relevant_info(vectorstore,
         # 执行相似性搜索，返回带分数的结果
         docs_and_scores = vectorstore.similarity_search_with_score(query, k=k)
         
-        if not docs_and_scores:
-            logger.info(f"检索到 0 条结果 (k={k})")
-            return []
-        
-        # 如果没有指定阈值，使用自适应阈值
-        # 对于 L2 距离，取前 k 个结果中最高的分数，然后乘以一个系数作为阈值
-        if score_threshold is None:
-            max_score = max(score for _, score in docs_and_scores)
-            # 使用最高分数的 1.5 倍作为阈值，确保包含所有前 k 个结果
-            score_threshold = max_score * 1.5
-            logger.debug(f"自适应阈值: {score_threshold:.2f} (基于最高分数 {max_score:.2f})")
-        
         # 过滤并格式化结果
         results = []
         for doc, score in docs_and_scores:
-            # 对于 L2 距离，分数越小越相似
+            if score < score_threshold:  # 分数越低表示越相似
-            # 如果阈值很大（> 1000），说明是 L2 距离，使用 < 比较
-            # 如果阈值很小（< 1），说明可能是余弦距离，使用 < 比较
-            if score < score_threshold:
                 results.append({
                     "content": doc.page_content,
                     "metadata": doc.metadata,
                     "similarity_score": float(score)
                 })
-            else:
-                logger.debug(f"文档因分数 {score:.2f} >= 阈值 {score_threshold:.2f} 被过滤")
         
-        threshold_str = f"{score_threshold:.2f}" if score_threshold is not None else "自适应"
+        logger.info(f"检索到 {len(results)} 条相关信息 (k={k}, 阈值={score_threshold})")
-        logger.info(f"检索到 {len(results)} 条相关信息 (k={k}, 阈值={threshold_str})")
-        if results:
-            logger.debug(f"相似度分数范围: {min(r['similarity_score'] for r in results):.2f} - {max(r['similarity_score'] for r in results):.2f}")
         return results
         
     except Exception as e:
-        logger.error(f"检索相关信息失败: {str(e)}", exc_info=True)
+        logger.error(f"检索相关信息失败: {str(e)}")
         return []
     
 def get_retriever(vectorstore,search_kwargs: Dict[str, Any] = None) -> Any: