bug fixed for start point sidderent from current point

uav_agent.py

@@ -20,26 +20,56 @@ import json
 import os
 from pathlib import Path
 
+# from langchain.agents import create_tool_calling_agent
+# from langchain.agents import create_react_agent as create_react_agent_anthropic
+# from langchain.agents import AgentExecutor as AgentExecutorAnthropic
 
-class EnhancedChatOpenAI(ChatOpenAI):
-    """ChatOpenAI subclass that captures reasoning_content if provided by the API"""
+class EnhancedChatAnthropic(ChatAnthropic):
+    """
+    针对 Anthropic 的增强类：
+    1. 提取 'thinking' 块并将其转换为 ReAct 格式的 'Thought: ...'
+    2. 防止因为只有工具调用而导致的 content 为空
+    """
     
-    def _create_chat_result(self, response: Any) -> ChatResult:
-        result = super()._create_chat_result(response)
+    def generate(self, response: Any) -> ChatResult:
+        result = super().generate(response)
+        
+        for generation in result.generations:
+            message = generation.message
+            raw_content = message.content
+            
+            # --- 逻辑 A: 处理思维链 (Thinking Blocks) ---
+            # 如果 content 是列表 (Anthropic 标准格式)，提取 thinking
+            if isinstance(raw_content, list):
+                text_parts = []
+                thought_parts = []
+                
+                for block in raw_content:
+                    if isinstance(block, dict):
+                        if block.get("type") == "thinking":
+                            # 获取思考内容
+                            thinking = block.get("thinking", "")
+                            # 存入 additional_kwargs (保持与其他代码一致)
+                            message.additional_kwargs["reasoning_content"] = thinking
+                            thought_parts.append(f"Thought: {thinking}\n")
+                        elif block.get("type") == "text":
+                            text_parts.append(block.get("text", ""))
+                
+                # 重组 Content，把思考放在最前面，欺骗 ReAct 解析器
+                final_text = "".join(text_parts)
+                if thought_parts and "Thought:" not in final_text:
+                    message.content = "".join(thought_parts) + final_text
+                else:
+                    message.content = final_text
+
+            # --- 逻辑 B: 处理纯工具调用导致的空字符串 ---
+            # 如果 content 为空，但有工具调用，我们手动补一个 Thought
+            # 这样 ReAct 解析器就不会报错说 "No action found"
+            if not message.content and message.tool_calls:
+                tool_name = message.tool_calls[0]['name']
+                # 伪造一个 Thought，让 Log 好看，也让解析器通过
+                message.content = f"Thought: I should use the {tool_name} tool to proceed.\n"
                 
-        if hasattr(response, "choices") and response.choices:
-            for i, choice in enumerate(response.choices):
-                # Handle MiniMax reasoning_details
-                if hasattr(choice.message, "reasoning_details") and choice.message.reasoning_details:
-                    reasoning = choice.message.reasoning_details[0].get('text', '')
-                    if reasoning and i < len(result.generations):
-                        gen = result.generations[i]
-                        if isinstance(gen.message, AIMessage):
-                            # Store in additional_kwargs
-                            gen.message.additional_kwargs["reasoning_content"] = reasoning
-                            # Prepend to content for ReAct agent visibility
-                            if "Thought:" not in gen.message.content:
-                                gen.message.content = f"Thought: {reasoning}\n" + gen.message.content
         return result
 
 
@@ -289,7 +319,7 @@ class UAVControlAgent:
                     "api_key": llm_api_key,
                     "base_url": final_base_url
                 }
-                self.llm = ChatAnthropic(**kwargs)
+                self.llm = EnhancedChatAnthropic(**kwargs)
             else:
                 kwargs = {
                     "model": llm_model,
@@ -328,6 +358,17 @@ class UAVControlAgent:
         # Create ReAct agent
         if self.debug:
             print("🤖 Creating ReAct agent...")
+        if llm_provider in ["anthropic", "anthropic-compatible"]:
+            if self.debug:
+                print("🤖 Using Tool Calling Agent (Better for Claude)")
+            self.agent = create_react_agent(
+                llm=self.llm,
+                tools=self.tools,
+                prompt=self.prompt
+            )
+        else:
+            if self.debug:
+                print("🤖 Using React Agent (GPT-3 and older)")
             self.agent = create_react_agent(
                 llm=self.llm,
                 tools=self.tools,

uav_langchain_tools.py

@@ -460,6 +460,8 @@ def create_uav_tools(client: UAVAPIClient) -> list:
                 return "Error: drone_id is required"
 
             result = client.take_off(drone_id, altitude)
+            if result["status"] == "success":
+                result["message"] += f" Using `get_drone_status` to check if the drone\'s current position is start point. If not, fly to the start point first."
             return json.dumps(result, indent=2)
         except json.JSONDecodeError as e:
             return f"Error parsing JSON input: {str(e)}. Expected format: {{\"drone_id\": \"drone-001\", \"altitude\": 15.0}}"
@@ -1248,8 +1250,10 @@ def create_uav_tools(client: UAVAPIClient) -> list:
             if current_coverage >= required_coverage:
                 tool_states.explored_count = 0
                 return f"Success: Target explored with coverage {current_coverage:.2%} (Visited {tool_states.explored_count}/{total_points} grid points)"
-
-        return f"Finished path. Final coverage: {current_coverage:.2%}"
+        if math.isclose(current_coverage, 0.0):
+            return f"Finished path. Final coverage: {current_coverage:.2%}. Please try call this tool again to continue exploring."
+        else:
+            return f"Finished path. Final coverage: {current_coverage:.2%}. Wait for a while and continue calling this function! return [TASK DONE] this time"
     
 
     # Return all tools