Update README.md

README.md

@@ -127,58 +127,105 @@ Save a legible drawing as `test.jpg` in your working directory, then run the fol
 ```python
 import base64
 from openai import OpenAI
+import json
 
 client = OpenAI(api_key="EMPTY", base_url="http://localhost:18124/v1")
 model = "LevelField"
 
+SYSTEM_PROMPT = (
+    "你是一位机械制造工艺设计专家。"
+    "请根据工程图纸，先从图纸中提取零件信息（标题栏、技术要求等），"
+    "然后推导出完整的工艺流程。请用中文输出。"
+    "如果图纸不清晰、不是机械工程图纸、或无法识别关键信息，请说明原因，不要编造内容。"
+)
+
+PROCESSES = [
+    "落料",
+    "普车", "数车", "小普车",
+    "普铣", "三轴", "四轴", "五轴", "卧加",
+    "平面磨", "内外圆磨", "外圆磨", "平磨", "磨床",
+    "中丝", "慢丝", "快丝", "线割",
+    "穿孔", "电火花", "深孔", "深孔钻", "摇臂钻", "钻床",
+    "热处理", "回火", "表面处理", "抛光",
+    "检验", "三坐标", "钳工",
+    "装配", "压配", "铜焊", "焊接", "热配",
+    "试压", "道序委外", "流转", "动平衡", "激光雕刻",
+]
+
+EQUIPMENT_MAP = {}
+
 
 def encode_image_base64(image_path):
     with open(image_path, "rb") as f:
         return base64.b64encode(f.read()).decode("utf-8")
 
 
-image_path = "test.jpg"
+image_path = "test.png"
 image_base64 = encode_image_base64(image_path)
 
-response = client.chat.completions.create(
+user_prompt = f"""【可用工序词表】
+{json.dumps(PROCESSES, ensure_ascii=False, indent=2)}
+
+【可用设备清单】
+{{}}
+
+【可用刀具清单】
+{{}}
+
+【可用检具清单】
+{{}}
+
+请分析这张工程图纸，根据上述工序词表、设备清单、刀具清单、检具清单和工艺规则，推导工艺流程并输出JSON。
+
+要求：
+1. 工序名称只能使用工序词表中的名称
+2. 设备只能从可用设备清单中选择（多台设备用分号分隔），清单中无对应工序时留空
+3. 刀具优先从刀具清单中选择，清单为空时根据工艺常识推导填写
+4. 检具优先从检具清单中选择，清单为空时根据工艺常识推导填写
+5. JSON中不要使用LaTeX格式，请使用Unicode字符（如φ）"""
+
+stream = client.chat.completions.create(
     model=model,
     messages=[
+        {"role": "system", "content": SYSTEM_PROMPT},
         {
             "role": "user",
             "content": [
-                {
-                    "type": "text",
-                    "text": (
-                        "您是一位工艺工程师。现在给您加工图纸<image>。"
-                        "请根据可供选择的工艺`车削,铣削,钻削,磨削,铸造,锻造,"
-                        "热处理,表面处理,电火花加工,钳加工,装配,齿加工,"
-                        "钣金成型,焊接,线切割,激光打标,检验,下料`，"
-                        "给出它的工艺序列。"
-                    ),
-                },
+                {"type": "text", "text": user_prompt},
                 {
                     "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{image_base64}"
-                    },
+                    "image_url": {"url": f"data:image/{'png' if image_path.lower().endswith('.png') else 'jpeg'};base64,{image_base64}"},
                 },
             ],
-        }
+        },
     ],
-    temperature=0.01,
-    max_tokens=2000,
+    temperature=0.85,
+    top_p=0.95,
+    max_tokens=16384,
+    presence_penalty=1.0,
+    stream=True,
     extra_body={
-        "chat_template_kwargs": {"enable_thinking": True}
+        "top_k": 20,
+        "chat_template_kwargs": {"enable_thinking": True},
     },
 )
 
-message = response.choices[0].message
-if hasattr(message, "reasoning") and message.reasoning:
-    print("【思考过程】")
-    print(message.reasoning)
-    print()
-print("【最终回答】")
-print(message.content)
+print("【思考过程】", flush=True)
+in_reasoning = True
+for chunk in stream:
+    if not chunk.choices:  
+        continue
+    delta = chunk.choices[0].delta
+    reasoning = getattr(delta, "reasoning", None)  # 思考内容
+    content = getattr(delta, "content", None)  # 最终回答
+    if reasoning:
+        print(reasoning, end="", flush=True)
+    if content:
+        if in_reasoning:  # 思考结束, 切到回答
+            print("\n\n【最终回答】", flush=True)
+            in_reasoning = False
+        print(content, end="", flush=True)
+print()
 ```
 
 This example enables thinking through `chat_template_kwargs` and prints the reasoning field when returned, followed by the final answer. Use it with the server configuration above, including `--reasoning-parser qwen3`.