Update README.md

README.md

@@ -115,15 +115,17 @@ prompt = prompt_engine.get_prompt(conversations)
 output_str = _inference(prompt, llm, params)
 result = prompt_engine.parse_generated_str(output_str)
 
-print(result) # 
+print(result)
+# {'role': 'assistant',
+#  'content': '深度學習（Deep Learning）是一種機器學習方法，它模仿人類大腦的神經網路結構來處理複雜的數據和任務。在深度學習中，模型由多層人工神經元組成，每個神經元之間有權重連接，並通過非線性轉換進行計算。這些層與層之間的相互作用使模型能夠學習複雜的函數關係或模式，從而解決各種問題，如圖像識別、自然語言理解、語音辨識等。深度學習通常需要大量的數據和強大的計算能力，因此經常使用圖形處理器（GPU）或特殊的加速器來執行。'}
 ```
 
 **Function Calling**
 
 ```python
-from mtkresearch.llm.prompt import MRPromptV2
+import json
 
-sys_prompt = 'You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.'
+from mtkresearch.llm.prompt import MRPromptV2
 
 functions = [
     {
@@ -146,6 +148,13 @@ functions = [
     }
 ]
 
+def faked_get_current_weather(location, unit=None):
+    return {'temperature': 30}
+
+mapping = {
+    'get_current_weather': faked_get_current_weather
+}
+
 prompt_engine = MRPromptV2()
 
 # stage 1: query
@@ -158,10 +167,35 @@ prompt = prompt_engine.get_prompt(conversations, functions=functions)
 output_str = _inference(prompt, llm, params)
 result = prompt_engine.parse_generated_str(output_str)
 
-print(result) #
+print(result) 
+# {'role': 'assistant', 
+#  'tool_calls': [
+#    {'id': 'call_U9bYCBRAbF639uUqfwehwSbw', 'type': 'function', 
+#     'function': {'name': 'get_current_weather', 'arguments': '{"location": "台北, 台灣", "unit": "攝氏"}'}}]}
 
 # stage 2: execute called functions
+conversations.append(result)
+
+tool_call = result['tool_calls'][0]
+func_name = tool_call['function']['name']
+func = mapping[func_name]
+arguments = json.loads(tool_call['function']['arguments'])
+called_result = func(**arguments)
 
 # stage 3: put executed results
+conversations.append(
+    {
+        'role': 'tool',
+        'tool_call_id': tool_call['id'],
+        'name': func_name,
+        'content': json.dumps(called_result)
+    }
+)
+
+prompt = prompt_engine.get_prompt(conversations, functions=functions)
 
+output_str2 = _inference(prompt, llm, params)
+result2 = prompt_engine.parse_generated_str(output_str2)
+print(result2)
+# {'role': 'assistant', 'content': '台北目前的溫度是攝氏30度。'}
 ```