questions_answers # REDROCK_CLIENT

app.py

@@ -1117,13 +1117,7 @@ def get_questions_answers(video_id, df_string, source="gcs"):
     return questions_answers
 
 def generate_questions_answers(df_string):
-    # 使用 OpenAI 生成基于上传数据的问题
-    if isinstance(df_string, str):
-        df_string_json = json.loads(df_string)
-    else:
-        df_string_json = df_string
-
-    # JSON FORMAT: [{"question": "問題", "answer": "答案"}, ...]
+    content_text = str(df_string)
     sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW"
     user_content = f"""
         請根據 {content_text} 生成三個問題跟答案，主要與學科有關，不要問跟情節故事相關的問題
@@ -1133,20 +1127,46 @@ def generate_questions_answers(df_string):
         並用 JSON 格式返回 questions_answers: [{{question: q1的敘述text, answer: q1的答案text}}, ...]
         k-v pair 的 key 是 question, value 是 answer
     """
-    messages = [
-        {"role": "system", "content": sys_content},
-        {"role": "user", "content": user_content}
-    ]
-    response_format = { "type": "json_object" }
-    request_payload = {
-        "model": "gpt-4-turbo",
-        "messages": messages,
-        "max_tokens": 4000,
-        "response_format": response_format
-    }
 
-    response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
-    questions_answers = json.loads(response.choices[0].message.content)["questions_answers"]
+    try:
+        # OPENAI
+        messages = [
+            {"role": "system", "content": sys_content},
+            {"role": "user", "content": user_content}
+        ]
+        response_format = { "type": "json_object" }
+        request_payload = {
+            "model": "gpt-4-turbo",
+            "messages": messages,
+            "max_tokens": 4000,
+            "response_format": response_format
+        }
+
+        response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
+        questions_answers = json.loads(response.choices[0].message.content)["questions_answers"]
+    except:
+        # REDROCK_CLIENT        
+        messages = [
+            {"role": "user", "content": user_content}
+        ]
+        model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
+        # model_id = "anthropic.claude-3-haiku-20240307-v1:0"
+        kwargs = {
+            "modelId": model_id,
+            "contentType": "application/json",
+            "accept": "application/json",
+            "body": json.dumps({
+                "anthropic_version": "bedrock-2023-05-31",
+                "max_tokens": 4000,
+                "system": sys_content,
+                "messages": messages
+            })
+        }
+        response = BEDROCK_CLIENT.invoke_model(**kwargs)
+        response_body = json.loads(response.get('body').read())
+        response_completion = response_body.get('content')[0].get('text')
+        questions_answers = json.loads(response_completion)["questions_answers"]
+
     print("=====json_response=====")
     print(questions_answers)
     print("=====json_response=====")