docs: fix Quick Start examples in Chat & Message API docs to match MemOSClient signatures

docs/cn/open_source/open_source_api/chat/chat.md

@@ -68,11 +68,11 @@ client = MemOSClient(api_key="...", base_url="...")
 # 发起对话请求
 res = client.chat(
     user_id="dev_user_01",
+    conversation_id="conv_r_study_001",
     query="根据我之前的偏好，推荐一套 R 语言数据清理方案",
-    readable_cube_ids=["private_cube_01", "public_kb_r_lang"], # 读：个人偏好+公共库
-    writable_cube_ids=["private_cube_01"],                      # 写：沉淀至个人空间
-    add_message_on_answer=True,                                 # 开启自动记忆回写
-    mode="fine"                                                 # 使用精细检索模式
+    knowledgebase_ids=["kb_r_lang"],  # 检索 R 语言公共知识库
+    add_message_on_answer=True,       # 开启自动记忆回写
+    temperature=0.7
 )
 
 if res:

docs/cn/open_source/open_source_api/message/feedback.md

@@ -46,24 +46,15 @@ from memos.api.client import MemOSClient
 
 client = MemOSClient(api_key="...", base_url="...")
 
-# 场景：修正 AI 关于用户职业的错误记忆
+# 场景：修正 AI 关于用户饮食偏好的错误记忆
 res = client.add_feedback(
     user_id="dev_user_01",
-    feedback_content="我不再减肥了，现在不需要控制饮食。",
-    history=[
-        {"role": "assistant", "content": "您正在减肥中，近期是否控制了摄入食物的热量？"},
-        {"role": "user", "content": "我不再减肥了..."}
-    ],
-    writable_cube_ids=["private_cube_01"],
-    # 指定具体的错误记忆 ID，以实现精准打击
-    retrieved_memory_ids=["mem_id_old_job_123"],
-    corrected_answer=True # 要求 AI 重新根据新事实回复我
+    conversation_id="conv_diet_001",
+    feedback_content="我不再减肥了，现在不需要控制饮食。"
 )
 
 if res and res.code == 200:
-    print(f"修正进度: {res.message}")
-    if res.data:
-        print(f"更正后的回复: {res.data}")
+    print(f"反馈已提交: {res.message}")
 ```
 
 

docs/cn/open_source/open_source_api/message/get_suggestion_queries.md

@@ -3,8 +3,6 @@ title: 获取建议问题 (Get Suggestions)
 desc: 基于当前对话语境或 Cube 内的近期记忆，自动生成 3 条后续对话建议。
 ---
 
-# 获取建议问题 (Get Suggestion Queries)
-
 **接口路径**：`POST /product/suggestions`
 **功能描述**：本接口用于实现“猜你想问”功能。系统会根据提供的对话上下文或目标 **MemCube** 中的近期记忆，通过大语言模型生成 3 个相关的建议问题，帮助用户延续对话。
 
@@ -41,27 +39,29 @@ desc: 基于当前对话语境或 Cube 内的近期记忆，自动生成 3 条
 
 ## 4. 快速上手示例
 
-使用 SDK 获取针对当前对话的中文建议：
+使用 HTTP 请求获取针对当前对话的中文建议：
 
 ```python
-from memos.api.client import MemOSClient
-
-client = MemOSClient(api_key="...", base_url="...")
-
-# 场景：根据刚刚关于“R语言”的对话生成建议
-res = client.get_suggestions(
-    user_id="dev_user_01",
-    mem_cube_id="private_cube_01",
-    language="zh",
-    message=[
-        {"role": "user", "content": "我想学习 R 语言的可视化。"},
-        {"role": "assistant", "content": "推荐您学习 ggplot2 包，它是 R 语言可视化的核心工具。"}
-    ]
+import requests
+
+# 场景：根据刚刚关于”R语言”的对话生成建议
+res = requests.post(
+    “http://localhost:8000/product/suggestions”,
+    json={
+        “user_id”: “dev_user_01”,
+        “mem_cube_id”: “private_cube_01”,
+        “language”: “zh”,
+        “message”: [
+            {“role”: “user”, “content”: “我想学习 R 语言的可视化。”},
+            {“role”: “assistant”, “content”: “推荐您学习 ggplot2 包，它是 R 语言可视化的核心工具。”}
+        ]
+    }
 )
 
-if res and res.code == 200:
-    # 示例输出: ["如何安装 ggplot2？", "有哪些经典的 ggplot2 教程？", "R 语言还有哪些可视化包？"]
-    print(f"建议问题: {res.data}")
+if res.status_code == 200:
+    data = res.json()
+    # 示例输出: [“如何安装 ggplot2？”, “有哪些经典的 ggplot2 教程？”, “R 语言还有哪些可视化包？”]
+    print(f”建议问题: {data['data']}”)
 ```
 
 ## 5. 使用场景建议

docs/en/open_source/open_source_api/chat/chat.md

@@ -70,11 +70,11 @@ client = MemOSClient(api_key="...", base_url="...")
 # Initiate a chat request
 res = client.chat(
     user_id="dev_user_01",
+    conversation_id="conv_r_study_001",
     query="Based on my previous preferences, recommend an R language data cleaning workflow",
-    readable_cube_ids=["private_cube_01", "public_kb_r_lang"], # Read: personal preferences + public knowledge base
-    writable_cube_ids=["private_cube_01"],                      # Write: persist to personal space
-    add_message_on_answer=True,                                 # Enable automatic memory write-back
-    mode="fine"                                                 # Use fine-grained retrieval mode
+    knowledgebase_ids=["kb_r_lang"],  # Retrieve from the public R language knowledge base
+    add_message_on_answer=True,       # Enable automatic memory write-back
+    temperature=0.7
 )
 
 if res:

docs/en/open_source/open_source_api/message/feedback.md

@@ -44,24 +44,15 @@ from memos.api.client import MemOSClient
 
 client = MemOSClient(api_key="...", base_url="...")
 
-# Scenario: Correct the AI's mistaken memory about the user's occupation
+# Scenario: Correct the AI's mistaken memory about the user's diet preference
 res = client.add_feedback(
     user_id="dev_user_01",
-    feedback_content="I am no longer on a diet and don't need to control my food intake anymore.",
-    history=[
-        {"role": "assistant", "content": "You're on a diet — have you been controlling your calorie intake recently?"},
-        {"role": "user", "content": "I'm not on a diet anymore..."}
-    ],
-    writable_cube_ids=["private_cube_01"],
-    # Specify the exact mistaken memory ID for precise correction
-    retrieved_memory_ids=["mem_id_old_job_123"],
-    corrected_answer=True # Ask the AI to respond again based on the updated facts
+    conversation_id="conv_diet_001",
+    feedback_content="I am no longer on a diet and don't need to control my food intake anymore."
 )
 
 if res and res.code == 200:
-    print(f"Correction progress: {res.message}")
-    if res.data:
-        print(f"Corrected response: {res.data}")
+    print(f"Feedback submitted: {res.message}")
 ```
 
 

docs/en/open_source/open_source_api/message/get_suggestion_queries.md

@@ -3,8 +3,6 @@ title: Get Suggestion Queries
 desc: Automatically generate 3 follow-up conversation suggestions based on the current dialogue context or recent memories within a Cube.
 ---
 
-# Get Suggestion Queries
-
 **API Path**: `POST /product/suggestions`
 **Description**: This API implements the "Guess What You Want to Ask" feature. Based on the provided conversation context or recent memories in the target **MemCube**, the system uses a large language model to generate 3 relevant suggested questions, helping users continue the conversation.
 
@@ -39,27 +37,29 @@ desc: Automatically generate 3 follow-up conversation suggestions based on the c
 
 ## 4. Quick Start
 
-Use the SDK to get Chinese-language suggestions for the current conversation:
+Use an HTTP request to get Chinese-language suggestions for the current conversation:
 
 ```python
-from memos.api.client import MemOSClient
-
-client = MemOSClient(api_key="...", base_url="...")
+import requests
 
 # Scenario: Generate suggestions based on a recent conversation about "R language"
-res = client.get_suggestions(
-    user_id="dev_user_01",
-    mem_cube_id="private_cube_01",
-    language="zh",
-    message=[
-        {"role": "user", "content": "I want to learn R language visualization."},
-        {"role": "assistant", "content": "I recommend learning the ggplot2 package — it's the core tool for R language visualization."}
-    ]
+res = requests.post(
+    "http://localhost:8000/product/suggestions",
+    json={
+        "user_id": "dev_user_01",
+        "mem_cube_id": "private_cube_01",
+        "language": "zh",
+        "message": [
+            {"role": "user", "content": "I want to learn R language visualization."},
+            {"role": "assistant", "content": "I recommend learning the ggplot2 package — it's the core tool for R language visualization."}
+        ]
+    }
 )
 
-if res and res.code == 200:
+if res.status_code == 200:
+    data = res.json()
     # Example output: ["How do I install ggplot2?", "What are some classic ggplot2 tutorials?", "What other visualization packages does R have?"]
-    print(f"Suggested questions: {res.data}")
+    print(f"Suggested questions: {data['data']}")
 ```
 
 ## 5. Suggested Use Cases