Fix errors in test documentation

docs/en/Cookbook/streaming.md

@@ -18,7 +18,7 @@ Let's first simply implement a streaming conversational robot with a front-end i
 import lazyllm
 
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True)  # or llm = lazyllm.OnlineChatModule(stream=True)
-lazyllm.WebModule(llm, port=23333).start().wait()
+lazyllm.WebModule(llm, port=23333, stream=True).start().wait()
 ```
 
 Isn't the implementation very simple? You just need to define the model using streaming, and leave the rest of the work to [WebModule][lazyllm.tools.webpages.WebModule] to handle it. Then the messages displayed to the user will be displayed in a streaming manner on the front-end interface.
@@ -129,7 +129,7 @@ Now there is only one last step left. We use [WebModule][lazyllm.tools.webpages.
 
 ```python
 import lazyllm
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 Now we have completed a conversational robot that supports streaming output and [FunctionCall][lazyllm.tools.agent.FunctionCall]. When there is information to show to the user, the interface will stream the message content. And [FunctionCall][lazyllm.tools.agent.FunctionCall] will execute normally.
@@ -186,7 +186,7 @@ def get_n_day_weather_forecast(location: str, num_days: int, unit: Literal["cels
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True).start()  # or llm = lazyllm.OnlineChatModule()
 tools = ["get_current_weather", "get_n_day_weather_forecast"]
 agent = FunctionCallAgent(llm, tools)
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 The effect is as follows:
@@ -255,7 +255,7 @@ def get_n_day_weather_forecast(location: str, num_days: int, unit: Literal["cels
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True, return_trace=True).start()  # or llm = lazyllm.OnlineChatModule(return_trace=True)
 tools = ["get_current_weather", "get_n_day_weather_forecast"]
 agent = FunctionCallAgent(llm, tools, return_trace=True)
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 The effect is as follows:

docs/zh/Cookbook/streaming.md

@@ -18,7 +18,7 @@
 import lazyllm
 
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True)  # or llm = lazyllm.OnlineChatModule(stream=True)
-lazyllm.WebModule(llm, port=23333).start().wait()
+lazyllm.WebModule(llm, port=23333, stream=True).start().wait()
 ```
 
 实现是不是很简单，只需要定义好模型使用流式，其余工作交给 [WebModule][lazyllm.tools.webpages.WebModule] 来处理即可，则在前端界面上会流式的显示展示给用户的消息。
@@ -129,7 +129,7 @@ agent = FunctionCallAgent(llm, tools)
 
 ```python
 import lazyllm
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 现在便完成了支持流式输出和 [FunctionCall][lazyllm.tools.agent.FunctionCall] 的对话机器人。当有给用户展示的信息时，界面便会流式的输出消息内容。而 [FunctionCall][lazyllm.tools.agent.FunctionCall] 会正常执行。
@@ -186,7 +186,7 @@ def get_n_day_weather_forecast(location: str, num_days: int, unit: Literal["cels
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True).start()  # or llm = lazyllm.OnlineChatModule()
 tools = ["get_current_weather", "get_n_day_weather_forecast"]
 agent = FunctionCallAgent(llm, tools)
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 效果如下：
@@ -256,7 +256,7 @@ def get_n_day_weather_forecast(location: str, num_days: int, unit: Literal["cels
 llm = lazyllm.TrainableModule("internlm2-chat-20b", stream=True, return_trace=True).start()  # or llm = lazyllm.OnlineChatModule(return_trace=True)
 tools = ["get_current_weather", "get_n_day_weather_forecast"]
 agent = FunctionCallAgent(llm, tools, return_trace=True)
-lazyllm.WebModule(agent, port=23333).start().wait()
+lazyllm.WebModule(agent, port=23333, stream=True).start().wait()
 ```
 
 效果如下：

lazyllm/docs/components.py

@@ -963,23 +963,23 @@
 >>> from lazyllm import ChatPrompter
 >>> p = ChatPrompter('hello world')
 >>> p.generate_prompt('this is my input')
-'<|start_system|>You are an AI-Agent developed by LazyLLM.hello world\\\\n\\\\n<|end_system|>\\\\n\\\\n\\\\n<|Human|>:\\\\nthis is my input\\\\n<|Assistant|>:\\\\n'
+'You are an AI-Agent developed by LazyLLM.hello world\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nthis is my input\\\\n\\\\n'
 >>> p.generate_prompt('this is my input', return_dict=True)
 {'messages': [{'role': 'system', 'content': 'You are an AI-Agent developed by LazyLLM.\\\\nhello world\\\\n\\\\n'}, {'role': 'user', 'content': 'this is my input'}]}
 >>>
 >>> p = ChatPrompter('hello world {instruction}', extro_keys=['knowledge'])
 >>> p.generate_prompt(dict(instruction='this is my ins', input='this is my inp', knowledge='LazyLLM-Knowledge'))
-'<|start_system|>You are an AI-Agent developed by LazyLLM.hello world this is my ins\\\\nHere are some extra messages you can referred to:\\\\n\\\\n### knowledge:\\\\nLazyLLM-Knowledge\\\\n\\\\n\\\\n<|end_system|>\\\\n\\\\n\\\\n<|Human|>:\\\\nthis is my inp\\\\n<|Assistant|>:\\\\n'
+'You are an AI-Agent developed by LazyLLM.hello world this is my ins\\\\nHere are some extra messages you can referred to:\\\\n\\\\n### knowledge:\\\\nLazyLLM-Knowledge\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nthis is my inp\\\\n\\\\n'
 >>> p.generate_prompt(dict(instruction='this is my ins', input='this is my inp', knowledge='LazyLLM-Knowledge'), return_dict=True)
 {'messages': [{'role': 'system', 'content': 'You are an AI-Agent developed by LazyLLM.\\\\nhello world this is my ins\\\\nHere are some extra messages you can referred to:\\\\n\\\\n### knowledge:\\\\nLazyLLM-Knowledge\\\\n\\\\n\\\\n'}, {'role': 'user', 'content': 'this is my inp'}]}
 >>> p.generate_prompt(dict(instruction='this is my ins', input='this is my inp', knowledge='LazyLLM-Knowledge'), history=[['s1', 'e1'], ['s2', 'e2']])
-'<|start_system|>You are an AI-Agent developed by LazyLLM.hello world this is my ins\\\\nHere are some extra messages you can referred to:\\\\n\\\\n### knowledge:\\\\nLazyLLM-Knowledge\\\\n\\\\n\\\\n<|end_system|>\\\\n\\\\n<|Human|>:s1<|Assistant|>:e1<|Human|>:s2<|Assistant|>:e2\\\\n<|Human|>:\\\\nthis is my inp\\\\n<|Assistant|>:\\\\n'
+'You are an AI-Agent developed by LazyLLM.hello world this is my ins\\\\nHere are some extra messages you can referred to:\\\\n\\\\n### knowledge:\\\\nLazyLLM-Knowledge\\\\n\\\\n\\\\n\\\\n\\\\ns1e1s2e2\\\\n\\\\nthis is my inp\\\\n\\\\n'
 >>>
 >>> p = ChatPrompter(dict(system="hello world", user="this is user instruction {input} "))
 >>> p.generate_prompt(dict(input="my input", query="this is user query"))
-'<|start_system|>You are an AI-Agent developed by LazyLLM.hello world\\\\n\\\\n<|end_system|>\\\\n\\\\n\\\\n<|Human|>:\\\\nthis is user instruction my input this is user query\\\\n<|Assistant|>:\\\\n'
+'You are an AI-Agent developed by LazyLLM.hello world\\\\n\\\\n\\\\n\\\\nthis is user instruction my input this is user query\\\\n\\\\n'
 >>> p.generate_prompt(dict(input="my input", query="this is user query"), return_dict=True)
-{'messages': [{'role': 'system', 'content': 'You are an AI-Agent developed by LazyLLM.\\\\nhello world\\\\n\\\\n'}, {'role': 'user', 'content': 'this is user instruction my input this is user query'}]}
+{'messages': [{'role': 'system', 'content': 'You are an AI-Agent developed by LazyLLM.\\\\nhello world'}, {'role': 'user', 'content': 'this is user instruction my input this is user query'}]}
 ''')
 
 # ============= MultiModal

lazyllm/docs/flow.py

@@ -115,9 +115,9 @@
 ... 
 >>> flow = lazyllm.pipeline(test1, lazyllm.pipeline(test2, test3))
 >>> flow.for_each(lambda x: callable(x), lambda x: print(x))
-<function test1 at 0x7f389c3d3ac0>
-<function test2 at 0x7f389c3d3b50>
-<function test3 at 0x7f389c3d3be0>
+<Function type=test1>
+<Function type=test2>
+<Function type=test3>
 """)
 
 add_chinese_doc('Parallel', """\

lazyllm/docs/module.py

@@ -42,10 +42,10 @@
 ... 
 >>> m = Module2()
 >>> m.submodules
-[<__main__.Module object at 0x7f3dc3bb16f0>]
+[<Module type=Module>]
 >>> m.m3 = Module()
 >>> m.submodules
-[<__main__.Module object at 0x7f3dc3bb16f0>, <__main__.Module object at 0x7f3dc3bb0be0>]
+[<Module type=Module>, <Module type=Module>]
 ''')
 
 add_chinese_doc('ModuleBase.forward', '''\
@@ -116,7 +116,7 @@
 
 add_example('ModuleBase.update', '''\
 >>> import lazyllm
->>> m = lazyllm.module.TrainableModule().finetune_method(lazyllm.finetune.dummy).deploy_method(lazyllm.deploy.dummy).mode('finetune').prompt(None)
+>>> m = lazyllm.module.TrainableModule().finetune_method(lazyllm.finetune.dummy).trainset("").deploy_method(lazyllm.deploy.dummy).mode('finetune').prompt(None)
 >>> m.evalset([1, 2, 3])
 >>> m.update()
 INFO: (lazyllm.launcher) PID: dummy finetune!, and init-args is {}
@@ -134,7 +134,7 @@
 
 add_example('ModuleBase.evalset', '''\
 >>> import lazyllm
->>> m = lazyllm.module.TrainableModule().deploy_method(layzllm.deploy.dummy).finetune_method(lazyllm.finetune.dummy).mode("finetune").prompt(None)
+>>> m = lazyllm.module.TrainableModule().deploy_method(lazyllm.deploy.dummy).finetune_method(lazyllm.finetune.dummy).trainset("").mode("finetune").prompt(None)
 >>> m.evalset([1, 2, 3])
 >>> m.update()
 INFO: (lazyllm.launcher) PID: dummy finetune!, and init-args is {}
@@ -743,45 +743,21 @@
 
 add_example('OnlineChatModule', '''\
 >>> import lazyllm
+>>> from functools import partial
 >>> m = lazyllm.OnlineChatModule(source="sensenova", stream=True)
 >>> query = "Hello!"
->>> resp = m(query)
->>> for r in resp:
-...     print(r)
+>>> with lazyllm.ThreadPoolExecutor(1) as executor:
+...     future = executor.submit(partial(m, llm_chat_history=[]), query)
+...     while True:
+...         if value := lazyllm.FileSystemQueue().dequeue():
+...             print(f"output: {''.join(value)}")
+...         elif future.done():
+...             break
+...     print(f"ret: {future.result()}")
 ...
-{'content': '你好'}
-{'content': '！'}
-{'content': '有什么'}
-{'content': '我可以'}
-{'content': '帮助'}
-{'content': '你的'}
-{'content': '吗'}
-{'content': '？'}
-{'content': ''}
->>> m = lazyllm.OnlineChatModule(source="sensenova", model="nova-ptc-s-v2", stream=False)
->>> train_file = "toy_chat_fine_tuning.jsonl"
->>> m.set_train_tasks(train_file=train_file, upload_url="https://file.sensenova.cn/v1/files")
->>> m._get_train_tasks()
-Num examples:
-First example:
-{'role': 'system', 'content': 'Marv is a factual chatbot that is also sarcastic.'}
-{'role': 'user', 'content': "What's the capital of France?"}
-{'role': 'assistant', 'content': "Paris, as if everyone doesn't know that already."}
-No errors found
-train file id: 7193d9a3-8b6e-4332-99cc-724dec75d9dd
-toy_chat_fine_tuning.jsonl upload success! file id is d632e591-f668-43a1-b5bf-49418e9c0fec
-fine tuning job ft-85f7bc96034441f2b64f9a5fff5d5b9c created, status: SUBMITTED
-fine tuning job ft-85f7bc96034441f2b64f9a5fff5d5b9c status: RUNNING
-...
-fine tuning job ft-85f7bc96034441f2b64f9a5fff5d5b9c status: SUCCEEDED
-fine tuned model: nova-ptc-s-v2:ft-fee492082cbe4a6d880d396f34f1bc50 finished  
->>> m._get_deploy_tasks()
-deployment c5aaf3bf-ef9b-4797-8c15-12ff04ed5372 created, status: SUBMITTED
-...
-deployment c5aaf3bf-ef9b-4797-8c15-12ff04ed5372 status: PENDING
-...
-deployment c5aaf3bf-ef9b-4797-8c15-12ff04ed5372 status: RUNNING
-deployment c5aaf3bf-ef9b-4797-8c15-12ff04ed5372 finished
+output: Hello
+output: ! How can I assist you today?
+ret: Hello! How can I assist you today?
 ''')
 
 add_chinese_doc('OnlineEmbeddingModule', '''\

lazyllm/docs/tools.py

@@ -201,16 +201,16 @@
 >>> doc1 = Document(dataset_path="your_files_path", create_ui=False)
 >>> doc2 = Document(dataset_path="your_files_path", create_ui=False)
 >>> doc1.add_reader("**/*.yml", YmlReader)
->>> print(doc1._local_file_reader)
-# {'**/*.yml': <class '__main__.YmlReader'>}
->>> print(doc2._local_file_reader)
-# {}
+>>> print(doc1._impl._local_file_reader)
+{'**/*.yml': <class '__main__.YmlReader'>}
+>>> print(doc2._impl._local_file_reader)
+{}
 >>> files = ["your_yml_files"]
 >>> Document.register_global_reader("**/*.yml", processYml)
 >>> doc1._impl._reader.load_data(input_files=files)
-# Call the class YmlReader.
+Call the class YmlReader.
 >>> doc2._impl._reader.load_data(input_files=files)
-# Call the function processYml.
+Call the function processYml.
 ''')
 
 add_english_doc('rag.readers.ReaderBase', '''