体验

动机

智能体开发者可以添加示例来指导规划和代码生成。此外，我们还提供了另一种将用户经验保存到长期记忆中的方式。实际应用中，如果用户要求TaskWeaver解决一个复杂问题， TaskWeaver可能一开始会出错。但经过多次尝试或用户提供更多指示后，智能体最终能解决问题。然而，下次用户提出类似问题甚至完全相同的问题时，智能体仍难以第一时间给出正确方案，因为它没有记住这些经验。因此，我们提出了一种称为"经验记忆"的机制。其核心思想是：用户可以通过命令让TaskWeaver保存聊天记录，从中提取经验提示并存入经验池。之后当智能体遇到类似请求时，就会从记忆中检索这些经验来指导其规划和代码生成。每条经验提示都记录了"遇到此类请求时应做或不应做"的事项。后续收到相似请求时，我们会将检索到的经验添加到提示中。

Save User Experience

快速开始

在本快速入门教程中，我们将向您展示如何启用经验功能并将聊天记录保存到经验池中。

要启用经验功能，您需要在配置文件中将planner.use_experience和code_generator.use_experience参数设置为true。这样，Planner和CodeInterpreter都可以利用经验来指导规划和代码生成。
启动与TaskWeaver的新对话。您会发现在项目目录中创建了experience目录。请注意，由于我们尚未保存任何聊天记录，目前该目录为空。
如果您认为当前聊天记录值得保存，可以在控制台聊天界面输入命令/save进行保存。您将在experience目录下发现一个名为raw_exp_{session_id}.yaml的新文件被创建。
重启TaskWeaver并开始新对话。在初始化阶段，TaskWeaver会读取raw_exp_{session_id}.yaml文件并生成一个名为exp_{session_id}.yaml的新摘要文件。这个过程可能需要一些时间，因为TaskWeaver需要调用LLM模型从聊天记录中提取经验技巧。
下次当用户向TaskWeaver发送类似查询时，系统将检索相关经验并将其加载到系统提示中（供Planner和CodeInterpreter使用）。通过这种方式，这些经验可用于指导未来的对话。

info

项目目录中会创建experience文件夹。您可以通过在配置文件中设置session.experience_dir参数来更改experience文件夹的位置。

示例演练

启动与TaskWeaver的新对话。向TaskWeaver发送请求"计算./project/sample_data/data_demo.csv的平均值"。
你会发现TaskWeaver犯了一个错误，计算了非数值列的平均值。经过多次尝试，TaskWeaver修复了这个问题。

=========================================================
 _____         _     _       __
|_   _|_ _ ___| | _ | |     / /__  ____ __   _____  _____
  | |/ _` / __| |/ /| | /| / / _ \/ __ `/ | / / _ \/ ___/
  | | (_| \__ \   < | |/ |/ /  __/ /_/ /| |/ /  __/ /
  |_|\__,_|___/_|\_\|__/|__/\___/\__,_/ |___/\___/_/
=========================================================
 TaskWeaver ▶  I am TaskWeaver, an AI assistant. To get started, could you please enter your request?
    Human   ▶   calculate the mean value of d:/demo_data.csv
 ╭───< Planner >
 ├─► [init_plan]
 │   1. load the data file
 │   2. calculate the mean value of the loaded data <sequentially depends on 1>
 │   3. report the result to the user <interactively depends on 2>
 ├─► [plan]
 │   1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data
 │   2. report the result to the user
 ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data
 ├──● Please load the data file d:/demo_data.csv and calculate the mean value of the loaded data
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas.
 ├─► [python]
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   data_df = pd.read_csv(data_file_path)
 │   mean_value = data_df.mean()
 │   mean_value
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] FAILURE
 ├─► [execution_result]
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     Cell In[1], line 5
 │       mean_value = data_df.mean()
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11335 in mean
 │       result = super().mean(axis, skipna, numeric_only, **kwargs)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\generic.py:11984 in mean
 │       return self._stat_function(
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\generic.py:11941 in _stat_function
 │       return self._reduce(
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11204 in _reduce
 │       res = df._mgr.reduce(blk_func)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\internals\managers.py:1459 in reduce
 │       nbs = blk.reduce(func)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\internals\blocks.py:377 in reduce
 │       result = func(self.values)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11136 in blk_func
 │       ret
 │   
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   data_df = pd.read_csv(data_file_path)
 │   mean_value = data_df.mean()
 │   mean_value
 │   ```
 │   
 │   
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     Cell In[1], line 5
 │       mean_value = data_df.mean()
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11335 in mean
 │       result = super().mean(axis, skipna, numeric_only, **kwargs)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\generic.py:11984 in mean
 │       return self._stat_function(
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\generic.py:11941 in _stat_function
 │       return self._reduce(
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11204 in _reduce
 │       res = df._mgr.reduce(blk_func)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\internals\managers.py:1459 in reduce
 │       nbs = blk.reduce(func)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\internals\blocks.py:377 in reduce
 │       result = func(self.values)
 │   
 │     File D:\python_project\Octopus\venv\Lib\site-packages\pandas\core\frame.py:11136 in blk_func
 │       ret
 │   
 ├─► [revise_message]
 │   The execution of the previous generated code has failed. If you think you can fix the problem by rewriting the code, please generate code and run it again.
 │   Otherwise, please explain the problem to me.
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas, considering the execution 
 │   error.
 ├─► [python]
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   data_df = pd.read_csv(data_file_path)
 │   mean_value = data_df.mean(axis=0, numeric_only=True)
 │   mean_value
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] SUCCESS
 ├─► [execution_result]
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │   Count    78172.75
 │   dtype: float64
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   data_df = pd.read_csv(data_file_path)
 │   mean_value = data_df.mean(axis=0, numeric_only=True)
 │   mean_value
 │   ```
 │   
 │   
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │   Count    78172.75
 │   dtype: float64
 ╰──● sending message to Planner
 ╭───< Planner >
 ├─► [init_plan]
 │   1. load the data file
 │   2. calculate the mean value of the loaded data <sequentially depends on 1>
 │   3. report the result to the user <interactively depends on 2>
 ├─► [plan]
 │   1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data
 │   2. report the result to the user
 ├─► [current_plan_step] 2. report the result to the user
 ├──● The mean value of the data in d:/demo_data.csv is 78172.75
 ╰──● sending message to User
 TaskWeaver ▶  The mean value of the data in d:/demo_data.csv is 78172.75

我们可以通过控制台聊天界面输入/save命令来保存聊天记录。之后，当我们重启TaskWeaver时，可以看到从上一次聊天记录中总结的经验如下：

exp_id: 20231228-073922-9b411afd
experience_text: "User Query: get the mean value of d:/demo_data.csv
  Error Resolution:
  - Error 1: The initial code failed to execute because it tried to calculate the mean value of all columns, including non-numeric columns.
  - Resolution 1: The code was revised to check for non-numeric columns and only calculate the mean value for numeric columns.
  Preferences:
  - Preference 1: The user prefers to calculate the mean value of the "Count" column in the data."
raw_experience_path: D:\project\experience\raw_exp_20231228-073922-9b411afd.yaml
embedding_model: text-embedding-ada-002
embedding: ...

向TaskWeaver发送类似的请求"计算./project/sample_data/data_demo.csv的方差值"。你会发现TaskWeaver不会再犯同样的错误，它会要求用户确认要计算方差值的列名。

=========================================================
 _____         _     _       __
|_   _|_ _ ___| | _ | |     / /__  ____ __   _____  _____
  | |/ _` / __| |/ /| | /| / / _ \/ __ `/ | / / _ \/ ___/
  | | (_| \__ \   < | |/ |/ /  __/ /_/ /| |/ /  __/ /
  |_|\__,_|___/_|\_\|__/|__/\___/\__,_/ |___/\___/_/
=========================================================
 TaskWeaver ▶  I am TaskWeaver, an AI assistant. To get started, could you please enter your request?
    Human   ▶  calculate the variance value of d:/demo_data.csv
 ╭───< Planner >TaskWeaver] preparing     <=�=>     
 ├─► [init_plan]
 │   1. load the data file
 │   2. confirm the columns to calculate variance <interactively depends on 1>
 │   3. calculate the variance value for the selected columns <sequentially depends on 2>
 │   4. report the result to the user <interactively depends on 3>
 ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calcu    <=�=>      
 │   1. instruct CodeInterpreter to load the data file
 │   2. confirm the columns to calculate variance
 ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file load the data file        <=�=>  
 ├──● Please load the data file d:/demo_data.csva file d:/demo_dat <=�=>         
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv using pandas.demo_data.csv using panda     <=�=>     
 ├─► [python]▶ [CodeInterpreter][python] import pandas as pd  data_file_path = "d:/demo_data.csv" df = pd.read_csv(data_fil      <=�=>    
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   df = pd.read_csv(data_file_path)
 │   df
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] SUCCESS] executing code       <=�=>   
 ├─► [execution_result]
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │                  TimeBucket  Count
 │   0    2023-02-01T00:00:00Z  67814
 │   1    2023-02-01T04:00:00Z  84569
 │   ..                    ...    ...
 │   718  2023-05-31T16:00:00Z  89912
 │   719  2023-05-31T20:00:00Z  87249
 │   
 │   [720 rows x 2 columns]
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   import pandas as pd
 │   
 │   data_file_path = "d:/demo_data.csv"
 │   df = pd.read_csv(data_file_path)
 │   df
 │   ```
 │   
 │   
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │                  TimeBucket  Count
 │   0    2023-02-01T00:00:00Z  67814
 │   1    2023-02-01T04:00:00Z  84569
 │   ..                    ...    ...
 │   718  2023-05-31T16:00:00Z  89912
 │   719  2023-05-31T20:00:00Z  87249
 │   
 │   [720 rows x 2 columns]
 ╰──● sending message to Planner
 ╭───< Planner >CodeInterpreter] finished  <=�=>        
 ├─► [init_plan]
 │   1. load the data file
 │   2. confirm the columns to calculate variance <interactively depends on 1>
 │   3. calculate the variance value for the selected columns <sequentially depends on 2>
 │   4. report the result to the user <interactively depends on 3>
 ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calculate var       <=�=>   
 │   1. instruct CodeInterpreter to load the data file
 │   2. confirm the columns to calculate variance
 ├─► [current_plan_step] 2. confirm the columns to calculate variance calculate varia      <=�=>    
 ├──● The data file d:/demo_data.csv is loaded. The columns in the data are:
 │   
 │   1. TimeBucket
 │   2. Count
 │   
 │   Please specify which column(s) you would like to calculate the variance for.
 ╰──● sending message to User
 TaskWeaver ▶  The data file d:/demo_data.csv is loaded. The columns in the data are:

1. TimeBucket
2. Count

Please specify which column(s) you would like to calculate the variance for.

Human   ▶  Count
 ╭───< Planner >TaskWeaver] preparing    <=�=>      
 ├─► [init_plan]
 │   1. load the data file
 │   2. confirm the columns to calculate variance <interactively depends on 1>
 │   3. calculate the variance value for the selected columns <sequentially depends on 2>
 │   4. report the result to the user <interactively depends on 3>
 ├─► [plan]
 │   1. instruct CodeInterpreter to load the data file
 │   2. confirm the columns to calculate variance
 │   3. instruct CodeInterpreter to calculate the variance value for the Count column
 │   4. report the result to the user
 ├─► [current_plan_step] 3. instruct CodeInterpreter to calculate the variance value for the Count columnor the Count col  <=�=>        
 ├──● Please calculate the variance value for the Count column in the loaded datalumn in the loaded          <=�=>
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] ProgramApe will generate a code snippet to calculate the variance value for the Count column in the loaded data. column in the loaded dat <=�=>         
 ├─► [python]▶ [CodeInterpreter][python] variance_count = df['Count'].var() varia     <=�=>     
 │   variance_count = df['Count'].var()
 │   variance_count
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] SUCCESS] executing code        <=�=>  
 ├─► [execution_result]
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │   263514550.45479834
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   variance_count = df['Count'].var()
 │   variance_count
 │   ```
 │   
 │   
 │   The execution of the generated python code above has succeeded
 │   
 │   The result of above Python code after execution is:
 │   263514550.45479834
 ╰──● sending message to Planner
 ╭───< Planner >CodeInterpreter] finished         <=�=> 
 ├─► [init_plan]
 │   1. load the data file
 │   2. confirm the columns to calculate variance <interactively depends on 1>
 │   3. calculate the variance value for the selected columns <sequentially depends on 2>
 │   4. report the result to the user <interactively depends on 3>
  ├─► [plan]
 │   1. instruct CodeInterpreter to load the data file
 │   2. confirm the columns to calculate variance
 │   3. instruct CodeInterpreter to calculate the variance value for the Count column
 │   4. report the result to the user
 ├─► [current_plan_step] 4. report the result to the user result to the u       <=�=>   
 ├──● The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45a.csv is 263,51    <=�=>      
 ╰──● sending message to User
 TaskWeaver ▶  The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45

动机​

快速开始​

示例演练​

动机

快速开始

示例演练