Dev

metagpt/environment/base_env.py

@@ -4,7 +4,7 @@
 
 import asyncio
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Iterable, Optional, Set, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, Optional, Set, Union
 
 from pydantic import BaseModel, ConfigDict, Field, SerializeAsAny, model_validator
 
@@ -104,7 +104,7 @@ class Environment(ExtEnv):
 
     desc: str = Field(default="")  # 环境描述
     roles: dict[str, SerializeAsAny["Role"]] = Field(default_factory=dict, validate_default=True)
-    member_addrs: dict["Role", Set] = Field(default_factory=dict, exclude=True)
+    member_addrs: Dict["Role", Set] = Field(default_factory=dict, exclude=True)
     history: str = ""  # For debug
     context: Context = Field(default_factory=Context, exclude=True)
 

tests/data/rsp_cache.json

@@ -394,5 +394,6 @@
     },
     "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
         "code": "from sklearn import datasets\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\niris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\niris_df['target'] = iris.target\niris_df['target_name'] = iris_df['target'].apply(lambda x: iris.target_names[x])\n\n# Plotting\nfig, ax = plt.subplots(figsize=(12, 8))\nfor target, target_name in zip(iris.target_names, iris.target_names):\n    subset = iris_df[iris_df['target_name'] == target_name]\n    ax.scatter(subset[iris.feature_names[0]], subset[iris.feature_names[1]], label=target_name)\n\nax.set_xlabel(iris.feature_names[0])\nax.set_ylabel(iris.feature_names[1])\nax.legend()\nplt.show()"
-    }
+    },
+    "\n## context\n\n\n-----\n\n## format example\n[CONTENT]\n{\n    \"invoice\": \"False\"\n}\n[/CONTENT]\n\n## nodes: \"<node>: <type>  # <instruction>\"\n- invoice: <class 'bool'>  # if it's a invoice file, return True else False\n\n\n## constraint\nLanguage: Please use the same language as Human INPUT.\nFormat: output wrapped inside [CONTENT][/CONTENT] like format example, nothing else.\n\n## action\nFollow instructions of nodes, generate output and make sure it follows the format example.\n": "[CONTENT]\n{\n    \"invoice\": \"True\"\n}\n[/CONTENT]"
 }