update examples

garylin2099 · Mar 12, 2024 · a680a1a · a680a1a
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diff --git a/examples/di/crawl_webpage.py b/examples/di/crawl_webpage.py
@@ -7,13 +7,31 @@
 
 from metagpt.roles.di.data_interpreter import DataInterpreter
 
+PAPER_LIST_REQ = """"
+Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
+and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*
+"""
+
+ECOMMERCE_REQ = """
+Get products data from website https://scrapeme.live/shop/ and save it as a csv file.
+**Notice: Firstly parse the web page encoding and the text HTML structure;
+The first page product name, price, product URL, and image URL must be saved in the csv;**
+"""
+
+NEWS_36KR_REQ = """从36kr创投平台https://pitchhub.36kr.com/financing-flash 所有初创企业融资的信息, **注意: 这是一个中文网站**;
+下面是一个大致流程, 你会根据每一步的运行结果对当前计划中的任务做出适当调整:
+1. 爬取并本地保存html结构;
+2. 直接打印第7个*`快讯`*关键词后2000个字符的html内容, 作为*快讯的html内容示例*;
+3. 反思*快讯的html内容示例*中的规律, 设计正则匹配表达式来获取*`快讯`*的标题、链接、时间;
+4. 筛选最近3天的初创企业融资*`快讯`*, 以list[dict]形式打印前5个。
+5. 将全部结果存在本地csv中
+"""
+
 
 async def main():
-    prompt = """Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
-    and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*"""
-    di = DataInterpreter(use_tools=True)
+    di = DataInterpreter(tools=["scrape_web_playwright"])
 
-    await di.run(prompt)
+    await di.run(ECOMMERCE_REQ)
 
 
 if __name__ == "__main__":

diff --git a/examples/di/data_visualization.py b/examples/di/data_visualization.py
@@ -4,7 +4,7 @@
 
 
 async def main(requirement: str = ""):
-    di = DataInterpreter(use_tools=False)
+    di = DataInterpreter()
     await di.run(requirement)
 
 

diff --git a/examples/di/email_summary.py b/examples/di/email_summary.py
@@ -22,7 +22,7 @@ async def main():
             Firstly, Please help me fetch the latest 5 senders and full letter contents.
             Then, summarize each of the 5 emails into one sentence (you can do this by yourself, no need to import other models to do this) and output them in a markdown format."""
 
-    di = DataInterpreter(use_tools=True)
+    di = DataInterpreter()
 
     await di.run(prompt)
 

diff --git a/examples/di/imitate_webpage.py b/examples/di/imitate_webpage.py
@@ -15,7 +15,7 @@ async def main():
 Secondly, convert image to a webpage including HTML, CSS and JS in one go. 
 Finally, save webpage in a text file. 
 Note: All required dependencies and environments have been fully installed and configured."""
-    di = DataInterpreter(use_tools=True)
+    di = DataInterpreter(tools=["GPTvGenerator"])
 
     await di.run(prompt)
 

diff --git a/examples/di/ml_engineer_with_tools.py → examples/di/machine_learning_with_tools.py b/examples/di/ml_engineer_with_tools.py → examples/di/machine_learning_with_tools.py
@@ -1,10 +1,10 @@
 import asyncio
 
-from metagpt.roles.di.ml_engineer import MLEngineer
+from metagpt.roles.di.data_interpreter import DataInterpreter
 
 
 async def main(requirement: str):
-    role = MLEngineer(auto_run=True, use_tools=True)
+    role = DataInterpreter(tools=["<all>"])
     await role.run(requirement)
 
 

diff --git a/examples/di/rm_image_background.py b/examples/di/rm_image_background.py
@@ -4,7 +4,7 @@
 
 
 async def main(requirement: str = ""):
-    di = DataInterpreter(use_tools=False)
+    di = DataInterpreter()
     await di.run(requirement)
 
 

diff --git a/examples/di/sd_tool_usage.py b/examples/di/sd_tool_usage.py
@@ -8,7 +8,7 @@
 
 
 async def main(requirement: str = ""):
-    di = DataInterpreter(use_tools=True, goal=requirement)
+    di = DataInterpreter(tools=["SDEngine"])
     await di.run(requirement)
 
 

diff --git a/examples/di/solve_math_problems.py b/examples/di/solve_math_problems.py
@@ -4,7 +4,7 @@
 
 
 async def main(requirement: str = ""):
-    di = DataInterpreter(use_tools=False)
+    di = DataInterpreter()
     await di.run(requirement)
 
 

diff --git a/metagpt/roles/di/data_interpreter.py b/metagpt/roles/di/data_interpreter.py
@@ -9,7 +9,7 @@
 from metagpt.actions.di.execute_nb_code import ExecuteNbCode
 from metagpt.actions.di.write_analysis_code import CheckData, WriteCodeWithTools
 from metagpt.logs import logger
-from metagpt.prompts.mi.write_analysis_code import DATA_INFO
+from metagpt.prompts.di.write_analysis_code import DATA_INFO
 from metagpt.roles import Role
 from metagpt.schema import Message, Task, TaskResult
 from metagpt.strategy.task_type import TaskType

diff --git a/metagpt/tools/libs/sd_engine.py b/metagpt/tools/libs/sd_engine.py
@@ -14,7 +14,6 @@
 from aiohttp import ClientSession
 from PIL import Image, PngImagePlugin
 
-#
 from metagpt.const import SD_OUTPUT_FILE_REPO, SOURCE_ROOT
 from metagpt.logs import logger
 from metagpt.tools.tool_registry import register_tool

diff --git a/metagpt/tools/tool_recommend.py b/metagpt/tools/tool_recommend.py
@@ -57,8 +57,7 @@ async def run(self, prompt):
 class ToolRecommender(BaseModel):
     """
     The default ToolRecommender:
-    1. Recall: If plan exists, use exact match between task type and tool type to recall tools;
-               If plan doesn't exist (e.g. we use ReAct), return all user-specified tools;
+    1. Recall: To be implemented in subclasses. Recall tools based on the given context and plan.
     2. Rank: Use LLM to select final candidates from recalled set.
     """