mirror of
https://github.com/FoundationAgents/MetaGPT.git
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feat: merge geekan:dev
This commit is contained in:
莘权 马
commit
1df853158b
9 changed files with 140 additions and 457 deletions
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@ -1,67 +0,0 @@
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from pathlib import Path
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from pydantic import Field
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from metagpt.actions.write_code import WriteCode
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from metagpt.llm import LLM
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from metagpt.logs import logger
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from metagpt.provider.base_llm import BaseLLM
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from metagpt.schema import Message
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from metagpt.utils.exceptions import handle_exception
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from metagpt.utils.highlight import highlight
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CLONE_PROMPT = """
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*context*
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Please convert the function code ```{source_code}``` into the the function format: ```{template_func}```.
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*Please Write code based on the following list and context*
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1. Write code start with ```, and end with ```.
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2. Please implement it in one function if possible, except for import statements. for exmaple:
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```python
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import pandas as pd
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def run(*args) -> pd.DataFrame:
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...
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```
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3. Do not use public member functions that do not exist in your design.
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4. The output function name, input parameters and return value must be the same as ```{template_func}```.
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5. Make sure the results before and after the code conversion are required to be exactly the same.
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6. Don't repeat my context in your replies.
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7. Return full results, for example, if the return value has df.head(), please return df.
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8. If you must use a third-party package, use the most popular ones, for example: pandas, numpy, ta, ...
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"""
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class CloneFunction(WriteCode):
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name: str = "CloneFunction"
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context: list[Message] = []
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llm: BaseLLM = Field(default_factory=LLM)
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def _save(self, code_path, code):
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if isinstance(code_path, str):
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code_path = Path(code_path)
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code_path.parent.mkdir(parents=True, exist_ok=True)
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code_path.write_text(code, encoding="utf-8")
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logger.info(f"Saving Code to {code_path}")
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async def run(self, template_func: str, source_code: str) -> str:
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"""将source_code转换成template_func一样的入参和返回类型"""
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prompt = CLONE_PROMPT.format(source_code=source_code, template_func=template_func)
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logger.info(f"query for CloneFunction: \n {prompt}")
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code = await self.write_code(prompt)
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logger.info(f"CloneFunction code is \n {highlight(code)}")
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return code
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@handle_exception
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def run_function_code(func_code: str, func_name: str, *args, **kwargs):
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"""Run function code from string code."""
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locals_ = {}
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exec(func_code, locals_)
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func = locals_[func_name]
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return func(*args, **kwargs), ""
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def run_function_script(code_script_path: str, func_name: str, *args, **kwargs):
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"""Run function code from script."""
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code_path = Path(code_script_path)
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code = code_path.read_text(encoding="utf-8")
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return run_function_code(code, func_name, *args, **kwargs)
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@ -49,6 +49,9 @@ class ZhiPuAIGPTAPI(BaseLLM):
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zhipuai.api_key = config.zhipuai_api_key
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# due to use openai sdk, set the api_key but it will't be used.
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# openai.api_key = zhipuai.api_key # due to use openai sdk, set the api_key but it will't be used.
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if config.openai_proxy:
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# FIXME: openai v1.x sdk has no proxy support
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openai.proxy = config.openai_proxy
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def _const_kwargs(self, messages: list[dict]) -> dict:
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kwargs = {"model": self.model, "prompt": messages, "temperature": 0.3}
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@ -90,4 +90,5 @@ class TutorialAssistant(Role):
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msg = await super().react()
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root_path = TUTORIAL_PATH / datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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await File.write(root_path, f"{self.main_title}.md", self.total_content.encode("utf-8"))
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msg.content = str(root_path / f"{self.main_title}.md")
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return msg
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@ -1,207 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@Time :
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@Author :
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@File : code_interpreter.py
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@Warning : open-interpreter 0.1.17 requires openai<0.29.0,>=0.28.0, but you have openai 1.6.0 which is incompatible.
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open-interpreter 0.1.17 requires tiktoken<0.5.0,>=0.4.0, but you have tiktoken 0.5.2 which is incompatible.
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"""
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# import inspect
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# import re
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# import textwrap
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# from pathlib import Path
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# from typing import Callable, Dict, List
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#
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# import wrapt
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# from interpreter.core.core import Interpreter
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#
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# from metagpt.actions.clone_function import (
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# CloneFunction,
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# run_function_code,
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# run_function_script,
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# )
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# from metagpt.config import CONFIG
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# from metagpt.logs import logger
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# from metagpt.utils.highlight import highlight
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#
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#
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# def extract_python_code(code: str):
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# """Extract code blocks: If the code comments are the same, only the last code block is kept."""
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# # Use regular expressions to match comment blocks and related code.
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# pattern = r"(#\s[^\n]*)\n(.*?)(?=\n\s*#|$)"
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# matches = re.findall(pattern, code, re.DOTALL)
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#
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# # Extract the last code block when encountering the same comment.
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# unique_comments = {}
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# for comment, code_block in matches:
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# unique_comments[comment] = code_block
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#
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# # concatenate into functional form
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# result_code = "\n".join([f"{comment}\n{code_block}" for comment, code_block in unique_comments.items()])
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# header_code = code[: code.find("#")]
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# code = header_code + result_code
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#
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# logger.info(f"Extract python code: \n {highlight(code)}")
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#
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# return code
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#
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#
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# class OpenCodeInterpreter(object):
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# """https://github.com/KillianLucas/open-interpreter"""
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#
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# def __init__(self, auto_run: bool = True) -> None:
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# interpreter = Interpreter()
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# interpreter.auto_run = auto_run
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# interpreter.model = CONFIG.openai_api_model or "gpt-3.5-turbo"
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# interpreter.api_key = CONFIG.openai_api_key
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# self.interpreter = interpreter
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#
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# def chat(self, query: str, reset: bool = True):
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# if reset:
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# self.interpreter.reset()
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# return self.interpreter.chat(query)
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#
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# @staticmethod
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# def extract_function(
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# query_respond: List, function_name: str, *, language: str = "python", function_format: str = None
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# ) -> str:
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# """create a function from query_respond."""
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# if language not in ("python"):
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# raise NotImplementedError(f"Not support to parse language {language}!")
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#
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# # set function form
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# if function_format is None:
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# assert language == "python", f"Expect python language for default function_format, but got {language}."
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# function_format = """def {function_name}():\n{code}"""
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# # Extract the code module in the open-interpreter respond message.
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# # The query_respond of open-interpreter before v0.1.4 is:
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# # [{'role': 'user', 'content': your query string},
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# # {'role': 'assistant', 'content': plan from llm, 'function_call': {
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# # "name": "run_code", "arguments": "{"language": "python", "code": code of first plan},
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# # "parsed_arguments": {"language": "python", "code": code of first plan}
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# # ...]
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# if "function_call" in query_respond[1]:
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# code = [
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# item["function_call"]["parsed_arguments"]["code"]
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# for item in query_respond
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# if "function_call" in item
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# and "parsed_arguments" in item["function_call"]
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# and "language" in item["function_call"]["parsed_arguments"]
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# and item["function_call"]["parsed_arguments"]["language"] == language
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# ]
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# # The query_respond of open-interpreter v0.1.7 is:
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# # [{'role': 'user', 'message': your query string},
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# # {'role': 'assistant', 'message': plan from llm, 'language': 'python',
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# # 'code': code of first plan, 'output': output of first plan code},
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# # ...]
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# elif "code" in query_respond[1]:
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# code = [
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# item["code"]
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# for item in query_respond
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# if "code" in item and "language" in item and item["language"] == language
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# ]
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# else:
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# raise ValueError(f"Unexpect message format in query_respond: {query_respond[1].keys()}")
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# # add indent.
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# indented_code_str = textwrap.indent("\n".join(code), " " * 4)
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# # Return the code after deduplication.
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# if language == "python":
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# return extract_python_code(function_format.format(function_name=function_name, code=indented_code_str))
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#
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#
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# def gen_query(func: Callable, args, kwargs) -> str:
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# # Get the annotation of the function as part of the query.
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# desc = func.__doc__
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# signature = inspect.signature(func)
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# # Get the signature of the wrapped function and the assignment of the input parameters as part of the query.
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# bound_args = signature.bind(*args, **kwargs)
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# bound_args.apply_defaults()
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# query = f"{desc}, {bound_args.arguments}, If you must use a third-party package, use the most popular ones, for example: pandas, numpy, ta, ..."
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# return query
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#
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#
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# def gen_template_fun(func: Callable) -> str:
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# return f"def {func.__name__}{str(inspect.signature(func))}\n # here is your code ..."
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#
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#
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# class OpenInterpreterDecorator(object):
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# def __init__(self, save_code: bool = False, code_file_path: str = None, clear_code: bool = False) -> None:
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# self.save_code = save_code
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# self.code_file_path = code_file_path
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# self.clear_code = clear_code
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#
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# def _have_code(self, rsp: List[Dict]):
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# # Is there any code generated?
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# return "code" in rsp[1] and rsp[1]["code"] not in ("", None)
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#
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# def _is_faild_plan(self, rsp: List[Dict]):
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# # is faild plan?
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# func_code = OpenCodeInterpreter.extract_function(rsp, "function")
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# # If there is no more than 1 '\n', the plan execution fails.
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# if isinstance(func_code, str) and func_code.count("\n") <= 1:
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# return True
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# return False
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#
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# def _check_respond(self, query: str, interpreter: OpenCodeInterpreter, respond: List[Dict], max_try: int = 3):
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# for _ in range(max_try):
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# # TODO: If no code or faild plan is generated, execute chat again, repeating no more than max_try times.
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# if self._have_code(respond) and not self._is_faild_plan(respond):
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# break
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# elif not self._have_code(respond):
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# logger.warning(f"llm did not return executable code, resend the query: \n{query}")
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# respond = interpreter.chat(query)
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# elif self._is_faild_plan(respond):
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# logger.warning(f"llm did not generate successful plan, resend the query: \n{query}")
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# respond = interpreter.chat(query)
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#
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# # Post-processing of respond
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# if not self._have_code(respond):
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# error_msg = f"OpenCodeInterpreter do not generate code for query: \n{query}"
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# logger.error(error_msg)
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# raise ValueError(error_msg)
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#
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# if self._is_faild_plan(respond):
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# error_msg = f"OpenCodeInterpreter do not generate code for query: \n{query}"
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# logger.error(error_msg)
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# raise ValueError(error_msg)
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# return respond
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#
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# def __call__(self, wrapped):
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# @wrapt.decorator
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# async def wrapper(wrapped: Callable, instance, args, kwargs):
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# # Get the decorated function name.
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# func_name = wrapped.__name__
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# # If the script exists locally and clearcode is not required, execute the function from the script.
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# if self.code_file_path and Path(self.code_file_path).is_file() and not self.clear_code:
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# return run_function_script(self.code_file_path, func_name, *args, **kwargs)
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#
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# # Auto run generate code by using open-interpreter.
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# interpreter = OpenCodeInterpreter()
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# query = gen_query(wrapped, args, kwargs)
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# logger.info(f"query for OpenCodeInterpreter: \n {query}")
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# respond = interpreter.chat(query)
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# # Make sure the response is as expected.
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# respond = self._check_respond(query, interpreter, respond, 3)
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# # Assemble the code blocks generated by open-interpreter into a function without parameters.
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# func_code = interpreter.extract_function(respond, func_name)
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# # Clone the `func_code` into wrapped, that is,
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# # keep the `func_code` and wrapped functions with the same input parameter and return value types.
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# template_func = gen_template_fun(wrapped)
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# cf = CloneFunction()
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# code = await cf.run(template_func=template_func, source_code=func_code)
|
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# # Display the generated function in the terminal.
|
||||
# logger_code = highlight(code, "python")
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# logger.info(f"Creating following Python function:\n{logger_code}")
|
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# # execute this function.
|
||||
# try:
|
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# res = run_function_code(code, func_name, *args, **kwargs)
|
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# if self.save_code and self.code_file_path:
|
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# cf._save(self.code_file_path, code)
|
||||
# except Exception as e:
|
||||
# logger.error(f"Could not evaluate Python code \n{logger_code}: \nError: {e}")
|
||||
# raise Exception("Could not evaluate Python code", e)
|
||||
# return res
|
||||
#
|
||||
# return wrapper(wrapped)
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||||
|
|
@ -1,58 +1,61 @@
|
|||
# #!/usr/bin/env python3
|
||||
# # _*_ coding: utf-8 _*_
|
||||
#
|
||||
# """
|
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# @Time : 2023/10/09 18:40:34
|
||||
# @Author : Stitch-z
|
||||
# @File : test_invoice_ocr.py
|
||||
# """
|
||||
#
|
||||
# import os
|
||||
# from pathlib import Path
|
||||
#
|
||||
# import pytest
|
||||
#
|
||||
# from metagpt.actions.invoice_ocr import GenerateTable, InvoiceOCR, ReplyQuestion
|
||||
#
|
||||
#
|
||||
# @pytest.mark.asyncio
|
||||
# @pytest.mark.parametrize(
|
||||
# "invoice_path",
|
||||
# [
|
||||
# "../../data/invoices/invoice-3.jpg",
|
||||
# "../../data/invoices/invoice-4.zip",
|
||||
# ],
|
||||
# )
|
||||
# async def test_invoice_ocr(invoice_path: str):
|
||||
# invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
# filename = os.path.basename(invoice_path)
|
||||
# resp = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
# assert isinstance(resp, list)
|
||||
#
|
||||
#
|
||||
# @pytest.mark.asyncio
|
||||
# @pytest.mark.parametrize(
|
||||
# ("invoice_path", "expected_result"),
|
||||
# [
|
||||
# ("../../data/invoices/invoice-1.pdf", [{"收款人": "小明", "城市": "深圳市", "总费用/元": "412.00", "开票日期": "2023年02月03日"}]),
|
||||
# ],
|
||||
# )
|
||||
# async def test_generate_table(invoice_path: str, expected_result: list[dict]):
|
||||
# invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
# filename = os.path.basename(invoice_path)
|
||||
# ocr_result = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
# table_data = await GenerateTable().run(ocr_results=ocr_result, filename=filename)
|
||||
# assert table_data == expected_result
|
||||
#
|
||||
#
|
||||
# @pytest.mark.asyncio
|
||||
# @pytest.mark.parametrize(
|
||||
# ("invoice_path", "query", "expected_result"),
|
||||
# [("../../data/invoices/invoice-1.pdf", "Invoicing date", "2023年02月03日")],
|
||||
# )
|
||||
# async def test_reply_question(invoice_path: str, query: dict, expected_result: str):
|
||||
# invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
# filename = os.path.basename(invoice_path)
|
||||
# ocr_result = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
# result = await ReplyQuestion().run(query=query, ocr_result=ocr_result)
|
||||
# assert expected_result in result
|
||||
#!/usr/bin/env python3
|
||||
# _*_ coding: utf-8 _*_
|
||||
|
||||
"""
|
||||
@Time : 2023/10/09 18:40:34
|
||||
@Author : Stitch-z
|
||||
@File : test_invoice_ocr.py
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from metagpt.actions.invoice_ocr import GenerateTable, InvoiceOCR, ReplyQuestion
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
"invoice_path",
|
||||
[
|
||||
"../../data/invoices/invoice-3.jpg",
|
||||
"../../data/invoices/invoice-4.zip",
|
||||
],
|
||||
)
|
||||
async def test_invoice_ocr(invoice_path: str):
|
||||
invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
filename = os.path.basename(invoice_path)
|
||||
resp = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
assert isinstance(resp, list)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
("invoice_path", "expected_result"),
|
||||
[
|
||||
(
|
||||
"../../data/invoices/invoice-1.pdf",
|
||||
[{"收款人": "小明", "城市": "深圳市", "总费用/元": "412.00", "开票日期": "2023年02月03日"}]
|
||||
),
|
||||
],
|
||||
)
|
||||
async def test_generate_table(invoice_path: str, expected_result: list[dict]):
|
||||
invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
filename = os.path.basename(invoice_path)
|
||||
ocr_result = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
table_data = await GenerateTable().run(ocr_results=ocr_result, filename=filename)
|
||||
assert json.dumps(table_data) == json.dumps(expected_result)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
("invoice_path", "query", "expected_result"),
|
||||
[("../../data/invoices/invoice-1.pdf", "Invoicing date", "2023年02月03日")],
|
||||
)
|
||||
async def test_reply_question(invoice_path: str, query: dict, expected_result: str):
|
||||
invoice_path = os.path.abspath(os.path.join(os.getcwd(), invoice_path))
|
||||
filename = os.path.basename(invoice_path)
|
||||
ocr_result = await InvoiceOCR().run(file_path=Path(invoice_path), filename=filename)
|
||||
result = await ReplyQuestion().run(query=query, ocr_result=ocr_result)
|
||||
assert expected_result in result
|
||||
|
|
|
|||
|
|
@ -51,3 +51,13 @@ async def test_zhipuai_acompletion(mocker):
|
|||
|
||||
resp = await zhipu_gpt.aask(prompt_msg)
|
||||
assert resp == resp_content
|
||||
|
||||
|
||||
def test_zhipuai_proxy(mocker):
|
||||
import openai
|
||||
|
||||
from metagpt.config import CONFIG
|
||||
|
||||
CONFIG.openai_proxy = "http://127.0.0.1:8080"
|
||||
_ = ZhiPuAIGPTAPI()
|
||||
assert openai.proxy == CONFIG.openai_proxy
|
||||
|
|
|
|||
|
|
@ -1,63 +1,59 @@
|
|||
# #!/usr/bin/env python3
|
||||
# # _*_ coding: utf-8 _*_
|
||||
#
|
||||
# """
|
||||
# @Time : 2023/9/21 23:11:27
|
||||
# @Author : Stitch-z
|
||||
# @File : test_invoice_ocr_assistant.py
|
||||
# """
|
||||
#
|
||||
# import json
|
||||
# from pathlib import Path
|
||||
#
|
||||
# import pandas as pd
|
||||
# import pytest
|
||||
#
|
||||
# from metagpt.roles.invoice_ocr_assistant import InvoiceOCRAssistant, InvoicePath
|
||||
# from metagpt.schema import Message
|
||||
#
|
||||
#
|
||||
# @pytest.mark.asyncio
|
||||
# @pytest.mark.parametrize(
|
||||
# ("query", "invoice_path", "invoice_table_path", "expected_result"),
|
||||
# [
|
||||
# (
|
||||
# "Invoicing date",
|
||||
# Path("../../data/invoices/invoice-1.pdf"),
|
||||
# Path("../../../data/invoice_table/invoice-1.xlsx"),
|
||||
# [{"收款人": "小明", "城市": "深圳市", "总费用/元": 412.00, "开票日期": "2023年02月03日"}],
|
||||
# ),
|
||||
# (
|
||||
# "Invoicing date",
|
||||
# Path("../../data/invoices/invoice-2.png"),
|
||||
# Path("../../../data/invoice_table/invoice-2.xlsx"),
|
||||
# [{"收款人": "铁头", "城市": "广州市", "总费用/元": 898.00, "开票日期": "2023年03月17日"}],
|
||||
# ),
|
||||
# (
|
||||
# "Invoicing date",
|
||||
# Path("../../data/invoices/invoice-3.jpg"),
|
||||
# Path("../../../data/invoice_table/invoice-3.xlsx"),
|
||||
# [{"收款人": "夏天", "城市": "福州市", "总费用/元": 2462.00, "开票日期": "2023年08月26日"}],
|
||||
# ),
|
||||
# (
|
||||
# "Invoicing date",
|
||||
# Path("../../data/invoices/invoice-4.zip"),
|
||||
# Path("../../../data/invoice_table/invoice-4.xlsx"),
|
||||
# [
|
||||
# {"收款人": "小明", "城市": "深圳市", "总费用/元": 412.00, "开票日期": "2023年02月03日"},
|
||||
# {"收款人": "铁头", "城市": "广州市", "总费用/元": 898.00, "开票日期": "2023年03月17日"},
|
||||
# {"收款人": "夏天", "城市": "福州市", "总费用/元": 2462.00, "开票日期": "2023年08月26日"},
|
||||
# ],
|
||||
# ),
|
||||
# ],
|
||||
# )
|
||||
# async def test_invoice_ocr_assistant(
|
||||
# query: str, invoice_path: Path, invoice_table_path: Path, expected_result: list[dict]
|
||||
# ):
|
||||
# invoice_path = Path.cwd() / invoice_path
|
||||
# role = InvoiceOCRAssistant()
|
||||
# await role.run(Message(content=query, instruct_content=InvoicePath(file_path=invoice_path)))
|
||||
# invoice_table_path = Path.cwd() / invoice_table_path
|
||||
# df = pd.read_excel(invoice_table_path)
|
||||
# dict_result = df.to_dict(orient="records")
|
||||
# assert json.dumps(dict_result) == json.dumps(expected_result)
|
||||
#!/usr/bin/env python3
|
||||
# _*_ coding: utf-8 _*_
|
||||
|
||||
"""
|
||||
@Time : 2023/9/21 23:11:27
|
||||
@Author : Stitch-z
|
||||
@File : test_invoice_ocr_assistant.py
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from metagpt.roles.invoice_ocr_assistant import InvoiceOCRAssistant, InvoicePath
|
||||
from metagpt.schema import Message
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(
|
||||
("query", "invoice_path", "invoice_table_path", "expected_result"),
|
||||
[
|
||||
(
|
||||
"Invoicing date",
|
||||
Path("../../data/invoices/invoice-1.pdf"),
|
||||
Path("../../../data/invoice_table/invoice-1.xlsx"),
|
||||
{"收款人": "小明", "城市": "深圳", "总费用/元": 412.00, "开票日期": "2023年02月03日"},
|
||||
),
|
||||
(
|
||||
"Invoicing date",
|
||||
Path("../../data/invoices/invoice-2.png"),
|
||||
Path("../../../data/invoice_table/invoice-2.xlsx"),
|
||||
{"收款人": "铁头", "城市": "广州", "总费用/元": 898.00, "开票日期": "2023年03月17日"},
|
||||
),
|
||||
(
|
||||
"Invoicing date",
|
||||
Path("../../data/invoices/invoice-3.jpg"),
|
||||
Path("../../../data/invoice_table/invoice-3.xlsx"),
|
||||
{"收款人": "夏天", "城市": "福州", "总费用/元": 2462.00, "开票日期": "2023年08月26日"},
|
||||
)
|
||||
],
|
||||
)
|
||||
async def test_invoice_ocr_assistant(
|
||||
query: str, invoice_path: Path, invoice_table_path: Path, expected_result: dict
|
||||
):
|
||||
invoice_path = Path.cwd() / invoice_path
|
||||
role = InvoiceOCRAssistant()
|
||||
await role.run(Message(content=query, instruct_content=InvoicePath(file_path=invoice_path)))
|
||||
invoice_table_path = Path.cwd() / invoice_table_path
|
||||
df = pd.read_excel(invoice_table_path)
|
||||
resp = df.to_dict(orient="records")
|
||||
assert isinstance(resp, list)
|
||||
assert len(resp) == 1
|
||||
resp = resp[0]
|
||||
assert expected_result["收款人"] == resp["收款人"]
|
||||
assert expected_result["城市"] in resp["城市"]
|
||||
assert int(expected_result["总费用/元"]) == int(resp["总费用/元"])
|
||||
assert expected_result["开票日期"] == resp["开票日期"]
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
@File : test_tutorial_assistant.py
|
||||
"""
|
||||
import shutil
|
||||
|
||||
import aiofiles
|
||||
import pytest
|
||||
|
||||
from metagpt.const import TUTORIAL_PATH
|
||||
|
|
@ -14,20 +14,17 @@ from metagpt.roles.tutorial_assistant import TutorialAssistant
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize(("language", "topic"), [("Chinese", "Write a tutorial about Python")])
|
||||
@pytest.mark.parametrize(("language", "topic"), [("Chinese", "Write a tutorial about pip")])
|
||||
async def test_tutorial_assistant(language: str, topic: str):
|
||||
shutil.rmtree(path=TUTORIAL_PATH, ignore_errors=True)
|
||||
|
||||
topic = "Write a tutorial about MySQL"
|
||||
role = TutorialAssistant(language=language)
|
||||
msg = await role.run(topic)
|
||||
assert "MySQL" in msg.content
|
||||
assert TUTORIAL_PATH.exists()
|
||||
# filename = msg.content
|
||||
# title = filename.split("/")[-1].split(".")[0]
|
||||
# async with aiofiles.open(filename, mode="r") as reader:
|
||||
# content = await reader.read()
|
||||
# assert content.startswith(f"# {title}")
|
||||
filename = msg.content
|
||||
async with aiofiles.open(filename, mode="r", encoding="utf-8") as reader:
|
||||
content = await reader.read()
|
||||
assert "pip" in content
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -1,53 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@Time :
|
||||
@Author :
|
||||
@File : test_code_interpreter.py
|
||||
@Warning : open-interpreter 0.1.17 requires openai<0.29.0,>=0.28.0, but you have openai 1.6.0 which is incompatible.
|
||||
open-interpreter 0.1.17 requires tiktoken<0.5.0,>=0.4.0, but you have tiktoken 0.5.2 which is incompatible.
|
||||
"""
|
||||
|
||||
# from pathlib import Path
|
||||
#
|
||||
# import pandas as pd
|
||||
# import pytest
|
||||
#
|
||||
# from metagpt.actions import Action
|
||||
# from metagpt.logs import logger
|
||||
# from metagpt.tools.code_interpreter import OpenCodeInterpreter, OpenInterpreterDecorator
|
||||
#
|
||||
# logger.add("./tests/data/test_ci.log")
|
||||
# stock = "./tests/data/baba_stock.csv"
|
||||
#
|
||||
#
|
||||
# # TODO: 需要一种表格数据格式，能够支持schame管理的，标注字段类型和字段含义。
|
||||
# class CreateStockIndicators(Action):
|
||||
# @OpenInterpreterDecorator(save_code=True, code_file_path="./tests/data/stock_indicators.py")
|
||||
# async def run(self, stock_path: str, indicators=["Simple Moving Average", "BollingerBands"]) -> pd.DataFrame:
|
||||
# """对stock_path中的股票数据, 使用pandas和ta计算indicators中的技术指标, 返回带有技术指标的股票数据，不需要去除空值, 不需要安装任何包；
|
||||
# 指标生成对应的三列: SMA, BB_upper, BB_lower
|
||||
# """
|
||||
# ...
|
||||
#
|
||||
#
|
||||
# @pytest.mark.asyncio
|
||||
# async def test_actions():
|
||||
# # 计算指标
|
||||
# indicators = ["Simple Moving Average", "BollingerBands"]
|
||||
# stocker = CreateStockIndicators()
|
||||
# df, msg = await stocker.run(stock, indicators=indicators)
|
||||
# assert isinstance(df, pd.DataFrame)
|
||||
# assert "Close" in df.columns
|
||||
# assert "Date" in df.columns
|
||||
# # 将df保存为文件，将文件路径传入到下一个action
|
||||
# df_path = "./tests/data/stock_indicators.csv"
|
||||
# df.to_csv(df_path)
|
||||
# assert Path(df_path).is_file()
|
||||
# # 可视化指标结果
|
||||
# figure_path = "./tests/data/figure_ci.png"
|
||||
# ci_ploter = OpenCodeInterpreter()
|
||||
# ci_ploter.chat(
|
||||
# f"使用seaborn对{df_path}中与股票布林带有关的数据列的Date, Close, SMA, BB_upper（布林带上界）, BB_lower（布林带下界）进行可视化, 可视化图片保存在{figure_path}中。不需要任何指标计算，把Date列转换为日期类型。要求图片优美，BB_upper, BB_lower之间使用合适的颜色填充。"
|
||||
# )
|
||||
# assert Path(figure_path).is_file()
|
||||
Loading…
Add table
Add a link
Reference in a new issue