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remove oi and clone_function
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geekan
parent
e86af605f5
commit
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3 changed files with 0 additions and 307 deletions
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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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@ -1,197 +0,0 @@
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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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import wrapt
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from interpreter.core.core import Interpreter
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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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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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# 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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# 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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logger.info(f"Extract python code: \n {highlight(code)}")
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return code
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class OpenCodeInterpreter(object):
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"""https://github.com/KillianLucas/open-interpreter"""
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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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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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@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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# 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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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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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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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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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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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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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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# 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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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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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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# 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.
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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.
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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)
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except Exception as e:
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logger.error(f"Could not evaluate Python code \n{logger_code}: \nError: {e}")
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raise Exception("Could not evaluate Python code", e)
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return res
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return wrapper(wrapped)
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# from pathlib import Path
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#
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# import pandas as pd
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# import pytest
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#
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# from metagpt.actions import Action
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# from metagpt.logs import logger
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# from metagpt.tools.code_interpreter import OpenCodeInterpreter, OpenInterpreterDecorator
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#
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# logger.add("./tests/data/test_ci.log")
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# stock = "./tests/data/baba_stock.csv"
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#
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#
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# # 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。
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# class CreateStockIndicators(Action):
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# @OpenInterpreterDecorator(save_code=True, code_file_path="./tests/data/stock_indicators.py")
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# async def run(self, stock_path: str, indicators=["Simple Moving Average", "BollingerBands"]) -> pd.DataFrame:
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# """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；
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# 指标生成对应的三列: SMA, BB_upper, BB_lower
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# """
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# ...
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#
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#
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# @pytest.mark.asyncio
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# async def test_actions():
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# # 计算指标
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# indicators = ["Simple Moving Average", "BollingerBands"]
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# stocker = CreateStockIndicators()
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# df, msg = await stocker.run(stock, indicators=indicators)
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# assert isinstance(df, pd.DataFrame)
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# assert "Close" in df.columns
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# assert "Date" in df.columns
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# # 将df保存为文件，将文件路径传入到下一个action
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# df_path = "./tests/data/stock_indicators.csv"
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# df.to_csv(df_path)
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# assert Path(df_path).is_file()
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# # 可视化指标结果
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# figure_path = "./tests/data/figure_ci.png"
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# ci_ploter = OpenCodeInterpreter()
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# ci_ploter.chat(
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# 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。"
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# )
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# assert Path(figure_path).is_file()
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