chore: import upstream snapshot with attribution
This commit is contained in:
@@ -0,0 +1,119 @@
|
||||
import re
|
||||
from typing import Type, Optional, List
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from superagi.agent.agent_prompt_builder import AgentPromptBuilder
|
||||
from superagi.helper.error_handler import ErrorHandler
|
||||
from superagi.helper.prompt_reader import PromptReader
|
||||
from superagi.helper.token_counter import TokenCounter
|
||||
from superagi.lib.logger import logger
|
||||
from superagi.llms.base_llm import BaseLlm
|
||||
from superagi.models.agent_execution import AgentExecution
|
||||
from superagi.models.agent_execution_feed import AgentExecutionFeed
|
||||
from superagi.resource_manager.file_manager import FileManager
|
||||
from superagi.tools.base_tool import BaseTool
|
||||
from superagi.tools.tool_response_query_manager import ToolResponseQueryManager
|
||||
from superagi.models.agent import Agent
|
||||
|
||||
class CodingSchema(BaseModel):
|
||||
code_description: str = Field(
|
||||
...,
|
||||
description="Description of the coding task",
|
||||
)
|
||||
|
||||
|
||||
class CodingTool(BaseTool):
|
||||
"""
|
||||
Used to generate code.
|
||||
|
||||
Attributes:
|
||||
llm: LLM used for code generation.
|
||||
name : The name of tool.
|
||||
description : The description of tool.
|
||||
args_schema : The args schema.
|
||||
goals : The goals.
|
||||
resource_manager: Manages the file resources
|
||||
"""
|
||||
llm: Optional[BaseLlm] = None
|
||||
agent_id: int = None
|
||||
agent_execution_id: int = None
|
||||
name = "CodingTool"
|
||||
description = (
|
||||
"You will get instructions for code to write. You will write a very long answer. "
|
||||
"Make sure that every detail of the architecture is, in the end, implemented as code. "
|
||||
"Think step by step and reason yourself to the right decisions to make sure we get it right. "
|
||||
"You will first lay out the names of the core classes, functions, methods that will be necessary, "
|
||||
"as well as a quick comment on their purpose. Then you will output the content of each file including each function and class and ALL code."
|
||||
)
|
||||
args_schema: Type[CodingSchema] = CodingSchema
|
||||
goals: List[str] = []
|
||||
resource_manager: Optional[FileManager] = None
|
||||
tool_response_manager: Optional[ToolResponseQueryManager] = None
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def _execute(self, code_description: str) -> str:
|
||||
"""
|
||||
Execute the write_code tool.
|
||||
|
||||
Args:
|
||||
code_description : The coding task description.
|
||||
code_file_name: The name of the file where the generated codes will be saved.
|
||||
|
||||
Returns:
|
||||
Generated code with where the code is being saved or error message.
|
||||
"""
|
||||
prompt = PromptReader.read_tools_prompt(__file__, "write_code.txt") + "\nUseful to know:\n" + PromptReader.read_tools_prompt(__file__, "generate_logic.txt")
|
||||
prompt = prompt.replace("{goals}", AgentPromptBuilder.add_list_items_to_string(self.goals))
|
||||
prompt = prompt.replace("{code_description}", code_description)
|
||||
spec_response = self.tool_response_manager.get_last_response("WriteSpecTool")
|
||||
if spec_response != "":
|
||||
prompt = prompt.replace("{spec}", "Use this specs for generating the code:\n" + spec_response)
|
||||
logger.info(prompt)
|
||||
messages = [{"role": "system", "content": prompt}]
|
||||
|
||||
organisation = Agent.find_org_by_agent_id(session=self.toolkit_config.session, agent_id=self.agent_id)
|
||||
total_tokens = TokenCounter.count_message_tokens(messages, self.llm.get_model())
|
||||
token_limit = TokenCounter(session=self.toolkit_config.session, organisation_id=organisation.id).token_limit(self.llm.get_model())
|
||||
|
||||
result = self.llm.chat_completion(messages, max_tokens=(token_limit - total_tokens - 100))
|
||||
|
||||
if 'error' in result and result['message'] is not None:
|
||||
ErrorHandler.handle_openai_errors(self.toolkit_config.session, self.agent_id, self.agent_execution_id, result['message'])
|
||||
|
||||
# Get all filenames and corresponding code blocks
|
||||
regex = r"(\S+?)\n```\S*\n(.+?)```"
|
||||
matches = re.finditer(regex, result["content"], re.DOTALL)
|
||||
|
||||
file_names = []
|
||||
# Save each file
|
||||
|
||||
for match in matches:
|
||||
# Get the filename
|
||||
file_name = re.sub(r'[<>"|?*]', "", match.group(1))
|
||||
if not file_name[0].isalnum():
|
||||
file_name = file_name[1:-1]
|
||||
|
||||
# Get the code
|
||||
code = match.group(2)
|
||||
|
||||
# Ensure file_name is not empty
|
||||
if not file_name.strip():
|
||||
continue
|
||||
|
||||
file_names.append(file_name)
|
||||
save_result = self.resource_manager.write_file(file_name, code)
|
||||
if save_result.startswith("Error"):
|
||||
return save_result
|
||||
|
||||
# Get README contents and save
|
||||
split_result = result["content"].split("```")
|
||||
if split_result:
|
||||
readme = split_result[0]
|
||||
save_readme_result = self.resource_manager.write_file("README.md", readme)
|
||||
if save_readme_result.startswith("Error"):
|
||||
return save_readme_result
|
||||
|
||||
return result["content"] + "\n Codes generated and saved successfully in " + ", ".join(file_names)
|
||||
Reference in New Issue
Block a user