You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
83 lines
2.1 KiB
83 lines
2.1 KiB
###### VERISON2
|
|
import inspect
|
|
import os
|
|
import threading
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
|
|
from swarm_models import OpenAIChat
|
|
from swarms.structs.majority_voting import MajorityVoting
|
|
from swarms.structs.stackoverflow_swarm import StackOverflowSwarm
|
|
from swarms.structs.task_queue_base import TaskQueueBase
|
|
|
|
##########
|
|
|
|
|
|
####################
|
|
load_dotenv()
|
|
|
|
api_key = os.getenv("OPENAI_API_KEY")
|
|
|
|
model = OpenAIChat(
|
|
openai_api_key=api_key,
|
|
max_tokens=4000,
|
|
)
|
|
|
|
|
|
def process_documentation(cls):
|
|
"""
|
|
Process the documentation for a given class using OpenAI model and save it in a Markdown file.
|
|
"""
|
|
doc = inspect.getdoc(cls)
|
|
source = inspect.getsource(cls)
|
|
input_content = (
|
|
"Class Name:"
|
|
f" {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource"
|
|
f" Code:\n{source}"
|
|
)
|
|
|
|
# Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content)
|
|
processed_content = model(
|
|
DOCUMENTATION_WRITER_SOP(input_content, "swarms.structs")
|
|
)
|
|
|
|
# doc_content = f"# {cls.__name__}\n\n{processed_content}\n"
|
|
doc_content = f"{processed_content}\n"
|
|
|
|
# Create the directory if it doesn't exist
|
|
dir_path = "docs/swarms/tokenizers"
|
|
os.makedirs(dir_path, exist_ok=True)
|
|
|
|
# Write the processed documentation to a Markdown file
|
|
file_path = os.path.join(dir_path, f"{cls.__name__.lower()}.md")
|
|
with open(file_path, "w") as file:
|
|
file.write(doc_content)
|
|
|
|
print(f"Documentation generated for {cls.__name__}.")
|
|
|
|
|
|
def main():
|
|
classes = [
|
|
MajorityVoting,
|
|
StackOverflowSwarm,
|
|
TaskQueueBase,
|
|
]
|
|
threads = []
|
|
for cls in classes:
|
|
thread = threading.Thread(
|
|
target=process_documentation, args=(cls,)
|
|
)
|
|
threads.append(thread)
|
|
thread.start()
|
|
|
|
# Wait for all threads to complete
|
|
for thread in threads:
|
|
thread.join()
|
|
|
|
print("Documentation generated in 'swarms.structs' directory.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|