mirror of https://github.com/microsoft/autogen.git
574 lines
18 KiB
Python
574 lines
18 KiB
Python
import base64
|
|
import hashlib
|
|
import os
|
|
import re
|
|
import shutil
|
|
from datetime import datetime
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, Tuple, Union
|
|
|
|
from dotenv import load_dotenv
|
|
from loguru import logger
|
|
|
|
from autogen.coding import DockerCommandLineCodeExecutor, LocalCommandLineCodeExecutor
|
|
from autogen.oai.client import ModelClient, OpenAIWrapper
|
|
|
|
from ..datamodel import CodeExecutionConfigTypes, Model, Skill
|
|
from ..version import APP_NAME
|
|
|
|
|
|
def md5_hash(text: str) -> str:
|
|
"""
|
|
Compute the MD5 hash of a given text.
|
|
|
|
:param text: The string to hash
|
|
:return: The MD5 hash of the text
|
|
"""
|
|
return hashlib.md5(text.encode()).hexdigest()
|
|
|
|
|
|
def check_and_cast_datetime_fields(obj: Any) -> Any:
|
|
if hasattr(obj, "created_at") and isinstance(obj.created_at, str):
|
|
obj.created_at = str_to_datetime(obj.created_at)
|
|
|
|
if hasattr(obj, "updated_at") and isinstance(obj.updated_at, str):
|
|
obj.updated_at = str_to_datetime(obj.updated_at)
|
|
|
|
return obj
|
|
|
|
|
|
def str_to_datetime(dt_str: str) -> datetime:
|
|
if dt_str[-1] == "Z":
|
|
# Replace 'Z' with '+00:00' for UTC timezone
|
|
dt_str = dt_str[:-1] + "+00:00"
|
|
return datetime.fromisoformat(dt_str)
|
|
|
|
|
|
def clear_folder(folder_path: str) -> None:
|
|
"""
|
|
Clear the contents of a folder.
|
|
|
|
:param folder_path: The path to the folder to clear.
|
|
"""
|
|
# exit if the folder does not exist
|
|
if not os.path.exists(folder_path):
|
|
return
|
|
# exit if the folder does not exist
|
|
if not os.path.exists(folder_path):
|
|
return
|
|
for file in os.listdir(folder_path):
|
|
file_path = os.path.join(folder_path, file)
|
|
if os.path.isfile(file_path):
|
|
os.remove(file_path)
|
|
elif os.path.isdir(file_path):
|
|
shutil.rmtree(file_path)
|
|
|
|
|
|
def get_file_type(file_path: str) -> str:
|
|
"""
|
|
|
|
|
|
Get file type determined by the file extension. If the file extension is not
|
|
recognized, 'unknown' will be used as the file type.
|
|
|
|
:param file_path: The path to the file to be serialized.
|
|
:return: A string containing the file type.
|
|
"""
|
|
|
|
# Extended list of file extensions for code and text files
|
|
CODE_EXTENSIONS = {
|
|
".py",
|
|
".js",
|
|
".jsx",
|
|
".java",
|
|
".c",
|
|
".cpp",
|
|
".cs",
|
|
".ts",
|
|
".tsx",
|
|
".html",
|
|
".css",
|
|
".scss",
|
|
".less",
|
|
".json",
|
|
".xml",
|
|
".yaml",
|
|
".yml",
|
|
".md",
|
|
".rst",
|
|
".tex",
|
|
".sh",
|
|
".bat",
|
|
".ps1",
|
|
".php",
|
|
".rb",
|
|
".go",
|
|
".swift",
|
|
".kt",
|
|
".hs",
|
|
".scala",
|
|
".lua",
|
|
".pl",
|
|
".sql",
|
|
".config",
|
|
}
|
|
|
|
# Supported spreadsheet extensions
|
|
CSV_EXTENSIONS = {".csv", ".xlsx"}
|
|
|
|
# Supported image extensions
|
|
IMAGE_EXTENSIONS = {
|
|
".png",
|
|
".jpg",
|
|
".jpeg",
|
|
".gif",
|
|
".bmp",
|
|
".tiff",
|
|
".svg",
|
|
".webp",
|
|
}
|
|
# Supported (web) video extensions
|
|
VIDEO_EXTENSIONS = {".mp4", ".webm", ".ogg", ".mov", ".avi", ".wmv"}
|
|
|
|
# Supported PDF extension
|
|
PDF_EXTENSION = ".pdf"
|
|
|
|
# Determine the file extension
|
|
_, file_extension = os.path.splitext(file_path)
|
|
|
|
# Determine the file type based on the extension
|
|
if file_extension in CODE_EXTENSIONS:
|
|
file_type = "code"
|
|
elif file_extension in CSV_EXTENSIONS:
|
|
file_type = "csv"
|
|
elif file_extension in IMAGE_EXTENSIONS:
|
|
file_type = "image"
|
|
elif file_extension == PDF_EXTENSION:
|
|
file_type = "pdf"
|
|
elif file_extension in VIDEO_EXTENSIONS:
|
|
file_type = "video"
|
|
else:
|
|
file_type = "unknown"
|
|
|
|
return file_type
|
|
|
|
|
|
def serialize_file(file_path: str) -> Tuple[str, str]:
|
|
"""
|
|
Reads a file from a given file path, base64 encodes its content,
|
|
and returns the base64 encoded string along with the file type.
|
|
|
|
The file type is determined by the file extension. If the file extension is not
|
|
recognized, 'unknown' will be used as the file type.
|
|
|
|
:param file_path: The path to the file to be serialized.
|
|
:return: A tuple containing the base64 encoded string of the file and the file type.
|
|
"""
|
|
|
|
file_type = get_file_type(file_path)
|
|
|
|
# Read the file and encode its contents
|
|
try:
|
|
with open(file_path, "rb") as file:
|
|
file_content = file.read()
|
|
base64_encoded_content = base64.b64encode(file_content).decode("utf-8")
|
|
except Exception as e:
|
|
raise IOError(f"An error occurred while reading the file: {e}") from e
|
|
|
|
return base64_encoded_content, file_type
|
|
|
|
|
|
def get_modified_files(start_timestamp: float, end_timestamp: float, source_dir: str) -> List[Dict[str, str]]:
|
|
"""
|
|
Identify files from source_dir that were modified within a specified timestamp range.
|
|
The function excludes files with certain file extensions and names.
|
|
|
|
:param start_timestamp: The floating-point number representing the start timestamp to filter modified files.
|
|
:param end_timestamp: The floating-point number representing the end timestamp to filter modified files.
|
|
:param source_dir: The directory to search for modified files.
|
|
|
|
:return: A list of dictionaries with details of relative file paths that were modified.
|
|
Dictionary format: {path: "", name: "", extension: "", type: ""}
|
|
Files with extensions "__pycache__", "*.pyc", "__init__.py", and "*.cache"
|
|
are ignored.
|
|
"""
|
|
modified_files = []
|
|
ignore_extensions = {".pyc", ".cache"}
|
|
ignore_files = {"__pycache__", "__init__.py"}
|
|
|
|
# Walk through the directory tree
|
|
for root, dirs, files in os.walk(source_dir):
|
|
# Update directories and files to exclude those to be ignored
|
|
dirs[:] = [d for d in dirs if d not in ignore_files]
|
|
files[:] = [f for f in files if f not in ignore_files and os.path.splitext(f)[1] not in ignore_extensions]
|
|
|
|
for file in files:
|
|
file_path = os.path.join(root, file)
|
|
file_mtime = os.path.getmtime(file_path)
|
|
|
|
# Verify if the file was modified within the given timestamp range
|
|
if start_timestamp <= file_mtime <= end_timestamp:
|
|
file_relative_path = (
|
|
"files/user" + file_path.split("files/user", 1)[1] if "files/user" in file_path else ""
|
|
)
|
|
file_type = get_file_type(file_path)
|
|
|
|
file_dict = {
|
|
"path": file_relative_path,
|
|
"name": os.path.basename(file),
|
|
# Remove the dot
|
|
"extension": os.path.splitext(file)[1].lstrip("."),
|
|
"type": file_type,
|
|
}
|
|
modified_files.append(file_dict)
|
|
|
|
# Sort the modified files by extension
|
|
modified_files.sort(key=lambda x: x["extension"])
|
|
return modified_files
|
|
|
|
|
|
def get_app_root() -> str:
|
|
"""
|
|
Get the root directory of the application.
|
|
|
|
:return: The root directory of the application.
|
|
"""
|
|
app_name = f".{APP_NAME}"
|
|
default_app_root = os.path.join(os.path.expanduser("~"), app_name)
|
|
if not os.path.exists(default_app_root):
|
|
os.makedirs(default_app_root, exist_ok=True)
|
|
app_root = os.environ.get("AUTOGENSTUDIO_APPDIR") or default_app_root
|
|
return app_root
|
|
|
|
|
|
def get_db_uri(app_root: str) -> str:
|
|
"""
|
|
Get the default database URI for the application.
|
|
|
|
:param app_root: The root directory of the application.
|
|
:return: The default database URI.
|
|
"""
|
|
db_uri = f"sqlite:///{os.path.join(app_root, 'database.sqlite')}"
|
|
db_uri = os.environ.get("AUTOGENSTUDIO_DATABASE_URI") or db_uri
|
|
logger.info(f"Using database URI: {db_uri}")
|
|
return db_uri
|
|
|
|
|
|
def init_app_folders(app_file_path: str) -> Dict[str, str]:
|
|
"""
|
|
Initialize folders needed for a web server, such as static file directories
|
|
and user-specific data directories. Also load any .env file if it exists.
|
|
|
|
:param root_file_path: The root directory where webserver folders will be created
|
|
:return: A dictionary with the path of each created folder
|
|
"""
|
|
app_root = get_app_root()
|
|
|
|
if not os.path.exists(app_root):
|
|
os.makedirs(app_root, exist_ok=True)
|
|
|
|
# load .env file if it exists
|
|
env_file = os.path.join(app_root, ".env")
|
|
if os.path.exists(env_file):
|
|
logger.info(f"Loaded environment variables from {env_file}")
|
|
load_dotenv(env_file)
|
|
|
|
files_static_root = os.path.join(app_root, "files/")
|
|
static_folder_root = os.path.join(app_file_path, "ui")
|
|
|
|
os.makedirs(files_static_root, exist_ok=True)
|
|
os.makedirs(os.path.join(files_static_root, "user"), exist_ok=True)
|
|
os.makedirs(static_folder_root, exist_ok=True)
|
|
folders = {
|
|
"files_static_root": files_static_root,
|
|
"static_folder_root": static_folder_root,
|
|
"app_root": app_root,
|
|
"database_engine_uri": get_db_uri(app_root=app_root),
|
|
}
|
|
logger.info(f"Initialized application data folder: {app_root}")
|
|
return folders
|
|
|
|
|
|
def get_skills_prompt(skills: List[Skill], work_dir: str) -> str:
|
|
"""
|
|
Create a prompt with the content of all skills and write the skills to a file named skills.py in the work_dir.
|
|
|
|
:param skills: A dictionary skills
|
|
:return: A string containing the content of all skills
|
|
"""
|
|
|
|
instruction = """
|
|
|
|
While solving the task you may use functions below which will be available in a file called skills.py .
|
|
To use a function skill.py in code, IMPORT THE FUNCTION FROM skills.py and then use the function.
|
|
If you need to install python packages, write shell code to
|
|
install via pip and use --quiet option.
|
|
|
|
"""
|
|
prompt = "" # filename: skills.py
|
|
|
|
for skill in skills:
|
|
if not isinstance(skill, Skill):
|
|
skill = Skill(**skill)
|
|
if skill.secrets:
|
|
for secret in skill.secrets:
|
|
if secret.get("value") is not None:
|
|
os.environ[secret["secret"]] = secret["value"]
|
|
prompt += f"""
|
|
|
|
##### Begin of {skill.name} #####
|
|
from skills import {skill.name} # Import the function from skills.py
|
|
|
|
{skill.content}
|
|
|
|
#### End of {skill.name} ####
|
|
|
|
"""
|
|
|
|
return instruction + prompt
|
|
|
|
|
|
def save_skills_to_file(skills: List[Skill], work_dir: str) -> None:
|
|
"""
|
|
Write the skills to a file named skills.py in the work_dir.
|
|
|
|
:param skills: A dictionary skills
|
|
"""
|
|
|
|
# TBD: Double check for duplicate skills?
|
|
|
|
# check if work_dir exists
|
|
if not os.path.exists(work_dir):
|
|
os.makedirs(work_dir)
|
|
|
|
skills_content = ""
|
|
for skill in skills:
|
|
if not isinstance(skill, Skill):
|
|
skill = Skill(**skill)
|
|
|
|
skills_content += f"""
|
|
|
|
##### Begin of {skill.name} #####
|
|
|
|
{skill.content}
|
|
|
|
#### End of {skill.name} ####
|
|
|
|
"""
|
|
|
|
# overwrite skills.py in work_dir
|
|
with open(os.path.join(work_dir, "skills.py"), "w", encoding="utf-8") as f:
|
|
f.write(skills_content)
|
|
|
|
|
|
def delete_files_in_folder(folders: Union[str, List[str]]) -> None:
|
|
"""
|
|
Delete all files and directories in the specified folders.
|
|
|
|
:param folders: A list of folders or a single folder string
|
|
"""
|
|
|
|
if isinstance(folders, str):
|
|
folders = [folders]
|
|
|
|
for folder in folders:
|
|
# Check if the folder exists
|
|
if not os.path.isdir(folder):
|
|
continue
|
|
|
|
# List all the entries in the directory
|
|
for entry in os.listdir(folder):
|
|
# Get the full path
|
|
path = os.path.join(folder, entry)
|
|
try:
|
|
if os.path.isfile(path) or os.path.islink(path):
|
|
# Remove the file or link
|
|
os.remove(path)
|
|
elif os.path.isdir(path):
|
|
# Remove the directory and all its content
|
|
shutil.rmtree(path)
|
|
except Exception as e:
|
|
# Print the error message and skip
|
|
logger.info(f"Failed to delete {path}. Reason: {e}")
|
|
|
|
|
|
def extract_successful_code_blocks(messages: List[Dict[str, str]]) -> List[str]:
|
|
"""
|
|
Parses through a list of messages containing code blocks and execution statuses,
|
|
returning the array of code blocks that executed successfully and retains
|
|
the backticks for Markdown rendering.
|
|
|
|
Parameters:
|
|
messages (List[Dict[str, str]]): A list of message dictionaries containing 'content' and 'role' keys.
|
|
|
|
Returns:
|
|
List[str]: A list containing the code blocks that were successfully executed, including backticks.
|
|
"""
|
|
successful_code_blocks = []
|
|
# Regex pattern to capture code blocks enclosed in triple backticks.
|
|
code_block_regex = r"```[\s\S]*?```"
|
|
|
|
for i, row in enumerate(messages):
|
|
message = row["message"]
|
|
if message["role"] == "user" and "execution succeeded" in message["content"]:
|
|
if i > 0 and messages[i - 1]["message"]["role"] == "assistant":
|
|
prev_content = messages[i - 1]["message"]["content"]
|
|
# Find all matches for code blocks
|
|
code_blocks = re.findall(code_block_regex, prev_content)
|
|
# Add the code blocks with backticks
|
|
successful_code_blocks.extend(code_blocks)
|
|
|
|
return successful_code_blocks
|
|
|
|
|
|
def sanitize_model(model: Model):
|
|
"""
|
|
Sanitize model dictionary to remove None values and empty strings and only keep valid keys.
|
|
"""
|
|
if isinstance(model, Model):
|
|
model = model.model_dump()
|
|
valid_keys = ["model", "base_url", "api_key", "api_type", "api_version"]
|
|
# only add key if value is not None
|
|
sanitized_model = {k: v for k, v in model.items() if (v is not None and v != "") and k in valid_keys}
|
|
return sanitized_model
|
|
|
|
|
|
def test_model(model: Model):
|
|
"""
|
|
Test the model endpoint by sending a simple message to the model and returning the response.
|
|
"""
|
|
|
|
print("Testing model", model)
|
|
|
|
sanitized_model = sanitize_model(model)
|
|
client = OpenAIWrapper(config_list=[sanitized_model])
|
|
response = client.create(
|
|
messages=[
|
|
{
|
|
"role": "system",
|
|
"content": "You are a helpful assistant that can add numbers. ONLY RETURN THE RESULT.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "2+2=",
|
|
},
|
|
],
|
|
cache_seed=None,
|
|
)
|
|
return response.choices[0].message.content
|
|
|
|
|
|
def load_code_execution_config(code_execution_type: CodeExecutionConfigTypes, work_dir: str):
|
|
"""
|
|
Load the code execution configuration based on the code execution type.
|
|
|
|
:param code_execution_type: The code execution type.
|
|
:param work_dir: The working directory to store code execution files.
|
|
:return: The code execution configuration.
|
|
|
|
"""
|
|
work_dir = Path(work_dir)
|
|
work_dir.mkdir(exist_ok=True)
|
|
executor = None
|
|
if code_execution_type == CodeExecutionConfigTypes.local:
|
|
executor = LocalCommandLineCodeExecutor(work_dir=work_dir)
|
|
elif code_execution_type == CodeExecutionConfigTypes.docker:
|
|
try:
|
|
executor = DockerCommandLineCodeExecutor(work_dir=work_dir)
|
|
except Exception as e:
|
|
logger.error(f"Error initializing Docker executor: {e}")
|
|
return False
|
|
elif code_execution_type == CodeExecutionConfigTypes.none:
|
|
return False
|
|
else:
|
|
raise ValueError(f"Invalid code execution type: {code_execution_type}")
|
|
code_execution_config = {
|
|
"executor": executor,
|
|
}
|
|
return code_execution_config
|
|
|
|
|
|
def summarize_chat_history(task: str, messages: List[Dict[str, str]], client: ModelClient):
|
|
"""
|
|
Summarize the chat history using the model endpoint and returning the response.
|
|
"""
|
|
summarization_system_prompt = f"""
|
|
You are a helpful assistant that is able to review the chat history between a set of agents (userproxy agents, assistants etc) as they try to address a given TASK and provide a summary. Be SUCCINCT but also comprehensive enough to allow others (who cannot see the chat history) understand and recreate the solution.
|
|
|
|
The task requested by the user is:
|
|
===
|
|
{task}
|
|
===
|
|
The summary should focus on extracting the actual solution to the task from the chat history (assuming the task was addressed) such that any other agent reading the summary will understand what the actual solution is. Use a neutral tone and DO NOT directly mention the agents. Instead only focus on the actions that were carried out (e.g. do not say 'assistant agent generated some code visualization code ..' instead say say 'visualization code was generated ..'. The answer should be framed as a response to the user task. E.g. if the task is "What is the height of the Eiffel tower", the summary should be "The height of the Eiffel Tower is ...").
|
|
"""
|
|
summarization_prompt = [
|
|
{
|
|
"role": "system",
|
|
"content": summarization_system_prompt,
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f"Summarize the following chat history. {str(messages)}",
|
|
},
|
|
]
|
|
response = client.create(messages=summarization_prompt, cache_seed=None)
|
|
return response.choices[0].message.content
|
|
|
|
|
|
def get_autogen_log(db_path="logs.db"):
|
|
"""
|
|
Fetches data the autogen logs database.
|
|
Args:
|
|
dbname (str): Name of the database file. Defaults to "logs.db".
|
|
table (str): Name of the table to query. Defaults to "chat_completions".
|
|
|
|
Returns:
|
|
list: A list of dictionaries, where each dictionary represents a row from the table.
|
|
"""
|
|
import json
|
|
import sqlite3
|
|
|
|
con = sqlite3.connect(db_path)
|
|
query = """
|
|
SELECT
|
|
chat_completions.*,
|
|
agents.name AS agent_name
|
|
FROM
|
|
chat_completions
|
|
JOIN
|
|
agents ON chat_completions.wrapper_id = agents.wrapper_id
|
|
"""
|
|
cursor = con.execute(query)
|
|
rows = cursor.fetchall()
|
|
column_names = [description[0] for description in cursor.description]
|
|
data = [dict(zip(column_names, row)) for row in rows]
|
|
for row in data:
|
|
response = json.loads(row["response"])
|
|
print(response)
|
|
total_tokens = response.get("usage", {}).get("total_tokens", 0)
|
|
row["total_tokens"] = total_tokens
|
|
con.close()
|
|
return data
|
|
|
|
|
|
def find_key_value(d, target_key):
|
|
"""
|
|
Recursively search for a key in a nested dictionary and return its value.
|
|
"""
|
|
if d is None:
|
|
return None
|
|
|
|
if isinstance(d, dict):
|
|
if target_key in d:
|
|
return d[target_key]
|
|
for k in d:
|
|
item = find_key_value(d[k], target_key)
|
|
if item is not None:
|
|
return item
|
|
elif isinstance(d, list):
|
|
for i in d:
|
|
item = find_key_value(i, target_key)
|
|
if item is not None:
|
|
return item
|
|
return None
|