Files
letta-server/memgpt/functions/functions.py

173 lines
7.6 KiB
Python

import importlib
import inspect
import os
import sys
import warnings
from types import ModuleType
from memgpt.constants import CLI_WARNING_PREFIX, MEMGPT_DIR
from memgpt.functions.schema_generator import generate_schema
USER_FUNCTIONS_DIR = os.path.join(MEMGPT_DIR, "functions")
sys.path.append(USER_FUNCTIONS_DIR)
def load_function_set(module: ModuleType) -> dict:
"""Load the functions and generate schema for them, given a module object"""
function_dict = {}
for attr_name in dir(module):
# Get the attribute
attr = getattr(module, attr_name)
# Check if it's a callable function and not a built-in or special method
if inspect.isfunction(attr) and attr.__module__ == module.__name__:
if attr_name in function_dict:
raise ValueError(f"Found a duplicate of function name '{attr_name}'")
generated_schema = generate_schema(attr)
function_dict[attr_name] = {
"python_function": attr,
"json_schema": generated_schema,
}
if len(function_dict) == 0:
raise ValueError(f"No functions found in module {module}")
return function_dict
def validate_function(module_name, module_full_path):
try:
file = os.path.basename(module_full_path)
spec = importlib.util.spec_from_file_location(module_name, module_full_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
except ModuleNotFoundError as e:
# Handle missing module imports
missing_package = str(e).split("'")[1] # Extract the name of the missing package
print(f"{CLI_WARNING_PREFIX}skipped loading python file '{module_full_path}'!")
return (
False,
f"'{file}' imports '{missing_package}', but '{missing_package}' is not installed locally - install python package '{missing_package}' to link functions from '{file}' to MemGPT.",
)
except SyntaxError as e:
# Handle syntax errors in the module
return False, f"{CLI_WARNING_PREFIX}skipped loading python file '{file}' due to a syntax error: {e}"
except Exception as e:
# Handle other general exceptions
return False, f"{CLI_WARNING_PREFIX}skipped loading python file '{file}': {e}"
return True, None
def write_function(module_name: str, function_name: str, function_code: str):
"""Write a function to a file in the user functions directory"""
# Create the user functions directory if it doesn't exist
if not os.path.exists(USER_FUNCTIONS_DIR):
os.makedirs(USER_FUNCTIONS_DIR)
# Write the function to a file
file_path = os.path.join(USER_FUNCTIONS_DIR, f"{module_name}.py")
with open(file_path, "a") as f:
f.write(function_code)
succ, error = validate_function(module_name, file_path)
# raise error if function cannot be loaded
if not succ:
raise ValueError(error)
def load_all_function_sets(merge: bool = True, ignore_duplicates: bool = True) -> dict:
from memgpt.utils import printd
# functions/examples/*.py
scripts_dir = os.path.dirname(os.path.abspath(__file__)) # Get the directory of the current script
function_sets_dir = os.path.join(scripts_dir, "function_sets") # Path to the function_sets directory
# List all .py files in the directory (excluding __init__.py)
example_module_files = [f for f in os.listdir(function_sets_dir) if f.endswith(".py") and f != "__init__.py"]
# ~/.memgpt/functions/*.py
# create if missing
if not os.path.exists(USER_FUNCTIONS_DIR):
os.makedirs(USER_FUNCTIONS_DIR)
user_module_files = [f for f in os.listdir(USER_FUNCTIONS_DIR) if f.endswith(".py") and f != "__init__.py"]
# combine them both (pull from both examples and user-provided)
# all_module_files = example_module_files + user_module_files
# Add user_scripts_dir to sys.path
if USER_FUNCTIONS_DIR not in sys.path:
sys.path.append(USER_FUNCTIONS_DIR)
schemas_and_functions = {}
for dir_path, module_files in [(function_sets_dir, example_module_files), (USER_FUNCTIONS_DIR, user_module_files)]:
for file in module_files:
tags = []
module_name = file[:-3] # Remove '.py' from filename
if dir_path == USER_FUNCTIONS_DIR:
# For user scripts, adjust the module name appropriately
module_full_path = os.path.join(dir_path, file)
printd(f"Loading user function set from '{module_full_path}'")
try:
spec = importlib.util.spec_from_file_location(module_name, module_full_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
except ModuleNotFoundError as e:
# Handle missing module imports
missing_package = str(e).split("'")[1] # Extract the name of the missing package
printd(f"{CLI_WARNING_PREFIX}skipped loading python file '{module_full_path}'!")
printd(
f"'{file}' imports '{missing_package}', but '{missing_package}' is not installed locally - install python package '{missing_package}' to link functions from '{file}' to MemGPT."
)
continue
except SyntaxError as e:
# Handle syntax errors in the module
printd(f"{CLI_WARNING_PREFIX}skipped loading python file '{file}' due to a syntax error: {e}")
continue
except Exception as e:
# Handle other general exceptions
printd(f"{CLI_WARNING_PREFIX}skipped loading python file '{file}': {e}")
continue
else:
# For built-in scripts, use the existing method
full_module_name = f"memgpt.functions.function_sets.{module_name}"
tags.append(f"memgpt-{module_name}")
try:
module = importlib.import_module(full_module_name)
except Exception as e:
# Handle other general exceptions
printd(f"{CLI_WARNING_PREFIX}skipped loading python module '{full_module_name}': {e}")
continue
try:
# Load the function set
function_set = load_function_set(module)
# Add the metadata tags
for k, v in function_set.items():
# print(function_set)
v["tags"] = tags
schemas_and_functions[module_name] = function_set
except ValueError as e:
err = f"Error loading function set '{module_name}': {e}"
printd(err)
warnings.warn(err)
if merge:
# Put all functions from all sets into the same level dict
merged_functions = {}
for set_name, function_set in schemas_and_functions.items():
for function_name, function_info in function_set.items():
if function_name in merged_functions:
err_msg = f"Duplicate function name '{function_name}' found in function set '{set_name}'"
if ignore_duplicates:
warnings.warn(err_msg, category=UserWarning, stacklevel=2)
else:
raise ValueError(err_msg)
else:
merged_functions[function_name] = function_info
return merged_functions
else:
# Nested dict where the top level is organized by the function set name
return schemas_and_functions