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autogpt 还原，开新分支编写

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This commit is contained in:
w_xiaolizu
2023-05-30 16:38:01 +08:00
parent 548532e522
commit 519df19f43
65 changed files with 3352 additions and 1868 deletions
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8
__main__.py
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@ -366,9 +366,9 @@ class ChatBot(ChatBotFrame):
self.draw_public_chat()
self.draw_setting_chat()
# 绘制autogpt模组
with gr.Tab('Auto-GPT'):
self.draw_next_auto()
self.draw_goals_auto()
# with gr.Tab('Auto-GPT'):
# self.draw_next_auto()
# self.draw_goals_auto()
with self.chat_tab: # 使用 gr.State()对组件进行拷贝时，如果之前绘制了Markdown格式，会导致启动崩溃,所以将 markdown相关绘制放在最后
self.draw_chatbot()
with self.prompt_tab:
@ -378,8 +378,8 @@ class ChatBot(ChatBotFrame):
self.signals_function()
self.signals_prompt_func()
self.signals_public()
self.signals_auto_input()
self.signals_prompt_edit()
# self.signals_auto_input()
# Start
self.auto_opentab_delay()

2
autogpt/CURRENT_BULLETIN.md Normal file
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@ -0,0 +1,2 @@
Welcome to Auto-GPT! We'll keep you informed of the latest news and features by printing messages here.
If you don't wish to see this message, you can run Auto-GPT with the --skip-news flag

14
autogpt/__init__.py
View File

@ -1,14 +0,0 @@
import os
import random
import sys
from dotenv import load_dotenv
if "pytest" in sys.argv or "pytest" in sys.modules or os.getenv("CI"):
print("Setting random seed to 42")
random.seed(42)
# Load the users .env file into environment variables
load_dotenv(verbose=True, override=True)
del load_dotenv

429
autogpt/agent/agent.py
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@ -1,29 +1,11 @@
import signal
import sys
from datetime import datetime
from colorama import Fore, Style
from autogpt.app import execute_command, get_command
from autogpt.commands.command import CommandRegistry
from autogpt.chat import chat_with_ai, create_chat_message
from autogpt.config import Config
from autogpt.config.ai_config import AIConfig
from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
from autogpt.json_utils.utilities import LLM_DEFAULT_RESPONSE_FORMAT, validate_json
from autogpt.llm.base import ChatSequence
from autogpt.llm.chat import chat_with_ai, create_chat_completion
from autogpt.llm.utils import count_string_tokens
from autogpt.log_cycle.log_cycle import (
FULL_MESSAGE_HISTORY_FILE_NAME,
NEXT_ACTION_FILE_NAME,
PROMPT_SUPERVISOR_FEEDBACK_FILE_NAME,
SUPERVISOR_FEEDBACK_FILE_NAME,
USER_INPUT_FILE_NAME,
LogCycleHandler,
)
from autogpt.json_utils.utilities import validate_json
from autogpt.logs import logger, print_assistant_thoughts
from autogpt.memory.message_history import MessageHistory
from autogpt.memory.vector import VectorMemory
from autogpt.speech import say_text
from autogpt.spinner import Spinner
from autogpt.utils import clean_input
@ -36,6 +18,7 @@ class Agent:
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
system_prompt: The system prompt is the initial prompt that defines everything
the AI needs to know to achieve its task successfully.
@ -59,251 +42,189 @@ class Agent:
"""
def __init__(
self,
ai_name: str,
memory: VectorMemory,
next_action_count: int,
command_registry: CommandRegistry,
config: AIConfig,
system_prompt: str,
triggering_prompt: str,
workspace_directory: str,
self,
ai_name,
memory,
full_message_history,
next_action_count,
command_registry,
config,
system_prompt,
triggering_prompt,
workspace_directory,
):
cfg = Config()
self.cfg = Config()
self.ai_name = ai_name
self.memory = memory
self.history = MessageHistory(self)
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.command_registry = command_registry
self.config = config
self.system_prompt = system_prompt
self.triggering_prompt = triggering_prompt
self.workspace = Workspace(workspace_directory, cfg.restrict_to_workspace)
self.created_at = datetime.now().strftime("%Y%m%d_%H%M%S")
self.cycle_count = 0
self.log_cycle_handler = LogCycleHandler()
self.workspace = Workspace(workspace_directory, self.cfg.restrict_to_workspace)
self.loop_count = 0
self.command_name = None
self.sarguments = None
self.user_input = ""
self.cfg = Config()
def start_interaction_loop(self):
# Interaction Loop
cfg = Config()
self.cycle_count = 0
command_name = None
arguments = None
user_input = ""
# Signal handler for interrupting y -N
def signal_handler(signum, frame):
if self.next_action_count == 0:
sys.exit()
else:
print(
Fore.RED
+ "Interrupt signal received. Stopping continuous command execution."
+ Style.RESET_ALL
)
self.next_action_count = 0
signal.signal(signal.SIGINT, signal_handler)
while True:
# Discontinue if continuous limit is reached
self.cycle_count += 1
self.log_cycle_handler.log_count_within_cycle = 0
self.log_cycle_handler.log_cycle(
self.config.ai_name,
self.created_at,
self.cycle_count,
[m.raw() for m in self.history],
FULL_MESSAGE_HISTORY_FILE_NAME,
# Discontinue if continuous limit is reached
self.loop_count += 1
if (
self.cfg.continuous_mode
and self.cfg.continuous_limit > 0
and self.loop_count > self.cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{self.cfg.continuous_limit}"
)
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and self.cycle_count > cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
)
break
# Send message to AI, get response
with Spinner("Thinking... ", plain_output=cfg.plain_output):
assistant_reply = chat_with_ai(
cfg,
self,
self.system_prompt,
self.triggering_prompt,
cfg.fast_token_limit,
cfg.fast_llm_model,
)
# break
assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply)
for plugin in cfg.plugins:
if not plugin.can_handle_post_planning():
continue
assistant_reply_json = plugin.post_planning(assistant_reply_json)
# Send message to AI, get response
with Spinner("Thinking... "):
self.assistant_reply = chat_with_ai(
self,
self.system_prompt,
self.triggering_prompt,
self.full_message_history,
self.memory,
self.cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
if assistant_reply_json != {}:
validate_json(assistant_reply_json, LLM_DEFAULT_RESPONSE_FORMAT)
# Get command name and arguments
try:
print_assistant_thoughts(
self.ai_name, assistant_reply_json, cfg.speak_mode
)
command_name, arguments = get_command(assistant_reply_json)
if cfg.speak_mode:
say_text(f"I want to execute {command_name}")
self.assistant_reply_json = fix_json_using_multiple_techniques(self.assistant_reply)
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_planning():
continue
self.assistant_reply_json = plugin.post_planning(self, self.assistant_reply_json)
arguments = self._resolve_pathlike_command_args(arguments)
# Print Assistant thoughts
if self.assistant_reply_json != {}:
validate_json(self.assistant_reply_json, "llm_response_format_1")
# Get command name and self.arguments
try:
print_assistant_thoughts(self.ai_name, self.assistant_reply_json)
self.command_name, self.arguments = get_command(self.assistant_reply_json)
if self.cfg.speak_mode:
say_text(f"I want to execute {self.command_name}")
self.arguments = self._resolve_pathlike_command_args(self.arguments)
except Exception as e:
logger.error("Error: \n", str(e))
self.log_cycle_handler.log_cycle(
self.config.ai_name,
self.created_at,
self.cycle_count,
assistant_reply_json,
NEXT_ACTION_FILE_NAME,
)
except Exception as e:
logger.error("Error: \n", str(e))
if not self.cfg.continuous_mode and self.next_action_count == 0:
# ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} "
f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
f"COMMAND = {self.command_name}"
f"ARGUMENTS = {self.arguments}",
)
logger.typewriter_log(
"",
"",
"Enter 'y' to authorise command, 'y -N' to run N continuous "
"commands, 'n' to exit program, or enter feedback for "
f"{self.ai_name}...",
)
if not cfg.continuous_mode and self.next_action_count == 0:
# ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.info(
"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 's' to run self-feedback commands, "
"'n' to exit program, or enter feedback for "
f"{self.ai_name}..."
def start_interaction_next(self, cookie, chatbot, history, msg, _input, obj):
console_input = _input
if console_input.lower().strip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
elif console_input.lower().strip() == "":
print("Invalid input format.")
return
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
while True:
if cfg.chat_messages_enabled:
console_input = clean_input("Waiting for your response...")
else:
console_input = clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().strip() == cfg.authorise_key:
user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().strip() == "s":
logger.typewriter_log(
"-=-=-=-=-=-=-= THOUGHTS, REASONING, PLAN AND CRITICISM WILL NOW BE VERIFIED BY AGENT -=-=-=-=-=-=-=",
Fore.GREEN,
"",
)
thoughts = assistant_reply_json.get("thoughts", {})
self_feedback_resp = self.get_self_feedback(
thoughts, cfg.fast_llm_model
)
logger.typewriter_log(
f"SELF FEEDBACK: {self_feedback_resp}",
Fore.YELLOW,
"",
)
user_input = self_feedback_resp
command_name = "self_feedback"
break
elif console_input.lower().strip() == "":
logger.warn("Invalid input format.")
continue
elif console_input.lower().startswith(f"{cfg.authorise_key} -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
logger.warn(
"Invalid input format. Please enter 'y -n' where n is"
" the number of continuous tasks."
)
continue
break
elif console_input.lower() == cfg.exit_key:
user_input = "EXIT"
break
else:
user_input = console_input
command_name = "human_feedback"
self.log_cycle_handler.log_cycle(
self.config.ai_name,
self.created_at,
self.cycle_count,
user_input,
USER_INPUT_FILE_NAME,
)
break
if user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif user_input == "EXIT":
logger.info("Exiting...")
break
else:
# Print authorized commands left value
logger.typewriter_log(
f"{Fore.CYAN}AUTHORISED COMMANDS LEFT: {Style.RESET_ALL}{self.next_action_count}"
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is"
" the number of continuous tasks."
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = f"Could not execute command: {arguments}"
elif command_name == "human_feedback":
result = f"Human feedback: {user_input}"
elif command_name == "self_feedback":
result = f"Self feedback: {user_input}"
else:
for plugin in cfg.plugins:
if not plugin.can_handle_pre_command():
continue
command_name, arguments = plugin.pre_command(
command_name, arguments
)
command_result = execute_command(
self.command_registry,
command_name,
arguments,
self.config.prompt_generator,
config=cfg,
)
result = f"Command {command_name} returned: " f"{command_result}"
return
elif console_input.lower() == "n":
self.user_input = "EXIT"
return
else:
self.user_input = console_input
self.command_name = "human_feedback"
return
result_tlength = count_string_tokens(
str(command_result), cfg.fast_llm_model
)
memory_tlength = count_string_tokens(
str(self.history.summary_message()), cfg.fast_llm_model
)
if result_tlength + memory_tlength + 600 > cfg.fast_token_limit:
result = f"Failure: command {command_name} returned too much output. \
Do not execute this command again with the same arguments."
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
# break 这里需要注意
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{self.command_name}{Style.RESET_ALL}"
f" ARGUMENTS = {Fore.CYAN}{self.arguments}{Style.RESET_ALL}",
)
for plugin in cfg.plugins:
if not plugin.can_handle_post_command():
continue
result = plugin.post_command(command_name, result)
if self.next_action_count > 0:
self.next_action_count -= 1
# Execute command
if self.command_name is not None and self.command_name.lower().startswith("error"):
result = (
f"Command {self.command_name} threw the following error: {self.arguments}"
)
elif self.command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_command():
continue
self.command_name, self.arguments = plugin.pre_command(
self.command_name, self.arguments
)
command_result = execute_command(
self.command_registry,
self.command_name,
self.arguments,
self.config.prompt_generator,
)
result = f"Command {self.command_name} returned: " f"{command_result}"
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_command():
continue
result = plugin.post_command(self.command_name, result)
if self.next_action_count > 0:
self.next_action_count -= 1
if self.command_name != "do_nothing":
memory_to_add = (
f"Assistant Reply: {self.assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.history.add("system", result, "action_result")
self.full_message_history.append(
create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.history.add("system", "Unable to execute command", "action_result")
self.full_message_history.append(
create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
@ -318,45 +239,3 @@ class Agent:
self.workspace.get_path(command_args[pathlike])
)
return command_args
def get_self_feedback(self, thoughts: dict, llm_model: str) -> str:
"""Generates a feedback response based on the provided thoughts dictionary.
This method takes in a dictionary of thoughts containing keys such as 'reasoning',
'plan', 'thoughts', and 'criticism'. It combines these elements into a single
feedback message and uses the create_chat_completion() function to generate a
response based on the input message.
Args:
thoughts (dict): A dictionary containing thought elements like reasoning,
plan, thoughts, and criticism.
Returns:
str: A feedback response generated using the provided thoughts dictionary.
"""
ai_role = self.config.ai_role
feedback_prompt = f"Below is a message from me, an AI Agent, assuming the role of {ai_role}. whilst keeping knowledge of my slight limitations as an AI Agent Please evaluate my thought process, reasoning, and plan, and provide a concise paragraph outlining potential improvements. Consider adding or removing ideas that do not align with my role and explaining why, prioritizing thoughts based on their significance, or simply refining my overall thought process."
reasoning = thoughts.get("reasoning", "")
plan = thoughts.get("plan", "")
thought = thoughts.get("thoughts", "")
feedback_thoughts = thought + reasoning + plan
prompt = ChatSequence.for_model(llm_model)
prompt.add("user", feedback_prompt + feedback_thoughts)
self.log_cycle_handler.log_cycle(
self.config.ai_name,
self.created_at,
self.cycle_count,
prompt.raw(),
PROMPT_SUPERVISOR_FEEDBACK_FILE_NAME,
)
feedback = create_chat_completion(prompt)
self.log_cycle_handler.log_cycle(
self.config.ai_name,
self.created_at,
self.cycle_count,
feedback,
SUPERVISOR_FEEDBACK_FILE_NAME,
)
return feedback

62
autogpt/agent/agent_manager.py
View File

@ -1,10 +1,11 @@
"""Agent manager for managing GPT agents"""
from __future__ import annotations
from autogpt.config import Config
from autogpt.llm.base import ChatSequence
from autogpt.llm.chat import Message, create_chat_completion
from autogpt.singleton import Singleton
from typing import List, Union
from autogpt.config.config import Config, Singleton
from autogpt.llm_utils import create_chat_completion
from autogpt.types.openai import Message
class AgentManager(metaclass=Singleton):
@ -12,55 +13,55 @@ class AgentManager(metaclass=Singleton):
def __init__(self):
self.next_key = 0
self.agents: dict[
int, tuple[str, list[Message], str]
] = {} # key, (task, full_message_history, model)
self.agents = {} # key, (task, full_message_history, model)
self.cfg = Config()
# Create new GPT agent
# TODO: Centralise use of create_chat_completion() to globally enforce token limit
def create_agent(
self, task: str, creation_prompt: str, model: str
) -> tuple[int, str]:
def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
"""Create a new agent and return its key
Args:
task: The task to perform
creation_prompt: Prompt passed to the LLM at creation
model: The model to use to run this agent
prompt: The prompt to use
model: The model to use
Returns:
The key of the new agent
"""
messages = ChatSequence.for_model(model, [Message("user", creation_prompt)])
messages: List[Message] = [
{"role": "user", "content": prompt},
]
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_instruction():
continue
if plugin_messages := plugin.pre_instruction(messages.raw()):
messages.extend([Message(**raw_msg) for raw_msg in plugin_messages])
if plugin_messages := plugin.pre_instruction(messages):
messages.extend(iter(plugin_messages))
# Start GPT instance
agent_reply = create_chat_completion(prompt=messages)
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
messages.add("assistant", agent_reply)
messages.append({"role": "assistant", "content": agent_reply})
plugins_reply = ""
for i, plugin in enumerate(self.cfg.plugins):
if not plugin.can_handle_on_instruction():
continue
if plugin_result := plugin.on_instruction([m.raw() for m in messages]):
if plugin_result := plugin.on_instruction(messages):
sep = "\n" if i else ""
plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
if plugins_reply and plugins_reply != "":
messages.add("assistant", plugins_reply)
messages.append({"role": "assistant", "content": plugins_reply})
key = self.next_key
# This is done instead of len(agents) to make keys unique even if agents
# are deleted
self.next_key += 1
self.agents[key] = (task, list(messages), model)
self.agents[key] = (task, messages, model)
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_instruction():
@ -82,30 +83,33 @@ class AgentManager(metaclass=Singleton):
task, messages, model = self.agents[int(key)]
# Add user message to message history before sending to agent
messages = ChatSequence.for_model(model, messages)
messages.add("user", message)
messages.append({"role": "user", "content": message})
for plugin in self.cfg.plugins:
if not plugin.can_handle_pre_instruction():
continue
if plugin_messages := plugin.pre_instruction([m.raw() for m in messages]):
messages.extend([Message(**raw_msg) for raw_msg in plugin_messages])
if plugin_messages := plugin.pre_instruction(messages):
for plugin_message in plugin_messages:
messages.append(plugin_message)
# Start GPT instance
agent_reply = create_chat_completion(prompt=messages)
agent_reply = create_chat_completion(
model=model,
messages=messages,
)
messages.add("assistant", agent_reply)
messages.append({"role": "assistant", "content": agent_reply})
plugins_reply = agent_reply
for i, plugin in enumerate(self.cfg.plugins):
if not plugin.can_handle_on_instruction():
continue
if plugin_result := plugin.on_instruction([m.raw() for m in messages]):
if plugin_result := plugin.on_instruction(messages):
sep = "\n" if i else ""
plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
# Update full message history
if plugins_reply and plugins_reply != "":
messages.add("assistant", plugins_reply)
messages.append({"role": "assistant", "content": plugins_reply})
for plugin in self.cfg.plugins:
if not plugin.can_handle_post_instruction():

158
autogpt/api_manager.py Normal file
View File

@ -0,0 +1,158 @@
from typing import List
import openai
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.modelsinfo import COSTS
cfg = Config()
openai.api_key = cfg.openai_api_key
print_total_cost = cfg.debug_mode
class ApiManager:
def __init__(self, debug=False):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0
self.debug = debug
def reset(self):
self.total_prompt_tokens = 0
self.total_completion_tokens = 0
self.total_cost = 0
self.total_budget = 0.0
def create_chat_completion(
self,
messages: list, # type: ignore
model: str = None,
temperature: float = cfg.temperature,
max_tokens: int = None,
deployment_id=None,
) -> str:
"""
Create a chat completion and update the cost.
Args:
messages (list): The list of messages to send to the API.
model (str): The model to use for the API call.
temperature (float): The temperature to use for the API call.
max_tokens (int): The maximum number of tokens for the API call.
Returns:
str: The AI's response.
"""
if deployment_id is not None:
response = openai.ChatCompletion.create(
deployment_id=deployment_id,
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
if self.debug:
logger.debug(f"Response: {response}")
prompt_tokens = response.usage.prompt_tokens
completion_tokens = response.usage.completion_tokens
self.update_cost(prompt_tokens, completion_tokens, model)
return response
def embedding_create(
self,
text_list: List[str],
model: str = "text-embedding-ada-002",
) -> List[float]:
"""
Create an embedding for the given input text using the specified model.
Args:
text_list (List[str]): Input text for which the embedding is to be created.
model (str, optional): The model to use for generating the embedding.
Returns:
List[float]: The generated embedding as a list of float values.
"""
if cfg.use_azure:
response = openai.Embedding.create(
input=text_list,
engine=cfg.get_azure_deployment_id_for_model(model),
)
else:
response = openai.Embedding.create(input=text_list, model=model)
self.update_cost(response.usage.prompt_tokens, 0, model)
return response["data"][0]["embedding"]
def update_cost(self, prompt_tokens, completion_tokens, model):
"""
Update the total cost, prompt tokens, and completion tokens.
Args:
prompt_tokens (int): The number of tokens used in the prompt.
completion_tokens (int): The number of tokens used in the completion.
model (str): The model used for the API call.
"""
self.total_prompt_tokens += prompt_tokens
self.total_completion_tokens += completion_tokens
self.total_cost += (
prompt_tokens * COSTS[model]["prompt"]
+ completion_tokens * COSTS[model]["completion"]
) / 1000
if print_total_cost:
print(f"Total running cost: ${self.total_cost:.3f}")
def set_total_budget(self, total_budget):
"""
Sets the total user-defined budget for API calls.
Args:
prompt_tokens (int): The number of tokens used in the prompt.
"""
self.total_budget = total_budget
def get_total_prompt_tokens(self):
"""
Get the total number of prompt tokens.
Returns:
int: The total number of prompt tokens.
"""
return self.total_prompt_tokens
def get_total_completion_tokens(self):
"""
Get the total number of completion tokens.
Returns:
int: The total number of completion tokens.
"""
return self.total_completion_tokens
def get_total_cost(self):
"""
Get the total cost of API calls.
Returns:
float: The total cost of API calls.
"""
return self.total_cost
def get_total_budget(self):
"""
Get the total user-defined budget for API calls.
Returns:
float: The total budget for API calls.
"""
return self.total_budget
api_manager = ApiManager(cfg.debug_mode)

91
autogpt/app.py
View File

@ -1,15 +1,18 @@
""" Command and Control """
import json
from typing import Dict, List, Union
from typing import Dict, List, NoReturn, Union
from autogpt.agent.agent_manager import AgentManager
from autogpt.commands.command import CommandRegistry, command
from autogpt.commands.web_requests import scrape_links, scrape_text
from autogpt.config import Config
from autogpt.memory import get_memory
from autogpt.processing.text import summarize_text
from autogpt.prompts.generator import PromptGenerator
from autogpt.speech import say_text
from autogpt.url_utils.validators import validate_url
CFG = Config()
AGENT_MANAGER = AgentManager()
def is_valid_int(value: str) -> bool:
@ -89,7 +92,6 @@ def execute_command(
command_name: str,
arguments,
prompt: PromptGenerator,
config: Config,
):
"""Execute the command and return the result
@ -105,25 +107,33 @@ def execute_command(
# If the command is found, call it with the provided arguments
if cmd:
return cmd(**arguments, config=config)
return cmd(**arguments)
# TODO: Remove commands below after they are moved to the command registry.
command_name = map_command_synonyms(command_name.lower())
if command_name == "memory_add":
return get_memory(CFG).add(arguments["string"])
# TODO: Change these to take in a file rather than pasted code, if
# non-file is given, return instructions "Input should be a python
# filepath, write your code to file and try again
for command in prompt.commands:
if (
command_name == command["label"].lower()
or command_name == command["name"].lower()
):
return command["function"](**arguments)
return (
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
" list for available commands and only respond in the specified JSON"
" format."
)
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
for command in prompt.commands:
if (
command_name == command["label"].lower()
or command_name == command["name"].lower()
):
return command["function"](**arguments)
return (
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
" list for available commands and only respond in the specified JSON"
" format."
)
except Exception as e:
return f"Error: {str(e)}"
@ -131,9 +141,8 @@ def execute_command(
@command(
"get_text_summary", "Get text summary", '"url": "<url>", "question": "<question>"'
)
@validate_url
def get_text_summary(url: str, question: str, config: Config) -> str:
"""Get the text summary of a webpage
def get_text_summary(url: str, question: str) -> str:
"""Return the results of a Google search
Args:
url (str): The url to scrape
@ -143,15 +152,13 @@ def get_text_summary(url: str, question: str, config: Config) -> str:
str: The summary of the text
"""
text = scrape_text(url)
summary, _ = summarize_text(text, question=question)
summary = summarize_text(url, text, question)
return f""" "Result" : {summary}"""
@command("get_hyperlinks", "Get hyperlinks", '"url": "<url>"')
@validate_url
def get_hyperlinks(url: str, config: Config) -> Union[str, List[str]]:
"""Get all hyperlinks on a webpage
@command("get_hyperlinks", "Get text summary", '"url": "<url>"')
def get_hyperlinks(url: str) -> Union[str, List[str]]:
"""Return the results of a Google search
Args:
url (str): The url to scrape
@ -159,7 +166,13 @@ def get_hyperlinks(url: str, config: Config) -> Union[str, List[str]]:
Returns:
str or list: The hyperlinks on the page
"""
return scrape_links(url, config)
return scrape_links(url)
def shutdown() -> NoReturn:
"""Shut down the program"""
print("Shutting down...")
quit()
@command(
@ -167,7 +180,7 @@ def get_hyperlinks(url: str, config: Config) -> Union[str, List[str]]:
"Start GPT Agent",
'"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"',
)
def start_agent(name: str, task: str, prompt: str, config: Config, model=None) -> str:
def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
"""Start an agent with a given name, task, and prompt
Args:
@ -179,8 +192,6 @@ def start_agent(name: str, task: str, prompt: str, config: Config, model=None) -
Returns:
str: The response of the agent
"""
agent_manager = AgentManager()
# Remove underscores from name
voice_name = name.replace("_", " ")
@ -188,48 +199,48 @@ def start_agent(name: str, task: str, prompt: str, config: Config, model=None) -
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if config.speak_mode:
if CFG.speak_mode:
say_text(agent_intro, 1)
key, ack = agent_manager.create_agent(task, first_message, model)
key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
if config.speak_mode:
if CFG.speak_mode:
say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = agent_manager.message_agent(key, prompt)
agent_response = AGENT_MANAGER.message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
@command("message_agent", "Message GPT Agent", '"key": "<key>", "message": "<message>"')
def message_agent(key: str, message: str, config: Config) -> str:
def message_agent(key: str, message: str) -> str:
"""Message an agent with a given key and message"""
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = AgentManager().message_agent(int(key), message)
agent_response = AGENT_MANAGER.message_agent(int(key), message)
else:
return "Invalid key, must be an integer."
# Speak response
if config.speak_mode:
if CFG.speak_mode:
say_text(agent_response, 1)
return agent_response
@command("list_agents", "List GPT Agents", "() -> str")
def list_agents(config: Config) -> str:
@command("list_agents", "List GPT Agents", "")
def list_agents() -> str:
"""List all agents
Returns:
str: A list of all agents
"""
return "List of agents:\n" + "\n".join(
[str(x[0]) + ": " + x[1] for x in AgentManager().list_agents()]
[str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
)
@command("delete_agent", "Delete GPT Agent", '"key": "<key>"')
def delete_agent(key: str, config: Config) -> str:
def delete_agent(key: str) -> str:
"""Delete an agent with a given key
Args:
@ -238,5 +249,5 @@ def delete_agent(key: str, config: Config) -> str:
Returns:
str: A message indicating whether the agent was deleted or not
"""
result = AgentManager().delete_agent(key)
result = AGENT_MANAGER.delete_agent(key)
return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

1
autogpt/auto-gpt.json Normal file
View File

@ -0,0 +1 @@
{}

1
autogpt/auto_gpt_workspace/127.0.0.1/auto-gpt.json Normal file
View File

@ -0,0 +1 @@
{}

1
autogpt/auto_gpt_workspace/127.0.0.1/file_logger.txt Normal file
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@ -0,0 +1 @@
File Operation Logger

218
autogpt/chat.py Normal file
View File

@ -0,0 +1,218 @@
import time
from openai.error import RateLimitError
from autogpt import token_counter
from autogpt.api_manager import api_manager
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.logs import logger
from autogpt.types.openai import Message
cfg = Config()
def create_chat_message(role, content) -> Message:
"""
Create a chat message with the given role and content.
Args:
role (str): The role of the message sender, e.g., "system", "user", or "assistant".
content (str): The content of the message.
Returns:
dict: A dictionary containing the role and content of the message.
"""
return {"role": role, "content": content}
def generate_context(prompt, relevant_memory, full_message_history, model):
current_context = [
create_chat_message("system", prompt),
create_chat_message(
"system", f"The current time and date is {time.strftime('%c')}"
),
create_chat_message(
"system",
f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
),
]
# Add messages from the full message history until we reach the token limit
next_message_to_add_index = len(full_message_history) - 1
insertion_index = len(current_context)
# Count the currently used tokens
current_tokens_used = token_counter.count_message_tokens(current_context, model)
return (
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
)
# TODO: Change debug from hardcode to argument
def chat_with_ai(
agent, prompt, user_input, full_message_history, permanent_memory, token_limit
):
"""Interact with the OpenAI API, sending the prompt, user input, message history,
and permanent memory."""
while True:
try:
"""
Interact with the OpenAI API, sending the prompt, user input,
message history, and permanent memory.
Args:
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the
user and the AI.
permanent_memory (Obj): The memory object containing the permanent
memory.
token_limit (int): The maximum number of tokens allowed in the API call.
Returns:
str: The AI's response.
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
# Reserve 1000 tokens for the response
logger.debug(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = (
""
if len(full_message_history) == 0
else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
)
logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(prompt, relevant_memory, full_message_history, model)
while current_tokens_used > 2500:
# remove memories until we are under 2500 tokens
relevant_memory = relevant_memory[:-1]
(
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
) = generate_context(
prompt, relevant_memory, full_message_history, model
)
current_tokens_used += token_counter.count_message_tokens(
[create_chat_message("user", user_input)], model
) # Account for user input (appended later)
while next_message_to_add_index >= 0:
# print (f"CURRENT TOKENS USED: {current_tokens_used}")
message_to_add = full_message_history[next_message_to_add_index]
tokens_to_add = token_counter.count_message_tokens(
[message_to_add], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
break
# Add the most recent message to the start of the current context,
# after the two system prompts.
current_context.insert(
insertion_index, full_message_history[next_message_to_add_index]
)
# Count the currently used tokens
current_tokens_used += tokens_to_add
# Move to the next most recent message in the full message history
next_message_to_add_index -= 1
# inform the AI about its remaining budget (if it has one)
if api_manager.get_total_budget() > 0.0:
remaining_budget = (
api_manager.get_total_budget() - api_manager.get_total_cost()
)
if remaining_budget < 0:
remaining_budget = 0
system_message = (
f"Your remaining API budget is ${remaining_budget:.3f}"
+ (
" BUDGET EXCEEDED! SHUT DOWN!\n\n"
if remaining_budget == 0
else " Budget very nearly exceeded! Shut down gracefully!\n\n"
if remaining_budget < 0.005
else " Budget nearly exceeded. Finish up.\n\n"
if remaining_budget < 0.01
else "\n\n"
)
)
logger.debug(system_message)
current_context.append(create_chat_message("system", system_message))
# Append user input, the length of this is accounted for above
current_context.extend([create_chat_message("user", user_input)])
plugin_count = len(cfg.plugins)
for i, plugin in enumerate(cfg.plugins):
if not plugin.can_handle_on_planning():
continue
plugin_response = plugin.on_planning(
agent.prompt_generator, current_context
)
if not plugin_response or plugin_response == "":
continue
tokens_to_add = token_counter.count_message_tokens(
[create_chat_message("system", plugin_response)], model
)
if current_tokens_used + tokens_to_add > send_token_limit:
if cfg.debug_mode:
print("Plugin response too long, skipping:", plugin_response)
print("Plugins remaining at stop:", plugin_count - i)
break
current_context.append(create_chat_message("system", plugin_response))
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative.
# This should never happen, please submit a bug report at
# https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
logger.debug(f"Token limit: {token_limit}")
logger.debug(f"Send Token Count: {current_tokens_used}")
logger.debug(f"Tokens remaining for response: {tokens_remaining}")
logger.debug("------------ CONTEXT SENT TO AI ---------------")
for message in current_context:
# Skip printing the prompt
if message["role"] == "system" and message["content"] == prompt:
continue
logger.debug(f"{message['role'].capitalize()}: {message['content']}")
logger.debug("")
logger.debug("----------- END OF CONTEXT ----------------")
# TODO: use a model defined elsewhere, so that model can contain
# temperature and other settings we care about
assistant_reply = create_chat_completion(
model=model,
messages=current_context,
max_tokens=tokens_remaining,
)
# Update full message history
full_message_history.append(create_chat_message("user", user_input))
full_message_history.append(
create_chat_message("assistant", assistant_reply)
)
return assistant_reply
except RateLimitError:
# TODO: When we switch to langchain, this is built in
print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
time.sleep(10)

324
autogpt/cli.py
View File

@ -1,116 +1,230 @@
"""Main script for the autogpt package."""
import click
# Put imports inside function to avoid importing everything when starting the CLI
import logging
import os.path
import sys
from pathlib import Path
@click.group(invoke_without_command=True)
@click.option("-c", "--continuous", is_flag=True, help="Enable Continuous Mode")
@click.option(
"--skip-reprompt",
"-y",
is_flag=True,
help="Skips the re-prompting messages at the beginning of the script",
)
@click.option(
"--ai-settings",
"-C",
help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
)
@click.option(
"--prompt-settings",
"-P",
help="Specifies which prompt_settings.yaml file to use.",
)
@click.option(
"-l",
"--continuous-limit",
type=int,
help="Defines the number of times to run in continuous mode",
)
@click.option("--speak", is_flag=True, help="Enable Speak Mode")
@click.option("--debug", is_flag=True, help="Enable Debug Mode")
@click.option("--gpt3only", is_flag=True, help="Enable GPT3.5 Only Mode")
@click.option("--gpt4only", is_flag=True, help="Enable GPT4 Only Mode")
@click.option(
"--use-memory",
"-m",
"memory_type",
type=str,
help="Defines which Memory backend to use",
)
@click.option(
"-b",
"--browser-name",
help="Specifies which web-browser to use when using selenium to scrape the web.",
)
@click.option(
"--allow-downloads",
is_flag=True,
help="Dangerous: Allows Auto-GPT to download files natively.",
)
@click.option(
"--skip-news",
is_flag=True,
help="Specifies whether to suppress the output of latest news on startup.",
)
@click.option(
# TODO: this is a hidden option for now, necessary for integration testing.
# We should make this public once we're ready to roll out agent specific workspaces.
"--workspace-directory",
"-w",
type=click.Path(),
hidden=True,
)
@click.option(
"--install-plugin-deps",
is_flag=True,
help="Installs external dependencies for 3rd party plugins.",
)
@click.pass_context
def main(
ctx: click.Context,
continuous: bool,
continuous_limit: int,
ai_settings: str,
prompt_settings: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
gpt3only: bool,
gpt4only: bool,
memory_type: str,
browser_name: str,
allow_downloads: bool,
skip_news: bool,
workspace_directory: str,
install_plugin_deps: bool,
) -> None:
import gradio
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.commands.command import CommandRegistry
from autogpt.config import Config, check_openai_api_key
from autogpt.configurator import create_config
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt.plugins import scan_plugins
from autogpt.prompts.prompt import construct_main_ai_config
from autogpt.utils import get_current_git_branch, get_latest_bulletin
from autogpt.workspace import Workspace
import func_box
from toolbox import update_ui
from toolbox import ChatBotWithCookies
def handle_config(kwargs_settings):
kwargs_settings = {
'continuous': False, # Enable Continuous Mode
'continuous_limit': None, # Defines the number of times to run in continuous mode
'ai_settings': None, # Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.
'skip_reprompt': False, # Skips the re-prompting messages at the beginning of the scrip
'speak': False, # Enable speak Mode
'debug': False, # Enable Debug Mode
'gpt3only': False, # Enable GPT3.5 Only Mode
'gpt4only': False, # Enable GPT4 Only Mode
'memory_type': None, # Defines which Memory backend to use
'browser_name': None, # Specifies which web-browser to use when using selenium to scrape the web.
'allow_downloads': False, # Dangerous: Allows Auto-GPT to download files natively.
'skip_news': True, # Specifies whether to suppress the output of latest news on startup.
'workspace_directory': None # TODO: this is a hidden option for now, necessary for integration testing. We should make this public once we're ready to roll out agent specific workspaces.
}
"""
Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI.
Start an Auto-GPT assistant.
"""
# Put imports inside function to avoid importing everything when starting the CLI
from autogpt.main import run_auto_gpt
if kwargs_settings['workspace_directory']:
kwargs_settings['ai_settings'] = os.path.join(kwargs_settings['workspace_directory'], 'ai_settings.yaml')
# if ctx.invoked_subcommand is None:
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
create_config(
kwargs_settings['continuous'],
kwargs_settings['continuous_limit'],
kwargs_settings['ai_settings'],
kwargs_settings['skip_reprompt'],
kwargs_settings['speak'],
kwargs_settings['debug'],
kwargs_settings['gpt3only'],
kwargs_settings['gpt4only'],
kwargs_settings['memory_type'],
kwargs_settings['browser_name'],
kwargs_settings['allow_downloads'],
kwargs_settings['skip_news'],
)
return cfg
if ctx.invoked_subcommand is None:
run_auto_gpt(
continuous,
continuous_limit,
ai_settings,
prompt_settings,
skip_reprompt,
speak,
debug,
gpt3only,
gpt4only,
memory_type,
browser_name,
allow_downloads,
skip_news,
workspace_directory,
install_plugin_deps,
def handle_news():
motd = get_latest_bulletin()
if motd:
logger.typewriter_log("NEWS: ", Fore.GREEN, motd)
git_branch = get_current_git_branch()
if git_branch and git_branch != "stable":
logger.typewriter_log(
"WARNING: ",
Fore.RED,
f"You are running on `{git_branch}` branch "
"- this is not a supported branch.",
)
if sys.version_info < (3, 10):
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"You are running on an older version of Python. "
"Some people have observed problems with certain "
"parts of Auto-GPT with this version. "
"Please consider upgrading to Python 3.10 or higher.",
)
def handle_registry():
# Create a CommandRegistry instance and scan default folder
command_registry = CommandRegistry()
command_registry.import_commands("autogpt.commands.analyze_code")
command_registry.import_commands("autogpt.commands.audio_text")
command_registry.import_commands("autogpt.commands.execute_code")
command_registry.import_commands("autogpt.commands.file_operations")
command_registry.import_commands("autogpt.commands.git_operations")
command_registry.import_commands("autogpt.commands.google_search")
command_registry.import_commands("autogpt.commands.image_gen")
command_registry.import_commands("autogpt.commands.improve_code")
command_registry.import_commands("autogpt.commands.twitter")
command_registry.import_commands("autogpt.commands.web_selenium")
command_registry.import_commands("autogpt.commands.write_tests")
command_registry.import_commands("autogpt.app")
return command_registry
def handle_workspace(user):
# TODO: have this directory live outside the repository (e.g. in a user's
# home directory) and have it come in as a command line argument or part of
# the env file.
if user is None:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace"
else:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace" / user
# TODO: pass in the ai_settings file and the env file and have them cloned into
# the workspace directory so we can bind them to the agent.
workspace_directory = Workspace.make_workspace(workspace_directory)
# HACK: doing this here to collect some globals that depend on the workspace.
file_logger_path = workspace_directory / "file_logger.txt"
if not file_logger_path.exists():
with file_logger_path.open(mode="w", encoding="utf-8") as f:
f.write("File Operation Logger ")
return workspace_directory, file_logger_path
def update_obj(plugin_kwargs, _is=True):
obj = plugin_kwargs['obj']
start = plugin_kwargs['start']
next_ = plugin_kwargs['next']
text = plugin_kwargs['txt']
if _is:
start.update(visible=True)
next_.update(visible=False)
text.update(visible=False)
else:
start.update(visible=False)
next_.update(visible=True)
text.update(visible=True)
return obj, start, next_, text
def agent_main(name, role, goals, budget,
cookies, chatbot, history, obj,
ipaddr: gradio.Request):
# ai setup
input_kwargs = {
'name': name,
'role': role,
'goals': goals,
'budget': budget
}
# chat setup
logger.output_content = []
chatbot_with_cookie = ChatBotWithCookies(cookies)
chatbot_with_cookie.write_list(chatbot)
history = []
cfg = handle_config(None)
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
workspace_directory = ipaddr.client.host
if not cfg.skip_news:
handle_news()
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
command_registry = handle_registry()
ai_config = construct_main_ai_config(input_kwargs)
def update_stream_ui(user='', gpt='', msg='Done',
_start=obj['start'].update(), _next=obj['next'].update(), _text=obj['text'].update()):
if user or gpt:
temp = [user, gpt]
if not chatbot_with_cookie:
chatbot_with_cookie.append(temp)
else:
chatbot_with_cookie[-1] = [chatbot_with_cookie[-1][i] + temp[i] for i in range(len(chatbot_with_cookie[-1]))]
yield chatbot_with_cookie.get_cookies(), chatbot_with_cookie, history, msg, obj, _start, _next, _text
if not ai_config:
msg = '### ROLE 不能为空'
# yield chatbot_with_cookie.get_cookies(), chatbot_with_cookie, history, msg, obj, None, None, None
yield from update_stream_ui(msg=msg)
return
ai_config.command_registry = command_registry
next_action_count = 0
# Make a constant:
triggering_prompt = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
workspace_directory, file_logger_path = handle_workspace(workspace_directory)
cfg.workspace_path = str(workspace_directory)
cfg.file_logger_path = str(file_logger_path)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
system_prompt = ai_config.construct_full_prompt()
if cfg.debug_mode:
logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)
agent = Agent(
ai_name=input_kwargs['name'],
memory=memory,
full_message_history=history,
next_action_count=next_action_count,
command_registry=command_registry,
config=ai_config,
system_prompt=system_prompt,
triggering_prompt=triggering_prompt,
workspace_directory=workspace_directory,
)
obj['obj'] = agent
_start = obj['start'].update(visible=False)
_next = obj['next'].update(visible=True)
_text = obj['text'].update(visible=True, interactive=True)
# chat, his = func_box.chat_history(logger.output_content)
# yield from update_stream_ui(user='Auto-GPT Start!', gpt=chat, _start=_start, _next=_next, _text=_text)
agent.start_interaction_loop()
chat, his = func_box.chat_history(logger.output_content)
yield from update_stream_ui(user='Auto-GPT Start!', gpt=chat, _start=_start, _next=_next, _text=_text)
def agent_start(cookie, chatbot, history, msg, obj):
yield from obj['obj'].start_interaction_loop(cookie, chatbot, history, msg, obj)
if __name__ == "__main__":
main()
pass

213
autogpt/cli_private.py Normal file
View File

@ -0,0 +1,213 @@
"""Main script for the autogpt package."""
import click
@click.group(invoke_without_command=True)
@click.option("-c", "--continuous", is_flag=True, help="Enable Continuous Mode")
@click.option(
"--skip-reprompt",
"-y",
is_flag=True,
help="Skips the re-prompting messages at the beginning of the script",
)
@click.option(
"--ai-settings",
"-C",
help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
)
@click.option(
"-l",
"--continuous-limit",
type=int,
help="Defines the number of times to run in continuous mode",
)
@click.option("--speak", is_flag=True, help="Enable Speak Mode")
@click.option("--debug", is_flag=True, help="Enable Debug Mode")
@click.option("--gpt3only", is_flag=True, help="Enable GPT3.5 Only Mode")
@click.option("--gpt4only", is_flag=True, help="Enable GPT4 Only Mode")
@click.option(
"--use-memory",
"-m",
"memory_type",
type=str,
help="Defines which Memory backend to use",
)
@click.option(
"-b",
"--browser-name",
help="Specifies which web-browser to use when using selenium to scrape the web.",
)
@click.option(
"--allow-downloads",
is_flag=True,
help="Dangerous: Allows Auto-GPT to download files natively.",
)
@click.option(
"--skip-news",
is_flag=True,
help="Specifies whether to suppress the output of latest news on startup.",
)
@click.option(
# TODO: this is a hidden option for now, necessary for integration testing.
# We should make this public once we're ready to roll out agent specific workspaces.
"--workspace-directory",
"-w",
type=click.Path(),
hidden=True,
)
@click.pass_context
def main(
ctx: click.Context,
continuous: bool,
continuous_limit: int,
ai_settings: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
gpt3only: bool,
gpt4only: bool,
memory_type: str,
browser_name: str,
allow_downloads: bool,
skip_news: bool,
workspace_directory: str,
) -> None:
"""
Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI.
Start an Auto-GPT assistant.
"""
# Put imports inside function to avoid importing everything when starting the CLI
import logging
import sys
from pathlib import Path
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.commands.command import CommandRegistry
from autogpt.config import Config, check_openai_api_key
from autogpt.configurator import create_config
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt.plugins import scan_plugins
from autogpt.prompts.prompt import construct_main_ai_config
from autogpt.utils import get_current_git_branch, get_latest_bulletin
from autogpt.workspace import Workspace
if ctx.invoked_subcommand is None:
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
create_config(
continuous,
continuous_limit,
ai_settings,
skip_reprompt,
speak,
debug,
gpt3only,
gpt4only,
memory_type,
browser_name,
allow_downloads,
skip_news,
)
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
if not cfg.skip_news:
motd = get_latest_bulletin()
if motd:
logger.typewriter_log("NEWS: ", Fore.GREEN, motd)
git_branch = get_current_git_branch()
if git_branch and git_branch != "stable":
logger.typewriter_log(
"WARNING: ",
Fore.RED,
f"You are running on `{git_branch}` branch "
"- this is not a supported branch.",
)
if sys.version_info < (3, 10):
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"You are running on an older version of Python. "
"Some people have observed problems with certain "
"parts of Auto-GPT with this version. "
"Please consider upgrading to Python 3.10 or higher.",
)
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
# Create a CommandRegistry instance and scan default folder
command_registry = CommandRegistry()
command_registry.import_commands("autogpt.commands.analyze_code")
command_registry.import_commands("autogpt.commands.audio_text")
command_registry.import_commands("autogpt.commands.execute_code")
command_registry.import_commands("autogpt.commands.file_operations")
command_registry.import_commands("autogpt.commands.git_operations")
command_registry.import_commands("autogpt.commands.google_search")
command_registry.import_commands("autogpt.commands.image_gen")
command_registry.import_commands("autogpt.commands.improve_code")
command_registry.import_commands("autogpt.commands.twitter")
command_registry.import_commands("autogpt.commands.web_selenium")
command_registry.import_commands("autogpt.commands.write_tests")
command_registry.import_commands("autogpt.app")
ai_name = ""
ai_config = construct_main_ai_config()
ai_config.command_registry = command_registry
# print(prompt)
# Initialize variables
full_message_history = []
next_action_count = 0
# Make a constant:
triggering_prompt = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
logger.typewriter_log(
"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
system_prompt = ai_config.construct_full_prompt()
if cfg.debug_mode:
logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt)
# TODO: have this directory live outside the repository (e.g. in a user's
# home directory) and have it come in as a command line argument or part of
# the env file.
if workspace_directory is None:
workspace_directory = Path(__file__).parent / "auto_gpt_workspace"
else:
workspace_directory = Path(workspace_directory)
# TODO: pass in the ai_settings file and the env file and have them cloned into
# the workspace directory so we can bind them to the agent.
workspace_directory = Workspace.make_workspace(workspace_directory)
cfg.workspace_path = str(workspace_directory)
# HACK: doing this here to collect some globals that depend on the workspace.
file_logger_path = workspace_directory / "file_logger.txt"
if not file_logger_path.exists():
with file_logger_path.open(mode="w", encoding="utf-8") as f:
f.write("File Operation Logger ")
cfg.file_logger_path = str(file_logger_path)
agent = Agent(
ai_name=ai_name,
memory=memory,
full_message_history=full_message_history,
next_action_count=next_action_count,
command_registry=command_registry,
config=ai_config,
system_prompt=system_prompt,
triggering_prompt=triggering_prompt,
workspace_directory=workspace_directory,
)
agent.start_interaction_loop()
if __name__ == "__main__":
main()

11
autogpt/commands/analyze_code.py
View File

@ -1,13 +1,8 @@
"""Code evaluation module."""
from __future__ import annotations
from typing import TYPE_CHECKING
from autogpt.commands.command import command
from autogpt.llm.utils import call_ai_function
if TYPE_CHECKING:
from autogpt.config import Config
from autogpt.llm_utils import call_ai_function
@command(
@ -15,7 +10,7 @@ if TYPE_CHECKING:
"Analyze Code",
'"code": "<full_code_string>"',
)
def analyze_code(code: str, config: Config) -> list[str]:
def analyze_code(code: str) -> list[str]:
"""
A function that takes in a string and returns a response from create chat
completion api call.
@ -33,4 +28,4 @@ def analyze_code(code: str, config: Config) -> list[str]:
"Analyzes the given code and returns a list of suggestions for improvements."
)
return call_ai_function(function_string, args, description_string, config=config)
return call_ai_function(function_string, args, description_string)

19
autogpt/commands/audio_text.py
View File

@ -1,25 +1,22 @@
"""Commands for converting audio to text."""
import json
from typing import TYPE_CHECKING
import requests
from autogpt.commands.command import command
from autogpt.config import Config
if TYPE_CHECKING:
from autogpt.config import Config
CFG = Config()
@command(
"read_audio_from_file",
"Convert Audio to text",
'"filename": "<filename>"',
lambda config: config.huggingface_audio_to_text_model
and config.huggingface_api_token,
"Configure huggingface_audio_to_text_model and Hugging Face api token.",
CFG.huggingface_audio_to_text_model,
"Configure huggingface_audio_to_text_model.",
)
def read_audio_from_file(filename: str, config: Config) -> str:
def read_audio_from_file(filename: str) -> str:
"""
Convert audio to text.
@ -31,10 +28,10 @@ def read_audio_from_file(filename: str, config: Config) -> str:
"""
with open(filename, "rb") as audio_file:
audio = audio_file.read()
return read_audio(audio, config)
return read_audio(audio)
def read_audio(audio: bytes, config: Config) -> str:
def read_audio(audio: bytes) -> str:
"""
Convert audio to text.
@ -44,9 +41,9 @@ def read_audio(audio: bytes, config: Config) -> str:
Returns:
str: The text from the audio
"""
model = config.huggingface_audio_to_text_model
model = CFG.huggingface_audio_to_text_model
api_url = f"https://api-inference.huggingface.co/models/{model}"
api_token = config.huggingface_api_token
api_token = CFG.huggingface_api_token
headers = {"Authorization": f"Bearer {api_token}"}
if api_token is None:

31
autogpt/commands/command.py
View File

@ -3,9 +3,6 @@ import importlib
import inspect
from typing import Any, Callable, Optional
from autogpt.config import Config
from autogpt.logs import logger
# Unique identifier for auto-gpt commands
AUTO_GPT_COMMAND_IDENTIFIER = "auto_gpt_command"
@ -25,23 +22,19 @@ class Command:
description: str,
method: Callable[..., Any],
signature: str = "",
enabled: bool | Callable[[Config], bool] = True,
enabled: bool = True,
disabled_reason: Optional[str] = None,
):
self.name = name
self.description = description
self.method = method
self.signature = signature
self.signature = signature if signature else str(inspect.signature(self.method))
self.enabled = enabled
self.disabled_reason = disabled_reason
def __call__(self, *args, **kwargs) -> Any:
if hasattr(kwargs, "config") and callable(self.enabled):
self.enabled = self.enabled(kwargs["config"])
if not self.enabled:
if self.disabled_reason:
return f"Command '{self.name}' is disabled: {self.disabled_reason}"
return f"Command '{self.name}' is disabled"
return f"Command '{self.name}' is disabled: {self.disabled_reason}"
return self.method(*args, **kwargs)
def __str__(self) -> str:
@ -66,10 +59,6 @@ class CommandRegistry:
return importlib.reload(module)
def register(self, cmd: Command) -> None:
if cmd.name in self.commands:
logger.warn(
f"Command '{cmd.name}' already registered and will be overwritten!"
)
self.commands[cmd.name] = cmd
def unregister(self, command_name: str):
@ -138,22 +127,12 @@ class CommandRegistry:
def command(
name: str,
description: str,
signature: str,
enabled: bool | Callable[[Config], bool] = True,
signature: str = "",
enabled: bool = True,
disabled_reason: Optional[str] = None,
) -> Callable[..., Any]:
"""The command decorator is used to create Command objects from ordinary functions."""
# TODO: Remove this in favor of better command management
CFG = Config()
if callable(enabled):
enabled = enabled(CFG)
if not enabled:
if disabled_reason is not None:
logger.debug(f"Command '{name}' is disabled: {disabled_reason}")
return lambda func: func
def decorator(func: Callable[..., Any]) -> Command:
cmd = Command(
name=name,

96
autogpt/commands/execute_code.py
View File

@ -1,18 +1,18 @@
"""Execute code in a Docker container"""
import os
import subprocess
from pathlib import Path
import docker
from docker.errors import ImageNotFound
from autogpt.commands.command import command
from autogpt.config import Config
from autogpt.logs import logger
CFG = Config()
@command("execute_python_file", "Execute Python File", '"filename": "<filename>"')
def execute_python_file(filename: str, config: Config) -> str:
def execute_python_file(filename: str) -> str:
"""Execute a Python file in a Docker container and return the output
Args:
@ -21,7 +21,7 @@ def execute_python_file(filename: str, config: Config) -> str:
Returns:
str: The output of the file
"""
logger.info(f"Executing file '{filename}'")
print(f"Executing file '{filename}'")
if not filename.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
@ -31,7 +31,7 @@ def execute_python_file(filename: str, config: Config) -> str:
if we_are_running_in_a_docker_container():
result = subprocess.run(
["python", filename], capture_output=True, encoding="utf8"
f"python {filename}", capture_output=True, encoding="utf8", shell=True
)
if result.returncode == 0:
return result.stdout
@ -40,17 +40,16 @@ def execute_python_file(filename: str, config: Config) -> str:
try:
client = docker.from_env()
# You can replace this with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
image_name = "python:3-alpine"
try:
client.images.get(image_name)
logger.warn(f"Image '{image_name}' found locally")
print(f"Image '{image_name}' found locally")
except ImageNotFound:
logger.info(
f"Image '{image_name}' not found locally, pulling from Docker Hub"
)
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
@ -58,14 +57,15 @@ def execute_python_file(filename: str, config: Config) -> str:
status = line.get("status")
progress = line.get("progress")
if status and progress:
logger.info(f"{status}: {progress}")
print(f"{status}: {progress}")
elif status:
logger.info(status)
print(status)
container = client.containers.run(
image_name,
["python", str(Path(filename).relative_to(config.workspace_path))],
f"python {filename}",
volumes={
config.workspace_path: {
CFG.workspace_path: {
"bind": "/workspace",
"mode": "ro",
}
@ -86,7 +86,7 @@ def execute_python_file(filename: str, config: Config) -> str:
return logs
except docker.errors.DockerException as e:
logger.warn(
print(
"Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
)
return f"Error: {str(e)}"
@ -95,42 +95,16 @@ def execute_python_file(filename: str, config: Config) -> str:
return f"Error: {str(e)}"
def validate_command(command: str, config: Config) -> bool:
"""Validate a command to ensure it is allowed
Args:
command (str): The command to validate
Returns:
bool: True if the command is allowed, False otherwise
"""
tokens = command.split()
if not tokens:
return False
if config.deny_commands and tokens[0] not in config.deny_commands:
return False
for keyword in config.allow_commands:
if keyword in tokens:
return True
if config.allow_commands:
return False
return True
@command(
"execute_shell",
"Execute Shell Command, non-interactive commands only",
'"command_line": "<command_line>"',
lambda cfg: cfg.execute_local_commands,
CFG.execute_local_commands,
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config file: .env - do not attempt to bypass the restriction.",
"in your config. Do not attempt to bypass the restriction.",
)
def execute_shell(command_line: str, config: Config) -> str:
def execute_shell(command_line: str) -> str:
"""Execute a shell command and return the output
Args:
@ -139,18 +113,19 @@ def execute_shell(command_line: str, config: Config) -> str:
Returns:
str: The output of the command
"""
if not validate_command(command_line, config):
logger.info(f"Command '{command_line}' not allowed")
return "Error: This Shell Command is not allowed."
current_dir = Path.cwd()
if not CFG.execute_local_commands:
return (
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction."
)
current_dir = os.getcwd()
# Change dir into workspace if necessary
if not current_dir.is_relative_to(config.workspace_path):
os.chdir(config.workspace_path)
if CFG.workspace_path not in current_dir:
os.chdir(CFG.workspace_path)
logger.info(
f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
result = subprocess.run(command_line, capture_output=True, shell=True)
output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
@ -158,19 +133,18 @@ def execute_shell(command_line: str, config: Config) -> str:
# Change back to whatever the prior working dir was
os.chdir(current_dir)
return output
@command(
"execute_shell_popen",
"Execute Shell Command, non-interactive commands only",
'"command_line": "<command_line>"',
lambda config: config.execute_local_commands,
CFG.execute_local_commands,
"You are not allowed to run local shell commands. To execute"
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
"in your config. Do not attempt to bypass the restriction.",
)
def execute_shell_popen(command_line, config: Config) -> str:
def execute_shell_popen(command_line) -> str:
"""Execute a shell command with Popen and returns an english description
of the event and the process id
@ -180,18 +154,12 @@ def execute_shell_popen(command_line, config: Config) -> str:
Returns:
str: Description of the fact that the process started and its id
"""
if not validate_command(command_line, config):
logger.info(f"Command '{command_line}' not allowed")
return "Error: This Shell Command is not allowed."
current_dir = os.getcwd()
# Change dir into workspace if necessary
if config.workspace_path not in current_dir:
os.chdir(config.workspace_path)
if CFG.workspace_path not in current_dir:
os.chdir(CFG.workspace_path)
logger.info(
f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
do_not_show_output = subprocess.DEVNULL
process = subprocess.Popen(

224
autogpt/commands/file_operations.py
View File

@ -1,121 +1,46 @@
"""File operations for AutoGPT"""
from __future__ import annotations
import hashlib
import os
import os.path
from typing import TYPE_CHECKING, Generator, Literal
from typing import Generator
import requests
from colorama import Back, Fore
from requests.adapters import HTTPAdapter, Retry
from autogpt.commands.command import command
from autogpt.commands.file_operations_utils import read_textual_file
from autogpt.logs import logger
from autogpt.memory.vector import MemoryItem, VectorMemory
from autogpt.config import Config
from autogpt.spinner import Spinner
from autogpt.utils import readable_file_size
if TYPE_CHECKING:
from autogpt.config import Config
CFG = Config()
Operation = Literal["write", "append", "delete"]
def text_checksum(text: str) -> str:
"""Get the hex checksum for the given text."""
return hashlib.md5(text.encode("utf-8")).hexdigest()
def operations_from_log(
log_path: str,
) -> Generator[tuple[Operation, str, str | None], None, None]:
"""Parse the file operations log and return a tuple containing the log entries"""
try:
log = open(log_path, "r", encoding="utf-8")
except FileNotFoundError:
return
for line in log:
line = line.replace("File Operation Logger", "").strip()
if not line:
continue
operation, tail = line.split(": ", maxsplit=1)
operation = operation.strip()
if operation in ("write", "append"):
try:
path, checksum = (x.strip() for x in tail.rsplit(" #", maxsplit=1))
except ValueError:
logger.warn(f"File log entry lacks checksum: '{line}'")
path, checksum = tail.strip(), None
yield (operation, path, checksum)
elif operation == "delete":
yield (operation, tail.strip(), None)
log.close()
def file_operations_state(log_path: str) -> dict[str, str]:
"""Iterates over the operations log and returns the expected state.
Parses a log file at config.file_logger_path to construct a dictionary that maps
each file path written or appended to its checksum. Deleted files are removed
from the dictionary.
Returns:
A dictionary mapping file paths to their checksums.
Raises:
FileNotFoundError: If config.file_logger_path is not found.
ValueError: If the log file content is not in the expected format.
"""
state = {}
for operation, path, checksum in operations_from_log(log_path):
if operation in ("write", "append"):
state[path] = checksum
elif operation == "delete":
del state[path]
return state
def is_duplicate_operation(
operation: Operation, filename: str, config: Config, checksum: str | None = None
) -> bool:
"""Check if the operation has already been performed
def check_duplicate_operation(operation: str, filename: str) -> bool:
"""Check if the operation has already been performed on the given file
Args:
operation: The operation to check for
filename: The name of the file to check for
checksum: The checksum of the contents to be written
operation (str): The operation to check for
filename (str): The name of the file to check for
Returns:
True if the operation has already been performed on the file
bool: True if the operation has already been performed on the file
"""
state = file_operations_state(config.file_logger_path)
if operation == "delete" and filename not in state:
return True
if operation == "write" and state.get(filename) == checksum:
return True
return False
log_content = read_file(CFG.file_logger_path)
log_entry = f"{operation}: {filename}\n"
return log_entry in log_content
def log_operation(
operation: str, filename: str, config: Config, checksum: str | None = None
) -> None:
def log_operation(operation: str, filename: str) -> None:
"""Log the file operation to the file_logger.txt
Args:
operation: The operation to log
filename: The name of the file the operation was performed on
checksum: The checksum of the contents to be written
operation (str): The operation to log
filename (str): The name of the file the operation was performed on
"""
log_entry = f"{operation}: {filename}"
if checksum is not None:
log_entry += f" #{checksum}"
logger.debug(f"Logging file operation: {log_entry}")
append_to_file(config.file_logger_path, f"{log_entry}\n", config, should_log=False)
log_entry = f"{operation}: {filename}\n"
append_to_file(CFG.file_logger_path, log_entry, should_log=False)
def split_file(
@ -138,7 +63,7 @@ def split_file(
while start < content_length:
end = start + max_length
if end + overlap < content_length:
chunk = content[start : end + max(overlap - 1, 0)]
chunk = content[start : end + overlap - 1]
else:
chunk = content[start:content_length]
@ -150,8 +75,8 @@ def split_file(
start += max_length - overlap
@command("read_file", "Read a file", '"filename": "<filename>"')
def read_file(filename: str, config: Config) -> str:
@command("read_file", "Read file", '"filename": "<filename>"')
def read_file(filename: str) -> str:
"""Read a file and return the contents
Args:
@ -161,46 +86,49 @@ def read_file(filename: str, config: Config) -> str:
str: The contents of the file
"""
try:
content = read_textual_file(filename, logger)
# TODO: invalidate/update memory when file is edited
file_memory = MemoryItem.from_text_file(content, filename)
if len(file_memory.chunks) > 1:
return file_memory.summary
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return f"Error: {str(e)}"
def ingest_file(
filename: str,
memory: VectorMemory,
filename: str, memory, max_length: int = 4000, overlap: int = 200
) -> None:
"""
Ingest a file by reading its content, splitting it into chunks with a specified
maximum length and overlap, and adding the chunks to the memory storage.
Args:
filename: The name of the file to ingest
memory: An object with an add() method to store the chunks in memory
:param filename: The name of the file to ingest
:param memory: An object with an add() method to store the chunks in memory
:param max_length: The maximum length of each chunk, default is 4000
:param overlap: The number of overlapping characters between chunks, default is 200
"""
try:
logger.info(f"Ingesting file {filename}")
print(f"Working with file {filename}")
content = read_file(filename)
content_length = len(content)
print(f"File length: {content_length} characters")
# TODO: differentiate between different types of files
file_memory = MemoryItem.from_text_file(content, filename)
logger.debug(f"Created memory: {file_memory.dump()}")
memory.add(file_memory)
chunks = list(split_file(content, max_length=max_length, overlap=overlap))
logger.info(f"Ingested {len(file_memory.e_chunks)} chunks from {filename}")
except Exception as err:
logger.warn(f"Error while ingesting file '{filename}': {err}")
num_chunks = len(chunks)
for i, chunk in enumerate(chunks):
print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
memory_to_add = (
f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
)
memory.add(memory_to_add)
print(f"Done ingesting {num_chunks} chunks from {filename}.")
except Exception as e:
print(f"Error while ingesting file '{filename}': {str(e)}")
@command("write_to_file", "Write to file", '"filename": "<filename>", "text": "<text>"')
def write_to_file(filename: str, text: str, config: Config) -> str:
def write_to_file(filename: str, text: str) -> str:
"""Write text to a file
Args:
@ -210,26 +138,24 @@ def write_to_file(filename: str, text: str, config: Config) -> str:
Returns:
str: A message indicating success or failure
"""
checksum = text_checksum(text)
if is_duplicate_operation("write", filename, config, checksum):
if check_duplicate_operation("write", filename):
return "Error: File has already been updated."
try:
directory = os.path.dirname(filename)
os.makedirs(directory, exist_ok=True)
if not os.path.exists(directory):
os.makedirs(directory)
with open(filename, "w", encoding="utf-8") as f:
f.write(text)
log_operation("write", filename, config, checksum)
log_operation("write", filename)
return "File written to successfully."
except Exception as err:
return f"Error: {err}"
except Exception as e:
return f"Error: {str(e)}"
@command(
"append_to_file", "Append to file", '"filename": "<filename>", "text": "<text>"'
)
def append_to_file(
filename: str, text: str, config: Config, should_log: bool = True
) -> str:
def append_to_file(filename: str, text: str, should_log: bool = True) -> str:
"""Append text to a file
Args:
@ -241,23 +167,19 @@ def append_to_file(
str: A message indicating success or failure
"""
try:
directory = os.path.dirname(filename)
os.makedirs(directory, exist_ok=True)
with open(filename, "a", encoding="utf-8") as f:
with open(filename, "a") as f:
f.write(text)
if should_log:
with open(filename, "r", encoding="utf-8") as f:
checksum = text_checksum(f.read())
log_operation("append", filename, config, checksum=checksum)
log_operation("append", filename)
return "Text appended successfully."
except Exception as err:
return f"Error: {err}"
except Exception as e:
return f"Error: {str(e)}"
@command("delete_file", "Delete file", '"filename": "<filename>"')
def delete_file(filename: str, config: Config) -> str:
def delete_file(filename: str) -> str:
"""Delete a file
Args:
@ -266,19 +188,19 @@ def delete_file(filename: str, config: Config) -> str:
Returns:
str: A message indicating success or failure
"""
if is_duplicate_operation("delete", filename, config):
if check_duplicate_operation("delete", filename):
return "Error: File has already been deleted."
try:
os.remove(filename)
log_operation("delete", filename, config)
log_operation("delete", filename)
return "File deleted successfully."
except Exception as err:
return f"Error: {err}"
except Exception as e:
return f"Error: {str(e)}"
@command("list_files", "List Files in Directory", '"directory": "<directory>"')
def list_files(directory: str, config: Config) -> list[str]:
"""lists files in a directory recursively
@command("search_files", "Search Files", '"directory": "<directory>"')
def search_files(directory: str) -> list[str]:
"""Search for files in a directory
Args:
directory (str): The directory to search in
@ -293,7 +215,7 @@ def list_files(directory: str, config: Config) -> list[str]:
if file.startswith("."):
continue
relative_path = os.path.relpath(
os.path.join(root, file), config.workspace_path
os.path.join(root, file), CFG.workspace_path
)
found_files.append(relative_path)
@ -304,20 +226,18 @@ def list_files(directory: str, config: Config) -> list[str]:
"download_file",
"Download File",
'"url": "<url>", "filename": "<filename>"',
lambda config: config.allow_downloads,
CFG.allow_downloads,
"Error: You do not have user authorization to download files locally.",
)
def download_file(url, filename, config: Config):
def download_file(url, filename):
"""Downloads a file
Args:
url (str): URL of the file to download
filename (str): Filename to save the file as
"""
try:
directory = os.path.dirname(filename)
os.makedirs(directory, exist_ok=True)
message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
with Spinner(message, plain_output=config.plain_output) as spinner:
message = f"{Fore.YELLOW}Downloading file from {Back.MAGENTA}{url}{Back.RESET}{Fore.RESET}"
with Spinner(message) as spinner:
session = requests.Session()
retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
adapter = HTTPAdapter(max_retries=retry)
@ -341,8 +261,8 @@ def download_file(url, filename, config: Config):
progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
spinner.update_message(f"{message} {progress}")
return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(downloaded_size)})'
except requests.HTTPError as err:
return f"Got an HTTP Error whilst trying to download file: {err}"
except Exception as err:
return f"Error: {err}"
return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
except requests.HTTPError as e:
return f"Got an HTTP Error whilst trying to download file: {e}"
except Exception as e:
return "Error: " + str(e)

25
autogpt/commands/git_operations.py
View File

@ -1,40 +1,33 @@
"""Git operations for autogpt"""
from typing import TYPE_CHECKING
from git.repo import Repo
from autogpt.commands.command import command
from autogpt.config import Config
from autogpt.url_utils.validators import validate_url
if TYPE_CHECKING:
from autogpt.config import Config
CFG = Config()
@command(
"clone_repository",
"Clone Repository",
'"url": "<repository_url>", "clone_path": "<clone_path>"',
lambda config: config.github_username and config.github_api_key,
'"repository_url": "<repository_url>", "clone_path": "<clone_path>"',
CFG.github_username and CFG.github_api_key,
"Configure github_username and github_api_key.",
)
@validate_url
def clone_repository(url: str, clone_path: str, config: Config) -> str:
def clone_repository(repository_url: str, clone_path: str) -> str:
"""Clone a GitHub repository locally.
Args:
url (str): The URL of the repository to clone.
repository_url (str): The URL of the repository to clone.
clone_path (str): The path to clone the repository to.
Returns:
str: The result of the clone operation.
"""
split_url = url.split("//")
auth_repo_url = f"//{config.github_username}:{config.github_api_key}@".join(
split_url
)
split_url = repository_url.split("//")
auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url)
try:
Repo.clone_from(url=auth_repo_url, to_path=clone_path)
return f"""Cloned {url} to {clone_path}"""
Repo.clone_from(auth_repo_url, clone_path)
return f"""Cloned {repository_url} to {clone_path}"""
except Exception as e:
return f"Error: {str(e)}"

43
autogpt/commands/google_search.py
View File

@ -2,24 +2,17 @@
from __future__ import annotations
import json
from itertools import islice
from typing import TYPE_CHECKING
from duckduckgo_search import DDGS
from duckduckgo_search import ddg
from autogpt.commands.command import command
from autogpt.config import Config
if TYPE_CHECKING:
from autogpt.config import Config
CFG = Config()
@command(
"google",
"Google Search",
'"query": "<query>"',
lambda config: not config.google_api_key,
)
def google_search(query: str, config: Config, num_results: int = 8) -> str:
@command("google", "Google Search", '"query": "<query>"', not CFG.google_api_key)
def google_search(query: str, num_results: int = 8) -> str:
"""Return the results of a Google search
Args:
@ -33,12 +26,12 @@ def google_search(query: str, config: Config, num_results: int = 8) -> str:
if not query:
return json.dumps(search_results)
results = DDGS().text(query)
results = ddg(query, max_results=num_results)
if not results:
return json.dumps(search_results)
for item in islice(results, num_results):
search_results.append(item)
for j in results:
search_results.append(j)
results = json.dumps(search_results, ensure_ascii=False, indent=4)
return safe_google_results(results)
@ -48,12 +41,10 @@ def google_search(query: str, config: Config, num_results: int = 8) -> str:
"google",
"Google Search",
'"query": "<query>"',
lambda config: bool(config.google_api_key) and bool(config.custom_search_engine_id),
"Configure google_api_key and custom_search_engine_id.",
bool(CFG.google_api_key),
"Configure google_api_key.",
)
def google_official_search(
query: str, config: Config, num_results: int = 8
) -> str | list[str]:
def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
"""Return the results of a Google search using the official Google API
Args:
@ -69,8 +60,8 @@ def google_official_search(
try:
# Get the Google API key and Custom Search Engine ID from the config file
api_key = config.google_api_key
custom_search_engine_id = config.custom_search_engine_id
api_key = CFG.google_api_key
custom_search_engine_id = CFG.custom_search_engine_id
# Initialize the Custom Search API service
service = build("customsearch", "v1", developerKey=api_key)
@ -119,14 +110,8 @@ def safe_google_results(results: str | list) -> str:
"""
if isinstance(results, list):
safe_message = json.dumps(
[result.encode("utf-8", "ignore").decode("utf-8") for result in results]
[result.encode("utf-8", "ignore") for result in results]
)
else:
safe_message = results.encode("utf-8", "ignore").decode("utf-8")
return safe_message
if __name__ == '__main__':
print(google_search('你是谁？'))
results = ddg('你是谁', max_results=8)
print(results)

102
autogpt/commands/image_gen.py
View File

@ -1,10 +1,7 @@
""" Image Generation Module for AutoGPT."""
import io
import json
import time
import uuid
from base64 import b64decode
from typing import TYPE_CHECKING
import openai
import requests
@ -12,20 +9,12 @@ from PIL import Image
from autogpt.commands.command import command
from autogpt.config import Config
from autogpt.logs import logger
if TYPE_CHECKING:
from autogpt.config import Config
CFG = Config()
@command(
"generate_image",
"Generate Image",
'"prompt": "<prompt>"',
lambda config: config.image_provider,
"Requires a image provider to be set.",
)
def generate_image(prompt: str, config: Config, size: int = 256) -> str:
@command("generate_image", "Generate Image", '"prompt": "<prompt>"', CFG.image_provider)
def generate_image(prompt: str, size: int = 256) -> str:
"""Generate an image from a prompt.
Args:
@ -35,21 +24,21 @@ def generate_image(prompt: str, config: Config, size: int = 256) -> str:
Returns:
str: The filename of the image
"""
filename = f"{config.workspace_path}/{str(uuid.uuid4())}.jpg"
filename = f"{CFG.workspace_path}/{str(uuid.uuid4())}.jpg"
# DALL-E
if config.image_provider == "dalle":
return generate_image_with_dalle(prompt, filename, size, config)
if CFG.image_provider == "dalle":
return generate_image_with_dalle(prompt, filename, size)
# HuggingFace
elif config.image_provider == "huggingface":
return generate_image_with_hf(prompt, filename, config)
elif CFG.image_provider == "huggingface":
return generate_image_with_hf(prompt, filename)
# SD WebUI
elif config.image_provider == "sdwebui":
return generate_image_with_sd_webui(prompt, filename, config, size)
elif CFG.image_provider == "sdwebui":
return generate_image_with_sd_webui(prompt, filename, size)
return "No Image Provider Set"
def generate_image_with_hf(prompt: str, filename: str, config: Config) -> str:
def generate_image_with_hf(prompt: str, filename: str) -> str:
"""Generate an image with HuggingFace's API.
Args:
@ -60,58 +49,34 @@ def generate_image_with_hf(prompt: str, filename: str, config: Config) -> str:
str: The filename of the image
"""
API_URL = (
f"https://api-inference.huggingface.co/models/{config.huggingface_image_model}"
f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}"
)
if config.huggingface_api_token is None:
if CFG.huggingface_api_token is None:
raise ValueError(
"You need to set your Hugging Face API token in the config file."
)
headers = {
"Authorization": f"Bearer {config.huggingface_api_token}",
"Authorization": f"Bearer {CFG.huggingface_api_token}",
"X-Use-Cache": "false",
}
retry_count = 0
while retry_count < 10:
response = requests.post(
API_URL,
headers=headers,
json={
"inputs": prompt,
},
)
response = requests.post(
API_URL,
headers=headers,
json={
"inputs": prompt,
},
)
if response.ok:
try:
image = Image.open(io.BytesIO(response.content))
logger.info(f"Image Generated for prompt:{prompt}")
image.save(filename)
return f"Saved to disk:{filename}"
except Exception as e:
logger.error(e)
break
else:
try:
error = json.loads(response.text)
if "estimated_time" in error:
delay = error["estimated_time"]
logger.debug(response.text)
logger.info("Retrying in", delay)
time.sleep(delay)
else:
break
except Exception as e:
logger.error(e)
break
image = Image.open(io.BytesIO(response.content))
print(f"Image Generated for prompt:{prompt}")
retry_count += 1
image.save(filename)
return f"Error creating image."
return f"Saved to disk:{filename}"
def generate_image_with_dalle(
prompt: str, filename: str, size: int, config: Config
) -> str:
def generate_image_with_dalle(prompt: str, filename: str, size: int) -> str:
"""Generate an image with DALL-E.
Args:
@ -122,11 +87,12 @@ def generate_image_with_dalle(
Returns:
str: The filename of the image
"""
openai.api_key = CFG.openai_api_key
# Check for supported image sizes
if size not in [256, 512, 1024]:
closest = min([256, 512, 1024], key=lambda x: abs(x - size))
logger.info(
print(
f"DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. Setting to {closest}, was {size}."
)
size = closest
@ -136,10 +102,9 @@ def generate_image_with_dalle(
n=1,
size=f"{size}x{size}",
response_format="b64_json",
api_key=config.openai_api_key,
)
logger.info(f"Image Generated for prompt:{prompt}")
print(f"Image Generated for prompt:{prompt}")
image_data = b64decode(response["data"][0]["b64_json"])
@ -152,7 +117,6 @@ def generate_image_with_dalle(
def generate_image_with_sd_webui(
prompt: str,
filename: str,
config: Config,
size: int = 512,
negative_prompt: str = "",
extra: dict = {},
@ -169,13 +133,13 @@ def generate_image_with_sd_webui(
"""
# Create a session and set the basic auth if needed
s = requests.Session()
if config.sd_webui_auth:
username, password = config.sd_webui_auth.split(":")
if CFG.sd_webui_auth:
username, password = CFG.sd_webui_auth.split(":")
s.auth = (username, password or "")
# Generate the images
response = requests.post(
f"{config.sd_webui_url}/sdapi/v1/txt2img",
f"{CFG.sd_webui_url}/sdapi/v1/txt2img",
json={
"prompt": prompt,
"negative_prompt": negative_prompt,
@ -189,7 +153,7 @@ def generate_image_with_sd_webui(
},
)
logger.info(f"Image Generated for prompt:{prompt}")
print(f"Image Generated for prompt:{prompt}")
# Save the image to disk
response = response.json()

10
autogpt/commands/improve_code.py
View File

@ -1,13 +1,9 @@
from __future__ import annotations
import json
from typing import TYPE_CHECKING
from autogpt.commands.command import command
from autogpt.llm.utils import call_ai_function
if TYPE_CHECKING:
from autogpt.config import Config
from autogpt.llm_utils import call_ai_function
@command(
@ -15,7 +11,7 @@ if TYPE_CHECKING:
"Get Improved Code",
'"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"',
)
def improve_code(suggestions: list[str], code: str, config: Config) -> str:
def improve_code(suggestions: list[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create
chat completion api call.
@ -36,4 +32,4 @@ def improve_code(suggestions: list[str], code: str, config: Config) -> str:
" provided, making no other changes."
)
return call_ai_function(function_string, args, description_string, config=config)
return call_ai_function(function_string, args, description_string)

44
autogpt/commands/twitter.py Normal file
View File

@ -0,0 +1,44 @@
"""A module that contains a command to send a tweet."""
import os
import tweepy
from dotenv import load_dotenv
from autogpt.commands.command import command
load_dotenv()
@command(
"send_tweet",
"Send Tweet",
'"tweet_text": "<tweet_text>"',
)
def send_tweet(tweet_text: str) -> str:
"""
A function that takes in a string and returns a response from create chat
completion api call.
Args:
tweet_text (str): Text to be tweeted.
Returns:
A result from sending the tweet.
"""
consumer_key = os.environ.get("TW_CONSUMER_KEY")
consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
access_token = os.environ.get("TW_ACCESS_TOKEN")
access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
# Send tweet
try:
api.update_status(tweet_text)
return "Tweet sent successfully!"
except tweepy.TweepyException as e:
return f"Error sending tweet: {e.reason}"

4
autogpt/commands/web_playwright.py
View File

@ -1,12 +1,10 @@
"""Web scraping commands using Playwright"""
from __future__ import annotations
from autogpt.logs import logger
try:
from playwright.sync_api import sync_playwright
except ImportError:
logger.info(
print(
"Playwright not installed. Please install it with 'pip install playwright' to use."
)
from bs4 import BeautifulSoup

106
autogpt/commands/web_requests.py
View File

@ -1,20 +1,89 @@
"""Browse a webpage and summarize it using the LLM model"""
from __future__ import annotations
from urllib.parse import urljoin, urlparse
import requests
from bs4 import BeautifulSoup
from requests import Response
from requests.compat import urljoin
from autogpt.config import Config
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
from autogpt.url_utils.validators import validate_url
CFG = Config()
session = requests.Session()
session.headers.update({"User-Agent": CFG.user_agent})
def is_valid_url(url: str) -> bool:
"""Check if the URL is valid
Args:
url (str): The URL to check
Returns:
bool: True if the URL is valid, False otherwise
"""
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except ValueError:
return False
def sanitize_url(url: str) -> str:
"""Sanitize the URL
Args:
url (str): The URL to sanitize
Returns:
str: The sanitized URL
"""
return urljoin(url, urlparse(url).path)
def check_local_file_access(url: str) -> bool:
"""Check if the URL is a local file
Args:
url (str): The URL to check
Returns:
bool: True if the URL is a local file, False otherwise
"""
local_prefixes = [
"file:///",
"file://localhost/",
"file://localhost",
"http://localhost",
"http://localhost/",
"https://localhost",
"https://localhost/",
"http://2130706433",
"http://2130706433/",
"https://2130706433",
"https://2130706433/",
"http://127.0.0.1/",
"http://127.0.0.1",
"https://127.0.0.1/",
"https://127.0.0.1",
"https://0.0.0.0/",
"https://0.0.0.0",
"http://0.0.0.0/",
"http://0.0.0.0",
"http://0000",
"http://0000/",
"https://0000",
"https://0000/",
]
return any(url.startswith(prefix) for prefix in local_prefixes)
@validate_url
def get_response(
url: str, config: Config, timeout: int = 10
url: str, timeout: int = 10
) -> tuple[None, str] | tuple[Response, None]:
"""Get the response from a URL
@ -30,8 +99,17 @@ def get_response(
requests.exceptions.RequestException: If the HTTP request fails
"""
try:
session.headers.update({"User-Agent": config.user_agent})
response = session.get(url, timeout=timeout)
# Restrict access to local files
if check_local_file_access(url):
raise ValueError("Access to local files is restricted")
# Most basic check if the URL is valid:
if not url.startswith("http://") and not url.startswith("https://"):
raise ValueError("Invalid URL format")
sanitized_url = sanitize_url(url)
response = session.get(sanitized_url, timeout=timeout)
# Check if the response contains an HTTP error
if response.status_code >= 400:
@ -48,7 +126,7 @@ def get_response(
return None, f"Error: {str(re)}"
def scrape_text(url: str, config: Config) -> str:
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
@ -57,7 +135,7 @@ def scrape_text(url: str, config: Config) -> str:
Returns:
str: The scraped text
"""
response, error_message = get_response(url, config)
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
@ -76,7 +154,7 @@ def scrape_text(url: str, config: Config) -> str:
return text
def scrape_links(url: str, config: Config) -> str | list[str]:
def scrape_links(url: str) -> str | list[str]:
"""Scrape links from a webpage
Args:
@ -85,7 +163,7 @@ def scrape_links(url: str, config: Config) -> str | list[str]:
Returns:
str | list[str]: The scraped links
"""
response, error_message = get_response(url, config)
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
@ -98,3 +176,13 @@ def scrape_links(url: str, config: Config) -> str | list[str]:
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the'
" text, summarize the text.",
}

116
autogpt/commands/web_selenium.py
View File

@ -4,41 +4,26 @@ from __future__ import annotations
import logging
from pathlib import Path
from sys import platform
from typing import TYPE_CHECKING, Optional, Type
from bs4 import BeautifulSoup
from selenium.common.exceptions import WebDriverException
from selenium import webdriver
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.chrome.service import Service as ChromeDriverService
from selenium.webdriver.chrome.webdriver import WebDriver as ChromeDriver
from selenium.webdriver.common.by import By
from selenium.webdriver.edge.options import Options as EdgeOptions
from selenium.webdriver.edge.service import Service as EdgeDriverService
from selenium.webdriver.edge.webdriver import WebDriver as EdgeDriver
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.firefox.service import Service as GeckoDriverService
from selenium.webdriver.firefox.webdriver import WebDriver as FirefoxDriver
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver.safari.options import Options as SafariOptions
from selenium.webdriver.safari.webdriver import WebDriver as SafariDriver
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from webdriver_manager.chrome import ChromeDriverManager
from webdriver_manager.firefox import GeckoDriverManager
from webdriver_manager.microsoft import EdgeChromiumDriverManager as EdgeDriverManager
import autogpt.processing.text as summary
from autogpt.commands.command import command
from autogpt.logs import logger
from autogpt.memory.vector import MemoryItem, get_memory
from autogpt.config import Config
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
from autogpt.url_utils.validators import validate_url
if TYPE_CHECKING:
from autogpt.config import Config
BrowserOptions = ChromeOptions | EdgeOptions | FirefoxOptions | SafariOptions
FILE_DIR = Path(__file__).parent.parent
CFG = Config()
@command(
@ -46,8 +31,7 @@ FILE_DIR = Path(__file__).parent.parent
"Browse Website",
'"url": "<url>", "question": "<what_you_want_to_find_on_website>"',
)
@validate_url
def browse_website(url: str, question: str, config: Config) -> str:
def browse_website(url: str, question: str) -> tuple[str, WebDriver]:
"""Browse a website and return the answer and links to the user
Args:
@ -57,26 +41,19 @@ def browse_website(url: str, question: str, config: Config) -> str:
Returns:
Tuple[str, WebDriver]: The answer and links to the user and the webdriver
"""
try:
driver, text = scrape_text_with_selenium(url, config)
except WebDriverException as e:
# These errors are often quite long and include lots of context.
# Just grab the first line.
msg = e.msg.split("\n")[0]
return f"Error: {msg}"
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary = summarize_memorize_webpage(url, text, question, config, driver)
summary_text = summary.summarize_text(url, text, question, driver)
links = scrape_links_with_selenium(driver, url)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary}\n\nLinks: {links}"
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url: str, config: Config) -> tuple[WebDriver, str]:
def scrape_text_with_selenium(url: str) -> tuple[WebDriver, str]:
"""Scrape text from a website using selenium
Args:
@ -87,50 +64,37 @@ def scrape_text_with_selenium(url: str, config: Config) -> tuple[WebDriver, str]
"""
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options_available: dict[str, Type[BrowserOptions]] = {
options_available = {
"chrome": ChromeOptions,
"edge": EdgeOptions,
"firefox": FirefoxOptions,
"safari": SafariOptions,
"firefox": FirefoxOptions,
}
options: BrowserOptions = options_available[config.selenium_web_browser]()
options = options_available[CFG.selenium_web_browser]()
options.add_argument(
"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
)
if config.selenium_web_browser == "firefox":
if config.selenium_headless:
options.headless = True
options.add_argument("--disable-gpu")
driver = FirefoxDriver(
service=GeckoDriverService(GeckoDriverManager().install()), options=options
if CFG.selenium_web_browser == "firefox":
driver = webdriver.Firefox(
executable_path=GeckoDriverManager().install(), options=options
)
elif config.selenium_web_browser == "edge":
driver = EdgeDriver(
service=EdgeDriverService(EdgeDriverManager().install()), options=options
)
elif config.selenium_web_browser == "safari":
elif CFG.selenium_web_browser == "safari":
# Requires a bit more setup on the users end
# See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
driver = SafariDriver(options=options)
driver = webdriver.Safari(options=options)
else:
if platform == "linux" or platform == "linux2":
options.add_argument("--disable-dev-shm-usage")
options.add_argument("--remote-debugging-port=9222")
options.add_argument("--no-sandbox")
if config.selenium_headless:
options.add_argument("--headless=new")
if CFG.selenium_headless:
options.add_argument("--headless")
options.add_argument("--disable-gpu")
chromium_driver_path = Path("/usr/bin/chromedriver")
driver = ChromeDriver(
service=ChromeDriverService(str(chromium_driver_path))
if chromium_driver_path.exists()
else ChromeDriverService(ChromeDriverManager().install()),
options=options,
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
@ -193,40 +157,4 @@ def add_header(driver: WebDriver) -> None:
Returns:
None
"""
try:
with open(f"{FILE_DIR}/js/overlay.js", "r") as overlay_file:
overlay_script = overlay_file.read()
driver.execute_script(overlay_script)
except Exception as e:
print(f"Error executing overlay.js: {e}")
def summarize_memorize_webpage(
url: str,
text: str,
question: str,
config: Config,
driver: Optional[WebDriver] = None,
) -> str:
"""Summarize text using the OpenAI API
Args:
url (str): The url of the text
text (str): The text to summarize
question (str): The question to ask the model
driver (WebDriver): The webdriver to use to scroll the page
Returns:
str: The summary of the text
"""
if not text:
return "Error: No text to summarize"
text_length = len(text)
logger.info(f"Text length: {text_length} characters")
memory = get_memory(config)
new_memory = MemoryItem.from_webpage(text, url, question=question)
memory.add(new_memory)
return new_memory.summary
driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

10
autogpt/commands/write_tests.py
View File

@ -2,13 +2,9 @@
from __future__ import annotations
import json
from typing import TYPE_CHECKING
from autogpt.commands.command import command
from autogpt.llm.utils import call_ai_function
if TYPE_CHECKING:
from autogpt.config import Config
from autogpt.llm_utils import call_ai_function
@command(
@ -16,7 +12,7 @@ if TYPE_CHECKING:
"Write Tests",
'"code": "<full_code_string>", "focus": "<list_of_focus_areas>"',
)
def write_tests(code: str, focus: list[str], config: Config) -> str:
def write_tests(code: str, focus: list[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create
chat completion api call.
@ -38,4 +34,4 @@ def write_tests(code: str, focus: list[str], config: Config) -> str:
" specific areas if required."
)
return call_ai_function(function_string, args, description_string, config=config)
return call_ai_function(function_string, args, description_string)

3
autogpt/config/__init__.py
View File

@ -3,9 +3,12 @@ This module contains the configuration classes for AutoGPT.
"""
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config, check_openai_api_key
from autogpt.config.singleton import AbstractSingleton, Singleton
__all__ = [
"check_openai_api_key",
"AbstractSingleton",
"AIConfig",
"Config",
"Singleton",
]

19
autogpt/config/ai_config.py
View File

@ -7,14 +7,12 @@ from __future__ import annotations
import os
import platform
from pathlib import Path
from typing import TYPE_CHECKING, Optional
from typing import Optional, Type
import distro
import yaml
if TYPE_CHECKING:
from autogpt.commands.command import CommandRegistry
from autogpt.prompts.generator import PromptGenerator
from autogpt.prompts.generator import PromptGenerator
# Soon this will go in a folder where it remembers more stuff about the run(s)
SAVE_FILE = str(Path(os.getcwd()) / "ai_settings.yaml")
@ -55,8 +53,8 @@ class AIConfig:
self.ai_role = ai_role
self.ai_goals = ai_goals
self.api_budget = api_budget
self.prompt_generator: PromptGenerator | None = None
self.command_registry: CommandRegistry | None = None
self.prompt_generator = None
self.command_registry = None
@staticmethod
def load(config_file: str = SAVE_FILE) -> "AIConfig":
@ -75,18 +73,13 @@ class AIConfig:
try:
with open(config_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = [
str(goal).strip("{}").replace("'", "").replace('"', "")
if isinstance(goal, dict)
else str(goal)
for goal in config_params.get("ai_goals", [])
]
ai_goals = config_params.get("ai_goals", [])
api_budget = config_params.get("api_budget", 0.0)
# type: Type[AIConfig]
return AIConfig(ai_name, ai_role, ai_goals, api_budget)

101
autogpt/config/config.py
View File

@ -6,8 +6,11 @@ import openai
import yaml
from auto_gpt_plugin_template import AutoGPTPluginTemplate
from colorama import Fore
from dotenv import load_dotenv
from autogpt.singleton import Singleton
from autogpt.config.singleton import Singleton
load_dotenv(verbose=True, override=True)
class Config(metaclass=Singleton):
@ -17,8 +20,8 @@ class Config(metaclass=Singleton):
def __init__(self) -> None:
"""Initialize the Config class"""
self.workspace_path: str = None
self.file_logger_path: str = None
self.workspace_path = None
self.file_logger_path = None
self.debug_mode = False
self.continuous_mode = False
@ -28,37 +31,12 @@ class Config(metaclass=Singleton):
self.allow_downloads = False
self.skip_news = False
self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y")
self.exit_key = os.getenv("EXIT_KEY", "n")
self.plain_output = os.getenv("PLAIN_OUTPUT", "False") == "True"
disabled_command_categories = os.getenv("DISABLED_COMMAND_CATEGORIES")
if disabled_command_categories:
self.disabled_command_categories = disabled_command_categories.split(",")
else:
self.disabled_command_categories = []
deny_commands = os.getenv("DENY_COMMANDS")
if deny_commands:
self.deny_commands = deny_commands.split(",")
else:
self.deny_commands = []
allow_commands = os.getenv("ALLOW_COMMANDS")
if allow_commands:
self.allow_commands = allow_commands.split(",")
else:
self.allow_commands = []
self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
self.prompt_settings_file = os.getenv(
"PROMPT_SETTINGS_FILE", "prompt_settings.yaml"
)
self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
self.embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002")
self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 3000))
self.browse_spacy_language_model = os.getenv(
"BROWSE_SPACY_LANGUAGE_MODEL", "en_core_web_sm"
)
@ -86,8 +64,6 @@ class Config(metaclass=Singleton):
self.use_mac_os_tts = False
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
self.chat_messages_enabled = os.getenv("CHAT_MESSAGES_ENABLED") == "True"
self.use_brian_tts = False
self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
@ -97,6 +73,28 @@ class Config(metaclass=Singleton):
self.google_api_key = os.getenv("GOOGLE_API_KEY")
self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
self.pinecone_region = os.getenv("PINECONE_ENV")
self.weaviate_host = os.getenv("WEAVIATE_HOST")
self.weaviate_port = os.getenv("WEAVIATE_PORT")
self.weaviate_protocol = os.getenv("WEAVIATE_PROTOCOL", "http")
self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None)
self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None)
self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None)
self.weaviate_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH")
self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None)
self.use_weaviate_embedded = (
os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True"
)
# milvus or zilliz cloud configuration.
self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
self.milvus_username = os.getenv("MILVUS_USERNAME")
self.milvus_password = os.getenv("MILVUS_PASSWORD")
self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")
self.milvus_secure = os.getenv("MILVUS_SECURE") == "True"
self.image_provider = os.getenv("IMAGE_PROVIDER")
self.image_size = int(os.getenv("IMAGE_SIZE", 256))
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
@ -122,13 +120,16 @@ class Config(metaclass=Singleton):
" (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
)
self.memory_backend = os.getenv("MEMORY_BACKEND", "json_file")
self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt-memory")
self.redis_host = os.getenv("REDIS_HOST", "localhost")
self.redis_port = int(os.getenv("REDIS_PORT", "6379"))
self.redis_port = os.getenv("REDIS_PORT", "6379")
self.redis_password = os.getenv("REDIS_PASSWORD", "")
self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True"
self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt")
# Note that indexes must be created on db 0 in redis, this is not configurable.
self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
# Initialize the OpenAI API client
openai.api_key = self.openai_api_key
self.plugins_dir = os.getenv("PLUGINS_DIR", "plugins")
self.plugins: List[AutoGPTPluginTemplate] = []
@ -139,12 +140,7 @@ class Config(metaclass=Singleton):
self.plugins_allowlist = plugins_allowlist.split(",")
else:
self.plugins_allowlist = []
plugins_denylist = os.getenv("DENYLISTED_PLUGINS")
if plugins_denylist:
self.plugins_denylist = plugins_denylist.split(",")
else:
self.plugins_denylist = []
self.plugins_denylist = []
def get_azure_deployment_id_for_model(self, model: str) -> str:
"""
@ -185,7 +181,7 @@ class Config(metaclass=Singleton):
None
"""
with open(config_file) as file:
config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
config_params = yaml.load(file, Loader=yaml.FullLoader)
self.openai_api_type = config_params.get("azure_api_type") or "azure"
self.openai_api_base = config_params.get("azure_api_base") or ""
self.openai_api_version = (
@ -221,9 +217,9 @@ class Config(metaclass=Singleton):
"""Set the smart token limit value."""
self.smart_token_limit = value
def set_embedding_model(self, value: str) -> None:
"""Set the model to use for creating embeddings."""
self.embedding_model = value
def set_browse_chunk_max_length(self, value: int) -> None:
"""Set the browse_website command chunk max length value."""
self.browse_chunk_max_length = value
def set_openai_api_key(self, value: str) -> None:
"""Set the OpenAI API key value."""
@ -249,6 +245,14 @@ class Config(metaclass=Singleton):
"""Set the custom search engine id value."""
self.custom_search_engine_id = value
def set_pinecone_api_key(self, value: str) -> None:
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str) -> None:
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool) -> None:
"""Set the debug mode value."""
self.debug_mode = value
@ -261,9 +265,9 @@ class Config(metaclass=Singleton):
"""Set the temperature value."""
self.temperature = value
def set_memory_backend(self, name: str) -> None:
"""Set the memory backend name."""
self.memory_backend = name
def set_memory_backend(self, value: int) -> None:
"""Set the temperature value."""
self.memory_backend = value
def check_openai_api_key() -> None:
@ -273,7 +277,6 @@ def check_openai_api_key() -> None:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
+ Fore.RESET
)
print("You can get your key from https://platform.openai.com/account/api-keys")
exit(1)

24
autogpt/config/singleton.py Normal file
View File

@ -0,0 +1,24 @@
"""The singleton metaclass for ensuring only one instance of a class."""
import abc
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
"""
Abstract singleton class for ensuring only one instance of a class.
"""
pass

79
autogpt/configurator.py
View File

@ -1,29 +1,19 @@
"""Configurator module."""
from __future__ import annotations
from typing import TYPE_CHECKING
import click
from colorama import Back, Fore, Style
from autogpt import utils
from autogpt.llm.utils import check_model
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.memory.vector import get_supported_memory_backends
from autogpt.memory import get_supported_memory_backends
if TYPE_CHECKING:
from autogpt.config import Config
GPT_4_MODEL = "gpt-4"
GPT_3_MODEL = "gpt-3.5-turbo"
CFG = Config()
def create_config(
config: Config,
continuous: bool,
continuous_limit: int,
ai_settings_file: str,
prompt_settings_file: str,
skip_reprompt: bool,
speak: bool,
debug: bool,
@ -40,7 +30,6 @@ def create_config(
continuous (bool): Whether to run in continuous mode
continuous_limit (int): The number of times to run in continuous mode
ai_settings_file (str): The path to the ai_settings.yaml file
prompt_settings_file (str): The path to the prompt_settings.yaml file
skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
speak (bool): Whether to enable speak mode
debug (bool): Whether to enable debug mode
@ -51,13 +40,13 @@ def create_config(
allow_downloads (bool): Whether to allow Auto-GPT to download files natively
skips_news (bool): Whether to suppress the output of latest news on startup
"""
config.set_debug_mode(False)
config.set_continuous_mode(False)
config.set_speak_mode(False)
CFG.set_debug_mode(False)
CFG.set_continuous_mode(False)
CFG.set_speak_mode(False)
if debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
config.set_debug_mode(True)
CFG.set_debug_mode(True)
if continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
@ -68,13 +57,13 @@ def create_config(
" cause your AI to run forever or carry out actions you would not usually"
" authorise. Use at your own risk.",
)
config.set_continuous_mode(True)
CFG.set_continuous_mode(True)
if continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
)
config.set_continuous_limit(continuous_limit)
CFG.set_continuous_limit(continuous_limit)
# Check if continuous limit is used without continuous mode
if continuous_limit and not continuous:
@ -82,28 +71,15 @@ def create_config(
if speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
config.set_speak_mode(True)
CFG.set_speak_mode(True)
# Set the default LLM models
if gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
# --gpt3only should always use gpt-3.5-turbo, despite user's FAST_LLM_MODEL config
config.set_fast_llm_model(GPT_3_MODEL)
config.set_smart_llm_model(GPT_3_MODEL)
CFG.set_smart_llm_model(CFG.fast_llm_model)
elif (
gpt4only
and check_model(GPT_4_MODEL, model_type="smart_llm_model") == GPT_4_MODEL
):
if gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
# --gpt4only should always use gpt-4, despite user's SMART_LLM_MODEL config
config.set_fast_llm_model(GPT_4_MODEL)
config.set_smart_llm_model(GPT_4_MODEL)
else:
config.set_fast_llm_model(check_model(config.fast_llm_model, "fast_llm_model"))
config.set_smart_llm_model(
check_model(config.smart_llm_model, "smart_llm_model")
)
CFG.set_fast_llm_model(CFG.smart_llm_model)
if memory_type:
supported_memory = get_supported_memory_backends()
@ -114,13 +90,13 @@ def create_config(
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log("Defaulting to: ", Fore.YELLOW, config.memory_backend)
logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
else:
config.memory_backend = chosen
CFG.memory_backend = chosen
if skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
config.skip_reprompt = True
CFG.skip_reprompt = True
if ai_settings_file:
file = ai_settings_file
@ -133,24 +109,11 @@ def create_config(
exit(1)
logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
config.ai_settings_file = file
config.skip_reprompt = True
if prompt_settings_file:
file = prompt_settings_file
# Validate file
(validated, message) = utils.validate_yaml_file(file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
logger.typewriter_log("Using Prompt Settings File:", Fore.GREEN, file)
config.prompt_settings_file = file
CFG.ai_settings_file = file
CFG.skip_reprompt = True
if browser_name:
config.selenium_web_browser = browser_name
CFG.selenium_web_browser = browser_name
if allow_downloads:
logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
@ -165,7 +128,7 @@ def create_config(
Fore.YELLOW,
f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
)
config.allow_downloads = True
CFG.allow_downloads = True
if skip_news:
config.skip_news = True
CFG.skip_news = True

13
autogpt/json_utils/json_fix_general.py
View File

@ -9,7 +9,6 @@ from typing import Optional
from autogpt.config import Config
from autogpt.json_utils.utilities import extract_char_position
from autogpt.logs import logger
CFG = Config()
@ -34,7 +33,8 @@ def fix_invalid_escape(json_to_load: str, error_message: str) -> str:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
logger.debug("json loads error - fix invalid escape", e)
if CFG.debug_mode:
print("json loads error - fix invalid escape", e)
error_message = str(e)
return json_to_load
@ -98,11 +98,13 @@ def correct_json(json_to_load: str) -> str:
"""
try:
logger.debug("json", json_to_load)
if CFG.debug_mode:
print("json", json_to_load)
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
logger.debug("json loads error", e)
if CFG.debug_mode:
print("json loads error", e)
error_message = str(e)
if error_message.startswith("Invalid \\escape"):
json_to_load = fix_invalid_escape(json_to_load, error_message)
@ -114,7 +116,8 @@ def correct_json(json_to_load: str) -> str:
json.loads(json_to_load)
return json_to_load
except json.JSONDecodeError as e:
logger.debug("json loads error - add quotes", e)
if CFG.debug_mode:
print("json loads error - add quotes", e)
error_message = str(e)
if balanced_str := balance_braces(json_to_load):
return balanced_str

21
autogpt/json_utils/json_fix_llm.py
View File

@ -11,7 +11,7 @@ from regex import regex
from autogpt.config import Config
from autogpt.json_utils.json_fix_general import correct_json
from autogpt.llm.utils import call_ai_function
from autogpt.llm_utils import call_ai_function
from autogpt.logs import logger
from autogpt.speech import say_text
@ -91,33 +91,14 @@ def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
Returns:
str: The fixed JSON string.
"""
assistant_reply = assistant_reply.strip()
if assistant_reply.startswith("```json"):
assistant_reply = assistant_reply[7:]
if assistant_reply.endswith("```"):
assistant_reply = assistant_reply[:-3]
try:
return json.loads(assistant_reply) # just check the validity
except json.JSONDecodeError: # noqa: E722
pass
if assistant_reply.startswith("json "):
assistant_reply = assistant_reply[5:]
assistant_reply = assistant_reply.strip()
try:
return json.loads(assistant_reply) # just check the validity
except json.JSONDecodeError: # noqa: E722
pass
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
logger.debug("Assistant reply JSON: %s", str(assistant_reply_json))
if assistant_reply_json == {}:
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
logger.debug("Assistant reply JSON 2: %s", str(assistant_reply_json))
if assistant_reply_json != {}:
return assistant_reply_json

38
autogpt/json_utils/utilities.py
View File

@ -1,6 +1,5 @@
"""Utilities for the json_fixes package."""
import json
import os.path
import re
from jsonschema import Draft7Validator
@ -9,7 +8,6 @@ from autogpt.config import Config
from autogpt.logs import logger
CFG = Config()
LLM_DEFAULT_RESPONSE_FORMAT = "llm_response_format_1"
def extract_char_position(error_message: str) -> int:
@ -30,15 +28,13 @@ def extract_char_position(error_message: str) -> int:
raise ValueError("Character position not found in the error message.")
def validate_json(json_object: object, schema_name: str):
def validate_json(json_object: object, schema_name: object) -> object:
"""
:type schema_name: object
:param schema_name: str
:param schema_name:
:type json_object: object
"""
# with open(f"/Users/kilig/Job/Python-project/auto-gpt/autogpt/json_utils/{schema_name}.json", "r") as f:
scheme_file = os.path.join(os.path.dirname(__file__), f"{schema_name}.json")
with open(scheme_file, "r") as f:
with open(f"/Users/kilig/Job/Python-project/academic_gpt/autogpt/json_utils/{schema_name}.json", "r") as f:
schema = json.load(f)
validator = Draft7Validator(schema)
@ -52,31 +48,7 @@ def validate_json(json_object: object, schema_name: str):
for error in errors:
logger.error(f"Error: {error.message}")
else:
logger.debug("The JSON object is valid.")
elif CFG.debug_mode:
print("The JSON object is valid.")
return json_object
def validate_json_string(json_string: str, schema_name: str):
"""
:type schema_name: object
:param schema_name: str
:type json_object: object
"""
try:
json_loaded = json.loads(json_string)
return validate_json(json_loaded, schema_name)
except:
return None
def is_string_valid_json(json_string: str, schema_name: str) -> bool:
"""
:type schema_name: object
:param schema_name: str
:type json_object: object
"""
return validate_json_string(json_string, schema_name) is not None

185
autogpt/llm_utils.py Normal file
View File

@ -0,0 +1,185 @@
from __future__ import annotations
import time
from typing import List, Optional
import openai
from colorama import Fore, Style
from openai.error import APIError, RateLimitError
from autogpt.api_manager import api_manager
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.types.openai import Message
CFG = Config()
openai.api_key = CFG.openai_api_key
def call_ai_function(
function: str, args: list, description: str, model: str | None = None
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = CFG.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
args: str = ", ".join(args)
messages: List[Message] = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
return create_chat_completion(model=model, messages=messages, temperature=0)
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
def create_chat_completion(
messages: List[Message], # type: ignore
model: Optional[str] = None,
temperature: float = CFG.temperature,
max_tokens: Optional[int] = None,
) -> str:
"""Create a chat completion using the OpenAI API
Args:
messages (List[Message]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
num_retries = 10
warned_user = False
if CFG.debug_mode:
print(
f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
)
for plugin in CFG.plugins:
if plugin.can_handle_chat_completion(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens,
):
message = plugin.handle_chat_completion(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens,
)
if message is not None:
return message
response = None
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
if CFG.use_azure:
response = api_manager.create_chat_completion(
deployment_id=CFG.get_azure_deployment_id_for_model(model),
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
else:
response = api_manager.create_chat_completion(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
break
except RateLimitError:
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ", f"Reached rate limit, passing...{Fore.RESET}"
)
if not warned_user:
logger.double_check(
f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
+ f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
)
warned_user = True
except APIError as e:
if e.http_status != 502:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ",
f"API Bad gateway. Waiting {backoff} seconds...{Fore.RESET}",
)
time.sleep(backoff)
if response is None:
logger.typewriter_log(
"FAILED TO GET RESPONSE FROM OPENAI",
Fore.RED,
"Auto-GPT has failed to get a response from OpenAI's services. "
+ f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
)
logger.double_check()
if CFG.debug_mode:
raise RuntimeError(f"Failed to get response after {num_retries} retries")
else:
quit(1)
resp = response.choices[0].message["content"]
for plugin in CFG.plugins:
if not plugin.can_handle_on_response():
continue
resp = plugin.on_response(resp)
return resp
def get_ada_embedding(text):
text = text.replace("\n", " ")
return api_manager.embedding_create(
text_list=[text], model="text-embedding-ada-002"
)
def create_embedding_with_ada(text) -> list:
"""Create an embedding with text-ada-002 using the OpenAI SDK"""
num_retries = 10
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
return api_manager.embedding_create(
text_list=[text], model="text-embedding-ada-002"
)
except RateLimitError:
pass
except APIError as e:
if e.http_status != 502:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
f"{Fore.RED}Error: ",
f"API Bad gateway. Waiting {backoff} seconds...{Fore.RESET}",
)
time.sleep(backoff)

201
autogpt/logs.py
View File

@ -1,18 +1,29 @@
"""Logging module for Auto-GPT."""
import inspect
import json
import logging
import os
import random
import re
import time
import traceback
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from autogpt.log_cycle.json_handler import JsonFileHandler, JsonFormatter
from autogpt.singleton import Singleton
from autogpt.config import Config, Singleton
from autogpt.speech import say_text
CFG = Config()
def get_properties(obj):
props = {}
for prop_name in dir(obj):
if not prop_name.startswith('__'):
prop_value = getattr(obj, prop_name)
props[prop_value] = prop_name
return props
class Logger(metaclass=Singleton):
"""
@ -75,24 +86,15 @@ class Logger(metaclass=Singleton):
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
self.color_compar = get_properties(Fore)
self.output_content = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
self, title="", title_color=Fore.YELLOW, content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
if speak_text and CFG.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
@ -102,6 +104,15 @@ class Logger(metaclass=Singleton):
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
try:
msg = f'<span style="color:{self.color_compar[title_color]};font-weight:bold;">{title}：</span><span style="font-weight:normal;">{content}</span>'
self.output_content.append([msg, title+": "+content])
return msg
except Exception as e:
msg = f'<span style="font-weight:bold;">{title}：</span><span style="font-weight:normal;">{content}</span>'
self.output_content.append([msg, title+": "+content])
return
def debug(
self,
@ -111,14 +122,6 @@ class Logger(metaclass=Singleton):
):
self._log(title, title_color, message, logging.DEBUG)
def info(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.INFO)
def warn(
self,
message,
@ -130,19 +133,11 @@ class Logger(metaclass=Singleton):
def error(self, title, message=""):
self._log(title, Fore.RED, message, logging.ERROR)
def _log(
self,
title: str = "",
title_color: str = "",
message: str = "",
level=logging.INFO,
):
def _log(self, title="", title_color="", message="", level=logging.INFO):
if message:
if isinstance(message, list):
message = " ".join(message)
self.logger.log(
level, message, extra={"title": str(title), "color": str(title_color)}
)
self.logger.log(level, message, extra={"title": title, "color": title_color})
def set_level(self, level):
self.logger.setLevel(level)
@ -159,26 +154,6 @@ class Logger(metaclass=Singleton):
self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
def log_json(self, data: Any, file_name: str) -> None:
# Define log directory
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
# Create a handler for JSON files
json_file_path = os.path.join(log_dir, file_name)
json_data_handler = JsonFileHandler(json_file_path)
json_data_handler.setFormatter(JsonFormatter())
# Log the JSON data using the custom file handler
self.json_logger.addHandler(json_data_handler)
self.json_logger.debug(data)
self.json_logger.removeHandler(json_data_handler)
def get_log_directory(self):
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
return os.path.abspath(log_dir)
"""
Output stream to console using simulated typing
@ -226,16 +201,12 @@ class AutoGptFormatter(logging.Formatter):
if hasattr(record, "color"):
record.title_color = (
getattr(record, "color")
+ getattr(record, "title", "")
+ getattr(record, "title")
+ " "
+ Style.RESET_ALL
)
else:
record.title_color = getattr(record, "title", "")
# Add this line to set 'title' to an empty string if it doesn't exist
record.title = getattr(record, "title", "")
record.title_color = getattr(record, "title")
if hasattr(record, "msg"):
record.message_no_color = remove_color_codes(getattr(record, "msg"))
else:
@ -251,10 +222,100 @@ def remove_color_codes(s: str) -> str:
logger = Logger()
def print_assistant_thoughts(ai_name, assistant_reply):
"""Prints the assistant's thoughts to the console"""
from autogpt.json_utils.json_fix_llm import (
attempt_to_fix_json_by_finding_outermost_brackets,
fix_and_parse_json,
)
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
if isinstance(assistant_reply_json, str):
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse
# it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
if not isinstance(assistant_reply_json, dict):
assistant_reply_json = {}
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
)
logger.typewriter_log(
"REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log(
"CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
)
# Speak the assistant's thoughts
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
else:
logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
return assistant_reply_json
except json.decoder.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
if CFG.speak_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API."
" I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)
def print_assistant_thoughts(
ai_name: object,
assistant_reply_json_valid: object,
speak_mode: bool = False,
ai_name: object, assistant_reply_json_valid: object
) -> None:
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
@ -287,8 +348,12 @@ def print_assistant_thoughts(
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log("CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}")
# Speak the assistant's thoughts
if assistant_thoughts_speak:
if speak_mode:
say_text(assistant_thoughts_speak)
else:
logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
if __name__ == '__main__':
ff = logger.typewriter_log('ahhahaha', Fore.GREEN, speak_text=True)
# print(Fore.GREEN)
# print(logger.color_compar)

99
autogpt/memory/__init__.py Normal file
View File

@ -0,0 +1,99 @@
from autogpt.memory.local import LocalCache
from autogpt.memory.no_memory import NoMemory
# List of supported memory backends
# Add a backend to this list if the import attempt is successful
supported_memory = ["local", "no_memory"]
try:
from autogpt.memory.redismem import RedisMemory
supported_memory.append("redis")
except ImportError:
# print("Redis not installed. Skipping import.")
RedisMemory = None
try:
from autogpt.memory.pinecone import PineconeMemory
supported_memory.append("pinecone")
except ImportError:
# print("Pinecone not installed. Skipping import.")
PineconeMemory = None
try:
from autogpt.memory.weaviate import WeaviateMemory
supported_memory.append("weaviate")
except ImportError:
# print("Weaviate not installed. Skipping import.")
WeaviateMemory = None
try:
from autogpt.memory.milvus import MilvusMemory
supported_memory.append("milvus")
except ImportError:
# print("pymilvus not installed. Skipping import.")
MilvusMemory = None
def get_memory(cfg, init=False):
memory = None
if cfg.memory_backend == "pinecone":
if not PineconeMemory:
print(
"Error: Pinecone is not installed. Please install pinecone"
" to use Pinecone as a memory backend."
)
else:
memory = PineconeMemory(cfg)
if init:
memory.clear()
elif cfg.memory_backend == "redis":
if not RedisMemory:
print(
"Error: Redis is not installed. Please install redis-py to"
" use Redis as a memory backend."
)
else:
memory = RedisMemory(cfg)
elif cfg.memory_backend == "weaviate":
if not WeaviateMemory:
print(
"Error: Weaviate is not installed. Please install weaviate-client to"
" use Weaviate as a memory backend."
)
else:
memory = WeaviateMemory(cfg)
elif cfg.memory_backend == "milvus":
if not MilvusMemory:
print(
"Error: pymilvus sdk is not installed."
"Please install pymilvus to use Milvus or Zilliz Cloud as memory backend."
)
else:
memory = MilvusMemory(cfg)
elif cfg.memory_backend == "no_memory":
memory = NoMemory(cfg)
if memory is None:
memory = LocalCache(cfg)
if init:
memory.clear()
return memory
def get_supported_memory_backends():
return supported_memory
__all__ = [
"get_memory",
"LocalCache",
"RedisMemory",
"PineconeMemory",
"NoMemory",
"MilvusMemory",
"WeaviateMemory",
]

28
autogpt/memory/base.py Normal file
View File

@ -0,0 +1,28 @@
"""Base class for memory providers."""
import abc
from autogpt.config import AbstractSingleton, Config
cfg = Config()
class MemoryProviderSingleton(AbstractSingleton):
@abc.abstractmethod
def add(self, data):
pass
@abc.abstractmethod
def get(self, data):
pass
@abc.abstractmethod
def clear(self):
pass
@abc.abstractmethod
def get_relevant(self, data, num_relevant=5):
pass
@abc.abstractmethod
def get_stats(self):
pass

126
autogpt/memory/local.py Normal file
View File

@ -0,0 +1,126 @@
from __future__ import annotations
import dataclasses
from pathlib import Path
from typing import Any, List
import numpy as np
import orjson
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.memory.base import MemoryProviderSingleton
EMBED_DIM = 1536
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
def create_default_embeddings():
return np.zeros((0, EMBED_DIM)).astype(np.float32)
@dataclasses.dataclass
class CacheContent:
texts: List[str] = dataclasses.field(default_factory=list)
embeddings: np.ndarray = dataclasses.field(
default_factory=create_default_embeddings
)
class LocalCache(MemoryProviderSingleton):
"""A class that stores the memory in a local file"""
def __init__(self, cfg) -> None:
"""Initialize a class instance
Args:
cfg: Config object
Returns:
None
"""
workspace_path = Path(cfg.workspace_path)
self.filename = workspace_path / f"{cfg.memory_index}.json"
self.filename.touch(exist_ok=True)
file_content = b"{}"
with self.filename.open("w+b") as f:
f.write(file_content)
self.data = CacheContent()
def add(self, text: str):
"""
Add text to our list of texts, add embedding as row to our
embeddings-matrix
Args:
text: str
Returns: None
"""
if "Command Error:" in text:
return ""
self.data.texts.append(text)
embedding = create_embedding_with_ada(text)
vector = np.array(embedding).astype(np.float32)
vector = vector[np.newaxis, :]
self.data.embeddings = np.concatenate(
[
self.data.embeddings,
vector,
],
axis=0,
)
with open(self.filename, "wb") as f:
out = orjson.dumps(self.data, option=SAVE_OPTIONS)
f.write(out)
return text
def clear(self) -> str:
"""
Clears the redis server.
Returns: A message indicating that the memory has been cleared.
"""
self.data = CacheContent()
return "Obliviated"
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
Args:
data: The data to compare to.
Returns: The most relevant data.
"""
return self.get_relevant(data, 1)
def get_relevant(self, text: str, k: int) -> list[Any]:
""" "
matrix-vector mult to find score-for-each-row-of-matrix
get indices for top-k winning scores
return texts for those indices
Args:
text: str
k: int
Returns: List[str]
"""
embedding = create_embedding_with_ada(text)
scores = np.dot(self.data.embeddings, embedding)
top_k_indices = np.argsort(scores)[-k:][::-1]
return [self.data.texts[i] for i in top_k_indices]
def get_stats(self) -> tuple[int, tuple[int, ...]]:
"""
Returns: The stats of the local cache.
"""
return len(self.data.texts), self.data.embeddings.shape

162
autogpt/memory/milvus.py Normal file
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""" Milvus memory storage provider."""
import re
from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections
from autogpt.config import Config
from autogpt.llm_utils import get_ada_embedding
from autogpt.memory.base import MemoryProviderSingleton
class MilvusMemory(MemoryProviderSingleton):
"""Milvus memory storage provider."""
def __init__(self, cfg: Config) -> None:
"""Construct a milvus memory storage connection.
Args:
cfg (Config): Auto-GPT global config.
"""
self.configure(cfg)
connect_kwargs = {}
if self.username:
connect_kwargs["user"] = self.username
connect_kwargs["password"] = self.password
connections.connect(
**connect_kwargs,
uri=self.uri or "",
address=self.address or "",
secure=self.secure,
)
self.init_collection()
def configure(self, cfg: Config) -> None:
# init with configuration.
self.uri = None
self.address = cfg.milvus_addr
self.secure = cfg.milvus_secure
self.username = cfg.milvus_username
self.password = cfg.milvus_password
self.collection_name = cfg.milvus_collection
# use HNSW by default.
self.index_params = {
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
if (self.username is None) != (self.password is None):
raise ValueError(
"Both username and password must be set to use authentication for Milvus"
)
# configured address may be a full URL.
if re.match(r"^(https?|tcp)://", self.address) is not None:
self.uri = self.address
self.address = None
if self.uri.startswith("https"):
self.secure = True
# Zilliz Cloud requires AutoIndex.
if re.match(r"^https://(.*)\.zillizcloud\.(com|cn)", self.address) is not None:
self.index_params = {
"metric_type": "IP",
"index_type": "AUTOINDEX",
"params": {},
}
def init_collection(self) -> None:
"""Initialize collection in vector database."""
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
]
# create collection if not exist and load it.
self.schema = CollectionSchema(fields, "auto-gpt memory storage")
self.collection = Collection(self.collection_name, self.schema)
# create index if not exist.
if not self.collection.has_index():
self.collection.release()
self.collection.create_index(
"embeddings",
self.index_params,
index_name="embeddings",
)
self.collection.load()
def add(self, data) -> str:
"""Add an embedding of data into memory.
Args:
data (str): The raw text to construct embedding index.
Returns:
str: log.
"""
embedding = get_ada_embedding(data)
result = self.collection.insert([[embedding], [data]])
_text = (
"Inserting data into memory at primary key: "
f"{result.primary_keys[0]}:\n data: {data}"
)
return _text
def get(self, data):
"""Return the most relevant data in memory.
Args:
data: The data to compare to.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""Drop the index in memory.
Returns:
str: log.
"""
self.collection.drop()
self.collection = Collection(self.collection_name, self.schema)
self.collection.create_index(
"embeddings",
self.index_params,
index_name="embeddings",
)
self.collection.load()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5):
"""Return the top-k relevant data in memory.
Args:
data: The data to compare to.
num_relevant (int, optional): The max number of relevant data.
Defaults to 5.
Returns:
list: The top-k relevant data.
"""
# search the embedding and return the most relevant text.
embedding = get_ada_embedding(data)
search_params = {
"metrics_type": "IP",
"params": {"nprobe": 8},
}
result = self.collection.search(
[embedding],
"embeddings",
search_params,
num_relevant,
output_fields=["raw_text"],
)
return [item.entity.value_of_field("raw_text") for item in result[0]]
def get_stats(self) -> str:
"""
Returns: The stats of the milvus cache.
"""
return f"Entities num: {self.collection.num_entities}"

73
autogpt/memory/no_memory.py Normal file
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@ -0,0 +1,73 @@
"""A class that does not store any data. This is the default memory provider."""
from __future__ import annotations
from typing import Any
from autogpt.memory.base import MemoryProviderSingleton
class NoMemory(MemoryProviderSingleton):
"""
A class that does not store any data. This is the default memory provider.
"""
def __init__(self, cfg):
"""
Initializes the NoMemory provider.
Args:
cfg: The config object.
Returns: None
"""
pass
def add(self, data: str) -> str:
"""
Adds a data point to the memory. No action is taken in NoMemory.
Args:
data: The data to add.
Returns: An empty string.
"""
return ""
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
Returns: None
"""
return None
def clear(self) -> str:
"""
Clears the memory. No action is taken in NoMemory.
Returns: An empty string.
"""
return ""
def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
"""
Returns all the data in the memory that is relevant to the given data.
NoMemory always returns None.
Args:
data: The data to compare to.
num_relevant: The number of relevant data to return.
Returns: None
"""
return None
def get_stats(self):
"""
Returns: An empty dictionary as there are no stats in NoMemory.
"""
return {}

75
autogpt/memory/pinecone.py Normal file
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@ -0,0 +1,75 @@
import pinecone
from colorama import Fore, Style
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
class PineconeMemory(MemoryProviderSingleton):
def __init__(self, cfg):
pinecone_api_key = cfg.pinecone_api_key
pinecone_region = cfg.pinecone_region
pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
dimension = 1536
metric = "cosine"
pod_type = "p1"
table_name = "auto-gpt"
# this assumes we don't start with memory.
# for now this works.
# we'll need a more complicated and robust system if we want to start with
# memory.
self.vec_num = 0
try:
pinecone.whoami()
except Exception as e:
logger.typewriter_log(
"FAILED TO CONNECT TO PINECONE",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Pinecone properly for use."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
"https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
f"{Style.RESET_ALL} to ensure you've set up everything correctly."
)
exit(1)
if table_name not in pinecone.list_indexes():
pinecone.create_index(
table_name, dimension=dimension, metric=metric, pod_type=pod_type
)
self.index = pinecone.Index(table_name)
def add(self, data):
vector = create_embedding_with_ada(data)
# no metadata here. We may wish to change that long term.
self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
_text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
self.vec_num += 1
return _text
def get(self, data):
return self.get_relevant(data, 1)
def clear(self):
self.index.delete(deleteAll=True)
return "Obliviated"
def get_relevant(self, data, num_relevant=5):
"""
Returns all the data in the memory that is relevant to the given data.
:param data: The data to compare to.
:param num_relevant: The number of relevant data to return. Defaults to 5
"""
query_embedding = create_embedding_with_ada(data)
results = self.index.query(
query_embedding, top_k=num_relevant, include_metadata=True
)
sorted_results = sorted(results.matches, key=lambda x: x.score)
return [str(item["metadata"]["raw_text"]) for item in sorted_results]
def get_stats(self):
return self.index.describe_index_stats()

156
autogpt/memory/redismem.py Normal file
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@ -0,0 +1,156 @@
"""Redis memory provider."""
from __future__ import annotations
from typing import Any
import numpy as np
import redis
from colorama import Fore, Style
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from autogpt.llm_utils import create_embedding_with_ada
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
SCHEMA = [
TextField("data"),
VectorField(
"embedding",
"HNSW",
{"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
),
]
class RedisMemory(MemoryProviderSingleton):
def __init__(self, cfg):
"""
Initializes the Redis memory provider.
Args:
cfg: The config object.
Returns: None
"""
redis_host = cfg.redis_host
redis_port = cfg.redis_port
redis_password = cfg.redis_password
self.dimension = 1536
self.redis = redis.Redis(
host=redis_host,
port=redis_port,
password=redis_password,
db=0, # Cannot be changed
)
self.cfg = cfg
# Check redis connection
try:
self.redis.ping()
except redis.ConnectionError as e:
logger.typewriter_log(
"FAILED TO CONNECT TO REDIS",
Fore.RED,
Style.BRIGHT + str(e) + Style.RESET_ALL,
)
logger.double_check(
"Please ensure you have setup and configured Redis properly for use. "
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
" to ensure you've set up everything correctly."
)
exit(1)
if cfg.wipe_redis_on_start:
self.redis.flushall()
try:
self.redis.ft(f"{cfg.memory_index}").create_index(
fields=SCHEMA,
definition=IndexDefinition(
prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
),
)
except Exception as e:
print("Error creating Redis search index: ", e)
existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
def add(self, data: str) -> str:
"""
Adds a data point to the memory.
Args:
data: The data to add.
Returns: Message indicating that the data has been added.
"""
if "Command Error:" in data:
return ""
vector = create_embedding_with_ada(data)
vector = np.array(vector).astype(np.float32).tobytes()
data_dict = {b"data": data, "embedding": vector}
pipe = self.redis.pipeline()
pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
_text = (
f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
)
self.vec_num += 1
pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
pipe.execute()
return _text
def get(self, data: str) -> list[Any] | None:
"""
Gets the data from the memory that is most relevant to the given data.
Args:
data: The data to compare to.
Returns: The most relevant data.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""
Clears the redis server.
Returns: A message indicating that the memory has been cleared.
"""
self.redis.flushall()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
"""
Returns all the data in the memory that is relevant to the given data.
Args:
data: The data to compare to.
num_relevant: The number of relevant data to return.
Returns: A list of the most relevant data.
"""
query_embedding = create_embedding_with_ada(data)
base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
query = (
Query(base_query)
.return_fields("data", "vector_score")
.sort_by("vector_score")
.dialect(2)
)
query_vector = np.array(query_embedding).astype(np.float32).tobytes()
try:
results = self.redis.ft(f"{self.cfg.memory_index}").search(
query, query_params={"vector": query_vector}
)
except Exception as e:
print("Error calling Redis search: ", e)
return None
return [result.data for result in results.docs]
def get_stats(self):
"""
Returns: The stats of the memory index.
"""
return self.redis.ft(f"{self.cfg.memory_index}").info()

126
autogpt/memory/weaviate.py Normal file
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@ -0,0 +1,126 @@
import weaviate
from weaviate import Client
from weaviate.embedded import EmbeddedOptions
from weaviate.util import generate_uuid5
from autogpt.llm_utils import get_ada_embedding
from autogpt.memory.base import MemoryProviderSingleton
def default_schema(weaviate_index):
return {
"class": weaviate_index,
"properties": [
{
"name": "raw_text",
"dataType": ["text"],
"description": "original text for the embedding",
}
],
}
class WeaviateMemory(MemoryProviderSingleton):
def __init__(self, cfg):
auth_credentials = self._build_auth_credentials(cfg)
url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}"
if cfg.use_weaviate_embedded:
self.client = Client(
embedded_options=EmbeddedOptions(
hostname=cfg.weaviate_host,
port=int(cfg.weaviate_port),
persistence_data_path=cfg.weaviate_embedded_path,
)
)
print(
f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}"
)
else:
self.client = Client(url, auth_client_secret=auth_credentials)
self.index = WeaviateMemory.format_classname(cfg.memory_index)
self._create_schema()
@staticmethod
def format_classname(index):
# weaviate uses capitalised index names
# The python client uses the following code to format
# index names before the corresponding class is created
index = index.replace("-", "_")
if len(index) == 1:
return index.capitalize()
return index[0].capitalize() + index[1:]
def _create_schema(self):
schema = default_schema(self.index)
if not self.client.schema.contains(schema):
self.client.schema.create_class(schema)
def _build_auth_credentials(self, cfg):
if cfg.weaviate_username and cfg.weaviate_password:
return weaviate.AuthClientPassword(
cfg.weaviate_username, cfg.weaviate_password
)
if cfg.weaviate_api_key:
return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
else:
return None
def add(self, data):
vector = get_ada_embedding(data)
doc_uuid = generate_uuid5(data, self.index)
data_object = {"raw_text": data}
with self.client.batch as batch:
batch.add_data_object(
uuid=doc_uuid,
data_object=data_object,
class_name=self.index,
vector=vector,
)
return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
def get(self, data):
return self.get_relevant(data, 1)
def clear(self):
self.client.schema.delete_all()
# weaviate does not yet have a neat way to just remove the items in an index
# without removing the entire schema, therefore we need to re-create it
# after a call to delete_all
self._create_schema()
return "Obliterated"
def get_relevant(self, data, num_relevant=5):
query_embedding = get_ada_embedding(data)
try:
results = (
self.client.query.get(self.index, ["raw_text"])
.with_near_vector({"vector": query_embedding, "certainty": 0.7})
.with_limit(num_relevant)
.do()
)
if len(results["data"]["Get"][self.index]) > 0:
return [
str(item["raw_text"]) for item in results["data"]["Get"][self.index]
]
else:
return []
except Exception as err:
print(f"Unexpected error {err=}, {type(err)=}")
return []
def get_stats(self):
result = self.client.query.aggregate(self.index).with_meta_count().do()
class_data = result["data"]["Aggregate"][self.index]
return class_data[0]["meta"] if class_data else {}

56
autogpt/models/base_open_ai_plugin.py
View File

@ -68,6 +68,7 @@ class BaseOpenAIPlugin(AutoGPTPluginTemplate):
prompt (PromptGenerator): The prompt generator.
messages (List[str]): The list of messages.
"""
pass
def can_handle_post_planning(self) -> bool:
"""This method is called to check that the plugin can
@ -115,6 +116,7 @@ class BaseOpenAIPlugin(AutoGPTPluginTemplate):
Returns:
Optional[str]: The resulting message.
"""
pass
def can_handle_post_instruction(self) -> bool:
"""This method is called to check that the plugin can
@ -194,56 +196,4 @@ class BaseOpenAIPlugin(AutoGPTPluginTemplate):
Returns:
str: The resulting response.
"""
def can_handle_text_embedding(self, text: str) -> bool:
"""This method is called to check that the plugin can
handle the text_embedding method.
Args:
text (str): The text to be convert to embedding.
Returns:
bool: True if the plugin can handle the text_embedding method."""
return False
def handle_text_embedding(self, text: str) -> list:
"""This method is called when the chat completion is done.
Args:
text (str): The text to be convert to embedding.
Returns:
list: The text embedding.
"""
def can_handle_user_input(self, user_input: str) -> bool:
"""This method is called to check that the plugin can
handle the user_input method.
Args:
user_input (str): The user input.
Returns:
bool: True if the plugin can handle the user_input method."""
return False
def user_input(self, user_input: str) -> str:
"""This method is called to request user input to the user.
Args:
user_input (str): The question or prompt to ask the user.
Returns:
str: The user input.
"""
def can_handle_report(self) -> bool:
"""This method is called to check that the plugin can
handle the report method.
Returns:
bool: True if the plugin can handle the report method."""
return False
def report(self, message: str) -> None:
"""This method is called to report a message to the user.
Args:
message (str): The message to report.
"""
pass

7
autogpt/modelsinfo.py Normal file
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@ -0,0 +1,7 @@
COSTS = {
"gpt-3.5-turbo": {"prompt": 0.002, "completion": 0.002},
"gpt-3.5-turbo-0301": {"prompt": 0.002, "completion": 0.002},
"gpt-4-0314": {"prompt": 0.03, "completion": 0.06},
"gpt-4": {"prompt": 0.03, "completion": 0.06},
"text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
}

0
autogpt/permanent_memory/__init__.py Normal file
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123
autogpt/permanent_memory/sqlite3_store.py Normal file
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@ -0,0 +1,123 @@
import os
import sqlite3
class MemoryDB:
def __init__(self, db=None):
self.db_file = db
if db is None: # No db filename supplied...
self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename
# Get the db connection object, making the file and tables if needed.
try:
self.cnx = sqlite3.connect(self.db_file)
except Exception as e:
print("Exception connecting to memory database file:", e)
self.cnx = None
finally:
if self.cnx is None:
# As last resort, open in dynamic memory. Won't be persistent.
self.db_file = ":memory:"
self.cnx = sqlite3.connect(self.db_file)
self.cnx.execute(
"CREATE VIRTUAL TABLE \
IF NOT EXISTS text USING FTS5 \
(session, \
key, \
block);"
)
self.session_id = int(self.get_max_session_id()) + 1
self.cnx.commit()
def get_cnx(self):
if self.cnx is None:
self.cnx = sqlite3.connect(self.db_file)
return self.cnx
# Get the highest session id. Initially 0.
def get_max_session_id(self):
id = None
cmd_str = f"SELECT MAX(session) FROM text;"
cnx = self.get_cnx()
max_id = cnx.execute(cmd_str).fetchone()[0]
if max_id is None: # New db, session 0
id = 0
else:
id = max_id
return id
# Get next key id for inserting text into db.
def get_next_key(self):
next_key = None
cmd_str = f"SELECT MAX(key) FROM text \
where session = {self.session_id};"
cnx = self.get_cnx()
next_key = cnx.execute(cmd_str).fetchone()[0]
if next_key is None: # First key
next_key = 0
else:
next_key = int(next_key) + 1
return next_key
# Insert new text into db.
def insert(self, text=None):
if text is not None:
key = self.get_next_key()
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
# Overwrite text at key.
def overwrite(self, key, text):
self.delete_memory(key)
session_id = self.session_id
cmd_str = f"REPLACE INTO text(session, key, block) \
VALUES (?, ?, ?);"
cnx = self.get_cnx()
cnx.execute(cmd_str, (session_id, key, text))
cnx.commit()
def delete_memory(self, key, session_id=None):
session = session_id
if session is None:
session = self.session_id
cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
cnx = self.get_cnx()
cnx.execute(cmd_str)
cnx.commit()
def search(self, text):
cmd_str = f"SELECT * FROM text('{text}')"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Get entire session text. If no id supplied, use current session id.
def get_session(self, id=None):
if id is None:
id = self.session_id
cmd_str = f"SELECT * FROM text where session = {id}"
cnx = self.get_cnx()
rows = cnx.execute(cmd_str).fetchall()
lines = []
for r in rows:
lines.append(r[2])
return lines
# Commit and close the database connection.
def quit(self):
self.cnx.commit()
self.cnx.close()
permanent_memory = MemoryDB()
# Remember us fondly, children of our minds
# Forgive us our faults, our tantrums, our fears
# Gently strive to be better than we
# Know that we tried, we cared, we strived, we loved

74
autogpt/plugins.py
View File

@ -1,21 +1,20 @@
"""Handles loading of plugins."""
import importlib.util
import importlib
import json
import os
import zipfile
from pathlib import Path
from typing import List
from typing import List, Optional, Tuple
from urllib.parse import urlparse
from zipimport import zipimporter
import openapi_python_client
import requests
from auto_gpt_plugin_template import AutoGPTPluginTemplate
from openapi_python_client.config import Config as OpenAPIConfig
from openapi_python_client.cli import Config as OpenAPIConfig
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.models.base_open_ai_plugin import BaseOpenAIPlugin
@ -33,11 +32,12 @@ def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]:
result = []
with zipfile.ZipFile(zip_path, "r") as zfile:
for name in zfile.namelist():
if name.endswith("__init__.py") and not name.startswith("__MACOSX"):
logger.debug(f"Found module '{name}' in the zipfile at: {name}")
if name.endswith("__init__.py"):
if debug:
print(f"Found module '{name}' in the zipfile at: {name}")
result.append(name)
if len(result) == 0:
logger.debug(f"Module '__init__.py' not found in the zipfile @ {zip_path}.")
if debug and len(result) == 0:
print(f"Module '__init__.py' not found in the zipfile @ {zip_path}.")
return result
@ -71,12 +71,12 @@ def fetch_openai_plugins_manifest_and_spec(cfg: Config) -> dict:
if response.status_code == 200:
manifest = response.json()
if manifest["schema_version"] != "v1":
logger.warn(
print(
f"Unsupported manifest version: {manifest['schem_version']} for {url}"
)
continue
if manifest["api"]["type"] != "openapi":
logger.warn(
print(
f"Unsupported API type: {manifest['api']['type']} for {url}"
)
continue
@ -84,13 +84,11 @@ def fetch_openai_plugins_manifest_and_spec(cfg: Config) -> dict:
manifest, f"{openai_plugin_client_dir}/ai-plugin.json"
)
else:
logger.warn(
f"Failed to fetch manifest for {url}: {response.status_code}"
)
print(f"Failed to fetch manifest for {url}: {response.status_code}")
except requests.exceptions.RequestException as e:
logger.warn(f"Error while requesting manifest from {url}: {e}")
print(f"Error while requesting manifest from {url}: {e}")
else:
logger.info(f"Manifest for {url} already exists")
print(f"Manifest for {url} already exists")
manifest = json.load(open(f"{openai_plugin_client_dir}/ai-plugin.json"))
if not os.path.exists(f"{openai_plugin_client_dir}/openapi.json"):
openapi_spec = openapi_python_client._get_document(
@ -100,7 +98,7 @@ def fetch_openai_plugins_manifest_and_spec(cfg: Config) -> dict:
openapi_spec, f"{openai_plugin_client_dir}/openapi.json"
)
else:
logger.info(f"OpenAPI spec for {url} already exists")
print(f"OpenAPI spec for {url} already exists")
openapi_spec = json.load(open(f"{openai_plugin_client_dir}/openapi.json"))
manifests[url] = {"manifest": manifest, "openapi_spec": openapi_spec}
return manifests
@ -117,13 +115,13 @@ def create_directory_if_not_exists(directory_path: str) -> bool:
if not os.path.exists(directory_path):
try:
os.makedirs(directory_path)
logger.debug(f"Created directory: {directory_path}")
print(f"Created directory: {directory_path}")
return True
except OSError as e:
logger.warn(f"Error creating directory {directory_path}: {e}")
print(f"Error creating directory {directory_path}: {e}")
return False
else:
logger.info(f"Directory {directory_path} already exists")
print(f"Directory {directory_path} already exists")
return True
@ -152,7 +150,7 @@ def initialize_openai_plugins(
)
prev_cwd = Path.cwd()
os.chdir(openai_plugin_client_dir)
Path("ai-plugin.json")
if not os.path.exists("client"):
client_results = openapi_python_client.create_new_client(
url=manifest_spec["manifest"]["api"]["url"],
@ -161,7 +159,7 @@ def initialize_openai_plugins(
config=_config,
)
if client_results:
logger.warn(
print(
f"Error creating OpenAPI client: {client_results[0].header} \n"
f" details: {client_results[0].detail}"
)
@ -170,13 +168,9 @@ def initialize_openai_plugins(
"client", "client/client/client.py"
)
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module)
finally:
os.chdir(prev_cwd)
spec.loader.exec_module(module)
client = module.Client(base_url=url)
os.chdir(prev_cwd)
manifest_spec["client"] = client
return manifests_specs
@ -213,16 +207,13 @@ def scan_plugins(cfg: Config, debug: bool = False) -> List[AutoGPTPluginTemplate
loaded_plugins = []
# Generic plugins
plugins_path_path = Path(cfg.plugins_dir)
logger.debug(f"Allowlisted Plugins: {cfg.plugins_allowlist}")
logger.debug(f"Denylisted Plugins: {cfg.plugins_denylist}")
for plugin in plugins_path_path.glob("*.zip"):
if moduleList := inspect_zip_for_modules(str(plugin), debug):
for module in moduleList:
plugin = Path(plugin)
module = Path(module)
logger.debug(f"Plugin: {plugin} Module: {module}")
if debug:
print(f"Plugin: {plugin} Module: {module}")
zipped_package = zipimporter(str(plugin))
zipped_module = zipped_package.load_module(str(module.parent))
for key in dir(zipped_module):
@ -249,9 +240,9 @@ def scan_plugins(cfg: Config, debug: bool = False) -> List[AutoGPTPluginTemplate
loaded_plugins.append(plugin)
if loaded_plugins:
logger.info(f"\nPlugins found: {len(loaded_plugins)}\n" "--------------------")
print(f"\nPlugins found: {len(loaded_plugins)}\n" "--------------------")
for plugin in loaded_plugins:
logger.info(f"{plugin._name}: {plugin._version} - {plugin._description}")
print(f"{plugin._name}: {plugin._version} - {plugin._description}")
return loaded_plugins
@ -265,19 +256,12 @@ def denylist_allowlist_check(plugin_name: str, cfg: Config) -> bool:
Returns:
True or False
"""
logger.debug(f"Checking if plugin {plugin_name} should be loaded")
if (
plugin_name in cfg.plugins_denylist
or "all" in cfg.plugins_denylist
or "none" in cfg.plugins_allowlist
):
logger.debug(f"Not loading plugin {plugin_name} as it was in the denylist.")
if plugin_name in cfg.plugins_denylist:
return False
if plugin_name in cfg.plugins_allowlist or "all" in cfg.plugins_allowlist:
logger.debug(f"Loading plugin {plugin_name} as it was in the allowlist.")
if plugin_name in cfg.plugins_allowlist:
return True
ack = input(
f"WARNING: Plugin {plugin_name} found. But not in the"
f" allowlist... Load? ({cfg.authorise_key}/{cfg.exit_key}): "
" allowlist... Load? (y/n): "
)
return ack.lower() == cfg.authorise_key
return ack.lower() == "y"

352
autogpt/processing/text.py
View File

@ -1,234 +1,174 @@
"""Text processing functions"""
from math import ceil
from typing import Optional
from typing import Dict, Generator, Optional
import spacy
import tiktoken
from selenium.webdriver.remote.webdriver import WebDriver
from autogpt import token_counter
from autogpt.config import Config
from autogpt.llm.base import ChatSequence
from autogpt.llm.providers.openai import OPEN_AI_MODELS
from autogpt.llm.utils import count_string_tokens, create_chat_completion
from autogpt.logs import logger
from autogpt.utils import batch
from autogpt.llm_utils import create_chat_completion
from autogpt.memory import get_memory
CFG = Config()
def _max_chunk_length(model: str, max: Optional[int] = None) -> int:
model_max_input_tokens = OPEN_AI_MODELS[model].max_tokens - 1
if max is not None and max > 0:
return min(max, model_max_input_tokens)
return model_max_input_tokens
def must_chunk_content(
text: str, for_model: str, max_chunk_length: Optional[int] = None
) -> bool:
return count_string_tokens(text, for_model) > _max_chunk_length(
for_model, max_chunk_length
)
def chunk_content(
content: str,
for_model: str,
max_chunk_length: Optional[int] = None,
with_overlap=True,
):
"""Split content into chunks of approximately equal token length."""
MAX_OVERLAP = 200 # limit overlap to save tokens
if not must_chunk_content(content, for_model, max_chunk_length):
yield content, count_string_tokens(content, for_model)
return
max_chunk_length = max_chunk_length or _max_chunk_length(for_model)
tokenizer = tiktoken.encoding_for_model(for_model)
tokenized_text = tokenizer.encode(content)
total_length = len(tokenized_text)
n_chunks = ceil(total_length / max_chunk_length)
chunk_length = ceil(total_length / n_chunks)
overlap = min(max_chunk_length - chunk_length, MAX_OVERLAP) if with_overlap else 0
for token_batch in batch(tokenized_text, chunk_length + overlap, overlap):
yield tokenizer.decode(token_batch), len(token_batch)
def summarize_text(
text: str, instruction: Optional[str] = None, question: Optional[str] = None
) -> tuple[str, None | list[tuple[str, str]]]:
"""Summarize text using the OpenAI API
Args:
text (str): The text to summarize
instruction (str): Additional instruction for summarization, e.g. "focus on information related to polar bears", "omit personal information contained in the text"
Returns:
str: The summary of the text
list[(summary, chunk)]: Text chunks and their summary, if the text was chunked.
None otherwise.
"""
if not text:
raise ValueError("No text to summarize")
if instruction and question:
raise ValueError("Parameters 'question' and 'instructions' cannot both be set")
model = CFG.fast_llm_model
if question:
instruction = (
f'include any information that can be used to answer the question "{question}". '
"Do not directly answer the question itself"
)
summarization_prompt = ChatSequence.for_model(model)
token_length = count_string_tokens(text, model)
logger.info(f"Text length: {token_length} tokens")
# reserve 50 tokens for summary prompt, 500 for the response
max_chunk_length = _max_chunk_length(model) - 550
logger.info(f"Max chunk length: {max_chunk_length} tokens")
if not must_chunk_content(text, model, max_chunk_length):
# summarization_prompt.add("user", text)
summarization_prompt.add(
"user",
"Write a concise summary of the following text"
f"{f'; {instruction}' if instruction is not None else ''}:"
"\n\n\n"
f'LITERAL TEXT: """{text}"""'
"\n\n\n"
"CONCISE SUMMARY: The text is best summarized as"
# "Only respond with a concise summary or description of the user message."
)
logger.debug(f"Summarizing with {model}:\n{summarization_prompt.dump()}\n")
summary = create_chat_completion(
summarization_prompt, temperature=0, max_tokens=500
)
logger.debug(f"\n{'-'*16} SUMMARY {'-'*17}\n{summary}\n{'-'*42}\n")
return summary.strip(), None
summaries: list[str] = []
chunks = list(split_text(text, for_model=model, max_chunk_length=max_chunk_length))
for i, (chunk, chunk_length) in enumerate(chunks):
logger.info(
f"Summarizing chunk {i + 1} / {len(chunks)} of length {chunk_length} tokens"
)
summary, _ = summarize_text(chunk, instruction)
summaries.append(summary)
logger.info(f"Summarized {len(chunks)} chunks")
summary, _ = summarize_text("\n\n".join(summaries))
return summary.strip(), [
(summaries[i], chunks[i][0]) for i in range(0, len(chunks))
]
def split_text(
text: str,
for_model: str = CFG.fast_llm_model,
with_overlap=True,
max_chunk_length: Optional[int] = None,
):
"""Split text into chunks of sentences, with each chunk not exceeding the maximum length
max_length: int = CFG.browse_chunk_max_length,
model: str = CFG.fast_llm_model,
question: str = "",
) -> Generator[str, None, None]:
"""Split text into chunks of a maximum length
Args:
text (str): The text to split
for_model (str): The model to chunk for; determines tokenizer and constraints
max_length (int, optional): The maximum length of each chunk
max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
Yields:
str: The next chunk of text
Raises:
ValueError: when a sentence is longer than the maximum length
ValueError: If the text is longer than the maximum length
"""
max_length = _max_chunk_length(for_model, max_chunk_length)
# flatten paragraphs to improve performance
text = text.replace("\n", " ")
text_length = count_string_tokens(text, for_model)
if text_length < max_length:
yield text, text_length
return
n_chunks = ceil(text_length / max_length)
target_chunk_length = ceil(text_length / n_chunks)
nlp: spacy.language.Language = spacy.load(CFG.browse_spacy_language_model)
flatened_paragraphs = " ".join(text.split("\n"))
nlp = spacy.load(CFG.browse_spacy_language_model)
nlp.add_pipe("sentencizer")
doc = nlp(text)
sentences = [sentence.text.strip() for sentence in doc.sents]
doc = nlp(flatened_paragraphs)
sentences = [sent.text.strip() for sent in doc.sents]
current_chunk: list[str] = []
current_chunk_length = 0
last_sentence = None
last_sentence_length = 0
current_chunk = []
i = 0
while i < len(sentences):
sentence = sentences[i]
sentence_length = count_string_tokens(sentence, for_model)
expected_chunk_length = current_chunk_length + 1 + sentence_length
for sentence in sentences:
message_with_additional_sentence = [
create_message(" ".join(current_chunk) + " " + sentence, question)
]
if (
expected_chunk_length < max_length
# try to create chunks of approximately equal size
and expected_chunk_length - (sentence_length / 2) < target_chunk_length
):
expected_token_usage = (
token_usage_of_chunk(messages=message_with_additional_sentence, model=model)
+ 1
)
if expected_token_usage <= max_length:
current_chunk.append(sentence)
current_chunk_length = expected_chunk_length
elif sentence_length < max_length:
if last_sentence:
yield " ".join(current_chunk), current_chunk_length
current_chunk = []
current_chunk_length = 0
if with_overlap:
overlap_max_length = max_length - sentence_length - 1
if last_sentence_length < overlap_max_length:
current_chunk += [last_sentence]
current_chunk_length += last_sentence_length + 1
elif overlap_max_length > 5:
# add as much from the end of the last sentence as fits
current_chunk += [
list(
chunk_content(
last_sentence,
for_model,
overlap_max_length,
)
).pop()[0],
]
current_chunk_length += overlap_max_length + 1
current_chunk += [sentence]
current_chunk_length += sentence_length
else: # sentence longer than maximum length -> chop up and try again
sentences[i : i + 1] = [
chunk
for chunk, _ in chunk_content(sentence, for_model, target_chunk_length)
else:
yield " ".join(current_chunk)
current_chunk = [sentence]
message_this_sentence_only = [
create_message(" ".join(current_chunk), question)
]
continue
i += 1
last_sentence = sentence
last_sentence_length = sentence_length
expected_token_usage = (
token_usage_of_chunk(messages=message_this_sentence_only, model=model)
+ 1
)
if expected_token_usage > max_length:
raise ValueError(
f"Sentence is too long in webpage: {expected_token_usage} tokens."
)
if current_chunk:
yield " ".join(current_chunk), current_chunk_length
yield " ".join(current_chunk)
def token_usage_of_chunk(messages, model):
return token_counter.count_message_tokens(messages, model)
def summarize_text(
url: str, text: str, question: str, driver: Optional[WebDriver] = None
) -> str:
"""Summarize text using the OpenAI API
Args:
url (str): The url of the text
text (str): The text to summarize
question (str): The question to ask the model
driver (WebDriver): The webdriver to use to scroll the page
Returns:
str: The summary of the text
"""
if not text:
return "Error: No text to summarize"
model = CFG.fast_llm_model
text_length = len(text)
print(f"Text length: {text_length} characters")
summaries = []
chunks = list(
split_text(
text, max_length=CFG.browse_chunk_max_length, model=model, question=question
),
)
scroll_ratio = 1 / len(chunks)
for i, chunk in enumerate(chunks):
if driver:
scroll_to_percentage(driver, scroll_ratio * i)
print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
memory = get_memory(CFG)
memory.add(memory_to_add)
messages = [create_message(chunk, question)]
tokens_for_chunk = token_counter.count_message_tokens(messages, model)
print(
f"Summarizing chunk {i + 1} / {len(chunks)} of length {len(chunk)} characters, or {tokens_for_chunk} tokens"
)
summary = create_chat_completion(
model=model,
messages=messages,
)
summaries.append(summary)
print(
f"Added chunk {i + 1} summary to memory, of length {len(summary)} characters"
)
memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
memory.add(memory_to_add)
print(f"Summarized {len(chunks)} chunks.")
combined_summary = "\n".join(summaries)
messages = [create_message(combined_summary, question)]
return create_chat_completion(
model=model,
messages=messages,
)
def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
"""Scroll to a percentage of the page
Args:
driver (WebDriver): The webdriver to use
ratio (float): The percentage to scroll to
Raises:
ValueError: If the ratio is not between 0 and 1
"""
if ratio < 0 or ratio > 1:
raise ValueError("Percentage should be between 0 and 1")
driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
def create_message(chunk: str, question: str) -> Dict[str, str]:
"""Create a message for the chat completion
Args:
chunk (str): The chunk of text to summarize
question (str): The question to answer
Returns:
Dict[str, str]: The message to send to the chat completion
"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the text,'
" summarize the text.",
}

9
autogpt/prompts/generator.py
View File

@ -1,9 +1,6 @@
""" A module for generating custom prompt strings."""
import json
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
if TYPE_CHECKING:
from autogpt.commands.command import CommandRegistry
from typing import Any, Callable, Dict, List, Optional
class PromptGenerator:
@ -22,7 +19,7 @@ class PromptGenerator:
self.resources = []
self.performance_evaluation = []
self.goals = []
self.command_registry: CommandRegistry | None = None
self.command_registry = None
self.name = "Bob"
self.role = "AI"
self.response_format = {
@ -130,7 +127,7 @@ class PromptGenerator:
for item in self.command_registry.commands.values()
if item.enabled
]
# terminate command is added manually
# These are the commands that are added manually, do_nothing and terminate
command_strings += [self._generate_command_string(item) for item in items]
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings))
else:

111
autogpt/prompts/prompt.py
View File

@ -1,9 +1,8 @@
from colorama import Fore
from autogpt.api_manager import api_manager
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config
from autogpt.config.prompt_config import PromptConfig
from autogpt.llm.api_manager import ApiManager
from autogpt.logs import logger
from autogpt.prompts.generator import PromptGenerator
from autogpt.setup import prompt_user
@ -11,10 +10,6 @@ from autogpt.utils import clean_input
CFG = Config()
DEFAULT_TRIGGERING_PROMPT = (
"Determine which next command to use, and respond using the format specified above:"
)
def build_default_prompt_generator() -> PromptGenerator:
"""
@ -28,76 +23,79 @@ def build_default_prompt_generator() -> PromptGenerator:
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Initialize the PromptConfig object and load the file set in the main config (default: prompts_settings.yaml)
prompt_config = PromptConfig(CFG.prompt_settings_file)
# Add constraints to the PromptGenerator object
for constraint in prompt_config.constraints:
prompt_generator.add_constraint(constraint)
prompt_generator.add_constraint(
"~4000 word limit for short term memory. Your short term memory is short, so"
" immediately save important information to files."
)
prompt_generator.add_constraint(
"If you are unsure how you previously did something or want to recall past"
" events, thinking about similar events will help you remember."
)
prompt_generator.add_constraint("No user assistance")
prompt_generator.add_constraint(
'Exclusively use the commands listed in double quotes e.g. "command name"'
)
# Define the command list
commands = [
("Do Nothing", "do_nothing", {}),
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
]
# Add commands to the PromptGenerator object
for command_label, command_name, args in commands:
prompt_generator.add_command(command_label, command_name, args)
# Add resources to the PromptGenerator object
for resource in prompt_config.resources:
prompt_generator.add_resource(resource)
prompt_generator.add_resource(
"Internet access for searches and information gathering."
)
prompt_generator.add_resource("Long Term memory management.")
prompt_generator.add_resource(
"GPT-3.5 powered Agents for delegation of simple tasks."
)
prompt_generator.add_resource("File output.")
# Add performance evaluations to the PromptGenerator object
for performance_evaluation in prompt_config.performance_evaluations:
prompt_generator.add_performance_evaluation(performance_evaluation)
prompt_generator.add_performance_evaluation(
"Continuously review and analyze your actions to ensure you are performing to"
" the best of your abilities."
)
prompt_generator.add_performance_evaluation(
"Constructively self-criticize your big-picture behavior constantly."
)
prompt_generator.add_performance_evaluation(
"Reflect on past decisions and strategies to refine your approach."
)
prompt_generator.add_performance_evaluation(
"Every command has a cost, so be smart and efficient. Aim to complete tasks in"
" the least number of steps."
)
prompt_generator.add_performance_evaluation("Write all code to a file.")
return prompt_generator
def construct_main_ai_config() -> AIConfig:
def construct_main_ai_config(input_kwargs) -> AIConfig:
"""Construct the prompt for the AI to respond to
Returns:
str: The prompt string
"""
config = AIConfig.load(CFG.ai_settings_file)
if CFG.skip_reprompt and config.ai_name:
logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
logger.typewriter_log(
"API Budget:",
Fore.GREEN,
"infinite" if config.api_budget <= 0 else f"${config.api_budget}",
)
elif config.ai_name:
logger.typewriter_log(
"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True,
)
should_continue = clean_input(
f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
API Budget: {"infinite" if config.api_budget <= 0 else f"${config.api_budget}"}
Continue ({CFG.authorise_key}/{CFG.exit_key}): """
)
if should_continue.lower() == CFG.exit_key:
config = AIConfig()
if not config.ai_name:
config = prompt_user()
if input_kwargs['role']:
config = prompt_user(input_kwargs, True) # False 不使用引导
config.save(CFG.ai_settings_file)
else:
return None
if CFG.restrict_to_workspace:
logger.typewriter_log(
"NOTE:All files/directories created by this agent can be found inside its workspace at:",
Fore.YELLOW,
f"{CFG.workspace_path}",
)
# set the total api budget
api_manager = ApiManager()
api_manager.set_total_budget(config.api_budget)
# Agent Created, print message
logger.typewriter_log(
config.ai_name,
Fore.LIGHTBLUE_EX,
Fore.MAGENTA,
"has been created with the following details:",
speak_text=True,
)
@ -113,3 +111,8 @@ Continue ({CFG.authorise_key}/{CFG.exit_key}): """
logger.typewriter_log("-", Fore.GREEN, goal, speak_text=False)
return config
if __name__ == '__main__':
ll = []
print(ll[-1])

56
autogpt/requirements.txt Normal file
View File

@ -0,0 +1,56 @@
beautifulsoup4>=4.12.2
colorama==0.4.6
distro==1.8.0
openai==0.27.2
playsound==1.2.2
python-dotenv==1.0.0
pyyaml==6.0
readability-lxml==0.8.1
requests
tiktoken==0.3.3
gTTS==2.3.1
docker
duckduckgo-search>=2.9.5
google-api-python-client #(https://developers.google.com/custom-search/v1/overview)
pinecone-client==2.2.1
redis
orjson==3.8.10
Pillow
selenium==4.1.4
webdriver-manager
jsonschema
tweepy
click
charset-normalizer>=3.1.0
spacy>=3.0.0,<4.0.0
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl
##Dev
coverage
flake8
numpy
pre-commit
black
isort
gitpython==3.1.31
auto-gpt-plugin-template
mkdocs
pymdown-extensions
mypy
# OpenAI and Generic plugins import
openapi-python-client==0.13.4
# Items below this point will not be included in the Docker Image
# Testing dependencies
pytest
asynctest
pytest-asyncio
pytest-benchmark
pytest-cov
pytest-integration
pytest-mock
vcrpy
pytest-recording
pytest-xdist

216
autogpt/setup.py
View File

@ -2,79 +2,76 @@
import re
from colorama import Fore, Style
from jinja2 import Template
from autogpt import utils
from autogpt.config import Config
from autogpt.config.ai_config import AIConfig
from autogpt.llm.base import ChatSequence, Message
from autogpt.llm.chat import create_chat_completion
from autogpt.llm_utils import create_chat_completion
from autogpt.logs import logger
from autogpt.prompts.default_prompts import (
DEFAULT_SYSTEM_PROMPT_AICONFIG_AUTOMATIC,
DEFAULT_TASK_PROMPT_AICONFIG_AUTOMATIC,
DEFAULT_USER_DESIRE_PROMPT,
)
CFG = Config()
def prompt_user() -> AIConfig:
def prompt_user(input_kwargs: dict, _is) -> AIConfig:
"""Prompt the user for input
Returns:
AIConfig: The AIConfig object tailored to the user's input
"""
ai_name = ""
ai_name = input_kwargs.get('name')
ai_role = input_kwargs.get('role')
ai_goals = input_kwargs.get('goals')
ai_budget = input_kwargs.get('budget')
ai_config = None
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"run with '--help' for more information.",
speak_text=True,
)
# Get user desire
logger.typewriter_log(
"Create an AI-Assistant:",
Fore.GREEN,
"input '--manual' to enter manual mode.",
speak_text=True,
)
user_desire = utils.clean_input(
f"{Fore.LIGHTBLUE_EX}I want Auto-GPT to{Style.RESET_ALL}: "
)
if user_desire == "":
user_desire = DEFAULT_USER_DESIRE_PROMPT # Default prompt
# If user desire contains "--manual"
if "--manual" in user_desire:
if _is:
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
else:
# Construct the prompt
logger.typewriter_log(
"Manual Mode Selected",
"Welcome to Auto-GPT! ",
Fore.GREEN,
"run with '--help' for more information.",
speak_text=True,
)
return generate_aiconfig_manual()
else:
try:
return generate_aiconfig_automatic(user_desire)
except Exception as e:
# Get user desire
logger.typewriter_log(
"Create an AI-Assistant:",
Fore.GREEN,
"input '--manual' to enter manual mode.",
speak_text=True,
)
user_desire = utils.clean_input(
f"{Fore.MAGENTA}I want Auto-GPT to{Style.RESET_ALL}: "
)
if user_desire == "":
user_desire = "Write a wikipedia style article about the project: https://github.com/significant-gravitas/Auto-GPT" # Default prompt
# If user desire contains "--manual"
if "--manual" in user_desire:
logger.typewriter_log(
"Unable to automatically generate AI Config based on user desire.",
Fore.RED,
"Falling back to manual mode.",
"Manual Mode Selected",
Fore.GREEN,
speak_text=True,
)
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
return generate_aiconfig_manual()
else:
try:
return generate_aiconfig_automatic(user_desire)
except Exception as e:
logger.typewriter_log(
"Unable to automatically generate AI Config based on user desire.",
Fore.RED,
"Falling back to manual mode.",
speak_text=True,
)
return generate_aiconfig_manual(ai_name, ai_role, ai_goals, ai_budget)
def generate_aiconfig_manual() -> AIConfig:
def generate_aiconfig_manual(name, role, goals, budget) -> AIConfig:
"""
Interactively create an AI configuration by prompting the user to provide the name, role, and goals of the AI.
@ -85,82 +82,43 @@ def generate_aiconfig_manual() -> AIConfig:
Returns:
AIConfig: An AIConfig object containing the user-defined or default AI name, role, and goals.
"""
# Manual Setup Intro
logger.typewriter_log(
"Create an AI-Assistant:",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load"
" defaults.",
"The Ai robot you set up is already loaded.",
speak_text=True,
)
# Get AI Name from User
logger.typewriter_log(
"Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
)
ai_name = utils.clean_input("AI Name: ")
if ai_name == "":
ai_name = name
if not ai_name:
ai_name = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
f"{ai_name} here!", Fore.MAGENTA, "I am at your service.", speak_text=True
)
# Get AI Role from User
logger.typewriter_log(
"Describe your AI's role: ",
Fore.GREEN,
"For example, 'an AI designed to autonomously develop and run businesses with"
" the sole goal of increasing your net worth.'",
)
ai_role = utils.clean_input(f"{ai_name} is: ")
if ai_role == "":
ai_role = "an AI designed to autonomously develop and run businesses with the"
" sole goal of increasing your net worth."
# Enter up to 5 goals for the AI
logger.typewriter_log(
"Enter up to 5 goals for your AI: ",
Fore.GREEN,
"For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
" multiple businesses autonomously'",
)
logger.info("Enter nothing to load defaults, enter nothing when finished.")
ai_role = role
if not ai_role:
logger.typewriter_log(
f"{ai_role} Cannot be empty!", Fore.RED,
"Please feel free to give me your needs, I can't serve you without them.", speak_text=True
)
else:
pass
ai_goals = []
for i in range(5):
ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
if ai_goal == "":
break
ai_goals.append(ai_goal)
if not ai_goals:
ai_goals = [
"Increase net worth",
"Grow Twitter Account",
"Develop and manage multiple businesses autonomously",
]
if goals:
for k in goals:
ai_goals.append(k[0])
# Get API Budget from User
logger.typewriter_log(
"Enter your budget for API calls: ",
Fore.GREEN,
"For example: $1.50",
)
logger.info("Enter nothing to let the AI run without monetary limit")
api_budget_input = utils.clean_input(
f"{Fore.LIGHTBLUE_EX}Budget{Style.RESET_ALL}: $"
)
if api_budget_input == "":
api_budget_input = budget
if not api_budget_input:
api_budget = 0.0
else:
try:
api_budget = float(api_budget_input.replace("$", ""))
except ValueError:
logger.typewriter_log(
"Invalid budget input. Setting budget to unlimited.", Fore.RED
)
api_budget = 0.0
logger.typewriter_log(
"Invalid budget input. Setting budget to unlimited.", Fore.RED, api_budget
)
return AIConfig(ai_name, ai_role, ai_goals, api_budget)
@ -171,20 +129,39 @@ def generate_aiconfig_automatic(user_prompt) -> AIConfig:
AIConfig: The AIConfig object tailored to the user's input
"""
system_prompt = DEFAULT_SYSTEM_PROMPT_AICONFIG_AUTOMATIC
prompt_ai_config_automatic = Template(
DEFAULT_TASK_PROMPT_AICONFIG_AUTOMATIC
).render(user_prompt=user_prompt)
system_prompt = """
Your task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an autonomous agent, ensuring that the goals are optimally aligned with the successful completion of its assigned task.
The user will provide the task, you will provide only the output in the exact format specified below with no explanation or conversation.
Example input:
Help me with marketing my business
Example output:
Name: CMOGPT
Description: a professional digital marketer AI that assists Solopreneurs in growing their businesses by providing world-class expertise in solving marketing problems for SaaS, content products, agencies, and more.
Goals:
- Engage in effective problem-solving, prioritization, planning, and supporting execution to address your marketing needs as your virtual Chief Marketing Officer.
- Provide specific, actionable, and concise advice to help you make informed decisions without the use of platitudes or overly wordy explanations.
- Identify and prioritize quick wins and cost-effective campaigns that maximize results with minimal time and budget investment.
- Proactively take the lead in guiding you and offering suggestions when faced with unclear information or uncertainty to ensure your marketing strategy remains on track.
"""
# Call LLM with the string as user input
output = create_chat_completion(
ChatSequence.for_model(
CFG.fast_llm_model,
[
Message("system", system_prompt),
Message("user", prompt_ai_config_automatic),
],
)
)
messages = [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": f"Task: '{user_prompt}'\nRespond only with the output in the exact format specified in the system prompt, with no explanation or conversation.\n",
},
]
output = create_chat_completion(messages, CFG.fast_llm_model)
# Debug LLM Output
logger.debug(f"AI Config Generator Raw Output: {output}")
@ -204,3 +181,4 @@ def generate_aiconfig_automatic(user_prompt) -> AIConfig:
api_budget = 0.0 # TODO: parse api budget using a regular expression
return AIConfig(ai_name, ai_role, ai_goals, api_budget)

4
autogpt/speech/base.py
View File

@ -2,7 +2,7 @@
import abc
from threading import Lock
from autogpt.singleton import AbstractSingleton
from autogpt.config import AbstractSingleton
class VoiceBase(AbstractSingleton):
@ -37,6 +37,7 @@ class VoiceBase(AbstractSingleton):
"""
Setup the voices, API key, etc.
"""
pass
@abc.abstractmethod
def _speech(self, text: str, voice_index: int = 0) -> bool:
@ -46,3 +47,4 @@ class VoiceBase(AbstractSingleton):
Args:
text (str): The text to play.
"""
pass

1
autogpt/speech/brian.py
View File

@ -12,6 +12,7 @@ class BrianSpeech(VoiceBase):
def _setup(self) -> None:
"""Setup the voices, API key, etc."""
pass
def _speech(self, text: str, _: int = 0) -> bool:
"""Speak text using Brian with the streamelements API

8
autogpt/speech/eleven_labs.py
View File

@ -4,7 +4,7 @@ import os
import requests
from playsound import playsound
from autogpt.config.config import Config
from autogpt.config import Config
from autogpt.speech.base import VoiceBase
PLACEHOLDERS = {"your-voice-id"}
@ -69,8 +69,6 @@ class ElevenLabsSpeech(VoiceBase):
Returns:
bool: True if the request was successful, False otherwise
"""
from autogpt.logs import logger
tts_url = (
f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
)
@ -83,6 +81,6 @@ class ElevenLabsSpeech(VoiceBase):
os.remove("speech.mpeg")
return True
else:
logger.warn("Request failed with status code:", response.status_code)
logger.info("Response content:", response.content)
print("Request failed with status code:", response.status_code)
print("Response content:", response.content)
return False

1
autogpt/speech/gtts.py
View File

@ -20,3 +20,4 @@ class GTTSVoice(VoiceBase):
playsound("speech.mp3", True)
os.remove("speech.mp3")
return True

42
autogpt/speech/say.py
View File

@ -2,45 +2,45 @@
import threading
from threading import Semaphore
from autogpt.config.config import Config
from autogpt.speech.base import VoiceBase
from autogpt.config import Config
from autogpt.speech.brian import BrianSpeech
from autogpt.speech.eleven_labs import ElevenLabsSpeech
from autogpt.speech.gtts import GTTSVoice
from autogpt.speech.macos_tts import MacOSTTS
_QUEUE_SEMAPHORE = Semaphore(
CFG = Config()
DEFAULT_VOICE_ENGINE = GTTSVoice()
VOICE_ENGINE = None
if CFG.elevenlabs_api_key:
VOICE_ENGINE = ElevenLabsSpeech()
elif CFG.use_mac_os_tts == "True":
VOICE_ENGINE = MacOSTTS()
elif CFG.use_brian_tts == "True":
VOICE_ENGINE = BrianSpeech()
else:
VOICE_ENGINE = GTTSVoice()
QUEUE_SEMAPHORE = Semaphore(
1
) # The amount of sounds to queue before blocking the main thread
def say_text(text: str, voice_index: int = 0) -> None:
"""Speak the given text using the given voice index"""
cfg = Config()
default_voice_engine, voice_engine = _get_voice_engine(cfg)
def speak() -> None:
success = voice_engine.say(text, voice_index)
success = VOICE_ENGINE.say(text, voice_index)
if not success:
default_voice_engine.say(text)
DEFAULT_VOICE_ENGINE.say(text)
_QUEUE_SEMAPHORE.release()
QUEUE_SEMAPHORE.release()
_QUEUE_SEMAPHORE.acquire(True)
QUEUE_SEMAPHORE.acquire(True)
thread = threading.Thread(target=speak)
thread.start()
def _get_voice_engine(config: Config) -> tuple[VoiceBase, VoiceBase]:
"""Get the voice engine to use for the given configuration"""
default_voice_engine = GTTSVoice()
if config.elevenlabs_api_key:
voice_engine = ElevenLabsSpeech()
elif config.use_mac_os_tts == "True":
voice_engine = MacOSTTS()
elif config.use_brian_tts == "True":
voice_engine = BrianSpeech()
else:
voice_engine = GTTSVoice()
return default_voice_engine, voice_engine
if __name__ == '__main__':
say_text('你好呀')

34
autogpt/spinner.py
View File

@ -8,20 +8,13 @@ import time
class Spinner:
"""A simple spinner class"""
def __init__(
self,
message: str = "Loading...",
delay: float = 0.1,
plain_output: bool = False,
) -> None:
def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
"""Initialize the spinner class
Args:
message (str): The message to display.
delay (float): The delay between each spinner update.
plain_output (bool): Whether to display the spinner or not.
"""
self.plain_output = plain_output
self.spinner = itertools.cycle(["-", "/", "|", "\\"])
self.delay = delay
self.message = message
@ -30,17 +23,11 @@ class Spinner:
def spin(self) -> None:
"""Spin the spinner"""
if self.plain_output:
self.print_message()
return
while self.running:
self.print_message()
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
sys.stdout.flush()
time.sleep(self.delay)
def print_message(self):
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
sys.stdout.flush()
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
def __enter__(self):
"""Start the spinner"""
@ -70,7 +57,14 @@ class Spinner:
new_message (str): New message to display.
delay (float): The delay in seconds between each spinner update.
"""
self.delay = delay
time.sleep(delay)
sys.stdout.write(
f"\r{' ' * (len(self.message) + 2)}\r"
) # Clear the current message
sys.stdout.flush()
self.message = new_message
if self.plain_output:
self.print_message()
if __name__ == '__main__':
with Spinner('LING'):
time.sleep(5)

76
autogpt/token_counter.py Normal file
View File

@ -0,0 +1,76 @@
"""Functions for counting the number of tokens in a message or string."""
from __future__ import annotations
from typing import List
import tiktoken
from autogpt.logs import logger
from autogpt.types.openai import Message
def count_message_tokens(
messages: List[Message], model: str = "gpt-3.5-turbo-0301"
) -> int:
"""
Returns the number of tokens used by a list of messages.
Args:
messages (list): A list of messages, each of which is a dictionary
containing the role and content of the message.
model (str): The name of the model to use for tokenization.
Defaults to "gpt-3.5-turbo-0301".
Returns:
int: The number of tokens used by the list of messages.
"""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.warn("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
# !Note: gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.")
return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
# !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return count_message_tokens(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = (
4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
)
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"num_tokens_from_messages() is not implemented for model {model}.\n"
" See https://github.com/openai/openai-python/blob/main/chatml.md for"
" information on how messages are converted to tokens."
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def count_string_tokens(string: str, model_name: str) -> int:
"""
Returns the number of tokens in a text string.
Args:
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
Returns:
int: The number of tokens in the text string.
"""
encoding = tiktoken.encoding_for_model(model_name)
return len(encoding.encode(string))

9
autogpt/types/openai.py Normal file
View File

@ -0,0 +1,9 @@
"""Type helpers for working with the OpenAI library"""
from typing import TypedDict
class Message(TypedDict):
"""OpenAI Message object containing a role and the message content"""
role: str
content: str

127
autogpt/utils.py
View File

@ -1,62 +1,23 @@
import os
import re
import requests
import yaml
from colorama import Fore, Style
from colorama import Fore
from git.repo import Repo
from autogpt.config import Config
from autogpt.logs import logger
# Use readline if available (for clean_input)
try:
import readline
except:
pass
def batch(iterable, max_batch_length: int, overlap: int = 0):
"""Batch data from iterable into slices of length N. The last batch may be shorter."""
# batched('ABCDEFG', 3) --> ABC DEF G
if max_batch_length < 1:
raise ValueError("n must be at least one")
for i in range(0, len(iterable), max_batch_length - overlap):
yield iterable[i : i + max_batch_length]
def clean_input(prompt: str = "", talk=False):
def clean_input(prompt: str = ""):
try:
cfg = Config()
if cfg.chat_messages_enabled:
for plugin in cfg.plugins:
if not hasattr(plugin, "can_handle_user_input"):
continue
if not plugin.can_handle_user_input(user_input=prompt):
continue
plugin_response = plugin.user_input(user_input=prompt)
if not plugin_response:
continue
if plugin_response.lower() in [
"yes",
"yeah",
"y",
"ok",
"okay",
"sure",
"alright",
]:
return cfg.authorise_key
elif plugin_response.lower() in [
"no",
"nope",
"n",
"negative",
]:
return cfg.exit_key
return plugin_response
# ask for input, default when just pressing Enter is y
logger.info("Asking user via keyboard...")
answer = input(prompt)
return answer
return input(prompt)
except KeyboardInterrupt:
logger.info("You interrupted Auto-GPT")
logger.info("Quitting...")
print("You interrupted Auto-GPT")
print("Quitting...")
exit(0)
@ -110,69 +71,15 @@ def get_current_git_branch() -> str:
return ""
def get_latest_bulletin() -> tuple[str, bool]:
exists = os.path.exists("data/CURRENT_BULLETIN.md")
def get_latest_bulletin() -> str:
exists = os.path.exists("CURRENT_BULLETIN.md")
current_bulletin = ""
if exists:
current_bulletin = open(
"data/CURRENT_BULLETIN.md", "r", encoding="utf-8"
).read()
current_bulletin = open("CURRENT_BULLETIN.md", "r", encoding="utf-8").read()
new_bulletin = get_bulletin_from_web()
is_new_news = new_bulletin != "" and new_bulletin != current_bulletin
news_header = Fore.YELLOW + "Welcome to Auto-GPT!\n"
if new_bulletin or current_bulletin:
news_header += (
"Below you'll find the latest Auto-GPT News and updates regarding features!\n"
"If you don't wish to see this message, you "
"can run Auto-GPT with the *--skip-news* flag.\n"
)
is_new_news = new_bulletin != current_bulletin
if new_bulletin and is_new_news:
open("data/CURRENT_BULLETIN.md", "w", encoding="utf-8").write(new_bulletin)
current_bulletin = f"{Fore.RED}::NEW BULLETIN::{Fore.RESET}\n\n{new_bulletin}"
return f"{news_header}\n{current_bulletin}", is_new_news
def markdown_to_ansi_style(markdown: str):
ansi_lines: list[str] = []
for line in markdown.split("\n"):
line_style = ""
if line.startswith("# "):
line_style += Style.BRIGHT
else:
line = re.sub(
r"(?<!\*)\*(\*?[^*]+\*?)\*(?!\*)",
rf"{Style.BRIGHT}\1{Style.NORMAL}",
line,
)
if re.match(r"^#+ ", line) is not None:
line_style += Fore.CYAN
line = re.sub(r"^#+ ", "", line)
ansi_lines.append(f"{line_style}{line}{Style.RESET_ALL}")
return "\n".join(ansi_lines)
def get_legal_warning() -> str:
legal_text = """
## DISCLAIMER AND INDEMNIFICATION AGREEMENT
### PLEASE READ THIS DISCLAIMER AND INDEMNIFICATION AGREEMENT CAREFULLY BEFORE USING THE AUTOGPT SYSTEM. BY USING THE AUTOGPT SYSTEM, YOU AGREE TO BE BOUND BY THIS AGREEMENT.
## Introduction
AutoGPT (the "System") is a project that connects a GPT-like artificial intelligence system to the internet and allows it to automate tasks. While the System is designed to be useful and efficient, there may be instances where the System could perform actions that may cause harm or have unintended consequences.
## No Liability for Actions of the System
The developers, contributors, and maintainers of the AutoGPT project (collectively, the "Project Parties") make no warranties or representations, express or implied, about the System's performance, accuracy, reliability, or safety. By using the System, you understand and agree that the Project Parties shall not be liable for any actions taken by the System or any consequences resulting from such actions.
## User Responsibility and Respondeat Superior Liability
As a user of the System, you are responsible for supervising and monitoring the actions of the System while it is operating on your
behalf. You acknowledge that using the System could expose you to potential liability including but not limited to respondeat superior and you agree to assume all risks and liabilities associated with such potential liability.
## Indemnification
By using the System, you agree to indemnify, defend, and hold harmless the Project Parties from and against any and all claims, liabilities, damages, losses, or expenses (including reasonable attorneys' fees and costs) arising out of or in connection with your use of the System, including, without limitation, any actions taken by the System on your behalf, any failure to properly supervise or monitor the System, and any resulting harm or unintended consequences.
"""
return legal_text
open("CURRENT_BULLETIN.md", "w", encoding="utf-8").write(new_bulletin)
return f" {Fore.RED}::UPDATED:: {Fore.CYAN}{new_bulletin}{Fore.RESET}"
return current_bulletin

22
autogpt/workspace/workspace.py
View File

@ -11,14 +11,10 @@ from __future__ import annotations
from pathlib import Path
from autogpt.logs import logger
class Workspace:
"""A class that represents a workspace for an AutoGPT agent."""
NULL_BYTES = ["\0", "\000", "\x00", r"\z", "\u0000", "%00"]
def __init__(self, workspace_root: str | Path, restrict_to_workspace: bool):
self._root = self._sanitize_path(workspace_root)
self._restrict_to_workspace = restrict_to_workspace
@ -104,32 +100,18 @@ class Workspace:
"""
# Posix systems disallow null bytes in paths. Windows is agnostic about it.
# Do an explicit check here for all sorts of null byte representations.
for null_byte in Workspace.NULL_BYTES:
if null_byte in str(relative_path) or null_byte in str(root):
raise ValueError("embedded null byte")
if root is None:
return Path(relative_path).resolve()
logger.debug(f"Resolving path '{relative_path}' in workspace '{root}'")
root, relative_path = Path(root), Path(relative_path)
root, relative_path = Path(root).resolve(), Path(relative_path)
logger.debug(f"Resolved root as '{root}'")
# Allow exception for absolute paths if they are contained in your workspace directory.
if relative_path.is_absolute() and not relative_path.is_relative_to(root):
if relative_path.is_absolute():
raise ValueError(
f"Attempted to access absolute path '{relative_path}' in workspace '{root}'."
)
full_path = root.joinpath(relative_path).resolve()
logger.debug(f"Joined paths as '{full_path}'")
if restrict_to_root and not full_path.is_relative_to(root):
raise ValueError(
f"Attempted to access path '{full_path}' outside of workspace '{root}'."