Significant-Gravitas
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AutoGPT
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Merge remote-tracking branch 'origin/master' 

# Conflicts:
#	autogpt/commands.py
pull/1381/head
Jedakiah 2023-04-15 00:43:06 +02:00
parent 7daa3fc8f9 b65b7acace
commit c60e654a9b
59 changed files with 2069 additions and 1039 deletions
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12
.flake8 Normal file
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@ -0,0 +1,12 @@
[flake8]
max-line-length = 88
extend-ignore = E203
exclude =
.tox,
__pycache__,
*.pyc,
.env
venv/*
.venv/*
reports/*
dist/*

4
.github/workflows/ci.yml vendored
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@ -32,11 +32,11 @@ jobs:
- name: Lint with flake8
continue-on-error: false
run: flake8 scripts/ tests/ --select E303,W293,W291,W292,E305,E231,E302
run: flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
- name: Run unittest tests with coverage
run: |
coverage run --source=scripts -m unittest discover tests
coverage run --source=autogpt -m unittest discover tests
- name: Generate coverage report
run: |

146
.gitignore vendored
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@ -1,7 +1,8 @@
scripts/keys.py
scripts/*json
scripts/node_modules/
scripts/__pycache__/keys.cpython-310.pyc
## Original ignores
autogpt/keys.py
autogpt/*json
autogpt/node_modules/
autogpt/__pycache__/keys.cpython-310.pyc
package-lock.json
*.pyc
auto_gpt_workspace/*
@ -19,10 +20,135 @@ log.txt
log-ingestion.txt
logs
# Coverage reports
.coverage
coverage.xml
htmlcov/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# For Macs Dev Environs: ignoring .Desktop Services_Store
.DS_Store
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
plugins/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
llama-*
vicuna-*

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.isort.cfg Normal file
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@ -0,0 +1,10 @@
[settings]
profile = black
multi_line_output = 3
include_trailing_comma = True
force_grid_wrap = 0
use_parentheses = True
ensure_newline_before_comments = True
line_length = 88
skip = venv,env,node_modules,.env,.venv,dist
sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER

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.pre-commit-config.yaml Normal file
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repos:
- repo: https://github.com/sourcery-ai/sourcery
rev: v1.1.0 # Get the latest tag from https://github.com/sourcery-ai/sourcery/tags
hooks:
- id: sourcery
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v0.9.2
hooks:
- id: check-added-large-files
args: [ '--maxkb=500' ]
- id: check-byte-order-marker
- id: check-case-conflict
- id: check-merge-conflict
- id: check-symlinks
- id: debug-statements
- repo: local
hooks:
- id: isort
name: isort-local
entry: isort
language: python
types: [ python ]
exclude: .+/(dist|.venv|venv|build)/.+
pass_filenames: true
- id: black
name: black-local
entry: black
language: python
types: [ python ]
exclude: .+/(dist|.venv|venv|build)/.+
pass_filenames: true

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.sourcery.yaml Normal file
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@ -0,0 +1,71 @@
# 🪄 This is your project's Sourcery configuration file.
# You can use it to get Sourcery working in the way you want, such as
# ignoring specific refactorings, skipping directories in your project,
# or writing custom rules.
# 📚 For a complete reference to this file, see the documentation at
# https://docs.sourcery.ai/Configuration/Project-Settings/
# This file was auto-generated by Sourcery on 2023-02-25 at 21:07.
version: '1' # The schema version of this config file
ignore: # A list of paths or files which Sourcery will ignore.
- .git
- venv
- .venv
- build
- dist
- env
- .env
- .tox
rule_settings:
enable:
- default
- gpsg
disable: [] # A list of rule IDs Sourcery will never suggest.
rule_types:
- refactoring
- suggestion
- comment
python_version: '3.9' # A string specifying the lowest Python version your project supports. Sourcery will not suggest refactorings requiring a higher Python version.
# rules: # A list of custom rules Sourcery will include in its analysis.
# - id: no-print-statements
# description: Do not use print statements in the test directory.
# pattern: print(...)
# language: python
# replacement:
# condition:
# explanation:
# paths:
# include:
# - test
# exclude:
# - conftest.py
# tests: []
# tags: []
# rule_tags: {} # Additional rule tags.
# metrics:
# quality_threshold: 25.0
# github:
# labels: []
# ignore_labels:
# - sourcery-ignore
# request_review: author
# sourcery_branch: sourcery/{base_branch}
# clone_detection:
# min_lines: 3
# min_duplicates: 2
# identical_clones_only: false
# proxy:
# url:
# ssl_certs_file:
# no_ssl_verify: false

32
CONTRIBUTING.md
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@ -8,41 +8,49 @@ To contribute to this GitHub project, you can follow these steps:
```
git clone https://github.com/<YOUR-GITHUB-USERNAME>/Auto-GPT
```
3. Create a new branch for your changes using the following command:
3. Install the project requirements
```
pip install -r requirements.txt
```
4. Install pre-commit hooks
```
pre-commit install
```
5. Create a new branch for your changes using the following command:
```
git checkout -b "branch-name"
```
4. Make your changes to the code or documentation.
6. Make your changes to the code or documentation.
- Example: Improve User Interface or Add Documentation.
5. Add the changes to the staging area using the following command:
7. Add the changes to the staging area using the following command:
```
git add .
```
6. Commit the changes with a meaningful commit message using the following command:
8. Commit the changes with a meaningful commit message using the following command:
```
git commit -m "your commit message"
```
7. Push the changes to your forked repository using the following command:
9. Push the changes to your forked repository using the following command:
```
git push origin branch-name
```
8. Go to the GitHub website and navigate to your forked repository.
10. Go to the GitHub website and navigate to your forked repository.
9. Click the "New pull request" button.
11. Click the "New pull request" button.
10. Select the branch you just pushed to and the branch you want to merge into on the original repository.
12. Select the branch you just pushed to and the branch you want to merge into on the original repository.
11. Add a description of your changes and click the "Create pull request" button.
13. Add a description of your changes and click the "Create pull request" button.
12. Wait for the project maintainer to review your changes and provide feedback.
14. Wait for the project maintainer to review your changes and provide feedback.
13. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
15. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
14. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
16. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
```
git fetch upstream

4
Dockerfile
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@ -17,7 +17,7 @@ COPY --chown=appuser:appuser requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt
# Copy the application files
COPY --chown=appuser:appuser scripts/ .
COPY --chown=appuser:appuser autogpt/ .
# Set the entrypoint
ENTRYPOINT ["python", "main.py"]
ENTRYPOINT ["python", "-m", "autogpt"]

36
README.md
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@ -1,4 +1,8 @@
# Auto-GPT: An Autonomous GPT-4 Experiment
### 🔴 🔴 🔴 Urgent: USE `stable` not `master` 🔴 🔴 🔴
We've improved our workflow. `master` will often be **in a broken state**.
Download the latest `stable` release here: https://github.com/Torantulino/Auto-GPT/releases/latest
This information SUPERCEDES any following information. Takes precedence. Do this not that.
![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
[![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)](https://twitter.com/SigGravitas)
@ -119,11 +123,11 @@ pip install -r requirements.txt
## 🔧 Usage
1. Run the `main.py` Python script in your terminal:
1. Run the `autogpt` Python module in your terminal:
_(Type this into your CMD window)_
```
python scripts/main.py
python -m autogpt
```
2. After each of action, enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter additional feedback for the AI.
@ -136,7 +140,21 @@ You will find activity and error logs in the folder `./output/logs`
To output debug logs:
```
python scripts/main.py --debug
python -m autogpt --debug
```
### Docker
You can also build this into a docker image and run it:
```
docker build -t autogpt .
docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt
```
You can pass extra arguments, for instance, running with `--gpt3only` and `--continuous` mode:
```
docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt --gpt3only --continuous
```
### Command Line Arguments
Here are some common arguments you can use when running Auto-GPT:
@ -152,7 +170,7 @@ Here are some common arguments you can use when running Auto-GPT:
Use this to use TTS for Auto-GPT
```
python scripts/main.py --speak
python -m autogpt --speak
```
## 🔍 Google API Keys Configuration
@ -328,10 +346,10 @@ Continuous mode is not recommended.
It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise.
Use at your own risk.
1. Run the `main.py` Python script in your terminal:
1. Run the `autogpt` python module in your terminal:
```
python scripts/main.py --continuous
python -m autogpt --speak --continuous
```
@ -342,7 +360,7 @@ python scripts/main.py --continuous
If you don't have access to the GPT4 api, this mode will allow you to use Auto-GPT!
```
python scripts/main.py --gpt3only
python -m autogpt --speak --gpt3only
```
It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer's system and data.
@ -415,8 +433,8 @@ This project uses [flake8](https://flake8.pycqa.org/en/latest/) for linting. We
To run the linter, run the following command:
```
flake8 scripts/ tests/
flake8 autogpt/ tests/
# Or, if you want to run flake8 with the same configuration as the CI:
flake8 scripts/ tests/ --select E303,W293,W291,W292,E305,E231,E302
flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
```

0
scripts/__init__.py → autogpt/__init__.py
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autogpt/__main__.py Normal file
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@ -0,0 +1,546 @@
import argparse
import json
import logging
import random
import time
import traceback
import yaml
from colorama import Fore, Style
from autogpt import chat
from autogpt import commands as cmd
from autogpt import speak, utils
from autogpt.ai_config import AIConfig
from autogpt.config import Config
from autogpt.json_parser import fix_and_parse_json
from autogpt.logger import logger
from autogpt.memory import get_memory, get_supported_memory_backends
from autogpt.prompt import get_prompt
from autogpt.spinner import Spinner
cfg = Config()
def check_openai_api_key():
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
if not cfg.openai_api_key:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)
def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
if cfg.speak_mode and cfg.debug_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
# Use regex to search for JSON objects
import regex
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError) as e:
if cfg.speak_mode:
speak.say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
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 as e:
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
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, assistant_thoughts_text
)
logger.typewriter_log("REASONING:", Fore.YELLOW, 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, assistant_thoughts_criticism)
# Speak the assistant's thoughts
if cfg.speak_mode and assistant_thoughts_speak:
speak.say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
if cfg.speak_mode:
speak.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 as e:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)
def construct_prompt():
"""Construct the prompt for the AI to respond to"""
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, config.ai_goals)
elif config.ai_name:
logger.typewriter_log(
f"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True,
)
should_continue = utils.clean_input(
f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
Continue (y/n): """
)
if should_continue.lower() == "n":
config = AIConfig()
if not config.ai_name:
config = prompt_user()
config.save()
# Get rid of this global:
global ai_name
ai_name = config.ai_name
full_prompt = config.construct_full_prompt()
return full_prompt
def prompt_user():
"""Prompt the user for input"""
ai_name = ""
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load defaults.",
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 = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.LIGHTBLUE_EX, "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'",
)
print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
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 len(ai_goals) == 0:
ai_goals = [
"Increase net worth",
"Grow Twitter Account",
"Develop and manage multiple businesses autonomously",
]
config = AIConfig(ai_name, ai_role, ai_goals)
return config
def parse_arguments():
"""Parses the arguments passed to the script"""
global cfg
cfg.set_debug_mode(False)
cfg.set_continuous_mode(False)
cfg.set_speak_mode(False)
parser = argparse.ArgumentParser(description="Process arguments.")
parser.add_argument(
"--continuous", "-c", action="store_true", help="Enable Continuous Mode"
)
parser.add_argument(
"--continuous-limit",
"-l",
type=int,
dest="continuous_limit",
help="Defines the number of times to run in continuous mode",
)
parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
parser.add_argument(
"--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
)
parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
parser.add_argument(
"--use-memory",
"-m",
dest="memory_type",
help="Defines which Memory backend to use",
)
parser.add_argument(
"--skip-reprompt",
"-y",
dest="skip_reprompt",
action="store_true",
help="Skips the re-prompting messages at the beginning of the script",
)
parser.add_argument(
"--ai-settings",
"-C",
dest="ai_settings_file",
help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
)
args = parser.parse_args()
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
cfg.set_debug_mode(True)
if args.continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.",
)
cfg.set_continuous_mode(True)
if args.continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
)
cfg.set_continuous_limit(args.continuous_limit)
# Check if continuous limit is used without continuous mode
if args.continuous_limit and not args.continuous:
parser.error("--continuous-limit can only be used with --continuous")
if args.speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
cfg.set_speak_mode(True)
if args.gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_smart_llm_model(cfg.fast_llm_model)
if args.gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_fast_llm_model(cfg.smart_llm_model)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
if not chosen in supported_memory:
logger.typewriter_log(
"ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log(f"Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
else:
cfg.memory_backend = chosen
if args.skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
cfg.skip_reprompt = True
if args.ai_settings_file:
file = args.ai_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 AI Settings File:", Fore.GREEN, file)
cfg.ai_settings_file = file
cfg.skip_reprompt = True
def main():
global ai_name, memory
# TODO: fill in llm values here
check_openai_api_key()
parse_arguments()
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
ai_name = ""
prompt = construct_prompt()
# print(prompt)
# Initialize variables
full_message_history = []
result = None
next_action_count = 0
# Make a constant:
user_input = "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)
print("Using memory of type: " + memory.__class__.__name__)
agent = Agent(
ai_name=ai_name,
memory=memory,
full_message_history=full_message_history,
next_action_count=next_action_count,
prompt=prompt,
user_input=user_input,
)
agent.start_interaction_loop()
class Agent:
"""Agent class for interacting with Auto-GPT.
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.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_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... "):
assistant_reply = chat.chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
# Get command name and arguments
try:
command_name, arguments = cmd.get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
speak.say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
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.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True,
)
while True:
console_input = utils.clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
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."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
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
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: " + arguments
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {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.full_message_history.append(
chat.create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
if __name__ == "__main__":
main()

289
autogpt/agent.py Normal file
View File

@ -0,0 +1,289 @@
import json
import traceback
from tkinter.ttk import Style
from colorama import Fore
import autogpt.chat
import autogpt.commands as cmd
import autogpt.speak
from autogpt.config import Config
from autogpt.logger import logger
from autogpt.spinner import Spinner
class Agent:
"""Agent class for interacting with Auto-GPT.
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.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
cfg = Config()
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_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... "):
assistant_reply = chat.chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
# Get command name and arguments
try:
command_name, arguments = cmd.get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
speak.say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
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.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True,
)
while True:
console_input = utils.clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
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."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
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
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: " + arguments
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {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.full_message_history.append(
chat.create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
cfg = Config()
if cfg.speak_mode and cfg.debug_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
# Use regex to search for JSON objects
import regex
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError) as e:
if cfg.speak_mode:
speak.say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
cfg = Config()
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
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 as e:
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
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, assistant_thoughts_text
)
logger.typewriter_log("REASONING:", Fore.YELLOW, 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, assistant_thoughts_criticism)
# Speak the assistant's thoughts
if cfg.speak_mode and assistant_thoughts_speak:
speak.say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
if cfg.speak_mode:
speak.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 as e:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)

6
scripts/agent_manager.py → autogpt/agent_manager.py
View File

@ -1,4 +1,4 @@
from llm_utils import create_chat_completion
from autogpt.llm_utils import create_chat_completion
next_key = 0
agents = {} # key, (task, full_message_history, model)
@ -12,7 +12,9 @@ def create_agent(task, prompt, model):
global next_key
global agents
messages = [{"role": "user", "content": prompt}, ]
messages = [
{"role": "user", "content": prompt},
]
# Start GPT instance
agent_reply = create_chat_completion(

30
scripts/ai_config.py → autogpt/ai_config.py
View File

@ -1,6 +1,8 @@
import yaml
import os
from prompt import get_prompt
import yaml
from autogpt.prompt import get_prompt
class AIConfig:
@ -13,7 +15,9 @@ class AIConfig:
ai_goals (list): The list of objectives the AI is supposed to complete.
"""
def __init__(self, ai_name: str="", ai_role: str="", ai_goals: list=[]) -> None:
def __init__(
self, ai_name: str = "", ai_role: str = "", ai_goals: list = []
) -> None:
"""
Initialize a class instance
@ -30,10 +34,10 @@ class AIConfig:
self.ai_goals = ai_goals
# Soon this will go in a folder where it remembers more stuff about the run(s)
SAVE_FILE = os.path.join(os.path.dirname(__file__), '..', 'ai_settings.yaml')
SAVE_FILE = os.path.join(os.path.dirname(__file__), "..", "ai_settings.yaml")
@classmethod
def load(cls: object, config_file: str=SAVE_FILE) -> object:
def load(cls: object, config_file: str = SAVE_FILE) -> object:
"""
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
else returns class with no parameters.
@ -47,7 +51,7 @@ class AIConfig:
"""
try:
with open(config_file, encoding='utf-8') as file:
with open(config_file, encoding="utf-8") as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
@ -58,7 +62,7 @@ class AIConfig:
return cls(ai_name, ai_role, ai_goals)
def save(self, config_file: str=SAVE_FILE) -> None:
def save(self, config_file: str = SAVE_FILE) -> None:
"""
Saves the class parameters to the specified file yaml file path as a yaml file.
@ -69,8 +73,12 @@ class AIConfig:
None
"""
config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
with open(config_file, "w", encoding='utf-8') as file:
config = {
"ai_name": self.ai_name,
"ai_role": self.ai_role,
"ai_goals": self.ai_goals,
}
with open(config_file, "w", encoding="utf-8") as file:
yaml.dump(config, file, allow_unicode=True)
def construct_full_prompt(self) -> str:
@ -87,7 +95,9 @@ class AIConfig:
prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications."""
# Construct full prompt
full_prompt = f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
full_prompt = (
f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
)
for i, goal in enumerate(self.ai_goals):
full_prompt += f"{i+1}. {goal}\n"

8
scripts/ai_functions.py → autogpt/ai_functions.py
View File

@ -1,7 +1,9 @@
from typing import List
import json
from config import Config
from call_ai_function import call_ai_function
from typing import List
from autogpt.call_ai_function import call_ai_function
from autogpt.config import Config
cfg = Config()

39
scripts/browse.py → autogpt/browse.py
View File

@ -1,15 +1,17 @@
from urllib.parse import urljoin, urlparse
import requests
from bs4 import BeautifulSoup
from memory import get_memory
from config import Config
from llm_utils import create_chat_completion
from urllib.parse import urlparse, urljoin
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.memory import get_memory
cfg = Config()
memory = get_memory(cfg)
session = requests.Session()
session.headers.update({'User-Agent': cfg.user_agent})
session.headers.update({"User-Agent": cfg.user_agent})
# Function to check if the URL is valid
@ -28,7 +30,12 @@ def sanitize_url(url):
# Define and check for local file address prefixes
def check_local_file_access(url):
local_prefixes = ['file:///', 'file://localhost', 'http://localhost', 'https://localhost']
local_prefixes = [
"file:///",
"file://localhost",
"http://localhost",
"https://localhost",
]
return any(url.startswith(prefix) for prefix in local_prefixes)
@ -36,11 +43,11 @@ def get_response(url, timeout=10):
try:
# Restrict access to local files
if check_local_file_access(url):
raise ValueError('Access to local files is restricted')
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')
if not url.startswith("http://") and not url.startswith("https://"):
raise ValueError("Invalid URL format")
sanitized_url = sanitize_url(url)
@ -74,7 +81,7 @@ def scrape_text(url):
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = '\n'.join(chunk for chunk in chunks if chunk)
text = "\n".join(chunk for chunk in chunks if chunk)
return text
@ -82,8 +89,8 @@ def scrape_text(url):
def extract_hyperlinks(soup):
"""Extract hyperlinks from a BeautifulSoup object"""
hyperlinks = []
for link in soup.find_all('a', href=True):
hyperlinks.append((link.text, link['href']))
for link in soup.find_all("a", href=True):
hyperlinks.append((link.text, link["href"]))
return hyperlinks
@ -134,7 +141,7 @@ 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, please answer the following question: \"{question}\" -- if the question cannot be answered using the text, please summarize the text."
"content": f'"""{chunk}""" Using the above text, please answer the following question: "{question}" -- if the question cannot be answered using the text, please summarize the text.',
}
@ -152,8 +159,7 @@ def summarize_text(url, text, question):
for i, chunk in enumerate(chunks):
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_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
memory.add(memory_to_add)
@ -168,8 +174,7 @@ def summarize_text(url, text, question):
summaries.append(summary)
print(f"Added chunk {i + 1} summary to memory")
memory_to_add = f"Source: {url}\n" \
f"Content summary part#{i + 1}: {summary}"
memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
memory.add(memory_to_add)

8
scripts/call_ai_function.py → autogpt/call_ai_function.py
View File

@ -1,8 +1,8 @@
from config import Config
from autogpt.config import Config
cfg = Config()
from llm_utils import create_chat_completion
from autogpt.llm_utils import create_chat_completion
# This is a magic function that can do anything with no-code. See
@ -23,8 +23,6 @@ def call_ai_function(function, args, description, model=None):
{"role": "user", "content": args},
]
response = create_chat_completion(
model=model, messages=messages, temperature=0
)
response = create_chat_completion(model=model, messages=messages, temperature=0)
return response

85
scripts/chat.py → autogpt/chat.py
View File

@ -1,11 +1,13 @@
import logging
import time
import openai
from dotenv import load_dotenv
from config import Config
import token_counter
from llm_utils import create_chat_completion
from logger import logger
import logging
from autogpt import token_counter
from autogpt.config import Config
from autogpt.llm_utils import create_chat_completion
from autogpt.logger import logger
cfg = Config()
@ -26,28 +28,33 @@ def create_chat_message(role, content):
def generate_context(prompt, relevant_memory, full_message_history, model):
current_context = [
create_chat_message("system", prompt),
create_chat_message(
"system", prompt),
"system", f"The current time and date is {time.strftime('%c')}"
),
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")]
"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
return (
next_message_to_add_index,
current_tokens_used,
insertion_index,
current_context,
)
# TODO: Change debug from hardcode to argument
def chat_with_ai(
prompt,
user_input,
full_message_history,
permanent_memory,
token_limit):
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:
@ -64,37 +71,57 @@ def chat_with_ai(
Returns:
str: The AI's response.
"""
model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
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)
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()}')
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)
(
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)
(
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)
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)
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])
current_context.insert(
insertion_index, full_message_history[next_message_to_add_index]
)
# Count the currently used tokens
current_tokens_used += tokens_to_add
@ -130,12 +157,10 @@ def chat_with_ai(
)
# Update full message history
full_message_history.append(create_chat_message("user", user_input))
full_message_history.append(
create_chat_message(
"user", user_input))
full_message_history.append(
create_chat_message(
"assistant", assistant_reply))
create_chat_message("assistant", assistant_reply)
)
return assistant_reply
except openai.error.RateLimitError:

0
scripts/commands.py → autogpt/commands.py
View File

49
scripts/config.py → autogpt/config.py
View File

@ -1,8 +1,10 @@
import abc
import os
import openai
import yaml
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
@ -17,9 +19,7 @@ class Singleton(abc.ABCMeta, type):
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)
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
@ -46,12 +46,14 @@ class Config(metaclass=Singleton):
self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 8192))
self.browse_summary_max_token = int(os.getenv("BROWSE_SUMMARY_MAX_TOKEN", 300))
self.browse_summary_max_token = int(os.getenv("BROWSE_SUMMARY_MAX_TOKEN", 300))
self.openai_api_key = os.getenv("OPENAI_API_KEY")
self.temperature = float(os.getenv("TEMPERATURE", "1"))
self.use_azure = os.getenv("USE_AZURE") == 'True'
self.execute_local_commands = os.getenv('EXECUTE_LOCAL_COMMANDS', 'False') == 'True'
self.use_azure = os.getenv("USE_AZURE") == "True"
self.execute_local_commands = (
os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
)
if self.use_azure:
self.load_azure_config()
@ -80,15 +82,18 @@ class Config(metaclass=Singleton):
# User agent headers to use when browsing web
# Some websites might just completely deny request with an error code if no user agent was found.
self.user_agent = os.getenv("USER_AGENT", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36")
self.user_agent = os.getenv(
"USER_AGENT",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
)
self.redis_host = os.getenv("REDIS_HOST", "localhost")
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')
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')
self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
# Initialize the OpenAI API client
openai.api_key = self.openai_api_key
@ -105,15 +110,19 @@ class Config(metaclass=Singleton):
if model == self.fast_llm_model:
return self.azure_model_to_deployment_id_map["fast_llm_model_deployment_id"]
elif model == self.smart_llm_model:
return self.azure_model_to_deployment_id_map["smart_llm_model_deployment_id"]
return self.azure_model_to_deployment_id_map[
"smart_llm_model_deployment_id"
]
elif model == "text-embedding-ada-002":
return self.azure_model_to_deployment_id_map["embedding_model_deployment_id"]
return self.azure_model_to_deployment_id_map[
"embedding_model_deployment_id"
]
else:
return ""
AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), '..', 'azure.yaml')
AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "..", "azure.yaml")
def load_azure_config(self, config_file: str=AZURE_CONFIG_FILE) -> None:
def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
"""
Loads the configuration parameters for Azure hosting from the specified file path as a yaml file.
@ -128,9 +137,15 @@ class Config(metaclass=Singleton):
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
self.openai_api_type = os.getenv("OPENAI_API_TYPE", config_params.get("azure_api_type", "azure"))
self.openai_api_base = os.getenv("OPENAI_AZURE_API_BASE", config_params.get("azure_api_base", ""))
self.openai_api_version = os.getenv("OPENAI_AZURE_API_VERSION", config_params.get("azure_api_version", ""))
self.openai_api_type = os.getenv(
"OPENAI_API_TYPE", config_params.get("azure_api_type", "azure")
)
self.openai_api_base = os.getenv(
"OPENAI_AZURE_API_BASE", config_params.get("azure_api_base", "")
)
self.openai_api_version = os.getenv(
"OPENAI_AZURE_API_VERSION", config_params.get("azure_api_version", "")
)
self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
def set_continuous_mode(self, value: bool):

56
scripts/data_ingestion.py → autogpt/data_ingestion.py
View File

@ -1,19 +1,22 @@
import argparse
import logging
from config import Config
from memory import get_memory
from file_operations import ingest_file, search_files
from autogpt.config import Config
from autogpt.file_operations import ingest_file, search_files
from autogpt.memory import get_memory
cfg = Config()
def configure_logging():
logging.basicConfig(filename='log-ingestion.txt',
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
return logging.getLogger('AutoGPT-Ingestion')
logging.basicConfig(
filename="log-ingestion.txt",
filemode="a",
format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
datefmt="%H:%M:%S",
level=logging.DEBUG,
)
return logging.getLogger("AutoGPT-Ingestion")
def ingest_directory(directory, memory, args):
@ -34,19 +37,38 @@ def ingest_directory(directory, memory, args):
def main():
logger = configure_logging()
parser = argparse.ArgumentParser(description="Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.")
parser = argparse.ArgumentParser(
description="Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script."
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--file", type=str, help="The file to ingest.")
group.add_argument("--dir", type=str, help="The directory containing the files to ingest.")
parser.add_argument("--init", action='store_true', help="Init the memory and wipe its content (default: False)", default=False)
parser.add_argument("--overlap", type=int, help="The overlap size between chunks when ingesting files (default: 200)", default=200)
parser.add_argument("--max_length", type=int, help="The max_length of each chunk when ingesting files (default: 4000)", default=4000)
group.add_argument(
"--dir", type=str, help="The directory containing the files to ingest."
)
parser.add_argument(
"--init",
action="store_true",
help="Init the memory and wipe its content (default: False)",
default=False,
)
parser.add_argument(
"--overlap",
type=int,
help="The overlap size between chunks when ingesting files (default: 200)",
default=200,
)
parser.add_argument(
"--max_length",
type=int,
help="The max_length of each chunk when ingesting files (default: 4000)",
default=4000,
)
args = parser.parse_args()
# Initialize memory
memory = get_memory(cfg, init=args.init)
print('Using memory of type: ' + memory.__class__.__name__)
print("Using memory of type: " + memory.__class__.__name__)
if args.file:
try:
@ -63,7 +85,9 @@ def main():
logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
print(f"Error while ingesting directory '{args.dir}': {str(e)}")
else:
print("Please provide either a file path (--file) or a directory name (--dir) inside the auto_gpt_workspace directory as input.")
print(
"Please provide either a file path (--file) or a directory name (--dir) inside the auto_gpt_workspace directory as input."
)
if __name__ == "__main__":

37
scripts/execute_code.py → autogpt/execute_code.py
View File

@ -1,7 +1,7 @@
import docker
import os
import subprocess
import docker
WORKSPACE_FOLDER = "auto_gpt_workspace"
@ -9,7 +9,7 @@ WORKSPACE_FOLDER = "auto_gpt_workspace"
def execute_python_file(file):
"""Execute a Python file in a Docker container and return the output"""
print (f"Executing file '{file}' in workspace '{WORKSPACE_FOLDER}'")
print(f"Executing file '{file}' in workspace '{WORKSPACE_FOLDER}'")
if not file.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
@ -20,7 +20,9 @@ def execute_python_file(file):
return f"Error: File '{file}' does not exist."
if we_are_running_in_a_docker_container():
result = subprocess.run(f'python {file_path}', capture_output=True, encoding="utf8", shell=True)
result = subprocess.run(
f"python {file_path}", capture_output=True, encoding="utf8", shell=True
)
if result.returncode == 0:
return result.stdout
else:
@ -29,18 +31,20 @@ def execute_python_file(file):
try:
client = docker.from_env()
image_name = 'python:3.10'
image_name = "python:3.10"
try:
client.images.get(image_name)
print(f"Image '{image_name}' found locally")
except docker.errors.ImageNotFound:
print(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):
# Print the status and progress, if available
status = line.get('status')
progress = line.get('progress')
status = line.get("status")
progress = line.get("progress")
if status and progress:
print(f"{status}: {progress}")
elif status:
@ -51,19 +55,21 @@ def execute_python_file(file):
# https://hub.docker.com/_/python
container = client.containers.run(
image_name,
f'python {file}',
f"python {file}",
volumes={
os.path.abspath(WORKSPACE_FOLDER): {
'bind': '/workspace',
'mode': 'ro'}},
working_dir='/workspace',
"bind": "/workspace",
"mode": "ro",
}
},
working_dir="/workspace",
stderr=True,
stdout=True,
detach=True,
)
output = container.wait()
logs = container.logs().decode('utf-8')
logs = container.logs().decode("utf-8")
container.remove()
# print(f"Execution complete. Output: {output}")
@ -76,14 +82,13 @@ def execute_python_file(file):
def execute_shell(command_line):
current_dir = os.getcwd()
if not WORKSPACE_FOLDER in current_dir: # Change dir into workspace if necessary
if not WORKSPACE_FOLDER in current_dir: # Change dir into workspace if necessary
work_dir = os.path.join(os.getcwd(), WORKSPACE_FOLDER)
os.chdir(work_dir)
print (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}"
@ -96,4 +101,4 @@ def execute_shell(command_line):
def we_are_running_in_a_docker_container():
os.path.exists('/.dockerenv')
os.path.exists("/.dockerenv")

13
scripts/file_operations.py → autogpt/file_operations.py
View File

@ -36,7 +36,7 @@ def split_file(content, max_length=4000, overlap=0):
while start < content_length:
end = start + max_length
if end + overlap < content_length:
chunk = content[start:end+overlap]
chunk = content[start : end + overlap]
else:
chunk = content[start:content_length]
yield chunk
@ -47,7 +47,7 @@ def read_file(filename):
"""Read a file and return the contents"""
try:
filepath = safe_join(working_directory, filename)
with open(filepath, "r", encoding='utf-8') as f:
with open(filepath, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
@ -75,8 +75,9 @@ def ingest_file(filename, memory, max_length=4000, overlap=200):
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_to_add = (
f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
)
memory.add(memory_to_add)
@ -92,7 +93,7 @@ def write_to_file(filename, text):
directory = os.path.dirname(filepath)
if not os.path.exists(directory):
os.makedirs(directory)
with open(filepath, "w", encoding='utf-8') as f:
with open(filepath, "w", encoding="utf-8") as f:
f.write(text)
return "File written to successfully."
except Exception as e:
@ -130,7 +131,7 @@ def search_files(directory):
for root, _, files in os.walk(search_directory):
for file in files:
if file.startswith('.'):
if file.startswith("."):
continue
relative_path = os.path.relpath(os.path.join(root, file), working_directory)
found_files.append(relative_path)

31
scripts/image_gen.py → autogpt/image_gen.py
View File

@ -1,24 +1,24 @@
import requests
import io
import os.path
from PIL import Image
from config import Config
import uuid
import openai
from base64 import b64decode
import openai
import requests
from PIL import Image
from autogpt.config import Config
cfg = Config()
working_directory = "auto_gpt_workspace"
def generate_image(prompt):
filename = str(uuid.uuid4()) + ".jpg"
# DALL-E
if cfg.image_provider == 'dalle':
if cfg.image_provider == "dalle":
openai.api_key = cfg.openai_api_key
response = openai.Image.create(
@ -38,14 +38,19 @@ def generate_image(prompt):
return "Saved to disk:" + filename
# STABLE DIFFUSION
elif cfg.image_provider == 'sd':
API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
elif cfg.image_provider == "sd":
API_URL = (
"https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
)
headers = {"Authorization": "Bearer " + cfg.huggingface_api_token}
response = requests.post(API_URL, headers=headers, json={
"inputs": prompt,
})
response = requests.post(
API_URL,
headers=headers,
json={
"inputs": prompt,
},
)
image = Image.open(io.BytesIO(response.content))
print("Image Generated for prompt:" + prompt)

29
autogpt/js/overlay.js Normal file
View File

@ -0,0 +1,29 @@
const overlay = document.createElement('div');
Object.assign(overlay.style, {
position: 'fixed',
zIndex: 999999,
top: 0,
left: 0,
width: '100%',
height: '100%',
background: 'rgba(0, 0, 0, 0.7)',
color: '#fff',
fontSize: '24px',
fontWeight: 'bold',
display: 'flex',
justifyContent: 'center',
alignItems: 'center',
});
const textContent = document.createElement('div');
Object.assign(textContent.style, {
textAlign: 'center',
});
textContent.textContent = 'AutoGPT Analyzing Page';
overlay.appendChild(textContent);
document.body.append(overlay);
document.body.style.overflow = 'hidden';
let dotCount = 0;
setInterval(() => {
textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
dotCount = (dotCount + 1) % 4;
}, 1000);

36
scripts/json_parser.py → autogpt/json_parser.py
View File

@ -1,9 +1,10 @@
import json
from typing import Any, Dict, Union
from call_ai_function import call_ai_function
from config import Config
from json_utils import correct_json
from logger import logger
from autogpt.call_ai_function import call_ai_function
from autogpt.config import Config
from autogpt.json_utils import correct_json
from autogpt.logger import logger
cfg = Config()
@ -28,12 +29,11 @@ JSON_SCHEMA = """
def fix_and_parse_json(
json_str: str,
try_to_fix_with_gpt: bool = True
json_str: str, try_to_fix_with_gpt: bool = True
) -> Union[str, Dict[Any, Any]]:
"""Fix and parse JSON string"""
try:
json_str = json_str.replace('\t', '')
json_str = json_str.replace("\t", "")
return json.loads(json_str)
except json.JSONDecodeError as _: # noqa: F841
try:
@ -52,15 +52,17 @@ def fix_and_parse_json(
brace_index = json_str.index("{")
json_str = json_str[brace_index:]
last_brace_index = json_str.rindex("}")
json_str = json_str[:last_brace_index+1]
json_str = json_str[: last_brace_index + 1]
return json.loads(json_str)
# Can throw a ValueError if there is no "{" or "}" in the json_str
except (json.JSONDecodeError, ValueError) as e: # noqa: F841
if try_to_fix_with_gpt:
logger.warn("Warning: Failed to parse AI output, attempting to fix."
"\n If you see this warning frequently, it's likely that"
" your prompt is confusing the AI. Try changing it up"
" slightly.")
logger.warn(
"Warning: Failed to parse AI output, attempting to fix."
"\n If you see this warning frequently, it's likely that"
" your prompt is confusing the AI. Try changing it up"
" slightly."
)
# Now try to fix this up using the ai_functions
ai_fixed_json = fix_json(json_str, JSON_SCHEMA)
@ -80,11 +82,13 @@ def fix_json(json_str: str, schema: str) -> str:
# Try to fix the JSON using GPT:
function_string = "def fix_json(json_str: str, schema:str=None) -> str:"
args = [f"'''{json_str}'''", f"'''{schema}'''"]
description_string = "Fixes the provided JSON string to make it parseable"\
" and fully compliant with the provided schema.\n If an object or"\
" field specified in the schema isn't contained within the correct"\
" JSON, it is omitted.\n This function is brilliant at guessing"\
description_string = (
"Fixes the provided JSON string to make it parseable"
" and fully compliant with the provided schema.\n If an object or"
" field specified in the schema isn't contained within the correct"
" JSON, it is omitted.\n This function is brilliant at guessing"
" when the format is incorrect."
)
# If it doesn't already start with a "`", add one:
if not json_str.startswith("`"):

38
scripts/json_utils.py → autogpt/json_utils.py
View File

@ -1,6 +1,7 @@
import re
import json
from config import Config
import re
from autogpt.config import Config
cfg = Config()
@ -17,7 +18,7 @@ def extract_char_position(error_message: str) -> int:
"""
import re
char_pattern = re.compile(r'\(char (\d+)\)')
char_pattern = re.compile(r"\(char (\d+)\)")
if match := char_pattern.search(error_message):
return int(match[1])
else:
@ -38,10 +39,8 @@ def add_quotes_to_property_names(json_string: str) -> str:
def replace_func(match):
return f'"{match.group(1)}":'
property_name_pattern = re.compile(r'(\w+):')
corrected_json_string = property_name_pattern.sub(
replace_func,
json_string)
property_name_pattern = re.compile(r"(\w+):")
corrected_json_string = property_name_pattern.sub(replace_func, json_string)
try:
json.loads(corrected_json_string)
@ -61,15 +60,15 @@ def balance_braces(json_string: str) -> str:
str: The JSON string with braces balanced.
"""
open_braces_count = json_string.count('{')
close_braces_count = json_string.count('}')
open_braces_count = json_string.count("{")
close_braces_count = json_string.count("}")
while open_braces_count > close_braces_count:
json_string += '}'
json_string += "}"
close_braces_count += 1
while close_braces_count > open_braces_count:
json_string = json_string.rstrip('}')
json_string = json_string.rstrip("}")
close_braces_count -= 1
try:
@ -80,16 +79,15 @@ def balance_braces(json_string: str) -> str:
def fix_invalid_escape(json_str: str, error_message: str) -> str:
while error_message.startswith('Invalid \\escape'):
while error_message.startswith("Invalid \\escape"):
bad_escape_location = extract_char_position(error_message)
json_str = json_str[:bad_escape_location] + \
json_str[bad_escape_location + 1:]
json_str = json_str[:bad_escape_location] + json_str[bad_escape_location + 1 :]
try:
json.loads(json_str)
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print('json loads error - fix invalid escape', e)
print("json loads error - fix invalid escape", e)
error_message = str(e)
return json_str
@ -109,18 +107,20 @@ def correct_json(json_str: str) -> str:
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print('json loads error', e)
print("json loads error", e)
error_message = str(e)
if error_message.startswith('Invalid \\escape'):
if error_message.startswith("Invalid \\escape"):
json_str = fix_invalid_escape(json_str, error_message)
if error_message.startswith('Expecting property name enclosed in double quotes'):
if error_message.startswith(
"Expecting property name enclosed in double quotes"
):
json_str = add_quotes_to_property_names(json_str)
try:
json.loads(json_str)
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print('json loads error - add quotes', e)
print("json loads error - add quotes", e)
error_message = str(e)
if balanced_str := balance_braces(json_str):
return balanced_str

22
scripts/llm_utils.py → autogpt/llm_utils.py
View File

@ -1,7 +1,9 @@
import time
import openai
from colorama import Fore
from config import Config
from autogpt.config import Config
cfg = Config()
@ -10,7 +12,9 @@ openai.api_key = cfg.openai_api_key
# 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, model=None, temperature=cfg.temperature, max_tokens=None)->str:
def create_chat_completion(
messages, model=None, temperature=cfg.temperature, max_tokens=None
) -> str:
"""Create a chat completion using the OpenAI API"""
response = None
num_retries = 5
@ -22,24 +26,30 @@ def create_chat_completion(messages, model=None, temperature=cfg.temperature, ma
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
max_tokens=max_tokens,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
max_tokens=max_tokens,
)
break
except openai.error.RateLimitError:
if cfg.debug_mode:
print(Fore.RED + "Error: ", "API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET)
print(
Fore.RED + "Error: ",
"API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET,
)
time.sleep(20)
except openai.error.APIError as e:
if e.http_status == 502:
if cfg.debug_mode:
print(Fore.RED + "Error: ", "API Bad gateway. Waiting 20 seconds..." + Fore.RESET)
print(
Fore.RED + "Error: ",
"API Bad gateway. Waiting 20 seconds..." + Fore.RESET,
)
time.sleep(20)
else:
raise

96
scripts/logger.py → autogpt/logger.py
View File

@ -4,35 +4,33 @@ import random
import re
import time
from logging import LogRecord
from colorama import Fore
from colorama import Style
from colorama import Fore, Style
import speak
from config import Config
from config import Singleton
from autogpt import speak
from autogpt.config import Config, Singleton
cfg = Config()
'''
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
'''
"""
class Logger(metaclass=Singleton):
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, '../logs')
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = AutoGptFormatter('%(title_color)s %(message)s')
console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
@ -47,35 +45,34 @@ class Logger(metaclass=Singleton):
# Info handler in activity.log
self.file_handler = logging.FileHandler(os.path.join(log_dir, log_file))
self.file_handler.setLevel(logging.DEBUG)
info_formatter = AutoGptFormatter('%(asctime)s %(levelname)s %(title)s %(message_no_color)s')
info_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(os.path.join(log_dir, error_file))
error_handler.setLevel(logging.ERROR)
error_formatter = AutoGptFormatter(
'%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s %(message_no_color)s')
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger('TYPER')
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger('LOGGER')
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
def typewriter_log(
self,
title='',
title_color='',
content='',
speak_text=False,
level=logging.INFO):
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and cfg.speak_mode:
speak.say_text(f"{title}. {content}")
@ -85,41 +82,34 @@ class Logger(metaclass=Singleton):
else:
content = ""
self.typing_logger.log(level, content, extra={'title': title, 'color': title_color})
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title='',
title_color='',
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def warn(
self,
message,
title='',
title_color='',
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.WARN)
def error(
self,
title,
message=''
):
def error(self, title, message=""):
self._log(title, Fore.RED, message, logging.ERROR)
def _log(
self,
title='',
title_color='',
message='',
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': title, 'color': title_color})
self.logger.log(level, message, extra={"title": title, "color": title_color})
def set_level(self, level):
self.logger.setLevel(level)
@ -132,9 +122,9 @@ class Logger(metaclass=Singleton):
self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
'''
"""
Output stream to console using simulated typing
'''
"""
class TypingConsoleHandler(logging.StreamHandler):
@ -173,21 +163,27 @@ class AutoGptFormatter(logging.Formatter):
Allows to handle custom placeholders 'title_color' and 'message_no_color'.
To use this formatter, make sure to pass 'color', 'title' as log extras.
"""
def format(self, record: LogRecord) -> str:
if (hasattr(record, 'color')):
record.title_color = getattr(record, 'color') + getattr(record, 'title') + " " + Style.RESET_ALL
if hasattr(record, "color"):
record.title_color = (
getattr(record, "color")
+ getattr(record, "title")
+ " "
+ Style.RESET_ALL
)
else:
record.title_color = getattr(record, 'title')
if hasattr(record, 'msg'):
record.message_no_color = remove_color_codes(getattr(record, 'msg'))
record.title_color = getattr(record, "title")
if hasattr(record, "msg"):
record.message_no_color = remove_color_codes(getattr(record, "msg"))
else:
record.message_no_color = ''
record.message_no_color = ""
return super().format(record)
def remove_color_codes(s: str) -> str:
ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
return ansi_escape.sub('', s)
ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
return ansi_escape.sub("", s)
logger = Logger()

36
scripts/memory/__init__.py → autogpt/memory/__init__.py
View File

@ -1,20 +1,22 @@
from memory.local import LocalCache
from memory.no_memory import NoMemory
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']
supported_memory = ["local", "no_memory"]
try:
from memory.redismem import RedisMemory
supported_memory.append('redis')
from autogpt.memory.redismem import RedisMemory
supported_memory.append("redis")
except ImportError:
print("Redis not installed. Skipping import.")
RedisMemory = None
try:
from memory.pinecone import PineconeMemory
supported_memory.append('pinecone')
from autogpt.memory.pinecone import PineconeMemory
supported_memory.append("pinecone")
except ImportError:
print("Pinecone not installed. Skipping import.")
PineconeMemory = None
@ -24,16 +26,20 @@ 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.")
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.")
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 == "no_memory":
@ -50,10 +56,4 @@ def get_supported_memory_backends():
return supported_memory
__all__ = [
"get_memory",
"LocalCache",
"RedisMemory",
"PineconeMemory",
"NoMemory"
]
__all__ = ["get_memory", "LocalCache", "RedisMemory", "PineconeMemory", "NoMemory"]

13
scripts/memory/base.py → autogpt/memory/base.py
View File

@ -1,17 +1,24 @@
"""Base class for memory providers."""
import abc
from config import AbstractSingleton, Config
import openai
from autogpt.config import AbstractSingleton, Config
cfg = Config()
def get_ada_embedding(text):
text = text.replace("\n", " ")
if cfg.use_azure:
return openai.Embedding.create(input=[text], engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"))["data"][0]["embedding"]
return openai.Embedding.create(
input=[text],
engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"),
)["data"][0]["embedding"]
else:
return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[
"data"
][0]["embedding"]
class MemoryProviderSingleton(AbstractSingleton):

29
scripts/memory/local.py → autogpt/memory/local.py
View File

@ -1,10 +1,11 @@
import dataclasses
import orjson
from typing import Any, List, Optional
import numpy as np
import os
from memory.base import MemoryProviderSingleton, get_ada_embedding
from typing import Any, List, Optional
import numpy as np
import orjson
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
EMBED_DIM = 1536
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
@ -23,16 +24,15 @@ class CacheContent:
class LocalCache(MemoryProviderSingleton):
# on load, load our database
def __init__(self, cfg) -> None:
self.filename = f"{cfg.memory_index}.json"
if os.path.exists(self.filename):
try:
with open(self.filename, 'w+b') as f:
with open(self.filename, "w+b") as f:
file_content = f.read()
if not file_content.strip():
file_content = b'{}'
file_content = b"{}"
f.write(file_content)
loaded = orjson.loads(file_content)
@ -41,7 +41,9 @@ class LocalCache(MemoryProviderSingleton):
print(f"Error: The file '{self.filename}' is not in JSON format.")
self.data = CacheContent()
else:
print(f"Warning: The file '{self.filename}' does not exist. Local memory would not be saved to a file.")
print(
f"Warning: The file '{self.filename}' does not exist. Local memory would not be saved to a file."
)
self.data = CacheContent()
def add(self, text: str):
@ -54,7 +56,7 @@ class LocalCache(MemoryProviderSingleton):
Returns: None
"""
if 'Command Error:' in text:
if "Command Error:" in text:
return ""
self.data.texts.append(text)
@ -70,11 +72,8 @@ class LocalCache(MemoryProviderSingleton):
axis=0,
)
with open(self.filename, 'wb') as f:
out = orjson.dumps(
self.data,
option=SAVE_OPTIONS
)
with open(self.filename, "wb") as f:
out = orjson.dumps(self.data, option=SAVE_OPTIONS)
f.write(out)
return text
@ -99,7 +98,7 @@ class LocalCache(MemoryProviderSingleton):
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

2
scripts/memory/no_memory.py → autogpt/memory/no_memory.py
View File

@ -1,6 +1,6 @@
from typing import Optional, List, Any
from memory.base import MemoryProviderSingleton
from autogpt.memory.base import MemoryProviderSingleton
class NoMemory(MemoryProviderSingleton):

30
scripts/memory/pinecone.py → autogpt/memory/pinecone.py
View File

@ -1,10 +1,10 @@
import pinecone
from memory.base import MemoryProviderSingleton, get_ada_embedding
from logger import logger
from colorama import Fore, Style
from autogpt.config import Config, Singleton
from autogpt.logger import logger
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
class PineconeMemory(MemoryProviderSingleton):
def __init__(self, cfg):
@ -23,13 +23,21 @@ class PineconeMemory(MemoryProviderSingleton):
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{Style.RESET_ALL} to ensure you've set up everything correctly.")
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{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)
pinecone.create_index(
table_name, dimension=dimension, metric=metric, pod_type=pod_type
)
self.index = pinecone.Index(table_name)
def add(self, data):
@ -54,9 +62,11 @@ class PineconeMemory(MemoryProviderSingleton):
:param num_relevant: The number of relevant data to return. Defaults to 5
"""
query_embedding = get_ada_embedding(data)
results = self.index.query(query_embedding, top_k=num_relevant, include_metadata=True)
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]
return [str(item["metadata"]["raw_text"]) for item in sorted_results]
def get_stats(self):
return self.index.describe_index_stats()

76
scripts/memory/redismem.py → autogpt/memory/redismem.py
View File

@ -1,26 +1,22 @@
"""Redis memory provider."""
from typing import Any, List, Optional
import redis
from redis.commands.search.field import VectorField, TextField
from redis.commands.search.query import Query
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
import numpy as np
from memory.base import MemoryProviderSingleton, get_ada_embedding
from logger import logger
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.logger import logger
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
SCHEMA = [
TextField("data"),
VectorField(
"embedding",
"HNSW",
{
"TYPE": "FLOAT32",
"DIM": 1536,
"DISTANCE_METRIC": "COSINE"
}
{"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
),
]
@ -43,7 +39,7 @@ class RedisMemory(MemoryProviderSingleton):
host=redis_host,
port=redis_port,
password=redis_password,
db=0 # Cannot be changed
db=0, # Cannot be changed
)
self.cfg = cfg
@ -51,9 +47,15 @@ class RedisMemory(MemoryProviderSingleton):
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}https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL} to ensure you've set up everything correctly.")
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}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:
@ -62,15 +64,13 @@ class RedisMemory(MemoryProviderSingleton):
self.redis.ft(f"{cfg.memory_index}").create_index(
fields=SCHEMA,
definition=IndexDefinition(
prefix=[f"{cfg.memory_index}:"],
index_type=IndexType.HASH
)
)
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
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:
"""
@ -81,20 +81,18 @@ class RedisMemory(MemoryProviderSingleton):
Returns: Message indicating that the data has been added.
"""
if 'Command Error:' in data:
if "Command Error:" in data:
return ""
vector = get_ada_embedding(data)
vector = np.array(vector).astype(np.float32).tobytes()
data_dict = {
b"data": data,
"embedding": vector
}
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}"
_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.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
pipe.execute()
return _text
@ -118,11 +116,7 @@ class RedisMemory(MemoryProviderSingleton):
self.redis.flushall()
return "Obliviated"
def get_relevant(
self,
data: str,
num_relevant: int = 5
) -> Optional[List[Any]]:
def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
"""
Returns all the data in the memory that is relevant to the given data.
Args:
@ -133,10 +127,12 @@ class RedisMemory(MemoryProviderSingleton):
"""
query_embedding = get_ada_embedding(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 = (
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:

105
autogpt/prompt.py Normal file
View File

@ -0,0 +1,105 @@
from autogpt.promptgenerator import PromptGenerator
def get_prompt():
"""
This function generates a prompt string that includes various constraints, commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Add constraints to the PromptGenerator object
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 = [
("Google Search", "google", {"input": "<search>"}),
(
"Browse Website",
"browse_website",
{"url": "<url>", "question": "<what_you_want_to_find_on_website>"},
),
(
"Start GPT Agent",
"start_agent",
{"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"},
),
(
"Message GPT Agent",
"message_agent",
{"key": "<key>", "message": "<message>"},
),
("List GPT Agents", "list_agents", {}),
("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
("Write to file", "write_to_file", {"file": "<file>", "text": "<text>"}),
("Read file", "read_file", {"file": "<file>"}),
("Append to file", "append_to_file", {"file": "<file>", "text": "<text>"}),
("Delete file", "delete_file", {"file": "<file>"}),
("Search Files", "search_files", {"directory": "<directory>"}),
("Evaluate Code", "evaluate_code", {"code": "<full_code_string>"}),
(
"Get Improved Code",
"improve_code",
{"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"},
),
(
"Write Tests",
"write_tests",
{"code": "<full_code_string>", "focus": "<list_of_focus_areas>"},
),
("Execute Python File", "execute_python_file", {"file": "<file>"}),
(
"Execute Shell Command, non-interactive commands only",
"execute_shell",
{"command_line": "<command_line>"},
),
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
("Generate Image", "generate_image", {"prompt": "<prompt>"}),
("Do Nothing", "do_nothing", {}),
]
# 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
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
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."
)
# Generate the prompt string
prompt_string = prompt_generator.generate_prompt_string()
return prompt_string

26
scripts/promptgenerator.py → autogpt/promptgenerator.py
View File

@ -20,14 +20,9 @@ class PromptGenerator:
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"speak": "thoughts summary to say to user"
"speak": "thoughts summary to say to user",
},
"command": {
"name": "command name",
"args": {
"arg name": "value"
}
}
"command": {"name": "command name", "args": {"arg name": "value"}},
}
def add_constraint(self, constraint):
@ -51,8 +46,7 @@ class PromptGenerator:
if args is None:
args = {}
command_args = {arg_key: arg_value for arg_key,
arg_value in args.items()}
command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
command = {
"label": command_label,
@ -72,8 +66,9 @@ class PromptGenerator:
Returns:
str: The formatted command string.
"""
args_string = ', '.join(
f'"{key}": "{value}"' for key, value in command['args'].items())
args_string = ", ".join(
f'"{key}": "{value}"' for key, value in command["args"].items()
)
return f'{command["label"]}: "{command["name"]}", args: {args_string}'
def add_resource(self, resource):
@ -94,7 +89,7 @@ class PromptGenerator:
"""
self.performance_evaluation.append(evaluation)
def _generate_numbered_list(self, items, item_type='list'):
def _generate_numbered_list(self, items, item_type="list"):
"""
Generate a numbered list from given items based on the item_type.
@ -105,8 +100,11 @@ class PromptGenerator:
Returns:
str: The formatted numbered list.
"""
if item_type == 'command':
return "\n".join(f"{i+1}. {self._generate_command_string(item)}" for i, item in enumerate(items))
if item_type == "command":
return "\n".join(
f"{i+1}. {self._generate_command_string(item)}"
for i, item in enumerate(items)
)
else:
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))

40
scripts/speak.py → autogpt/speak.py
View File

@ -1,12 +1,16 @@
import os
from playsound import playsound
import requests
from config import Config
from playsound import playsound
from autogpt.config import Config
cfg = Config()
import gtts
import threading
from threading import Lock, Semaphore
import gtts
# Default voice IDs
default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
@ -19,26 +23,29 @@ placeholders = {"your-voice-id"}
# Use custom voice IDs if provided and not placeholders, otherwise use default voice IDs
voices = [
custom_voice_1 if custom_voice_1 and custom_voice_1 not in placeholders else default_voices[0],
custom_voice_2 if custom_voice_2 and custom_voice_2 not in placeholders else default_voices[1]
custom_voice_1
if custom_voice_1 and custom_voice_1 not in placeholders
else default_voices[0],
custom_voice_2
if custom_voice_2 and custom_voice_2 not in placeholders
else default_voices[1],
]
tts_headers = {
"Content-Type": "application/json",
"xi-api-key": cfg.elevenlabs_api_key
}
tts_headers = {"Content-Type": "application/json", "xi-api-key": cfg.elevenlabs_api_key}
mutex_lock = Lock() # Ensure only one sound is played at a time
queue_semaphore = Semaphore(1) # The amount of sounds to queue before blocking the main thread
mutex_lock = Lock() # Ensure only one sound is played at a time
queue_semaphore = Semaphore(
1
) # The amount of sounds to queue before blocking the main thread
def eleven_labs_speech(text, voice_index=0):
"""Speak text using elevenlabs.io's API"""
tts_url = "https://api.elevenlabs.io/v1/text-to-speech/{voice_id}".format(
voice_id=voices[voice_index])
voice_id=voices[voice_index]
)
formatted_message = {"text": text}
response = requests.post(
tts_url, headers=tts_headers, json=formatted_message)
response = requests.post(tts_url, headers=tts_headers, json=formatted_message)
if response.status_code == 200:
with mutex_lock:
@ -90,12 +97,11 @@ def macos_tts_speech(text, voice_index=0):
def say_text(text, voice_index=0):
def speak():
if not cfg.elevenlabs_api_key:
if cfg.use_mac_os_tts == 'True':
if cfg.use_mac_os_tts == "True":
macos_tts_speech(text)
elif cfg.use_brian_tts == 'True':
elif cfg.use_brian_tts == "True":
success = brian_speech(text)
if not success:
gtts_speech(text)

5
scripts/spinner.py → autogpt/spinner.py
View File

@ -1,14 +1,15 @@
import itertools
import sys
import threading
import itertools
import time
class Spinner:
"""A simple spinner class"""
def __init__(self, message="Loading...", delay=0.1):
"""Initialize the spinner class"""
self.spinner = itertools.cycle(['-', '/', '|', '\\'])
self.spinner = itertools.cycle(["-", "/", "|", "\\"])
self.delay = delay
self.message = message
self.running = False

67
autogpt/summary.py Normal file
View File

@ -0,0 +1,67 @@
from autogpt.llm_utils import create_chat_completion
def summarize_text(driver, text, question):
if not text:
return "Error: No text to summarize"
text_length = len(text)
print(f"Text length: {text_length} characters")
summaries = []
chunks = list(split_text(text))
scroll_ratio = 1 / len(chunks)
for i, chunk in enumerate(chunks):
scroll_to_percentage(driver, scroll_ratio * i)
print(f"Summarizing chunk {i + 1} / {len(chunks)}")
messages = [create_message(chunk, question)]
summary = create_chat_completion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=300,
)
summaries.append(summary)
print(f"Summarized {len(chunks)} chunks.")
combined_summary = "\n".join(summaries)
messages = [create_message(combined_summary, question)]
return create_chat_completion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=300,
)
def split_text(text, max_length=8192):
paragraphs = text.split("\n")
current_length = 0
current_chunk = []
for paragraph in paragraphs:
if current_length + len(paragraph) + 1 <= max_length:
current_chunk.append(paragraph)
current_length += len(paragraph) + 1
else:
yield "\n".join(current_chunk)
current_chunk = [paragraph]
current_length = len(paragraph) + 1
if current_chunk:
yield "\n".join(current_chunk)
def create_message(chunk, question):
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, please answer the following question: "{question}" -- if the question cannot be answered using the text, please summarize the text.',
}
def scroll_to_percentage(driver, ratio):
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});")

15
scripts/token_counter.py → autogpt/token_counter.py
View File

@ -1,8 +1,11 @@
from typing import Dict, List
import tiktoken
from typing import List, Dict
def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5-turbo-0301") -> int:
def count_message_tokens(
messages: List[Dict[str, str]], model: str = "gpt-3.5-turbo-0301"
) -> int:
"""
Returns the number of tokens used by a list of messages.
@ -25,13 +28,17 @@ def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5
# !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_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}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
raise NotImplementedError(
f"""num_tokens_from_messages() is not implemented for model {model}. 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

7
scripts/utils.py → autogpt/utils.py
View File

@ -2,7 +2,7 @@ import yaml
from colorama import Fore
def clean_input(prompt: str=''):
def clean_input(prompt: str = ""):
try:
return input(prompt)
except KeyboardInterrupt:
@ -18,6 +18,9 @@ def validate_yaml_file(file: str):
except FileNotFoundError:
return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found")
except yaml.YAMLError as e:
return (False, f"There was an issue while trying to read with your AI Settings file: {e}")
return (
False,
f"There was an issue while trying to read with your AI Settings file: {e}",
)
return (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")

91
autogpt/web.py Normal file
View File

@ -0,0 +1,91 @@
from duckduckgo_search import ddg
from selenium import webdriver
import autogpt.summary as summary
from bs4 import BeautifulSoup
import json
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.chrome.service import Service as ChromeService
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import os
import logging
from pathlib import Path
from autogpt.config import Config
file_dir = Path(__file__).parent
cfg = Config()
def browse_website(url, question):
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary_text = summary.summarize_text(driver, text, question)
links = scrape_links_with_selenium(driver)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url):
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options = Options()
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"
)
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
# Get the HTML content directly from the browser's DOM
page_source = driver.execute_script("return document.body.outerHTML;")
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return driver, text
def scrape_links_with_selenium(driver):
page_source = driver.page_source
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup)
return format_hyperlinks(hyperlinks)
def close_browser(driver):
driver.quit()
def extract_hyperlinks(soup):
return [(link.text, link["href"]) for link in soup.find_all("a", href=True)]
def format_hyperlinks(hyperlinks):
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
def add_header(driver):
driver.execute_script(open(f"{file_dir}/js/overlay.js", "r").read())

2
docker-compose.yml
View File

@ -8,7 +8,7 @@ services:
- redis
build: ./
volumes:
- "./scripts:/app"
- "./autogpt:/app"
- ".env:/app/.env"
profiles: ["exclude-from-up"]

2
main.py
View File

@ -1 +1 @@
from scripts.main import main
from autogpt import main

11
pyproject.toml Normal file
View File

@ -0,0 +1,11 @@
[project]
name = "auto-gpt"
version = "0.1.0"
description = "A GPT based ai agent"
readme = "README.md"
[tool.black]
line-length = 88
target-version = ['py310']
include = '\.pyi?$'
extend-exclude = ""

6
requirements.txt
View File

@ -15,6 +15,12 @@ pinecone-client==2.2.1
redis
orjson
Pillow
selenium
webdriver-manager
coverage
flake8
numpy
pre-commit
black
sourcery
isort

472
scripts/main.py
View File

@ -1,465 +1,11 @@
import json
import random
import commands as cmd
import utils
from memory import get_memory, get_supported_memory_backends
import chat
from colorama import Fore, Style
from spinner import Spinner
import time
import speak
from config import Config
from json_parser import fix_and_parse_json
from ai_config import AIConfig
import traceback
import yaml
import argparse
from logger import logger
import logging
from prompt import get_prompt
from colorama import Style, init
cfg = Config()
# Initialize colorama
init(autoreset=True)
def check_openai_api_key():
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
if not cfg.openai_api_key:
print(
Fore.RED +
"Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)
def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("I have received an invalid JSON response from the OpenAI API. Trying to fix it now.")
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
# Use regex to search for JSON objects
import regex
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(title="Apparently json was fixed.", title_color=Fore.GREEN)
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError) as e:
if cfg.speak_mode:
speak.say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
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 as e:
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
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, assistant_thoughts_text)
logger.typewriter_log("REASONING:", Fore.YELLOW, 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, assistant_thoughts_criticism)
# Speak the assistant's thoughts
if cfg.speak_mode and assistant_thoughts_speak:
speak.say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
if cfg.speak_mode:
speak.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 as e:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)
def construct_prompt():
"""Construct the prompt for the AI to respond to"""
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, config.ai_goals)
elif config.ai_name:
logger.typewriter_log(
f"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True)
should_continue = utils.clean_input(f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
Continue (y/n): """)
if should_continue.lower() == "n":
config = AIConfig()
if not config.ai_name:
config = prompt_user()
config.save()
# Get rid of this global:
global ai_name
ai_name = config.ai_name
full_prompt = config.construct_full_prompt()
return full_prompt
def prompt_user():
"""Prompt the user for input"""
ai_name = ""
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load defaults.",
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 = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!",
Fore.LIGHTBLUE_EX,
"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'")
print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
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 len(ai_goals) == 0:
ai_goals = ["Increase net worth", "Grow Twitter Account",
"Develop and manage multiple businesses autonomously"]
config = AIConfig(ai_name, ai_role, ai_goals)
return config
def parse_arguments():
"""Parses the arguments passed to the script"""
global cfg
cfg.set_debug_mode(False)
cfg.set_continuous_mode(False)
cfg.set_speak_mode(False)
parser = argparse.ArgumentParser(description='Process arguments.')
parser.add_argument('--continuous', '-c', action='store_true', help='Enable Continuous Mode')
parser.add_argument('--continuous-limit', '-l', type=int, dest="continuous_limit", help='Defines the number of times to run in continuous mode')
parser.add_argument('--speak', action='store_true', help='Enable Speak Mode')
parser.add_argument('--debug', action='store_true', help='Enable Debug Mode')
parser.add_argument('--gpt3only', action='store_true', help='Enable GPT3.5 Only Mode')
parser.add_argument('--gpt4only', action='store_true', help='Enable GPT4 Only Mode')
parser.add_argument('--use-memory', '-m', dest="memory_type", help='Defines which Memory backend to use')
parser.add_argument('--skip-reprompt', '-y', dest='skip_reprompt', action='store_true', help='Skips the re-prompting messages at the beginning of the script')
parser.add_argument('--ai-settings', '-C', dest='ai_settings_file', help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.")
args = parser.parse_args()
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
cfg.set_debug_mode(True)
if args.continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.")
cfg.set_continuous_mode(True)
if args.continuous_limit:
logger.typewriter_log(
"Continuous Limit: ",
Fore.GREEN,
f"{args.continuous_limit}")
cfg.set_continuous_limit(args.continuous_limit)
# Check if continuous limit is used without continuous mode
if args.continuous_limit and not args.continuous:
parser.error("--continuous-limit can only be used with --continuous")
if args.speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
cfg.set_speak_mode(True)
if args.gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_smart_llm_model(cfg.fast_llm_model)
if args.gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_fast_llm_model(cfg.smart_llm_model)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
if not chosen in supported_memory:
logger.typewriter_log("ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ", Fore.RED, f'{supported_memory}')
logger.typewriter_log(f"Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
else:
cfg.memory_backend = chosen
if args.skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
cfg.skip_reprompt = True
if args.ai_settings_file:
file = args.ai_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 AI Settings File:", Fore.GREEN, file)
cfg.ai_settings_file = file
cfg.skip_reprompt = True
def main():
global ai_name, memory
# TODO: fill in llm values here
check_openai_api_key()
parse_arguments()
logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
ai_name = ""
prompt = construct_prompt()
# print(prompt)
# Initialize variables
full_message_history = []
result = None
next_action_count = 0
# Make a constant:
user_input = "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)
print('Using memory of type: ' + memory.__class__.__name__)
agent = Agent(
ai_name=ai_name,
memory=memory,
full_message_history=full_message_history,
next_action_count=next_action_count,
prompt=prompt,
user_input=user_input
)
agent.start_interaction_loop()
class Agent:
"""Agent class for interacting with Auto-GPT.
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.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if cfg.continuous_mode and cfg.continuous_limit > 0 and loop_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... "):
assistant_reply = chat.chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
# Get command name and arguments
try:
command_name, arguments = cmd.get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply))
if cfg.speak_mode:
speak.say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
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.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True)
while True:
console_input = utils.clean_input(Fore.MAGENTA + "Input:" + Style.RESET_ALL)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(int(console_input.split(" ")[1]))
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.")
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
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
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = f"Command {command_name} threw the following error: " + arguments
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = f"Assistant Reply: {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.full_message_history.append(chat.create_chat_message("system", result))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message(
"system", "Unable to execute command"))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, "Unable to execute command")
if __name__ == "__main__":
main()
# Use the bold ANSI style
print(
f"""{Style.BRIGHT}Please run:
python -m autogpt
"""
)

63
scripts/prompt.py
View File

@ -1,63 +0,0 @@
from promptgenerator import PromptGenerator
def get_prompt():
"""
This function generates a prompt string that includes various constraints, commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Add constraints to the PromptGenerator object
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 = [
("Google Search", "google", {"input": "<search>"}),
("Browse Website", "browse_website", {"url": "<url>", "question": "<what_you_want_to_find_on_website>"}),
("Start GPT Agent", "start_agent", {"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"}),
("Message GPT Agent", "message_agent", {"key": "<key>", "message": "<message>"}),
("List GPT Agents", "list_agents", {}),
("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
("Write to file", "write_to_file", {"file": "<file>", "text": "<text>"}),
("Read file", "read_file", {"file": "<file>"}),
("Append to file", "append_to_file", {"file": "<file>", "text": "<text>"}),
("Delete file", "delete_file", {"file": "<file>"}),
("Search Files", "search_files", {"directory": "<directory>"}),
("Evaluate Code", "evaluate_code", {"code": "<full_code_string>"}),
("Get Improved Code", "improve_code", {"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"}),
("Write Tests", "write_tests", {"code": "<full_code_string>", "focus": "<list_of_focus_areas>"}),
("Execute Python File", "execute_python_file", {"file": "<file>"}),
("Execute Shell Command, non-interactive commands only", "execute_shell", { "command_line": "<command_line>"}),
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
("Generate Image", "generate_image", {"prompt": "<prompt>"}),
("Do Nothing", "do_nothing", {}),
]
# 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
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
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.")
# Generate the prompt string
prompt_string = prompt_generator.generate_prompt_string()
return prompt_string

4
tests.py
View File

@ -1,8 +1,8 @@
import unittest
if __name__ == "__main__":
# Load all tests from the 'scripts/tests' package
suite = unittest.defaultTestLoader.discover('scripts/tests')
# Load all tests from the 'autogpt/tests' package
suite = unittest.defaultTestLoader.discover("autogpt/tests")
# Run the tests
unittest.TextTestRunner().run(suite)

7
tests/context.py
View File

@ -1,5 +1,6 @@
import sys
import os
import sys
sys.path.insert(0, os.path.abspath(
os.path.join(os.path.dirname(__file__), '../scripts')))
sys.path.insert(
0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../scripts"))
)

22
tests/integration/memory_tests.py
View File

@ -1,18 +1,16 @@
import unittest
import random
import string
import sys
import unittest
from pathlib import Path
# Add the parent directory of the 'scripts' folder to the Python path
sys.path.append(str(Path(__file__).resolve().parent.parent.parent / 'scripts'))
from config import Config
from memory.local import LocalCache
from autogpt.config import Config
from autogpt.memory.local import LocalCache
class TestLocalCache(unittest.TestCase):
def random_string(self, length):
return ''.join(random.choice(string.ascii_letters) for _ in range(length))
return "".join(random.choice(string.ascii_letters) for _ in range(length))
def setUp(self):
cfg = cfg = Config()
@ -21,10 +19,10 @@ class TestLocalCache(unittest.TestCase):
# Add example texts to the cache
self.example_texts = [
'The quick brown fox jumps over the lazy dog',
'I love machine learning and natural language processing',
'The cake is a lie, but the pie is always true',
'ChatGPT is an advanced AI model for conversation'
"The quick brown fox jumps over the lazy dog",
"I love machine learning and natural language processing",
"The cake is a lie, but the pie is always true",
"ChatGPT is an advanced AI model for conversation",
]
for text in self.example_texts:
@ -47,5 +45,5 @@ class TestLocalCache(unittest.TestCase):
self.assertIn(self.example_texts[1], relevant_texts)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

24
tests/local_cache_test.py
View File

@ -1,21 +1,23 @@
import os
import sys
# Probably a better way:
sys.path.append(os.path.abspath('../scripts'))
from memory.local import LocalCache
from autogpt.memory.local import LocalCache
def MockConfig():
return type('MockConfig', (object,), {
'debug_mode': False,
'continuous_mode': False,
'speak_mode': False,
'memory_index': 'auto-gpt',
})
return type(
"MockConfig",
(object,),
{
"debug_mode": False,
"continuous_mode": False,
"speak_mode": False,
"memory_index": "auto-gpt",
},
)
class TestLocalCache(unittest.TestCase):
def setUp(self):
self.cfg = MockConfig()
self.cache = LocalCache(self.cfg)
@ -50,5 +52,5 @@ class TestLocalCache(unittest.TestCase):
self.assertEqual(stats, (1, self.cache.data.embeddings.shape))
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

14
tests/promptgenerator_tests.py
View File

@ -1,16 +1,13 @@
# Import the required libraries for unit testing
import unittest
import sys
import os
import sys
import unittest
# Add the path to the "scripts" directory to import the PromptGenerator module
sys.path.append(os.path.abspath("../scripts"))
from promptgenerator import PromptGenerator
from autogpt.promptgenerator import PromptGenerator
# Create a test class for the PromptGenerator, subclassed from unittest.TestCase
class promptgenerator_tests(unittest.TestCase):
# Set up the initial state for each test method by creating an instance of PromptGenerator
def setUp(self):
self.generator = PromptGenerator()
@ -69,7 +66,8 @@ class promptgenerator_tests(unittest.TestCase):
self.generator.add_constraint(constraint)
for command in commands:
self.generator.add_command(
command["label"], command["name"], command["args"])
command["label"], command["name"], command["args"]
)
for resource in resources:
self.generator.add_resource(resource)
for evaluation in evaluations:
@ -97,5 +95,5 @@ class promptgenerator_tests(unittest.TestCase):
# Run the tests when this script is executed
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

6
tests/test_config.py
View File

@ -1,9 +1,9 @@
import unittest
from scripts.config import Config
from autogpt.config import Config
class TestConfig(unittest.TestCase):
def test_singleton(self):
config1 = Config()
config2 = Config()
@ -55,5 +55,5 @@ class TestConfig(unittest.TestCase):
self.assertTrue(config.debug_mode)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

68
tests/test_json_parser.py
View File

@ -1,7 +1,7 @@
import unittest
import tests.context
from scripts.json_parser import fix_and_parse_json
import tests.context
from autogpt.json_parser import fix_and_parse_json
class TestParseJson(unittest.TestCase):
@ -21,7 +21,7 @@ class TestParseJson(unittest.TestCase):
# Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
with self.assertRaises(Exception):
fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
def test_invalid_json_major_without_gpt(self):
# Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@ -51,23 +51,22 @@ class TestParseJson(unittest.TestCase):
}
}"""
good_obj = {
"command": {
"name": "browse_website",
"args": {
"url": "https://github.com/Torantulino/Auto-GPT"
}
},
"thoughts":
{
"text": "I suggest we start browsing the repository to find any issues that we can fix.",
"reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
"plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
"criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
"speak": "I will start browsing the repository to find any issues we can fix."
}
}
"command": {
"name": "browse_website",
"args": {"url": "https://github.com/Torantulino/Auto-GPT"},
},
"thoughts": {
"text": "I suggest we start browsing the repository to find any issues that we can fix.",
"reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
"plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
"criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
"speak": "I will start browsing the repository to find any issues we can fix.",
},
}
# Assert that this raises an exception:
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
)
def test_invalid_json_leading_sentence_with_gpt(self):
# Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@ -90,24 +89,23 @@ class TestParseJson(unittest.TestCase):
}
}"""
good_obj = {
"command": {
"name": "browse_website",
"args": {
"url": "https://github.com/Torantulino/Auto-GPT"
"command": {
"name": "browse_website",
"args": {"url": "https://github.com/Torantulino/Auto-GPT"},
},
"thoughts": {
"text": "Browsing the repository to identify potential bugs",
"reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
"plan": "- Analyze the repository for potential bugs and areas of improvement",
"criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
"speak": "I am browsing the repository to identify potential bugs.",
},
}
},
"thoughts":
{
"text": "Browsing the repository to identify potential bugs",
"reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
"plan": "- Analyze the repository for potential bugs and areas of improvement",
"criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
"speak": "I am browsing the repository to identify potential bugs."
}
}
# Assert that this raises an exception:
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

79
tests/unit/json_tests.py
View File

@ -1,9 +1,6 @@
import unittest
import os
import sys
# Probably a better way:
sys.path.append(os.path.abspath('../scripts'))
from json_parser import fix_and_parse_json
from autogpt.json_parser import fix_and_parse_json
class TestParseJson(unittest.TestCase):
@ -16,12 +13,18 @@ class TestParseJson(unittest.TestCase):
def test_invalid_json_minor(self):
# Test that an invalid JSON string can be fixed with gpt
json_str = '{"name": "John", "age": 30, "city": "New York",}'
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), {"name": "John", "age": 30, "city": "New York"})
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=False),
{"name": "John", "age": 30, "city": "New York"},
)
def test_invalid_json_major_with_gpt(self):
# Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=True), {"name": "John", "age": 30, "city": "New York"})
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=True),
{"name": "John", "age": 30, "city": "New York"},
)
def test_invalid_json_major_without_gpt(self):
# Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@ -51,23 +54,22 @@ class TestParseJson(unittest.TestCase):
}
}"""
good_obj = {
"command": {
"name": "browse_website",
"args": {
"url": "https://github.com/Torantulino/Auto-GPT"
}
},
"thoughts":
{
"text": "I suggest we start browsing the repository to find any issues that we can fix.",
"reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
"plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
"criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
"speak": "I will start browsing the repository to find any issues we can fix."
}
}
"command": {
"name": "browse_website",
"args": {"url": "https://github.com/Torantulino/Auto-GPT"},
},
"thoughts": {
"text": "I suggest we start browsing the repository to find any issues that we can fix.",
"reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
"plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
"criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
"speak": "I will start browsing the repository to find any issues we can fix.",
},
}
# Assert that this raises an exception:
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
)
def test_invalid_json_leading_sentence_with_gpt(self):
# Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@ -90,24 +92,23 @@ class TestParseJson(unittest.TestCase):
}
}"""
good_obj = {
"command": {
"name": "browse_website",
"args": {
"url": "https://github.com/Torantulino/Auto-GPT"
"command": {
"name": "browse_website",
"args": {"url": "https://github.com/Torantulino/Auto-GPT"},
},
"thoughts": {
"text": "Browsing the repository to identify potential bugs",
"reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
"plan": "- Analyze the repository for potential bugs and areas of improvement",
"criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
"speak": "I am browsing the repository to identify potential bugs.",
},
}
},
"thoughts":
{
"text": "Browsing the repository to identify potential bugs",
"reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
"plan": "- Analyze the repository for potential bugs and areas of improvement",
"criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
"speak": "I am browsing the repository to identify potential bugs."
}
}
# Assert that this raises an exception:
self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
self.assertEqual(
fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

14
tests/unit/test_browse_scrape_links.py
View File

@ -1,4 +1,3 @@
# Generated by CodiumAI
# Dependencies:
@ -39,7 +38,6 @@ requests and parse HTML content, respectively.
class TestScrapeLinks:
# Tests that the function returns a list of formatted hyperlinks when
# provided with a valid url that returns a webpage with hyperlinks.
def test_valid_url_with_hyperlinks(self):
@ -54,8 +52,10 @@ class TestScrapeLinks:
# Mock the requests.get() function to return a response with sample HTML containing hyperlinks
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = "<html><body><a href='https://www.google.com'>Google</a></body></html>"
mocker.patch('requests.get', return_value=mock_response)
mock_response.text = (
"<html><body><a href='https://www.google.com'>Google</a></body></html>"
)
mocker.patch("requests.get", return_value=mock_response)
# Call the function with a valid URL
result = scrape_links("https://www.example.com")
@ -68,7 +68,7 @@ class TestScrapeLinks:
# Mock the requests.get() function to return an HTTP error response
mock_response = mocker.Mock()
mock_response.status_code = 404
mocker.patch('requests.get', return_value=mock_response)
mocker.patch("requests.get", return_value=mock_response)
# Call the function with an invalid URL
result = scrape_links("https://www.invalidurl.com")
@ -82,7 +82,7 @@ class TestScrapeLinks:
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = "<html><body><p>No hyperlinks here</p></body></html>"
mocker.patch('requests.get', return_value=mock_response)
mocker.patch("requests.get", return_value=mock_response)
# Call the function with a URL containing no hyperlinks
result = scrape_links("https://www.example.com")
@ -105,7 +105,7 @@ class TestScrapeLinks:
</body>
</html>
"""
mocker.patch('requests.get', return_value=mock_response)
mocker.patch("requests.get", return_value=mock_response)
# Call the function being tested
result = scrape_links("https://www.example.com")

6
tests/unit/test_browse_scrape_text.py
View File

@ -1,9 +1,8 @@
# Generated by CodiumAI
import requests
from scripts.browse import scrape_text
from autogpt.browse import scrape_text
"""
Code Analysis
@ -35,7 +34,6 @@ Additional aspects:
class TestScrapeText:
# Tests that scrape_text() returns the expected text when given a valid URL.
def test_scrape_text_with_valid_url(self, mocker):
# Mock the requests.get() method to return a response with expected text
@ -74,7 +72,7 @@ class TestScrapeText:
# Tests that the function returns an error message when the response status code is an http error (>=400).
def test_http_error(self, mocker):
# Mock the requests.get() method to return a response with a 404 status code
mocker.patch('requests.get', return_value=mocker.Mock(status_code=404))
mocker.patch("requests.get", return_value=mocker.Mock(status_code=404))
# Call the function with a URL
result = scrape_text("https://www.example.com")