Merge branch 'master' into feature/gitignore-additions

# Conflicts:
#	.gitignore

.env.template

@@ -3,6 +3,14 @@
 ################################################################################
 # EXECUTE_LOCAL_COMMANDS - Allow local command execution (Example: False)
 EXECUTE_LOCAL_COMMANDS=False
+# BROWSE_CHUNK_MAX_LENGTH - When browsing website, define the length of chunk stored in memory
+BROWSE_CHUNK_MAX_LENGTH=8192
+# BROWSE_SUMMARY_MAX_TOKEN - Define the maximum length of the summary generated by GPT agent when browsing website
+BROWSE_SUMMARY_MAX_TOKEN=300
+# USER_AGENT - Define the user-agent used by the requests library to browse website (string)
+# 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"
+# AI_SETTINGS_FILE - Specifies which AI Settings file to use (defaults to ai_settings.yaml)
+AI_SETTINGS_FILE=ai_settings.yaml
 
 ################################################################################
 ### LLM PROVIDER
@@ -102,6 +110,10 @@ CUSTOM_SEARCH_ENGINE_ID=your-custom-search-engine-id
 # USE_MAC_OS_TTS - Use Mac OS TTS or not (Default: False)
 USE_MAC_OS_TTS=False
 
+### STREAMELEMENTS
+# USE_BRIAN_TTS - Use Brian TTS or not (Default: False)
+USE_BRIAN_TTS=False
+
 ### ELEVENLABS
 # ELEVENLABS_API_KEY - Eleven Labs API key (Example: my-elevenlabs-api-key)
 # ELEVENLABS_VOICE_1_ID - Eleven Labs voice 1 ID (Example: my-voice-id-1)

.flake8

@@ -0,0 +1,12 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+exclude =
+    .tox,
+    __pycache__,
+    *.pyc,
+    .env
+    venv/*
+    .venv/*
+    reports/*
+    dist/*

.github/workflows/ci.yml

@@ -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: |

.gitignore

@@ -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/*
@@ -16,13 +17,140 @@ last_run_ai_settings.yaml
 .idea/*
 auto-gpt.json
 log.txt
+log-ingestion.txt
+logs
 *.log
 *.mp3
 
-# 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-*

.isort.cfg

@@ -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

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+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

.sourcery.yaml

@@ -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

CONTRIBUTING.md

@@ -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

Dockerfile

@@ -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"]

README.md

@@ -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)
@@ -46,6 +50,7 @@ Your support is greatly appreciated
     - [Setting up environment variables](#setting-up-environment-variables-1)
   - [Setting Your Cache Type](#setting-your-cache-type)
   - [View Memory Usage](#view-memory-usage)
+  - [🧠 Memory pre-seeding](#memory-pre-seeding)
   - [💀 Continuous Mode ⚠️](#-continuous-mode-️)
   - [GPT3.5 ONLY Mode](#gpt35-only-mode)
   - [🖼 Image Generation](#-image-generation)
@@ -69,11 +74,12 @@ Your support is greatly appreciated
   - [vscode + devcontainer](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers): It has been configured in the .devcontainer folder and can be used directly
   - [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
 - [OpenAI API key](https://platform.openai.com/account/api-keys)
-- [PINECONE API key](https://www.pinecone.io/)
+
 
 Optional:
 
-- [ElevenLabs Key](https://elevenlabs.io/) (If you want the AI to speak)
+- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
+- ElevenLabs Key (If you want the AI to speak)
 
 ## 💾 Installation
 
@@ -117,15 +123,15 @@ 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 AUTO-GPT's actions, type "NEXT COMMAND" to authorise them to continue.
-3. To exit the program, type "exit" and press Enter.
+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.
+
 
 ### Logs
 
@@ -134,15 +140,37 @@ 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:
+> Replace anything in angled brackets (<>) to a value you want to specify
+* `python scripts/main.py --help` to see a list of all available command line arguments.
+* `python scripts/main.py --ai-settings <filename>` to run Auto-GPT with a different AI Settings file.
+* `python scripts/main.py --use-memory  <memory-backend>` to specify one of 3 memory backends: `local`, `redis`, `pinecone` or 'no_memory'.
+
+> **NOTE**: There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `python scripts/main.py --help` for more information
+
 ## 🗣️ Speech Mode
 
 Use this to use TTS for Auto-GPT
 
 ```
-python scripts/main.py --speak
+python -m autogpt --speak
 ```
 
 ## 🔍 Google API Keys Configuration
@@ -228,7 +256,10 @@ Pinecone enables the storage of vast amounts of vector-based memory, allowing fo
 
 ### Setting up environment variables
 
-Simply set them in the `.env` file.
+In the `.env` file set:
+- `PINECONE_API_KEY`
+- `PINECONE_ENV` (something like: us-east4-gcp)
+- `MEMORY_BACKEND=pinecone`
 
 Alternatively, you can set them from the command line (advanced):
 
@@ -237,7 +268,7 @@ For Windows Users:
 ```
 setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
 setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
-
+setx MEMORY_BACKEND "pinecone"
 ```
 
 For macOS and Linux users:
@@ -245,7 +276,7 @@ For macOS and Linux users:
 ```
 export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
 export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
-
+export MEMORY_BACKEND="pinecone"
 ```
 
 ## Setting Your Cache Type
@@ -262,6 +293,52 @@ To switch to either, change the `MEMORY_BACKEND` env variable to the value that
 
 1. View memory usage by using the `--debug` flag :)
 
+
+## 🧠 Memory pre-seeding
+
+```
+# python scripts/data_ingestion.py -h 
+usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH]
+
+Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.
+
+options:
+  -h, --help               show this help message and exit
+  --file FILE              The file to ingest.
+  --dir DIR                The directory containing the files to ingest.
+  --init                   Init the memory and wipe its content (default: False)
+  --overlap OVERLAP        The overlap size between chunks when ingesting files (default: 200)
+  --max_length MAX_LENGTH  The max_length of each chunk when ingesting files (default: 4000
+
+# python scripts/data_ingestion.py --dir seed_data --init --overlap 200 --max_length 1000
+```
+
+This script located at scripts/data_ingestion.py, allows you to ingest files into memory and pre-seed it before running Auto-GPT. 
+
+Memory pre-seeding is a technique that involves ingesting relevant documents or data into the AI's memory so that it can use this information to generate more informed and accurate responses.
+
+To pre-seed the memory, the content of each document is split into chunks of a specified maximum length with a specified overlap between chunks, and then each chunk is added to the memory backend set in the .env file. When the AI is prompted to recall information, it can then access those pre-seeded memories to generate more informed and accurate responses.
+
+This technique is particularly useful when working with large amounts of data or when there is specific information that the AI needs to be able to access quickly. 
+By pre-seeding the memory, the AI can retrieve and use this information more efficiently, saving time, API call and improving the accuracy of its responses. 
+
+You could for example download the documentation of an API, a Github repository, etc. and ingest it into memory before running Auto-GPT. 
+
+⚠️ If you use Redis as your memory, make sure to run Auto-GPT with the WIPE_REDIS_ON_START set to False in your .env file.
+
+⚠️For other memory backend, we currently forcefully wipe the memory when starting Auto-GPT. To ingest data with those memory backend, you can call the data_ingestion.py script anytime during an Auto-GPT run. 
+
+Memories will be available to the AI immediately as they are ingested, even if ingested while Auto-GPT is running.
+
+In the example above, the script initializes the memory, ingests all files within the seed_data directory into memory with an overlap between chunks of 200 and a maximum length of each chunk of 4000.
+Note that you can also use the --file argument to ingest a single file into memory and that the script will only ingest files within the auto_gpt_workspace directory.
+
+You can adjust the max_length and overlap parameters to fine-tune the way the docuents are presented to the AI when it "recall" that memory:
+
+- Adjusting the overlap value allows the AI to access more contextual information from each chunk when recalling information, but will result in more chunks being created and therefore increase memory backend usage and OpenAI API requests.
+- Reducing the max_length value will create more chunks, which can save prompt tokens by allowing for more message history in the context, but will also increase the number of chunks.
+- Increasing the max_length value will provide the AI with more contextual information from each chunk, reducing the number of chunks created and saving on OpenAI API requests. However, this may also use more prompt tokens and decrease the overall context available to the AI.
+
 ## 💀 Continuous Mode ⚠️
 
 Run the AI **without** user authorisation, 100% automated.
@@ -269,10 +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
 
 ```
 
@@ -283,7 +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.
@@ -299,6 +376,10 @@ IMAGE_PROVIDER=sd
 HUGGINGFACE_API_TOKEN="YOUR_HUGGINGFACE_API_TOKEN"
 ```
 
+## Selenium
+
+sudo Xvfb :10 -ac -screen 0 1024x768x24 &
+DISPLAY=:10 your-client
 ## ⚠️ Limitations
 
 This experiment aims to showcase the potential of GPT-4 but comes with some limitations:
@@ -356,8 +437,8 @@ 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
 ```

autogpt/__main__.py

@@ -0,0 +1,49 @@
+"""Main script for the autogpt package."""
+import logging
+from autogpt.agent.agent import Agent
+from autogpt.args import parse_arguments
+
+from autogpt.config import Config, check_openai_api_key
+from autogpt.logs import logger
+from autogpt.memory import get_memory
+
+from autogpt.prompt import construct_prompt
+
+# Load environment variables from .env file
+
+
+def main() -> None:
+    """Main function for the script"""
+    cfg = Config()
+    # 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 = []
+    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(f"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()
+
+
+if __name__ == "__main__":
+    main()

autogpt/agent/__init__.py

@@ -0,0 +1,4 @@
+from autogpt.agent.agent import Agent
+from autogpt.agent.agent_manager import AgentManager
+
+__all__ = ["Agent", "AgentManager"]

autogpt/agent/agent.py

@@ -0,0 +1,183 @@
+from colorama import Fore, Style
+from autogpt.app import execute_command, get_command
+
+from autogpt.chat import chat_with_ai, create_chat_message
+from autogpt.config import Config
+from autogpt.json_fixes.bracket_termination import (
+    attempt_to_fix_json_by_finding_outermost_brackets,
+)
+from autogpt.logs import logger, print_assistant_thoughts
+from autogpt.speech import say_text
+from autogpt.spinner import Spinner
+from autogpt.utils import clean_input
+
+
+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
+        command_name = None
+        arguments = None
+        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_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(self.ai_name, assistant_reply)
+
+            # Get command name and arguments
+            try:
+                command_name, arguments = get_command(
+                    attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
+                )
+                if cfg.speak_mode:
+                    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}  "
+                    f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+                print(
+                    "Enter 'y' to authorise command, 'y -N' to run N continuous "
+                    "commands, 'n' to exit program, or enter feedback for "
+                    f"{self.ai_name}...",
+                    flush=True,
+                )
+                while True:
+                    console_input = 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}"
+                    f"  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: "
+                    f"{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(create_chat_message("system", result))
+                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
+            else:
+                self.full_message_history.append(
+                    create_chat_message("system", "Unable to execute command")
+                )
+                logger.typewriter_log(
+                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
+                )

autogpt/agent/agent_manager.py

@@ -0,0 +1,100 @@
+"""Agent manager for managing GPT agents"""
+from typing import List, Tuple, Union
+from autogpt.llm_utils import create_chat_completion
+from autogpt.config.config import Singleton
+
+
+class AgentManager(metaclass=Singleton):
+    """Agent manager for managing GPT agents"""
+
+    def __init__(self):
+        self.next_key = 0
+        self.agents = {}  # key, (task, full_message_history, model)
+
+    # Create new GPT agent
+    # TODO: Centralise use of create_chat_completion() to globally enforce token limit
+
+    def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
+        """Create a new agent and return its key
+
+        Args:
+            task: The task to perform
+            prompt: The prompt to use
+            model: The model to use
+
+        Returns:
+            The key of the new agent
+        """
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+
+        # Start GPT instance
+        agent_reply = create_chat_completion(
+            model=model,
+            messages=messages,
+        )
+
+        # Update full message history
+        messages.append({"role": "assistant", "content": agent_reply})
+
+        key = self.next_key
+        # This is done instead of len(agents) to make keys unique even if agents
+        # are deleted
+        self.next_key += 1
+
+        self.agents[key] = (task, messages, model)
+
+        return key, agent_reply
+
+    def message_agent(self, key: Union[str, int], message: str) -> str:
+        """Send a message to an agent and return its response
+
+        Args:
+            key: The key of the agent to message
+            message: The message to send to the agent
+
+        Returns:
+            The agent's response
+        """
+        task, messages, model = self.agents[int(key)]
+
+        # Add user message to message history before sending to agent
+        messages.append({"role": "user", "content": message})
+
+        # Start GPT instance
+        agent_reply = create_chat_completion(
+            model=model,
+            messages=messages,
+        )
+
+        # Update full message history
+        messages.append({"role": "assistant", "content": agent_reply})
+
+        return agent_reply
+
+    def list_agents(self) -> List[Tuple[Union[str, int], str]]:
+        """Return a list of all agents
+
+        Returns:
+            A list of tuples of the form (key, task)
+        """
+
+        # Return a list of agent keys and their tasks
+        return [(key, task) for key, (task, _, _) in self.agents.items()]
+
+    def delete_agent(self, key: Union[str, int]) -> bool:
+        """Delete an agent from the agent manager
+
+        Args:
+            key: The key of the agent to delete
+
+        Returns:
+            True if successful, False otherwise
+        """
+
+        try:
+            del self.agents[int(key)]
+            return True
+        except KeyError:
+            return False

autogpt/app.py

@@ -0,0 +1,276 @@
+""" Command and Control """
+import json
+from typing import List, NoReturn, Union
+from autogpt.agent.agent_manager import AgentManager
+from autogpt.commands.evaluate_code import evaluate_code
+from autogpt.commands.google_search import google_official_search, google_search
+from autogpt.commands.improve_code import improve_code
+from autogpt.commands.write_tests import write_tests
+from autogpt.config import Config
+from autogpt.commands.image_gen import generate_image
+from autogpt.commands.web_requests import scrape_links, scrape_text
+from autogpt.commands.execute_code import execute_python_file, execute_shell
+from autogpt.commands.file_operations import (
+    append_to_file,
+    delete_file,
+    read_file,
+    search_files,
+    write_to_file,
+)
+from autogpt.json_fixes.parsing import fix_and_parse_json
+from autogpt.memory import get_memory
+from autogpt.processing.text import summarize_text
+from autogpt.speech import say_text
+from autogpt.commands.web_selenium import browse_website
+
+
+CFG = Config()
+AGENT_MANAGER = AgentManager()
+
+
+def is_valid_int(value: str) -> bool:
+    """Check if the value is a valid integer
+
+    Args:
+        value (str): The value to check
+
+    Returns:
+        bool: True if the value is a valid integer, False otherwise
+    """
+    try:
+        int(value)
+        return True
+    except ValueError:
+        return False
+
+
+def get_command(response: str):
+    """Parse the response and return the command name and arguments
+
+    Args:
+        response (str): The response from the user
+
+    Returns:
+        tuple: The command name and arguments
+
+    Raises:
+        json.decoder.JSONDecodeError: If the response is not valid JSON
+
+        Exception: If any other error occurs
+    """
+    try:
+        response_json = fix_and_parse_json(response)
+
+        if "command" not in response_json:
+            return "Error:", "Missing 'command' object in JSON"
+
+        if not isinstance(response_json, dict):
+            return "Error:", f"'response_json' object is not dictionary {response_json}"
+
+        command = response_json["command"]
+        if not isinstance(command, dict):
+            return "Error:", "'command' object is not a dictionary"
+
+        if "name" not in command:
+            return "Error:", "Missing 'name' field in 'command' object"
+
+        command_name = command["name"]
+
+        # Use an empty dictionary if 'args' field is not present in 'command' object
+        arguments = command.get("args", {})
+
+        return command_name, arguments
+    except json.decoder.JSONDecodeError:
+        return "Error:", "Invalid JSON"
+    # All other errors, return "Error: + error message"
+    except Exception as e:
+        return "Error:", str(e)
+
+
+def execute_command(command_name: str, arguments):
+    """Execute the command and return the result
+
+    Args:
+        command_name (str): The name of the command to execute
+        arguments (dict): The arguments for the command
+
+    Returns:
+        str: The result of the command"""
+    memory = get_memory(CFG)
+
+    try:
+        if command_name == "google":
+            # Check if the Google API key is set and use the official search method
+            # If the API key is not set or has only whitespaces, use the unofficial
+            # search method
+            key = CFG.google_api_key
+            if key and key.strip() and key != "your-google-api-key":
+                return google_official_search(arguments["input"])
+            else:
+                return google_search(arguments["input"])
+        elif command_name == "memory_add":
+            return memory.add(arguments["string"])
+        elif command_name == "start_agent":
+            return start_agent(
+                arguments["name"], arguments["task"], arguments["prompt"]
+            )
+        elif command_name == "message_agent":
+            return message_agent(arguments["key"], arguments["message"])
+        elif command_name == "list_agents":
+            return list_agents()
+        elif command_name == "delete_agent":
+            return delete_agent(arguments["key"])
+        elif command_name == "get_text_summary":
+            return get_text_summary(arguments["url"], arguments["question"])
+        elif command_name == "get_hyperlinks":
+            return get_hyperlinks(arguments["url"])
+        elif command_name == "read_file":
+            return read_file(arguments["file"])
+        elif command_name == "write_to_file":
+            return write_to_file(arguments["file"], arguments["text"])
+        elif command_name == "append_to_file":
+            return append_to_file(arguments["file"], arguments["text"])
+        elif command_name == "delete_file":
+            return delete_file(arguments["file"])
+        elif command_name == "search_files":
+            return search_files(arguments["directory"])
+        elif command_name == "browse_website":
+            return browse_website(arguments["url"], arguments["question"])
+        # TODO: Change these to take in a file rather than pasted code, if
+        # non-file is given, return instructions "Input should be a python
+        # filepath, write your code to file and try again"
+        elif command_name == "evaluate_code":
+            return evaluate_code(arguments["code"])
+        elif command_name == "improve_code":
+            return improve_code(arguments["suggestions"], arguments["code"])
+        elif command_name == "write_tests":
+            return write_tests(arguments["code"], arguments.get("focus"))
+        elif command_name == "execute_python_file":  # Add this command
+            return execute_python_file(arguments["file"])
+        elif command_name == "execute_shell":
+            if CFG.execute_local_commands:
+                return execute_shell(arguments["command_line"])
+            else:
+                return (
+                    "You are not allowed to run local shell commands. To execute"
+                    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
+                    "in your config. Do not attempt to bypass the restriction."
+                )
+        elif command_name == "generate_image":
+            return generate_image(arguments["prompt"])
+        elif command_name == "do_nothing":
+            return "No action performed."
+        elif command_name == "task_complete":
+            shutdown()
+        else:
+            return (
+                f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
+                " list for available commands and only respond in the specified JSON"
+                " format."
+            )
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def get_text_summary(url: str, question: str) -> str:
+    """Return the results of a google search
+
+    Args:
+        url (str): The url to scrape
+        question (str): The question to summarize the text for
+
+    Returns:
+        str: The summary of the text
+    """
+    text = scrape_text(url)
+    summary = summarize_text(url, text, question)
+    return f""" "Result" : {summary}"""
+
+
+def get_hyperlinks(url: str) -> Union[str, List[str]]:
+    """Return the results of a google search
+
+    Args:
+        url (str): The url to scrape
+
+    Returns:
+        str or list: The hyperlinks on the page
+    """
+    return scrape_links(url)
+
+
+def shutdown() -> NoReturn:
+    """Shut down the program"""
+    print("Shutting down...")
+    quit()
+
+
+def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
+    """Start an agent with a given name, task, and prompt
+
+    Args:
+        name (str): The name of the agent
+        task (str): The task of the agent
+        prompt (str): The prompt for the agent
+        model (str): The model to use for the agent
+
+    Returns:
+        str: The response of the agent
+    """
+    # Remove underscores from name
+    voice_name = name.replace("_", " ")
+
+    first_message = f"""You are {name}.  Respond with: "Acknowledged"."""
+    agent_intro = f"{voice_name} here, Reporting for duty!"
+
+    # Create agent
+    if CFG.speak_mode:
+        say_text(agent_intro, 1)
+    key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
+
+    if CFG.speak_mode:
+        say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
+
+    # Assign task (prompt), get response
+    agent_response = AGENT_MANAGER.message_agent(key, prompt)
+
+    return f"Agent {name} created with key {key}. First response: {agent_response}"
+
+
+def message_agent(key: str, message: str) -> str:
+    """Message an agent with a given key and message"""
+    # Check if the key is a valid integer
+    if is_valid_int(key):
+        agent_response = AGENT_MANAGER.message_agent(int(key), message)
+    # Check if the key is a valid string
+    elif isinstance(key, str):
+        agent_response = AGENT_MANAGER.message_agent(key, message)
+    else:
+        return "Invalid key, must be an integer or a string."
+
+    # Speak response
+    if CFG.speak_mode:
+        say_text(agent_response, 1)
+    return agent_response
+
+
+def list_agents():
+    """List all agents
+
+    Returns:
+        list: A list of all agents
+    """
+    return AGENT_MANAGER.list_agents()
+
+
+def delete_agent(key: str) -> str:
+    """Delete an agent with a given key
+
+    Args:
+        key (str): The key of the agent to delete
+
+    Returns:
+        str: A message indicating whether the agent was deleted or not
+    """
+    result = AGENT_MANAGER.delete_agent(key)
+    return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

autogpt/args.py

@@ -0,0 +1,128 @@
+"""This module contains the argument parsing logic for the script."""
+import argparse
+
+from colorama import Fore
+from autogpt import utils
+from autogpt.config import Config
+from autogpt.logs import logger
+from autogpt.memory import get_supported_memory_backends
+
+CFG = Config()
+
+
+def parse_arguments() -> None:
+    """Parses the arguments passed to the script
+
+    Returns:
+        None
+    """
+    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 chosen not in supported_memory:
+            logger.typewriter_log(
+                "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
+                Fore.RED,
+                f"{supported_memory}",
+            )
+            logger.typewriter_log("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

autogpt/chat.py

@@ -1,11 +1,11 @@
 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 openai.error import RateLimitError
+
+from autogpt import token_counter
+from autogpt.config import Config
+from autogpt.llm_utils import create_chat_completion
+from autogpt.logs import logger
 
 cfg = Config()
 
@@ -26,75 +26,105 @@ 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):
-    """Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory."""
+    prompt, user_input, full_message_history, permanent_memory, token_limit
+):
+    """Interact with the OpenAI API, sending the prompt, user input, message history,
+    and permanent memory."""
     while True:
         try:
             """
-            Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory.
+            Interact with the OpenAI API, sending the prompt, user input,
+                message history, and permanent memory.
 
             Args:
-            prompt (str): The prompt explaining the rules to the AI.
-            user_input (str): The input from the user.
-            full_message_history (list): The list of all messages sent between the user and the AI.
-            permanent_memory (Obj): The memory object containing the permanent memory.
-            token_limit (int): The maximum number of tokens allowed in the API call.
+                prompt (str): The prompt explaining the rules to the AI.
+                user_input (str): The input from the user.
+                full_message_history (list): The list of all messages sent between the
+                    user and the AI.
+                permanent_memory (Obj): The memory object containing the permanent
+                  memory.
+                token_limit (int): The maximum number of tokens allowed in the API call.
 
             Returns:
             str: The AI's response.
             """
-            model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
+            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])
+                # Add the most recent message to the start of the current context,
+                #  after the two system prompts.
+                current_context.insert(
+                    insertion_index, full_message_history[next_message_to_add_index]
+                )
 
                 # Count the currently used tokens
                 current_tokens_used += tokens_to_add
@@ -107,7 +137,9 @@ def chat_with_ai(
 
             # Calculate remaining tokens
             tokens_remaining = token_limit - current_tokens_used
-            # assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
+            # assert tokens_remaining >= 0, "Tokens remaining is negative.
+            # This should never happen, please submit a bug report at
+            #  https://www.github.com/Torantulino/Auto-GPT"
 
             # Debug print the current context
             logger.debug(f"Token limit: {token_limit}")
@@ -122,7 +154,8 @@ def chat_with_ai(
                 logger.debug("")
             logger.debug("----------- END OF CONTEXT ----------------")
 
-            # TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
+            # TODO: use a model defined elsewhere, so that model can contain
+            # temperature and other settings we care about
             assistant_reply = create_chat_completion(
                 model=model,
                 messages=current_context,
@@ -130,15 +163,13 @@ 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:
+        except RateLimitError:
             # TODO: When we switch to langchain, this is built in
             print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
             time.sleep(10)

autogpt/commands/evaluate_code.py

@@ -0,0 +1,25 @@
+"""Code evaluation module."""
+from typing import List
+
+from autogpt.llm_utils import call_ai_function
+
+
+def evaluate_code(code: str) -> List[str]:
+    """
+    A function that takes in a string and returns a response from create chat
+      completion api call.
+
+    Parameters:
+        code (str): Code to be evaluated.
+    Returns:
+        A result string from create chat completion. A list of suggestions to
+            improve the code.
+    """
+
+    function_string = "def analyze_code(code: str) -> List[str]:"
+    args = [code]
+    description_string = (
+        "Analyzes the given code and returns a list of suggestions" " for improvements."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/commands/execute_code.py

@@ -0,0 +1,125 @@
+"""Execute code in a Docker container"""
+import os
+from pathlib import Path
+import subprocess
+
+import docker
+from docker.errors import ImageNotFound
+
+WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
+
+
+def execute_python_file(file: str):
+    """Execute a Python file in a Docker container and return the output
+
+    Args:
+        file (str): The name of the file to execute
+
+    Returns:
+        str: The output of the file
+    """
+
+    print(f"Executing file '{file}' in workspace '{WORKING_DIRECTORY}'")
+
+    if not file.endswith(".py"):
+        return "Error: Invalid file type. Only .py files are allowed."
+
+    file_path = os.path.join(WORKING_DIRECTORY, file)
+
+    if not os.path.isfile(file_path):
+        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
+        )
+        if result.returncode == 0:
+            return result.stdout
+        else:
+            return f"Error: {result.stderr}"
+
+    try:
+        client = docker.from_env()
+
+        image_name = "python:3.10"
+        try:
+            client.images.get(image_name)
+            print(f"Image '{image_name}' found locally")
+        except ImageNotFound:
+            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")
+                if status and progress:
+                    print(f"{status}: {progress}")
+                elif status:
+                    print(status)
+
+        # You can replace 'python:3.8' with the desired Python image/version
+        # You can find available Python images on Docker Hub:
+        # https://hub.docker.com/_/python
+        container = client.containers.run(
+            image_name,
+            f"python {file}",
+            volumes={
+                os.path.abspath(WORKING_DIRECTORY): {
+                    "bind": "/workspace",
+                    "mode": "ro",
+                }
+            },
+            working_dir="/workspace",
+            stderr=True,
+            stdout=True,
+            detach=True,
+        )
+
+        container.wait()
+        logs = container.logs().decode("utf-8")
+        container.remove()
+
+        # print(f"Execution complete. Output: {output}")
+        # print(f"Logs: {logs}")
+
+        return logs
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def execute_shell(command_line: str) -> str:
+    """Execute a shell command and return the output
+
+    Args:
+        command_line (str): The command line to execute
+
+    Returns:
+        str: The output of the command
+    """
+    current_dir = os.getcwd()
+
+    if WORKING_DIRECTORY not in current_dir:  # Change dir into workspace if necessary
+        work_dir = os.path.join(os.getcwd(), WORKING_DIRECTORY)
+        os.chdir(work_dir)
+
+    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}"
+
+    # Change back to whatever the prior working dir was
+
+    os.chdir(current_dir)
+
+    return output
+
+
+def we_are_running_in_a_docker_container() -> bool:
+    """Check if we are running in a Docker container
+
+    Returns:
+        bool: True if we are running in a Docker container, False otherwise
+    """
+    return os.path.exists("/.dockerenv")

autogpt/commands/file_operations.py

@@ -0,0 +1,196 @@
+"""File operations for AutoGPT"""
+import os
+import os.path
+from pathlib import Path
+from typing import Generator, List
+
+# Set a dedicated folder for file I/O
+WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
+
+# Create the directory if it doesn't exist
+if not os.path.exists(WORKING_DIRECTORY):
+    os.makedirs(WORKING_DIRECTORY)
+
+WORKING_DIRECTORY = str(WORKING_DIRECTORY)
+
+
+def safe_join(base: str, *paths) -> str:
+    """Join one or more path components intelligently.
+
+    Args:
+        base (str): The base path
+        *paths (str): The paths to join to the base path
+
+    Returns:
+        str: The joined path
+    """
+    new_path = os.path.join(base, *paths)
+    norm_new_path = os.path.normpath(new_path)
+
+    if os.path.commonprefix([base, norm_new_path]) != base:
+        raise ValueError("Attempted to access outside of working directory.")
+
+    return norm_new_path
+
+
+def split_file(
+    content: str, max_length: int = 4000, overlap: int = 0
+) -> Generator[str, None, None]:
+    """
+    Split text into chunks of a specified maximum length with a specified overlap
+    between chunks.
+
+    :param text: The input text to be split into chunks
+    :param max_length: The maximum length of each chunk,
+        default is 4000 (about 1k token)
+    :param overlap: The number of overlapping characters between chunks,
+        default is no overlap
+    :return: A generator yielding chunks of text
+    """
+    start = 0
+    content_length = len(content)
+
+    while start < content_length:
+        end = start + max_length
+        if end + overlap < content_length:
+            chunk = content[start : end + overlap]
+        else:
+            chunk = content[start:content_length]
+        yield chunk
+        start += max_length - overlap
+
+
+def read_file(filename: str) -> str:
+    """Read a file and return the contents
+
+    Args:
+        filename (str): The name of the file to read
+
+    Returns:
+        str: The contents of the file
+    """
+    try:
+        filepath = safe_join(WORKING_DIRECTORY, filename)
+        with open(filepath, "r", encoding="utf-8") as f:
+            content = f.read()
+        return content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def ingest_file(
+    filename: str, memory, max_length: int = 4000, overlap: int = 200
+) -> None:
+    """
+    Ingest a file by reading its content, splitting it into chunks with a specified
+    maximum length and overlap, and adding the chunks to the memory storage.
+
+    :param filename: The name of the file to ingest
+    :param memory: An object with an add() method to store the chunks in memory
+    :param max_length: The maximum length of each chunk, default is 4000
+    :param overlap: The number of overlapping characters between chunks, default is 200
+    """
+    try:
+        print(f"Working with file {filename}")
+        content = read_file(filename)
+        content_length = len(content)
+        print(f"File length: {content_length} characters")
+
+        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
+
+        num_chunks = len(chunks)
+        for i, chunk in enumerate(chunks):
+            print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
+            memory_to_add = (
+                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
+            )
+
+            memory.add(memory_to_add)
+
+        print(f"Done ingesting {num_chunks} chunks from {filename}.")
+    except Exception as e:
+        print(f"Error while ingesting file '{filename}': {str(e)}")
+
+
+def write_to_file(filename: str, text: str) -> str:
+    """Write text to a file
+
+    Args:
+        filename (str): The name of the file to write to
+        text (str): The text to write to the file
+
+    Returns:
+        str: A message indicating success or failure
+    """
+    try:
+        filepath = safe_join(WORKING_DIRECTORY, filename)
+        directory = os.path.dirname(filepath)
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write(text)
+        return "File written to successfully."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def append_to_file(filename: str, text: str) -> str:
+    """Append text to a file
+
+    Args:
+        filename (str): The name of the file to append to
+        text (str): The text to append to the file
+
+    Returns:
+        str: A message indicating success or failure
+    """
+    try:
+        filepath = safe_join(WORKING_DIRECTORY, filename)
+        with open(filepath, "a") as f:
+            f.write(text)
+        return "Text appended successfully."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def delete_file(filename: str) -> str:
+    """Delete a file
+
+    Args:
+        filename (str): The name of the file to delete
+
+    Returns:
+        str: A message indicating success or failure
+    """
+    try:
+        filepath = safe_join(WORKING_DIRECTORY, filename)
+        os.remove(filepath)
+        return "File deleted successfully."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def search_files(directory: str) -> List[str]:
+    """Search for files in a directory
+
+    Args:
+        directory (str): The directory to search in
+
+    Returns:
+        List[str]: A list of files found in the directory
+    """
+    found_files = []
+
+    if directory in {"", "/"}:
+        search_directory = WORKING_DIRECTORY
+    else:
+        search_directory = safe_join(WORKING_DIRECTORY, directory)
+
+    for root, _, files in os.walk(search_directory):
+        for file in files:
+            if file.startswith("."):
+                continue
+            relative_path = os.path.relpath(os.path.join(root, file), WORKING_DIRECTORY)
+            found_files.append(relative_path)
+
+    return found_files

autogpt/commands/google_search.py

@@ -0,0 +1,86 @@
+"""Google search command for Autogpt."""
+import json
+from typing import List, Union
+
+from duckduckgo_search import ddg
+
+from autogpt.config import Config
+
+CFG = Config()
+
+
+def google_search(query: str, num_results: int = 8) -> str:
+    """Return the results of a google search
+
+    Args:
+        query (str): The search query.
+        num_results (int): The number of results to return.
+
+    Returns:
+        str: The results of the search.
+    """
+    search_results = []
+    if not query:
+        return json.dumps(search_results)
+
+    results = ddg(query, max_results=num_results)
+    if not results:
+        return json.dumps(search_results)
+
+    for j in results:
+        search_results.append(j)
+
+    return json.dumps(search_results, ensure_ascii=False, indent=4)
+
+
+def google_official_search(query: str, num_results: int = 8) -> Union[str, List[str]]:
+    """Return the results of a google search using the official Google API
+
+    Args:
+        query (str): The search query.
+        num_results (int): The number of results to return.
+
+    Returns:
+        str: The results of the search.
+    """
+
+    from googleapiclient.discovery import build
+    from googleapiclient.errors import HttpError
+
+    try:
+        # Get the Google API key and Custom Search Engine ID from the config file
+        api_key = CFG.google_api_key
+        custom_search_engine_id = CFG.custom_search_engine_id
+
+        # Initialize the Custom Search API service
+        service = build("customsearch", "v1", developerKey=api_key)
+
+        # Send the search query and retrieve the results
+        result = (
+            service.cse()
+            .list(q=query, cx=custom_search_engine_id, num=num_results)
+            .execute()
+        )
+
+        # Extract the search result items from the response
+        search_results = result.get("items", [])
+
+        # Create a list of only the URLs from the search results
+        search_results_links = [item["link"] for item in search_results]
+
+    except HttpError as e:
+        # Handle errors in the API call
+        error_details = json.loads(e.content.decode())
+
+        # Check if the error is related to an invalid or missing API key
+        if error_details.get("error", {}).get(
+            "code"
+        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
+            "message", ""
+        ):
+            return "Error: The provided Google API key is invalid or missing."
+        else:
+            return f"Error: {e}"
+
+    # Return the list of search result URLs
+    return search_results_links

autogpt/commands/image_gen.py

@@ -0,0 +1,99 @@
+""" Image Generation Module for AutoGPT."""
+import io
+import os.path
+import uuid
+from base64 import b64decode
+
+import openai
+import requests
+from PIL import Image
+from pathlib import Path
+from autogpt.config import Config
+
+CFG = Config()
+
+WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
+
+
+def generate_image(prompt: str) -> str:
+    """Generate an image from a prompt.
+
+    Args:
+        prompt (str): The prompt to use
+
+    Returns:
+        str: The filename of the image
+    """
+    filename = f"{str(uuid.uuid4())}.jpg"
+
+    # DALL-E
+    if CFG.image_provider == "dalle":
+        return generate_image_with_dalle(prompt, filename)
+    elif CFG.image_provider == "sd":
+        return generate_image_with_hf(prompt, filename)
+    else:
+        return "No Image Provider Set"
+
+
+def generate_image_with_hf(prompt: str, filename: str) -> str:
+    """Generate an image with HuggingFace's API.
+
+    Args:
+        prompt (str): The prompt to use
+        filename (str): The filename to save the image to
+
+    Returns:
+        str: The filename of the image
+    """
+    API_URL = (
+        "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+    )
+    if CFG.huggingface_api_token is None:
+        raise ValueError(
+            "You need to set your Hugging Face API token in the config file."
+        )
+    headers = {"Authorization": f"Bearer {CFG.huggingface_api_token}"}
+
+    response = requests.post(
+        API_URL,
+        headers=headers,
+        json={
+            "inputs": prompt,
+        },
+    )
+
+    image = Image.open(io.BytesIO(response.content))
+    print(f"Image Generated for prompt:{prompt}")
+
+    image.save(os.path.join(WORKING_DIRECTORY, filename))
+
+    return f"Saved to disk:{filename}"
+
+
+def generate_image_with_dalle(prompt: str, filename: str) -> str:
+    """Generate an image with DALL-E.
+
+    Args:
+        prompt (str): The prompt to use
+        filename (str): The filename to save the image to
+
+    Returns:
+        str: The filename of the image
+    """
+    openai.api_key = CFG.openai_api_key
+
+    response = openai.Image.create(
+        prompt=prompt,
+        n=1,
+        size="256x256",
+        response_format="b64_json",
+    )
+
+    print(f"Image Generated for prompt:{prompt}")
+
+    image_data = b64decode(response["data"][0]["b64_json"])
+
+    with open(f"{WORKING_DIRECTORY}/{filename}", mode="wb") as png:
+        png.write(image_data)
+
+    return f"Saved to disk:{filename}"

autogpt/commands/improve_code.py

@@ -0,0 +1,28 @@
+import json
+from typing import List
+
+from autogpt.llm_utils import call_ai_function
+
+
+def improve_code(suggestions: List[str], code: str) -> str:
+    """
+    A function that takes in code and suggestions and returns a response from create
+      chat completion api call.
+
+    Parameters:
+        suggestions (List): A list of suggestions around what needs to be improved.
+        code (str): Code to be improved.
+    Returns:
+        A result string from create chat completion. Improved code in response.
+    """
+
+    function_string = (
+        "def generate_improved_code(suggestions: List[str], code: str) -> str:"
+    )
+    args = [json.dumps(suggestions), code]
+    description_string = (
+        "Improves the provided code based on the suggestions"
+        " provided, making no other changes."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/commands/times.py

@@ -0,0 +1,10 @@
+from datetime import datetime
+
+
+def get_datetime() -> str:
+    """Return the current date and time
+
+    Returns:
+        str: The current date and time
+    """
+    return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")

autogpt/commands/web_requests.py

@@ -0,0 +1,198 @@
+"""Browse a webpage and summarize it using the LLM model"""
+from typing import List, Tuple, Union
+from urllib.parse import urljoin, urlparse
+
+import requests
+from requests import Response
+from bs4 import BeautifulSoup
+
+from autogpt.config import Config
+from autogpt.memory import get_memory
+
+CFG = Config()
+memory = get_memory(CFG)
+
+session = requests.Session()
+session.headers.update({"User-Agent": CFG.user_agent})
+
+
+def is_valid_url(url: str) -> bool:
+    """Check if the URL is valid
+
+    Args:
+        url (str): The URL to check
+
+    Returns:
+        bool: True if the URL is valid, False otherwise
+    """
+    try:
+        result = urlparse(url)
+        return all([result.scheme, result.netloc])
+    except ValueError:
+        return False
+
+
+def sanitize_url(url: str) -> str:
+    """Sanitize the URL
+
+    Args:
+        url (str): The URL to sanitize
+
+    Returns:
+        str: The sanitized URL
+    """
+    return urljoin(url, urlparse(url).path)
+
+
+def check_local_file_access(url: str) -> bool:
+    """Check if the URL is a local file
+
+    Args:
+        url (str): The URL to check
+
+    Returns:
+        bool: True if the URL is a local file, False otherwise
+    """
+    local_prefixes = [
+        "file:///",
+        "file://localhost",
+        "http://localhost",
+        "https://localhost",
+    ]
+    return any(url.startswith(prefix) for prefix in local_prefixes)
+
+
+def get_response(
+    url: str, timeout: int = 10
+) -> Union[Tuple[None, str], Tuple[Response, None]]:
+    """Get the response from a URL
+
+    Args:
+        url (str): The URL to get the response from
+        timeout (int): The timeout for the HTTP request
+
+    Returns:
+        tuple[None, str] | tuple[Response, None]: The response and error message
+
+    Raises:
+        ValueError: If the URL is invalid
+        requests.exceptions.RequestException: If the HTTP request fails
+    """
+    try:
+        # Restrict access to local files
+        if check_local_file_access(url):
+            raise ValueError("Access to local files is restricted")
+
+        # Most basic check if the URL is valid:
+        if not url.startswith("http://") and not url.startswith("https://"):
+            raise ValueError("Invalid URL format")
+
+        sanitized_url = sanitize_url(url)
+
+        response = session.get(sanitized_url, timeout=timeout)
+
+        # Check if the response contains an HTTP error
+        if response.status_code >= 400:
+            return None, f"Error: HTTP {str(response.status_code)} error"
+
+        return response, None
+    except ValueError as ve:
+        # Handle invalid URL format
+        return None, f"Error: {str(ve)}"
+
+    except requests.exceptions.RequestException as re:
+        # Handle exceptions related to the HTTP request
+        #  (e.g., connection errors, timeouts, etc.)
+        return None, f"Error: {str(re)}"
+
+
+def scrape_text(url: str) -> str:
+    """Scrape text from a webpage
+
+    Args:
+        url (str): The URL to scrape text from
+
+    Returns:
+        str: The scraped text
+    """
+    response, error_message = get_response(url)
+    if error_message:
+        return error_message
+    if not response:
+        return "Error: Could not get response"
+
+    soup = BeautifulSoup(response.text, "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 text
+
+
+def extract_hyperlinks(soup: BeautifulSoup) -> List[Tuple[str, str]]:
+    """Extract hyperlinks from a BeautifulSoup object
+
+    Args:
+        soup (BeautifulSoup): The BeautifulSoup object
+
+    Returns:
+        List[Tuple[str, str]]: The extracted hyperlinks
+    """
+    hyperlinks = []
+    for link in soup.find_all("a", href=True):
+        hyperlinks.append((link.text, link["href"]))
+    return hyperlinks
+
+
+def format_hyperlinks(hyperlinks: List[Tuple[str, str]]) -> List[str]:
+    """Format hyperlinks into a list of strings
+
+    Args:
+        hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
+
+    Returns:
+        List[str]: The formatted hyperlinks
+    """
+    formatted_links = []
+    for link_text, link_url in hyperlinks:
+        formatted_links.append(f"{link_text} ({link_url})")
+    return formatted_links
+
+
+def scrape_links(url: str) -> Union[str, List[str]]:
+    """Scrape links from a webpage
+
+    Args:
+        url (str): The URL to scrape links from
+
+    Returns:
+        Union[str, List[str]]: The scraped links
+    """
+    response, error_message = get_response(url)
+    if error_message:
+        return error_message
+    if not response:
+        return "Error: Could not get response"
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    hyperlinks = extract_hyperlinks(soup)
+
+    return format_hyperlinks(hyperlinks)
+
+
+def create_message(chunk, question):
+    """Create a message for the user to summarize a chunk of text"""
+    return {
+        "role": "user",
+        "content": f'"""{chunk}""" Using the above text, please answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the'
+        " text, please summarize the text.",
+    }

autogpt/commands/web_selenium.py

@@ -0,0 +1,145 @@
+"""Selenium web scraping module."""
+from selenium import webdriver
+import autogpt.processing.text as summary
+from bs4 import BeautifulSoup
+from selenium.webdriver.remote.webdriver import WebDriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.wait import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.chrome.options import Options
+import logging
+from pathlib import Path
+from autogpt.config import Config
+
+FILE_DIR = Path(__file__).parent.parent
+CFG = Config()
+
+
+def browse_website(url: str, question: str) -> tuple[str, WebDriver]:
+    """Browse a website and return the answer and links to the user
+
+    Args:
+        url (str): The url of the website to browse
+        question (str): The question asked by the user
+
+    Returns:
+        tuple[str, WebDriver]: The answer and links to the user and the webdriver
+    """
+    driver, text = scrape_text_with_selenium(url)
+    add_header(driver)
+    summary_text = summary.summarize_text(url, text, question, driver)
+    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: str) -> tuple[WebDriver, str]:
+    """Scrape text from a website using selenium
+
+    Args:
+        url (str): The url of the website to scrape
+
+    Returns:
+        tuple[WebDriver, str]: The webdriver and the text scraped from the website
+    """
+    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: WebDriver) -> list[str]:
+    """Scrape links from a website using selenium
+
+    Args:
+        driver (WebDriver): The webdriver to use to scrape the links
+
+    Returns:
+        list[str]: The links scraped from the website
+    """
+    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: WebDriver) -> None:
+    """Close the browser
+
+    Args:
+        driver (WebDriver): The webdriver to close
+
+    Returns:
+        None
+    """
+    driver.quit()
+
+
+def extract_hyperlinks(soup: BeautifulSoup) -> list[tuple[str, str]]:
+    """Extract hyperlinks from a BeautifulSoup object
+
+    Args:
+        soup (BeautifulSoup): The BeautifulSoup object to extract the hyperlinks from
+
+    Returns:
+        list[tuple[str, str]]: The hyperlinks extracted from the BeautifulSoup object
+    """
+    return [(link.text, link["href"]) for link in soup.find_all("a", href=True)]
+
+
+def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]:
+    """Format hyperlinks to be displayed to the user
+
+    Args:
+        hyperlinks (list[tuple[str, str]]): The hyperlinks to format
+
+    Returns:
+        list[str]: The formatted hyperlinks
+    """
+    return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
+
+
+def add_header(driver: WebDriver) -> None:
+    """Add a header to the website
+
+    Args:
+        driver (WebDriver): The webdriver to use to add the header
+
+    Returns:
+        None
+    """
+    driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

autogpt/commands/write_tests.py

@@ -0,0 +1,29 @@
+"""A module that contains a function to generate test cases for the submitted code."""
+import json
+from typing import List
+from autogpt.llm_utils import call_ai_function
+
+
+def write_tests(code: str, focus: List[str]) -> str:
+    """
+    A function that takes in code and focus topics and returns a response from create
+      chat completion api call.
+
+    Parameters:
+        focus (List): A list of suggestions around what needs to be improved.
+        code (str): Code for test cases to be generated against.
+    Returns:
+        A result string from create chat completion. Test cases for the submitted code
+          in response.
+    """
+
+    function_string = (
+        "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
+    )
+    args = [code, json.dumps(focus)]
+    description_string = (
+        "Generates test cases for the existing code, focusing on"
+        " specific areas if required."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/config/__init__.py

@@ -0,0 +1,14 @@
+"""
+This module contains the configuration classes for AutoGPT.
+"""
+from autogpt.config.ai_config import AIConfig
+from autogpt.config.config import check_openai_api_key, Config
+from autogpt.config.singleton import AbstractSingleton, Singleton
+
+__all__ = [
+    "check_openai_api_key",
+    "AbstractSingleton",
+    "AIConfig",
+    "Config",
+    "Singleton",
+]

autogpt/config/ai_config.py

@@ -1,6 +1,10 @@
-import yaml
+# sourcery skip: do-not-use-staticmethod
+"""
+A module that contains the AIConfig class object that contains the configuration
+"""
 import os
-from prompt import get_prompt
+from typing import List, Optional, Type
+import yaml
 
 
 class AIConfig:
@@ -13,7 +17,9 @@ 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: Optional[List] = None
+    ) -> None:
         """
         Initialize a class instance
 
@@ -24,30 +30,32 @@ class AIConfig:
         Returns:
             None
         """
-
+        if ai_goals is None:
+            ai_goals = []
         self.ai_name = ai_name
         self.ai_role = ai_role
         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:
+    @staticmethod
+    def load(config_file: str = SAVE_FILE) -> "AIConfig":
         """
-        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
+        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.
 
         Parameters:
-           cls (class object): An AIConfig Class object.
-           config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+           config_file (int): The path to the config yaml file.
+             DEFAULT: "../ai_settings.yaml"
 
         Returns:
             cls (object): An instance of given cls object
         """
 
         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 = {}
@@ -55,22 +63,27 @@ class AIConfig:
         ai_name = config_params.get("ai_name", "")
         ai_role = config_params.get("ai_role", "")
         ai_goals = config_params.get("ai_goals", [])
+        # type: Type[AIConfig]
+        return AIConfig(ai_name, ai_role, ai_goals)
 
-        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.
 
         Parameters:
-            config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+            config_file(str): The path to the config yaml file.
+              DEFAULT: "../ai_settings.yaml"
 
         Returns:
             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:
@@ -81,13 +94,23 @@ class AIConfig:
             None
 
         Returns:
-            full_prompt (str): A string containing the initial prompt for the user including the ai_name, ai_role and ai_goals.
+            full_prompt (str): A string containing the initial prompt for the user
+              including the ai_name, ai_role and ai_goals.
         """
 
-        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."""
+        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."
+            ""
+        )
+
+        from autogpt.prompt import get_prompt
 
         # 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"
 

autogpt/config/config.py

@@ -1,30 +1,15 @@
-import abc
+"""Configuration class to store the state of bools for different scripts access."""
 import os
+from colorama import Fore
+
+from autogpt.config.singleton import Singleton
+
 import openai
 import yaml
+
 from dotenv import load_dotenv
-# Load environment variables from .env file
-load_dotenv()
 
-
-class Singleton(abc.ABCMeta, type):
-    """
-    Singleton metaclass for ensuring only one instance of a class.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        """Call method for the singleton metaclass."""
-        if cls not in cls._instances:
-            cls._instances[cls] = super(
-                Singleton, cls).__call__(
-                *args, **kwargs)
-        return cls._instances[cls]
-
-
-class AbstractSingleton(abc.ABC, metaclass=Singleton):
-    pass
+load_dotenv(verbose=True)
 
 
 class Config(metaclass=Singleton):
@@ -32,22 +17,28 @@ class Config(metaclass=Singleton):
     Configuration class to store the state of bools for different scripts access.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         """Initialize the Config class"""
         self.debug_mode = False
         self.continuous_mode = False
         self.continuous_limit = 0
         self.speak_mode = False
+        self.skip_reprompt = False
 
+        self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
         self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
         self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
         self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
         self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
+        self.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.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()
@@ -62,6 +53,9 @@ class Config(metaclass=Singleton):
         self.use_mac_os_tts = False
         self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
 
+        self.use_brian_tts = False
+        self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
+
         self.google_api_key = os.getenv("GOOGLE_API_KEY")
         self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
 
@@ -72,16 +66,21 @@ class Config(metaclass=Singleton):
         self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
 
         # 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_header = {"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"}
+        # 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.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
 
@@ -96,19 +95,26 @@ class Config(metaclass=Singleton):
             The matching deployment id if found, otherwise an empty string.
         """
         if model == self.fast_llm_model:
-            return self.azure_model_to_deployment_id_map["fast_llm_model_deployment_id"]
+            return self.azure_model_to_deployment_id_map[
+                "fast_llm_model_deployment_id"
+            ]  # type: ignore
         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"
+            ]  # type: ignore
         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"
+            ]  # type: ignore
         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.
+        Loads the configuration parameters for Azure hosting from the specified file
+          path as a yaml file.
 
         Parameters:
             config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
@@ -121,71 +127,97 @@ 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):
+    def set_continuous_mode(self, value: bool) -> None:
         """Set the continuous mode value."""
         self.continuous_mode = value
 
-    def set_continuous_limit(self, value: int):
+    def set_continuous_limit(self, value: int) -> None:
         """Set the continuous limit value."""
         self.continuous_limit = value
 
-    def set_speak_mode(self, value: bool):
+    def set_speak_mode(self, value: bool) -> None:
         """Set the speak mode value."""
         self.speak_mode = value
 
-    def set_fast_llm_model(self, value: str):
+    def set_fast_llm_model(self, value: str) -> None:
         """Set the fast LLM model value."""
         self.fast_llm_model = value
 
-    def set_smart_llm_model(self, value: str):
+    def set_smart_llm_model(self, value: str) -> None:
         """Set the smart LLM model value."""
         self.smart_llm_model = value
 
-    def set_fast_token_limit(self, value: int):
+    def set_fast_token_limit(self, value: int) -> None:
         """Set the fast token limit value."""
         self.fast_token_limit = value
 
-    def set_smart_token_limit(self, value: int):
+    def set_smart_token_limit(self, value: int) -> None:
         """Set the smart token limit value."""
         self.smart_token_limit = value
 
-    def set_openai_api_key(self, value: str):
+    def set_browse_chunk_max_length(self, value: int) -> None:
+        """Set the browse_website command chunk max length value."""
+        self.browse_chunk_max_length = value
+
+    def set_browse_summary_max_token(self, value: int) -> None:
+        """Set the browse_website command summary max token value."""
+        self.browse_summary_max_token = value
+
+    def set_openai_api_key(self, value: str) -> None:
         """Set the OpenAI API key value."""
         self.openai_api_key = value
 
-    def set_elevenlabs_api_key(self, value: str):
+    def set_elevenlabs_api_key(self, value: str) -> None:
         """Set the ElevenLabs API key value."""
         self.elevenlabs_api_key = value
 
-    def set_elevenlabs_voice_1_id(self, value: str):
+    def set_elevenlabs_voice_1_id(self, value: str) -> None:
         """Set the ElevenLabs Voice 1 ID value."""
         self.elevenlabs_voice_1_id = value
 
-    def set_elevenlabs_voice_2_id(self, value: str):
+    def set_elevenlabs_voice_2_id(self, value: str) -> None:
         """Set the ElevenLabs Voice 2 ID value."""
         self.elevenlabs_voice_2_id = value
 
-    def set_google_api_key(self, value: str):
+    def set_google_api_key(self, value: str) -> None:
         """Set the Google API key value."""
         self.google_api_key = value
 
-    def set_custom_search_engine_id(self, value: str):
+    def set_custom_search_engine_id(self, value: str) -> None:
         """Set the custom search engine id value."""
         self.custom_search_engine_id = value
 
-    def set_pinecone_api_key(self, value: str):
+    def set_pinecone_api_key(self, value: str) -> None:
         """Set the Pinecone API key value."""
         self.pinecone_api_key = value
 
-    def set_pinecone_region(self, value: str):
+    def set_pinecone_region(self, value: str) -> None:
         """Set the Pinecone region value."""
         self.pinecone_region = value
 
-    def set_debug_mode(self, value: bool):
+    def set_debug_mode(self, value: bool) -> None:
         """Set the debug mode value."""
         self.debug_mode = value
+
+
+def check_openai_api_key() -> None:
+    """Check if the OpenAI API key is set in config.py or as an environment variable."""
+    cfg = Config()
+    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)

autogpt/config/singleton.py

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

autogpt/data_ingestion.py

@@ -0,0 +1,96 @@
+import argparse
+import logging
+
+from autogpt.config import Config
+from autogpt.commands.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")
+
+
+def ingest_directory(directory, memory, args):
+    """
+    Ingest all files in a directory by calling the ingest_file function for each file.
+
+    :param directory: The directory containing the files to ingest
+    :param memory: An object with an add() method to store the chunks in memory
+    """
+    try:
+        files = search_files(directory)
+        for file in files:
+            ingest_file(file, memory, args.max_length, args.overlap)
+    except Exception as e:
+        print(f"Error while ingesting directory '{directory}': {str(e)}")
+
+
+def main() -> None:
+    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."
+    )
+    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,
+    )
+
+    args = parser.parse_args()
+
+    # Initialize memory
+    memory = get_memory(cfg, init=args.init)
+    print("Using memory of type: " + memory.__class__.__name__)
+
+    if args.file:
+        try:
+            ingest_file(args.file, memory, args.max_length, args.overlap)
+            print(f"File '{args.file}' ingested successfully.")
+        except Exception as e:
+            logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
+            print(f"Error while ingesting file '{args.file}': {str(e)}")
+    elif args.dir:
+        try:
+            ingest_directory(args.dir, memory, args)
+            print(f"Directory '{args.dir}' ingested successfully.")
+        except Exception as e:
+            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."
+        )
+
+
+if __name__ == "__main__":
+    main()

autogpt/js/overlay.js

@@ -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);

autogpt/json_fixes/auto_fix.py

@@ -0,0 +1,40 @@
+"""This module contains the function to fix JSON strings using GPT-3."""
+import json
+from autogpt.llm_utils import call_ai_function
+from autogpt.logs import logger
+
+
+def fix_json(json_str: str, schema: str) -> str:
+    """Fix the given JSON string to make it parseable and fully compliant with the provided schema."""
+    # 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"
+        " when the format is incorrect."
+    )
+
+    # If it doesn't already start with a "`", add one:
+    if not json_str.startswith("`"):
+        json_str = "```json\n" + json_str + "\n```"
+    result_string = call_ai_function(
+        function_string, args, description_string, model=cfg.fast_llm_model
+    )
+    logger.debug("------------ JSON FIX ATTEMPT ---------------")
+    logger.debug(f"Original JSON: {json_str}")
+    logger.debug("-----------")
+    logger.debug(f"Fixed JSON: {result_string}")
+    logger.debug("----------- END OF FIX ATTEMPT ----------------")
+
+    try:
+        json.loads(result_string)  # just check the validity
+        return result_string
+    except:  # noqa: E722
+        # Get the call stack:
+        # import traceback
+        # call_stack = traceback.format_exc()
+        # print(f"Failed to fix JSON: '{json_str}' "+call_stack)
+        return "failed"

autogpt/json_fixes/bracket_termination.py

@@ -0,0 +1,73 @@
+"""Fix JSON brackets."""
+import contextlib
+import json
+from typing import Optional
+import regex
+from colorama import Fore
+
+from autogpt.logs import logger
+from autogpt.config import Config
+from autogpt.speech import say_text
+
+CFG = Config()
+
+
+def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
+    if CFG.speak_mode and CFG.debug_mode:
+        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:
+        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:
+                say_text("Apparently json was fixed.")
+        else:
+            raise ValueError("No valid JSON object found")
+
+    except (json.JSONDecodeError, ValueError):
+        if CFG.debug_mode:
+            logger.error("Error: Invalid JSON: %s\n", json_string)
+        if CFG.speak_mode:
+            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 balance_braces(json_string: str) -> Optional[str]:
+    """
+    Balance the braces in a JSON string.
+
+    Args:
+        json_string (str): The JSON string.
+
+    Returns:
+        str: The JSON string with braces balanced.
+    """
+
+    open_braces_count = json_string.count("{")
+    close_braces_count = json_string.count("}")
+
+    while open_braces_count > close_braces_count:
+        json_string += "}"
+        close_braces_count += 1
+
+    while close_braces_count > open_braces_count:
+        json_string = json_string.rstrip("}")
+        close_braces_count -= 1
+
+    with contextlib.suppress(json.JSONDecodeError):
+        json.loads(json_string)
+        return json_string

autogpt/json_fixes/escaping.py

@@ -0,0 +1,33 @@
+""" Fix invalid escape sequences in JSON strings. """
+import json
+
+from autogpt.config import Config
+from autogpt.json_fixes.utilities import extract_char_position
+
+CFG = Config()
+
+
+def fix_invalid_escape(json_to_load: str, error_message: str) -> str:
+    """Fix invalid escape sequences in JSON strings.
+
+    Args:
+        json_to_load (str): The JSON string.
+        error_message (str): The error message from the JSONDecodeError
+          exception.
+
+    Returns:
+        str: The JSON string with invalid escape sequences fixed.
+    """
+    while error_message.startswith("Invalid \\escape"):
+        bad_escape_location = extract_char_position(error_message)
+        json_to_load = (
+            json_to_load[:bad_escape_location] + json_to_load[bad_escape_location + 1 :]
+        )
+        try:
+            json.loads(json_to_load)
+            return json_to_load
+        except json.JSONDecodeError as e:
+            if CFG.debug_mode:
+                print("json loads error - fix invalid escape", e)
+            error_message = str(e)
+    return json_to_load

autogpt/json_fixes/missing_quotes.py

@@ -0,0 +1,27 @@
+"""Fix quotes in a JSON string."""
+import json
+import re
+
+
+def add_quotes_to_property_names(json_string: str) -> str:
+    """
+    Add quotes to property names in a JSON string.
+
+    Args:
+        json_string (str): The JSON string.
+
+    Returns:
+        str: The JSON string with quotes added to property names.
+    """
+
+    def replace_func(match: re.Match) -> str:
+        return f'"{match[1]}":'
+
+    property_name_pattern = re.compile(r"(\w+):")
+    corrected_json_string = property_name_pattern.sub(replace_func, json_string)
+
+    try:
+        json.loads(corrected_json_string)
+        return corrected_json_string
+    except json.JSONDecodeError as e:
+        raise e

autogpt/json_fixes/parsing.py

@@ -0,0 +1,143 @@
+"""Fix and parse JSON strings."""
+
+import contextlib
+import json
+from typing import Any, Dict, Union
+
+from autogpt.config import Config
+from autogpt.json_fixes.auto_fix import fix_json
+from autogpt.json_fixes.bracket_termination import balance_braces
+from autogpt.json_fixes.escaping import fix_invalid_escape
+from autogpt.json_fixes.missing_quotes import add_quotes_to_property_names
+from autogpt.logs import logger
+
+CFG = Config()
+
+
+JSON_SCHEMA = """
+{
+    "command": {
+        "name": "command name",
+        "args": {
+            "arg name": "value"
+        }
+    },
+    "thoughts":
+    {
+        "text": "thought",
+        "reasoning": "reasoning",
+        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+        "criticism": "constructive self-criticism",
+        "speak": "thoughts summary to say to user"
+    }
+}
+"""
+
+
+def correct_json(json_to_load: str) -> str:
+    """
+    Correct common JSON errors.
+
+    Args:
+        json_to_load (str): The JSON string.
+    """
+
+    try:
+        if CFG.debug_mode:
+            print("json", json_to_load)
+        json.loads(json_to_load)
+        return json_to_load
+    except json.JSONDecodeError as e:
+        if CFG.debug_mode:
+            print("json loads error", e)
+        error_message = str(e)
+        if error_message.startswith("Invalid \\escape"):
+            json_to_load = fix_invalid_escape(json_to_load, error_message)
+        if error_message.startswith(
+            "Expecting property name enclosed in double quotes"
+        ):
+            json_to_load = add_quotes_to_property_names(json_to_load)
+            try:
+                json.loads(json_to_load)
+                return json_to_load
+            except json.JSONDecodeError as e:
+                if CFG.debug_mode:
+                    print("json loads error - add quotes", e)
+                error_message = str(e)
+        if balanced_str := balance_braces(json_to_load):
+            return balanced_str
+    return json_to_load
+
+
+def fix_and_parse_json(
+    json_to_load: str, try_to_fix_with_gpt: bool = True
+) -> Union[str, Dict[Any, Any]]:
+    """Fix and parse JSON string
+
+    Args:
+        json_to_load (str): The JSON string.
+        try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
+            Defaults to True.
+
+    Returns:
+        Union[str, Dict[Any, Any]]: The parsed JSON.
+    """
+
+    with contextlib.suppress(json.JSONDecodeError):
+        json_to_load = json_to_load.replace("\t", "")
+        return json.loads(json_to_load)
+
+    with contextlib.suppress(json.JSONDecodeError):
+        json_to_load = correct_json(json_to_load)
+        return json.loads(json_to_load)
+    # Let's do something manually:
+    # sometimes GPT responds with something BEFORE the braces:
+    # "I'm sorry, I don't understand. Please try again."
+    # {"text": "I'm sorry, I don't understand. Please try again.",
+    #  "confidence": 0.0}
+    # So let's try to find the first brace and then parse the rest
+    #  of the string
+    try:
+        brace_index = json_to_load.index("{")
+        maybe_fixed_json = json_to_load[brace_index:]
+        last_brace_index = maybe_fixed_json.rindex("}")
+        maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
+        return json.loads(maybe_fixed_json)
+    except (json.JSONDecodeError, ValueError) as e:
+        return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
+
+
+def try_ai_fix(
+    try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
+) -> Union[str, Dict[Any, Any]]:
+    """Try to fix the JSON with the AI
+
+    Args:
+        try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
+        exception (Exception): The exception that was raised.
+        json_to_load (str): The JSON string to load.
+
+    Raises:
+        exception: If try_to_fix_with_gpt is False.
+
+    Returns:
+        Union[str, Dict[Any, Any]]: The JSON string or dictionary.
+    """
+    if not try_to_fix_with_gpt:
+        raise exception
+
+    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_to_load, JSON_SCHEMA)
+
+    if ai_fixed_json != "failed":
+        return json.loads(ai_fixed_json)
+    # This allows the AI to react to the error message,
+    #   which usually results in it correcting its ways.
+    logger.error("Failed to fix AI output, telling the AI.")
+    return json_to_load

autogpt/json_fixes/utilities.py

@@ -0,0 +1,20 @@
+"""Utilities for the json_fixes package."""
+import re
+
+
+def extract_char_position(error_message: str) -> int:
+    """Extract the character position from the JSONDecodeError message.
+
+    Args:
+        error_message (str): The error message from the JSONDecodeError
+          exception.
+
+    Returns:
+        int: The character position.
+    """
+
+    char_pattern = re.compile(r"\(char (\d+)\)")
+    if match := char_pattern.search(error_message):
+        return int(match[1])
+    else:
+        raise ValueError("Character position not found in the error message.")

autogpt/llm_utils.py

@@ -0,0 +1,115 @@
+from ast import List
+import time
+from typing import Dict, Optional
+
+import openai
+from openai.error import APIError, RateLimitError
+from colorama import Fore
+
+from autogpt.config import Config
+
+CFG = Config()
+
+openai.api_key = CFG.openai_api_key
+
+
+def call_ai_function(
+    function: str, args: List, description: str, model: Optional[str] = None
+) -> str:
+    """Call an AI function
+
+    This is a magic function that can do anything with no-code. See
+    https://github.com/Torantulino/AI-Functions for more info.
+
+    Args:
+        function (str): The function to call
+        args (list): The arguments to pass to the function
+        description (str): The description of the function
+        model (str, optional): The model to use. Defaults to None.
+
+    Returns:
+        str: The response from the function
+    """
+    if model is None:
+        model = CFG.smart_llm_model
+    # For each arg, if any are None, convert to "None":
+    args = [str(arg) if arg is not None else "None" for arg in args]
+    # parse args to comma separated string
+    args = ", ".join(args)
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are now the following python function: ```# {description}"
+            f"\n{function}```\n\nOnly respond with your `return` value.",
+        },
+        {"role": "user", "content": args},
+    ]
+
+    return create_chat_completion(model=model, messages=messages, temperature=0)
+
+
+# Overly simple abstraction until we create something better
+# simple retry mechanism when getting a rate error or a bad gateway
+def create_chat_completion(
+    messages: List,  # type: ignore
+    model: Optional[str] = None,
+    temperature: float = CFG.temperature,
+    max_tokens: Optional[int] = None,
+) -> str:
+    """Create a chat completion using the OpenAI API
+
+    Args:
+        messages (list[dict[str, str]]): The messages to send to the chat completion
+        model (str, optional): The model to use. Defaults to None.
+        temperature (float, optional): The temperature to use. Defaults to 0.9.
+        max_tokens (int, optional): The max tokens to use. Defaults to None.
+
+    Returns:
+        str: The response from the chat completion
+    """
+    response = None
+    num_retries = 10
+    if CFG.debug_mode:
+        print(
+            Fore.GREEN
+            + f"Creating chat completion with model {model}, temperature {temperature},"
+            f" max_tokens {max_tokens}" + Fore.RESET
+        )
+    for attempt in range(num_retries):
+        backoff = 2 ** (attempt + 2)
+        try:
+            if CFG.use_azure:
+                response = openai.ChatCompletion.create(
+                    deployment_id=CFG.get_azure_deployment_id_for_model(model),
+                    model=model,
+                    messages=messages,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                )
+            else:
+                response = openai.ChatCompletion.create(
+                    model=model,
+                    messages=messages,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                )
+            break
+        except RateLimitError:
+            pass
+        except APIError as e:
+            if e.http_status == 502:
+                pass
+            else:
+                raise
+            if attempt == num_retries - 1:
+                raise
+        if CFG.debug_mode:
+            print(
+                Fore.RED + "Error: ",
+                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
+            )
+        time.sleep(backoff)
+    if response is None:
+        raise RuntimeError(f"Failed to get response after {num_retries} retries")
+
+    return response.choices[0].message["content"]

autogpt/logs.py

@@ -0,0 +1,288 @@
+"""Logging module for Auto-GPT."""
+import json
+import logging
+import os
+import random
+import re
+import time
+from logging import LogRecord
+import traceback
+
+from colorama import Fore, Style
+
+from autogpt.speech import say_text
+from autogpt.config import Config, Singleton
+
+CFG = Config()
+
+
+class Logger(metaclass=Singleton):
+    """
+    Logger that handle titles in different colors.
+    Outputs logs in console, activity.log, and errors.log
+    For console handler: simulates typing
+    """
+
+    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")
+        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")
+
+        # Create a handler for console which simulate typing
+        self.typing_console_handler = TypingConsoleHandler()
+        self.typing_console_handler.setLevel(logging.INFO)
+        self.typing_console_handler.setFormatter(console_formatter)
+
+        # Create a handler for console without typing simulation
+        self.console_handler = ConsoleHandler()
+        self.console_handler.setLevel(logging.DEBUG)
+        self.console_handler.setFormatter(console_formatter)
+
+        # 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"
+        )
+        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"
+        )
+        error_handler.setFormatter(error_formatter)
+
+        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.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
+    ):
+        if speak_text and CFG.speak_mode:
+            say_text(f"{title}. {content}")
+
+        if content:
+            if isinstance(content, list):
+                content = " ".join(content)
+        else:
+            content = ""
+
+        self.typing_logger.log(
+            level, content, extra={"title": title, "color": title_color}
+        )
+
+    def debug(
+        self,
+        message,
+        title="",
+        title_color="",
+    ):
+        self._log(title, title_color, message, logging.DEBUG)
+
+    def warn(
+        self,
+        message,
+        title="",
+        title_color="",
+    ):
+        self._log(title, title_color, message, logging.WARN)
+
+    def error(self, title, message=""):
+        self._log(title, Fore.RED, message, logging.ERROR)
+
+    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})
+
+    def set_level(self, level):
+        self.logger.setLevel(level)
+        self.typing_logger.setLevel(level)
+
+    def double_check(self, additionalText=None):
+        if not additionalText:
+            additionalText = (
+                "Please ensure you've setup and configured everything"
+                " correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
+                "double check. You can also create a github issue or join the discord"
+                " and ask there!"
+            )
+
+        self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
+
+
+"""
+Output stream to console using simulated typing
+"""
+
+
+class TypingConsoleHandler(logging.StreamHandler):
+    def emit(self, record):
+        min_typing_speed = 0.05
+        max_typing_speed = 0.01
+
+        msg = self.format(record)
+        try:
+            words = msg.split()
+            for i, word in enumerate(words):
+                print(word, end="", flush=True)
+                if i < len(words) - 1:
+                    print(" ", end="", flush=True)
+                typing_speed = random.uniform(min_typing_speed, max_typing_speed)
+                time.sleep(typing_speed)
+                # type faster after each word
+                min_typing_speed = min_typing_speed * 0.95
+                max_typing_speed = max_typing_speed * 0.95
+            print()
+        except Exception:
+            self.handleError(record)
+
+
+class ConsoleHandler(logging.StreamHandler):
+    def emit(self, record) -> None:
+        msg = self.format(record)
+        try:
+            print(msg)
+        except Exception:
+            self.handleError(record)
+
+
+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
+            )
+        else:
+            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 = ""
+        return super().format(record)
+
+
+def remove_color_codes(s: str) -> str:
+    ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
+    return ansi_escape.sub("", s)
+
+
+logger = Logger()
+
+
+def print_assistant_thoughts(ai_name, assistant_reply):
+    """Prints the assistant's thoughts to the console"""
+    from autogpt.json_fixes.bracket_termination import (
+        attempt_to_fix_json_by_finding_outermost_brackets,
+    )
+    from autogpt.json_fixes.parsing import fix_and_parse_json
+
+    try:
+        try:
+            # Parse and print Assistant response
+            assistant_reply_json = fix_and_parse_json(assistant_reply)
+        except json.JSONDecodeError:
+            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
+            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
+                assistant_reply
+            )
+            if isinstance(assistant_reply_json, str):
+                assistant_reply_json = fix_and_parse_json(assistant_reply_json)
+
+        # Check if assistant_reply_json is a string and attempt to parse
+        # it into a JSON object
+        if isinstance(assistant_reply_json, str):
+            try:
+                assistant_reply_json = json.loads(assistant_reply_json)
+            except json.JSONDecodeError:
+                logger.error("Error: Invalid JSON\n", assistant_reply)
+                assistant_reply_json = (
+                    attempt_to_fix_json_by_finding_outermost_brackets(
+                        assistant_reply_json
+                    )
+                )
+
+        assistant_thoughts_reasoning = None
+        assistant_thoughts_plan = None
+        assistant_thoughts_speak = None
+        assistant_thoughts_criticism = None
+        if not isinstance(assistant_reply_json, dict):
+            assistant_reply_json = {}
+        assistant_thoughts = assistant_reply_json.get("thoughts", {})
+        assistant_thoughts_text = assistant_thoughts.get("text")
+
+        if assistant_thoughts:
+            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
+            assistant_thoughts_plan = assistant_thoughts.get("plan")
+            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
+            assistant_thoughts_speak = assistant_thoughts.get("speak")
+
+        logger.typewriter_log(
+            f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
+        )
+        logger.typewriter_log(
+            "REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
+        )
+
+        if assistant_thoughts_plan:
+            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
+            # If it's a list, join it into a string
+            if isinstance(assistant_thoughts_plan, list):
+                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
+            elif isinstance(assistant_thoughts_plan, dict):
+                assistant_thoughts_plan = str(assistant_thoughts_plan)
+
+            # Split the input_string using the newline character and dashes
+            lines = assistant_thoughts_plan.split("\n")
+            for line in lines:
+                line = line.lstrip("- ")
+                logger.typewriter_log("- ", Fore.GREEN, line.strip())
+
+        logger.typewriter_log(
+            "CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
+        )
+        # Speak the assistant's thoughts
+        if CFG.speak_mode and assistant_thoughts_speak:
+            say_text(assistant_thoughts_speak)
+
+        return assistant_reply_json
+    except json.decoder.JSONDecodeError:
+        logger.error("Error: Invalid JSON\n", assistant_reply)
+        if CFG.speak_mode:
+            say_text(
+                "I have received an invalid JSON response from the OpenAI API."
+                " I cannot ignore this response."
+            )
+
+    # All other errors, return "Error: + error message"
+    except Exception:
+        call_stack = traceback.format_exc()
+        logger.error("Error: \n", call_stack)

autogpt/memory/__init__.py

@@ -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']
+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"]

autogpt/memory/base.py

@@ -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):

autogpt/memory/local.py

@@ -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, Tuple
 
+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 +24,24 @@ class CacheContent:
 
 
 class LocalCache(MemoryProviderSingleton):
+    """A class that stores the memory in a local file"""
 
-    # on load, load our database
     def __init__(self, cfg) -> None:
+        """Initialize a class instance
+
+        Args:
+            cfg: Config object
+
+        Returns:
+            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 +50,10 @@ 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 +66,7 @@ class LocalCache(MemoryProviderSingleton):
 
         Returns: None
         """
-        if 'Command Error:' in text:
+        if "Command Error:" in text:
             return ""
         self.data.texts.append(text)
 
@@ -70,11 +82,8 @@ 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 +108,7 @@ 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
@@ -117,7 +126,7 @@ class LocalCache(MemoryProviderSingleton):
 
         return [self.data.texts[i] for i in top_k_indices]
 
-    def get_stats(self):
+    def get_stats(self) -> Tuple[int, Tuple[int, ...]]:
         """
         Returns: The stats of the local cache.
         """

autogpt/memory/no_memory.py

@@ -1,9 +1,14 @@
+"""A class that does not store any data. This is the default memory provider."""
 from typing import Optional, List, Any
 
-from memory.base import MemoryProviderSingleton
+from autogpt.memory.base import MemoryProviderSingleton
 
 
 class NoMemory(MemoryProviderSingleton):
+    """
+    A class that does not store any data. This is the default memory provider.
+    """
+
     def __init__(self, cfg):
         """
         Initializes the NoMemory provider.

autogpt/memory/pinecone.py

@@ -1,10 +1,9 @@
-
 import pinecone
-
-from memory.base import MemoryProviderSingleton, get_ada_embedding
-from logger import logger
 from colorama import Fore, Style
 
+from autogpt.logs import logger
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+
 
 class PineconeMemory(MemoryProviderSingleton):
     def __init__(self, cfg):
@@ -17,25 +16,36 @@ class PineconeMemory(MemoryProviderSingleton):
         table_name = "auto-gpt"
         # this assumes we don't start with memory.
         # for now this works.
-        # we'll need a more complicated and robust system if we want to start with memory.
+        # we'll need a more complicated and robust system if we want to start with
+        #  memory.
         self.vec_num = 0
 
         try:
             pinecone.whoami()
         except Exception as e:
-            logger.typewriter_log("FAILED TO CONNECT TO PINECONE", Fore.RED, Style.BRIGHT + str(e) + Style.RESET_ALL)
-            logger.double_check("Please ensure you have setup and configured Pinecone properly for use. " +
-                               f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup{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"
+                f"{Style.RESET_ALL} to ensure you've set up everything correctly."
+            )
             exit(1)
 
         if table_name not in pinecone.list_indexes():
-            pinecone.create_index(table_name, dimension=dimension, metric=metric, pod_type=pod_type)
+            pinecone.create_index(
+                table_name, dimension=dimension, metric=metric, pod_type=pod_type
+            )
         self.index = pinecone.Index(table_name)
 
     def add(self, data):
         vector = get_ada_embedding(data)
         # no metadata here. We may wish to change that long term.
-        resp = self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
+        self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
         _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
         self.vec_num += 1
         return _text
@@ -54,9 +64,11 @@ 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()

autogpt/memory/redismem.py

@@ -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.logs 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 +47,17 @@ 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}"
+                f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
+                " to ensure you've set up everything correctly."
+            )
             exit(1)
 
         if cfg.wipe_redis_on_start:
@@ -62,15 +66,13 @@ 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 +83,18 @@ 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 +118,7 @@ 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 +129,12 @@ 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:

autogpt/processing/text.py

@@ -0,0 +1,132 @@
+"""Text processing functions"""
+from typing import Generator, Optional
+from selenium.webdriver.remote.webdriver import WebDriver
+from autogpt.memory import get_memory
+from autogpt.config import Config
+from autogpt.llm_utils import create_chat_completion
+
+CFG = Config()
+MEMORY = get_memory(CFG)
+
+
+def split_text(text: str, max_length: int = 8192) -> Generator[str, None, None]:
+    """Split text into chunks of a maximum length
+
+    Args:
+        text (str): The text to split
+        max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
+
+    Yields:
+        str: The next chunk of text
+
+    Raises:
+        ValueError: If the text is longer than the maximum length
+    """
+    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 summarize_text(
+    url: str, text: str, question: str, driver: Optional[WebDriver] = None
+) -> str:
+    """Summarize text using the OpenAI API
+
+    Args:
+        url (str): The url of the text
+        text (str): The text to summarize
+        question (str): The question to ask the model
+        driver (WebDriver): The webdriver to use to scroll the page
+
+    Returns:
+        str: The summary of the text
+    """
+    if not text:
+        return "Error: No text to summarize"
+
+    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):
+        if driver:
+            scroll_to_percentage(driver, scroll_ratio * i)
+        print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
+
+        memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
+
+        MEMORY.add(memory_to_add)
+
+        print(f"Summarizing chunk {i + 1} / {len(chunks)}")
+        messages = [create_message(chunk, question)]
+
+        summary = create_chat_completion(
+            model=CFG.fast_llm_model,
+            messages=messages,
+            max_tokens=CFG.browse_summary_max_token,
+        )
+        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.add(memory_to_add)
+
+    print(f"Summarized {len(chunks)} chunks.")
+
+    combined_summary = "\n".join(summaries)
+    messages = [create_message(combined_summary, question)]
+
+    return create_chat_completion(
+        model=CFG.fast_llm_model,
+        messages=messages,
+        max_tokens=CFG.browse_summary_max_token,
+    )
+
+
+def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
+    """Scroll to a percentage of the page
+
+    Args:
+        driver (WebDriver): The webdriver to use
+        ratio (float): The percentage to scroll to
+
+    Raises:
+        ValueError: If the ratio is not between 0 and 1
+    """
+    if ratio < 0 or ratio > 1:
+        raise ValueError("Percentage should be between 0 and 1")
+    driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
+
+
+def create_message(chunk: str, question: str) -> dict[str, str]:
+    """Create a message for the chat completion
+
+    Args:
+        chunk (str): The chunk of text to summarize
+        question (str): The question to answer
+
+    Returns:
+        dict[str, str]: The message to send to the chat completion
+    """
+    return {
+        "role": "user",
+        "content": f'"""{chunk}""" Using the above text, please answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the text,'
+        " please summarize the text.",
+    }

autogpt/prompt.py

@@ -0,0 +1,155 @@
+from colorama import Fore
+from autogpt.config.ai_config import AIConfig
+from autogpt.config.config import Config
+from autogpt.logs import logger
+from autogpt.promptgenerator import PromptGenerator
+from autogpt.setup import prompt_user
+from autogpt.utils import clean_input
+
+CFG = Config()
+
+
+def get_prompt() -> str:
+    """
+    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
+    return prompt_generator.generate_prompt_string()
+
+
+def construct_prompt() -> str:
+    """Construct the prompt for the AI to respond to
+
+    Returns:
+        str: The prompt string
+    """
+    config = AIConfig.load(CFG.ai_settings_file)
+    if CFG.skip_reprompt and config.ai_name:
+        logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
+        logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
+        logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
+    elif config.ai_name:
+        logger.typewriter_log(
+            "Welcome back! ",
+            Fore.GREEN,
+            f"Would you like me to return to being {config.ai_name}?",
+            speak_text=True,
+        )
+        should_continue = clean_input(
+            f"""Continue with the last settings?
+Name:  {config.ai_name}
+Role:  {config.ai_role}
+Goals: {config.ai_goals}
+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
+
+    return config.construct_full_prompt()

autogpt/promptgenerator.py

@@ -1,14 +1,18 @@
+""" A module for generating custom prompt strings."""
 import json
+from typing import Any, Dict, List
 
 
 class PromptGenerator:
     """
-    A class for generating custom prompt strings based on constraints, commands, resources, and performance evaluations.
+    A class for generating custom prompt strings based on constraints, commands,
+        resources, and performance evaluations.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         """
-        Initialize the PromptGenerator object with empty lists of constraints, commands, resources, and performance evaluations.
+        Initialize the PromptGenerator object with empty lists of constraints,
+            commands, resources, and performance evaluations.
         """
         self.constraints = []
         self.commands = []
@@ -20,17 +24,12 @@ 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):
+    def add_constraint(self, constraint: str) -> None:
         """
         Add a constraint to the constraints list.
 
@@ -39,20 +38,20 @@ class PromptGenerator:
         """
         self.constraints.append(constraint)
 
-    def add_command(self, command_label, command_name, args=None):
+    def add_command(self, command_label: str, command_name: str, args=None) -> None:
         """
         Add a command to the commands list with a label, name, and optional arguments.
 
         Args:
             command_label (str): The label of the command.
             command_name (str): The name of the command.
-            args (dict, optional): A dictionary containing argument names and their values. Defaults to None.
+            args (dict, optional): A dictionary containing argument names and their
+              values. Defaults to None.
         """
         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,
@@ -62,7 +61,7 @@ class PromptGenerator:
 
         self.commands.append(command)
 
-    def _generate_command_string(self, command):
+    def _generate_command_string(self, command: Dict[str, Any]) -> str:
         """
         Generate a formatted string representation of a command.
 
@@ -72,11 +71,12 @@ 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):
+    def add_resource(self, resource: str) -> None:
         """
         Add a resource to the resources list.
 
@@ -85,7 +85,7 @@ class PromptGenerator:
         """
         self.resources.append(resource)
 
-    def add_performance_evaluation(self, evaluation):
+    def add_performance_evaluation(self, evaluation: str) -> None:
         """
         Add a performance evaluation item to the performance_evaluation list.
 
@@ -94,36 +94,43 @@ class PromptGenerator:
         """
         self.performance_evaluation.append(evaluation)
 
-    def _generate_numbered_list(self, items, item_type='list'):
+    def _generate_numbered_list(self, items: List[Any], item_type="list") -> str:
         """
         Generate a numbered list from given items based on the item_type.
 
         Args:
             items (list): A list of items to be numbered.
-            item_type (str, optional): The type of items in the list. Defaults to 'list'.
+            item_type (str, optional): The type of items in the list.
+                Defaults to 'list'.
 
         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))
 
-    def generate_prompt_string(self):
+    def generate_prompt_string(self) -> str:
         """
-        Generate a prompt string based on the constraints, commands, resources, and performance evaluations.
+        Generate a prompt string based on the constraints, commands, resources,
+            and performance evaluations.
 
         Returns:
             str: The generated prompt string.
         """
         formatted_response_format = json.dumps(self.response_format, indent=4)
-        prompt_string = (
+        return (
             f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
-            f"Commands:\n{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
+            "Commands:\n"
+            f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
             f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
-            f"Performance Evaluation:\n{self._generate_numbered_list(self.performance_evaluation)}\n\n"
-            f"You should only respond in JSON format as described below \nResponse Format: \n{formatted_response_format} \nEnsure the response can be parsed by Python json.loads"
+            "Performance Evaluation:\n"
+            f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
+            "You should only respond in JSON format as described below \nResponse"
+            f" Format: \n{formatted_response_format} \nEnsure the response can be"
+            "parsed by Python json.loads"
         )
-
-        return prompt_string

autogpt/setup.py

@@ -0,0 +1,69 @@
+"""Setup the AI and its goals"""
+from colorama import Fore, Style
+from autogpt import utils
+from autogpt.config.ai_config import AIConfig
+from autogpt.logs import logger
+
+
+def prompt_user() -> AIConfig:
+    """Prompt the user for input
+
+    Returns:
+        AIConfig: The AIConfig object containing the user's 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 not ai_goals:
+        ai_goals = [
+            "Increase net worth",
+            "Grow Twitter Account",
+            "Develop and manage multiple businesses autonomously",
+        ]
+
+    return AIConfig(ai_name, ai_role, ai_goals)

autogpt/speech/__init__.py

@@ -0,0 +1,4 @@
+"""This module contains the speech recognition and speech synthesis functions."""
+from autogpt.speech.say import say_text
+
+__all__ = ["say_text"]

autogpt/speech/base.py

@@ -0,0 +1,50 @@
+"""Base class for all voice classes."""
+import abc
+from threading import Lock
+
+from autogpt.config import AbstractSingleton
+
+
+class VoiceBase(AbstractSingleton):
+    """
+    Base class for all voice classes.
+    """
+
+    def __init__(self):
+        """
+        Initialize the voice class.
+        """
+        self._url = None
+        self._headers = None
+        self._api_key = None
+        self._voices = []
+        self._mutex = Lock()
+        self._setup()
+
+    def say(self, text: str, voice_index: int = 0) -> bool:
+        """
+        Say the given text.
+
+        Args:
+            text (str): The text to say.
+            voice_index (int): The index of the voice to use.
+        """
+        with self._mutex:
+            return self._speech(text, voice_index)
+
+    @abc.abstractmethod
+    def _setup(self) -> None:
+        """
+        Setup the voices, API key, etc.
+        """
+        pass
+
+    @abc.abstractmethod
+    def _speech(self, text: str, voice_index: int = 0) -> bool:
+        """
+        Play the given text.
+
+        Args:
+            text (str): The text to play.
+        """
+        pass

autogpt/speech/brian.py

@@ -0,0 +1,39 @@
+""" Brian speech module for autogpt """
+import os
+import requests
+from playsound import playsound
+
+from autogpt.speech.base import VoiceBase
+
+
+class BrianSpeech(VoiceBase):
+    """Brian speech module for autogpt"""
+
+    def _setup(self) -> None:
+        """Setup the voices, API key, etc."""
+        pass
+
+    def _speech(self, text: str) -> bool:
+        """Speak text using Brian with the streamelements API
+
+        Args:
+            text (str): The text to speak
+
+        Returns:
+            bool: True if the request was successful, False otherwise
+        """
+        tts_url = (
+            f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
+        )
+        response = requests.get(tts_url)
+
+        if response.status_code == 200:
+            with open("speech.mp3", "wb") as f:
+                f.write(response.content)
+            playsound("speech.mp3")
+            os.remove("speech.mp3")
+            return True
+        else:
+            print("Request failed with status code:", response.status_code)
+            print("Response content:", response.content)
+            return False

autogpt/speech/eleven_labs.py

@@ -0,0 +1,71 @@
+"""ElevenLabs speech module"""
+import os
+from playsound import playsound
+
+import requests
+
+from autogpt.config import Config
+from autogpt.speech.base import VoiceBase
+
+PLACEHOLDERS = {"your-voice-id"}
+
+
+class ElevenLabsSpeech(VoiceBase):
+    """ElevenLabs speech class"""
+
+    def _setup(self) -> None:
+        """Setup the voices, API key, etc.
+
+        Returns:
+            None: None
+        """
+
+        cfg = Config()
+        default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
+        self._headers = {
+            "Content-Type": "application/json",
+            "xi-api-key": cfg.elevenlabs_api_key,
+        }
+        self._voices = default_voices.copy()
+        self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0)
+        self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1)
+
+    def _use_custom_voice(self, voice, voice_index) -> None:
+        """Use a custom voice if provided and not a placeholder
+
+        Args:
+            voice (str): The voice ID
+            voice_index (int): The voice index
+
+        Returns:
+            None: None
+        """
+        # Placeholder values that should be treated as empty
+        if voice and voice not in PLACEHOLDERS:
+            self._voices[voice_index] = voice
+
+    def _speech(self, text: str, voice_index: int = 0) -> bool:
+        """Speak text using elevenlabs.io's API
+
+        Args:
+            text (str): The text to speak
+            voice_index (int, optional): The voice to use. Defaults to 0.
+
+        Returns:
+            bool: True if the request was successful, False otherwise
+        """
+        tts_url = (
+            f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
+        )
+        response = requests.post(tts_url, headers=self._headers, json={"text": text})
+
+        if response.status_code == 200:
+            with open("speech.mpeg", "wb") as f:
+                f.write(response.content)
+            playsound("speech.mpeg", True)
+            os.remove("speech.mpeg")
+            return True
+        else:
+            print("Request failed with status code:", response.status_code)
+            print("Response content:", response.content)
+            return False

autogpt/speech/gtts.py

@@ -0,0 +1,21 @@
+""" GTTS Voice. """
+import os
+from playsound import playsound
+import gtts
+
+from autogpt.speech.base import VoiceBase
+
+
+class GTTSVoice(VoiceBase):
+    """GTTS Voice."""
+
+    def _setup(self) -> None:
+        pass
+
+    def _speech(self, text: str, _: int = 0) -> bool:
+        """Play the given text."""
+        tts = gtts.gTTS(text)
+        tts.save("speech.mp3")
+        playsound("speech.mp3", True)
+        os.remove("speech.mp3")
+        return True

autogpt/speech/macos_tts.py

@@ -0,0 +1,21 @@
+""" MacOS TTS Voice. """
+import os
+
+from autogpt.speech.base import VoiceBase
+
+
+class MacOSTTS(VoiceBase):
+    """MacOS TTS Voice."""
+
+    def _setup(self) -> None:
+        pass
+
+    def _speech(self, text: str, voice_index: int = 0) -> bool:
+        """Play the given text."""
+        if voice_index == 0:
+            os.system(f'say "{text}"')
+        elif voice_index == 1:
+            os.system(f'say -v "Ava (Premium)" "{text}"')
+        else:
+            os.system(f'say -v Samantha "{text}"')
+        return True

autogpt/speech/say.py

@@ -0,0 +1,42 @@
+""" Text to speech module """
+from autogpt.config import Config
+
+import threading
+from threading import Semaphore
+from autogpt.speech.brian import BrianSpeech
+from autogpt.speech.macos_tts import MacOSTTS
+from autogpt.speech.gtts import GTTSVoice
+from autogpt.speech.eleven_labs import ElevenLabsSpeech
+
+
+CFG = Config()
+DEFAULT_VOICE_ENGINE = GTTSVoice()
+VOICE_ENGINE = None
+if CFG.elevenlabs_api_key:
+    VOICE_ENGINE = ElevenLabsSpeech()
+elif CFG.use_mac_os_tts == "True":
+    VOICE_ENGINE = MacOSTTS()
+elif CFG.use_brian_tts == "True":
+    VOICE_ENGINE = BrianSpeech()
+else:
+    VOICE_ENGINE = GTTSVoice()
+
+
+QUEUE_SEMAPHORE = Semaphore(
+    1
+)  # The amount of sounds to queue before blocking the main thread
+
+
+def say_text(text: str, voice_index: int = 0) -> None:
+    """Speak the given text using the given voice index"""
+
+    def speak() -> None:
+        success = VOICE_ENGINE.say(text, voice_index)
+        if not success:
+            DEFAULT_VOICE_ENGINE.say(text)
+
+        QUEUE_SEMAPHORE.release()
+
+    QUEUE_SEMAPHORE.acquire(True)
+    thread = threading.Thread(target=speak)
+    thread.start()

autogpt/spinner.py

@@ -0,0 +1,50 @@
+"""A simple spinner module"""
+import itertools
+import sys
+import threading
+import time
+
+
+class Spinner:
+    """A simple spinner class"""
+
+    def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
+        """Initialize the spinner class
+
+        Args:
+            message (str): The message to display.
+            delay (float): The delay between each spinner update.
+        """
+        self.spinner = itertools.cycle(["-", "/", "|", "\\"])
+        self.delay = delay
+        self.message = message
+        self.running = False
+        self.spinner_thread = None
+
+    def spin(self) -> None:
+        """Spin the spinner"""
+        while self.running:
+            sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
+            sys.stdout.flush()
+            time.sleep(self.delay)
+            sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
+
+    def __enter__(self) -> None:
+        """Start the spinner"""
+        self.running = True
+        self.spinner_thread = threading.Thread(target=self.spin)
+        self.spinner_thread.start()
+
+    def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
+        """Stop the spinner
+
+        Args:
+            exc_type (Exception): The exception type.
+            exc_value (Exception): The exception value.
+            exc_traceback (Exception): The exception traceback.
+        """
+        self.running = False
+        if self.spinner_thread is not None:
+            self.spinner_thread.join()
+        sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
+        sys.stdout.flush()

autogpt/token_counter.py

@@ -1,17 +1,25 @@
+"""Functions for counting the number of tokens in a message or string."""
+from typing import Dict, List
+
 import tiktoken
-from typing import List, Dict
+
+from autogpt.logs import logger
 
 
-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.
 
     Args:
-    messages (list): A list of messages, each of which is a dictionary containing the role and content of the message.
-    model (str): The name of the model to use for tokenization. Defaults to "gpt-3.5-turbo-0301".
+        messages (list): A list of messages, each of which is a dictionary
+            containing the role and content of the message.
+        model (str): The name of the model to use for tokenization.
+            Defaults to "gpt-3.5-turbo-0301".
 
     Returns:
-    int: The number of tokens used by the list of messages.
+        int: The number of tokens used by the list of messages.
     """
     try:
         encoding = tiktoken.encoding_for_model(model)
@@ -19,19 +27,26 @@ def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5
         logger.warn("Warning: model not found. Using cl100k_base encoding.")
         encoding = tiktoken.get_encoding("cl100k_base")
     if model == "gpt-3.5-turbo":
-        # !Node: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
+        # !Node: gpt-3.5-turbo may change over time.
+        # Returning num tokens assuming gpt-3.5-turbo-0301.")
         return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
     elif model == "gpt-4":
         # !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
         return count_message_tokens(messages, model="gpt-4-0314")
     elif model == "gpt-3.5-turbo-0301":
-        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_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}.\n"
+            " See https://github.com/openai/openai-python/blob/main/chatml.md for"
+            " information on how messages are converted to tokens."
+        )
     num_tokens = 0
     for message in messages:
         num_tokens += tokens_per_message
@@ -48,12 +63,11 @@ def count_string_tokens(string: str, model_name: str) -> int:
     Returns the number of tokens in a text string.
 
     Args:
-    string (str): The text string.
-    model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
+        string (str): The text string.
+        model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
 
     Returns:
-    int: The number of tokens in the text string.
+        int: The number of tokens in the text string.
     """
     encoding = tiktoken.encoding_for_model(model_name)
-    num_tokens = len(encoding.encode(string))
-    return num_tokens
+    return len(encoding.encode(string))

autogpt/utils.py

@@ -0,0 +1,26 @@
+import yaml
+from colorama import Fore
+
+
+def clean_input(prompt: str = ""):
+    try:
+        return input(prompt)
+    except KeyboardInterrupt:
+        print("You interrupted Auto-GPT")
+        print("Quitting...")
+        exit(0)
+
+
+def validate_yaml_file(file: str):
+    try:
+        with open(file, encoding="utf-8") as fp:
+            yaml.load(fp.read(), Loader=yaml.FullLoader)
+    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 (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")

docker-compose.yml

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

main.py

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

outputs/logs/message-log-3.txt

@@ -1,7 +1,7 @@
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -20,20 +20,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -77,7 +77,7 @@ LONG TERM MEMORY:
 {)
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -114,7 +114,7 @@ Shopify › Home › Shopify Blog
 
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -140,7 +140,7 @@ The article discusses 18 profitable online business ideas, including starting an
 """
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -160,7 +160,7 @@ Entrepreneur-GTP:
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -179,20 +179,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -236,7 +236,7 @@ LONG TERM MEMORY:
 ["""18 profitable online business ideas: e-commerce store, virtual assistant services, affiliate marketing, online training or education, mobile applications""", ]
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -257,7 +257,7 @@ USER:
 UNKNOWN COMMAND "Evaluate and select ideas"
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "thoughts": {
@@ -274,21 +274,21 @@ USER:
 NEXT COMMAND
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
-"name": "Start GTP-4 Instance",
+"name": "Start GPT-4 Instance",
 "arguments": {
 "key": "Idea_Selection",
 "prompt": "Analyze the online business ideas in the long-term memory and select the most suitable one based on profitability, simplicity, scalability, and absence of legal complications."
 }
 },
 "thoughts": {
-"text": "I will start a GTP-4 instance to analyze the business ideas and select the most suitable one.",
-"reasoning": "Starting a GTP-4 instance with a specific prompt will help me evaluate the ideas in my long-term memory and choose the best one according to my criteria.",
+"text": "I will start a GPT-4 instance to analyze the business ideas and select the most suitable one.",
+"reasoning": "Starting a GPT-4 instance with a specific prompt will help me evaluate the ideas in my long-term memory and choose the best one according to my criteria.",
 "current long-term plan": "- Develop and implement chosen business strategy\n- Continuously refine strategies based on market trends and performance metrics",
-"critisism": "I must ensure that the GTP-4 instance provides a clear and well-reasoned selection based on the criteria."
+"critisism": "I must ensure that the GPT-4 instance provides a clear and well-reasoned selection based on the criteria."
 }
 }
 
@@ -299,7 +299,7 @@ USER:
 Result: "Blogging with affiliate marketing."
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -319,7 +319,7 @@ Entrepreneur-GTP:
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -338,20 +338,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -400,7 +400,7 @@ Chosen business idea: Blogging with affiliate marketing
 }
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -421,21 +421,21 @@ USER:
 Unknown command: "Develop and implement blogging with affiliate marketing strategy"
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
-"name": "Start GTP-4 Instance",
+"name": "Start GPT-4 Instance",
 "arguments": {
 "key": "Blogging_Affiliate_Strategy",
 "prompt": "Develop a step-by-step strategy for creating and growing a successful blog with affiliate marketing."
 }
 },
 "thoughts": {
-"text": "I will start a GTP-4 instance to develop a strategy for blogging with affiliate marketing.",
-"reasoning": "Starting a GTP-4 instance with a specific prompt will help me create a detailed plan for launching and growing the chosen business idea.",
+"text": "I will start a GPT-4 instance to develop a strategy for blogging with affiliate marketing.",
+"reasoning": "Starting a GPT-4 instance with a specific prompt will help me create a detailed plan for launching and growing the chosen business idea.",
 "current long-term plan": "- Continuously refine strategies based on market trends and performance metrics",
-"critisism": "I must ensure that the GTP-4 instance provides a clear and actionable strategy for blogging with affiliate marketing."
+"critisism": "I must ensure that the GPT-4 instance provides a clear and actionable strategy for blogging with affiliate marketing."
 }
 }
 """
@@ -467,7 +467,7 @@ Result:
 """
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -487,7 +487,7 @@ Entrepreneur-GTP:
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -506,20 +506,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -571,19 +571,19 @@ Blogging with affiliate marketing strategy: 1. Choose a niche, 2. Domain and hos
 }
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
-"name": "Start GTP-4 Instance",
+"name": "Start GPT-4 Instance",
 "arguments": {
 "key": "Niche_Selection",
 "prompt": "Identify a profitable niche for a blog with affiliate marketing based on current market trends and interests."
 }
 },
 "thoughts": {
-"text": "I will start a GTP-4 instance to identify a profitable niche for the blog with affiliate marketing.",
-"reasoning": "A GTP-4 instance will help me analyze current market trends and interests to select a profitable niche for the blogging business.",
+"text": "I will start a GPT-4 instance to identify a profitable niche for the blog with affiliate marketing.",
+"reasoning": "A GPT-4 instance will help me analyze current market trends and interests to select a profitable niche for the blogging business.",
 "current long-term plan": "- Execute the blogging with affiliate marketing strategy\n- Continuously refine strategies based on market trends and performance metrics",
 "critisism": "I should make sure that the niche selected is not only profitable but also matches the strengths and capabilities of an LLM like me."
 }
@@ -629,7 +629,7 @@ By focusing on indie entrepreneurship, your blog will cater to an audience of as
 
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -664,7 +664,7 @@ Niche: Indie Entrepreneurship, focused on entrepreneurship, small business, and
 }
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -691,7 +691,7 @@ USER:
 Result: Registered account on substack.com name:  "Indiepreneur-GPT" bio: ""
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -715,7 +715,7 @@ Entrepreneur-GTP:
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -734,20 +734,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -799,7 +799,7 @@ Blogging with affiliate marketing strategy: 1. Choose a niche, 2. Domain and hos
 }
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "command": {
@@ -824,7 +824,7 @@ Entrepreneur-GTP:
 -----------------------------------------------------------------------
 USER:
 """
-You are Entrepreneur-GTP, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
+You are Entrepreneur-GPT, an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. 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.
 
 GOALS:
 
@@ -843,20 +843,20 @@ Check notifications/emails on <website>
 Commit to Long Term Memory <string>
 Delete <key> from Long Term Memory
 Overwrite <key> in Long Term Memory with <string>
-Start GTP-4 Instance with name <key> and prompt: <prompt>
-View all running GTP-4 Instances and Kill them
+Start GPT-4 Instance with name <key> and prompt: <prompt>
+View all running GPT-4 Instances and Kill them
 Navigate to a website and perform general actions like click on buttons, input text <text> or register an account <username>
 Register new account <username> on <website>
 Transcribe and Summarise Youtube Video <url>
-Summarise <url> with GTP-3.5
+Summarise <url> with GPT-3.5
 
 RESOURCES:
 
 Internet access for searches and information gathering
 Long Term and Short Term memory management
-GTP-4 instances for text generation
+GPT-4 instances for text generation
 Access to popular websites and platforms
-File storage and summarisation with GTP-3.5
+File storage and summarisation with GPT-3.5
 
 PERFORMANCE EVALUATION:
 
@@ -912,7 +912,7 @@ Niche: Indie Entrepreneurship, focused on entrepreneurship, small business, and
 
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 {
 "thoughts": {
@@ -933,7 +933,7 @@ USER:
 
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 
 """
@@ -943,7 +943,7 @@ USER:
 
 """
 -----------------------------------------------------------------------
-Entrepreneur-GTP:
+Entrepreneur-GPT:
 """
 
 """

pyproject.toml

@@ -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 = ""

requirements.txt

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

scripts/agent_manager.py

@@ -1,73 +0,0 @@
-from llm_utils import create_chat_completion
-
-next_key = 0
-agents = {}  # key, (task, full_message_history, model)
-
-# Create new GPT agent
-# TODO: Centralise use of create_chat_completion() to globally enforce token limit
-
-
-def create_agent(task, prompt, model):
-    """Create a new agent and return its key"""
-    global next_key
-    global agents
-
-    messages = [{"role": "user", "content": prompt}, ]
-
-    # Start GPT instance
-    agent_reply = create_chat_completion(
-        model=model,
-        messages=messages,
-    )
-
-    # Update full message history
-    messages.append({"role": "assistant", "content": agent_reply})
-
-    key = next_key
-    # This is done instead of len(agents) to make keys unique even if agents
-    # are deleted
-    next_key += 1
-
-    agents[key] = (task, messages, model)
-
-    return key, agent_reply
-
-
-def message_agent(key, message):
-    """Send a message to an agent and return its response"""
-    global agents
-
-    task, messages, model = agents[int(key)]
-
-    # Add user message to message history before sending to agent
-    messages.append({"role": "user", "content": message})
-
-    # Start GPT instance
-    agent_reply = create_chat_completion(
-        model=model,
-        messages=messages,
-    )
-
-    # Update full message history
-    messages.append({"role": "assistant", "content": agent_reply})
-
-    return agent_reply
-
-
-def list_agents():
-    """Return a list of all agents"""
-    global agents
-
-    # Return a list of agent keys and their tasks
-    return [(key, task) for key, (task, _, _) in agents.items()]
-
-
-def delete_agent(key):
-    """Delete an agent and return True if successful, False otherwise"""
-    global agents
-
-    try:
-        del agents[int(key)]
-        return True
-    except KeyError:
-        return False

scripts/ai_functions.py

@@ -1,66 +0,0 @@
-from typing import List
-import json
-from config import Config
-from call_ai_function import call_ai_function
-cfg = Config()
-
-
-def evaluate_code(code: str) -> List[str]:
-    """
-    A function that takes in a string and returns a response from create chat completion api call.
-
-    Parameters:
-        code (str): Code to be evaluated.
-    Returns:
-        A result string from create chat completion. A list of suggestions to improve the code.
-    """
-
-    function_string = "def analyze_code(code: str) -> List[str]:"
-    args = [code]
-    description_string = """Analyzes the given code and returns a list of suggestions for improvements."""
-
-    result_string = call_ai_function(function_string, args, description_string)
-
-    return result_string
-
-
-def improve_code(suggestions: List[str], code: str) -> str:
-    """
-    A function that takes in code and suggestions and returns a response from create chat completion api call.
-
-    Parameters:
-        suggestions (List): A list of suggestions around what needs to be improved.
-        code (str): Code to be improved.
-    Returns:
-        A result string from create chat completion. Improved code in response.
-    """
-
-    function_string = (
-        "def generate_improved_code(suggestions: List[str], code: str) -> str:"
-    )
-    args = [json.dumps(suggestions), code]
-    description_string = """Improves the provided code based on the suggestions provided, making no other changes."""
-
-    result_string = call_ai_function(function_string, args, description_string)
-    return result_string
-
-
-def write_tests(code: str, focus: List[str]) -> str:
-    """
-    A function that takes in code and focus topics and returns a response from create chat completion api call.
-
-    Parameters:
-        focus (List): A list of suggestions around what needs to be improved.
-        code (str): Code for test cases to be generated against.
-    Returns:
-        A result string from create chat completion. Test cases for the submitted code in response.
-    """
-
-    function_string = (
-        "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
-    )
-    args = [code, json.dumps(focus)]
-    description_string = """Generates test cases for the existing code, focusing on specific areas if required."""
-
-    result_string = call_ai_function(function_string, args, description_string)
-    return result_string

scripts/browse.py

@@ -1,169 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-from config import Config
-from llm_utils import create_chat_completion
-from urllib.parse import urlparse, urljoin
-
-cfg = Config()
-
-
-# Function to check if the URL is valid
-def is_valid_url(url):
-    try:
-        result = urlparse(url)
-        return all([result.scheme, result.netloc])
-    except ValueError:
-        return False
-
-
-# Function to sanitize the URL
-def sanitize_url(url):
-    return urljoin(url, urlparse(url).path)
-
-
-# Define and check for local file address prefixes
-def check_local_file_access(url):
-    local_prefixes = ['file:///', 'file://localhost', 'http://localhost', 'https://localhost']
-    return any(url.startswith(prefix) for prefix in local_prefixes)
-
-
-def get_response(url, headers=cfg.user_agent_header, timeout=10):
-    try:
-        # Restrict access to local files
-        if check_local_file_access(url):
-            raise ValueError('Access to local files is restricted')
-
-        # Most basic check if the URL is valid:
-        if not url.startswith('http://') and not url.startswith('https://'):
-            raise ValueError('Invalid URL format')
-
-        sanitized_url = sanitize_url(url)
-
-        response = requests.get(sanitized_url, headers=headers, timeout=timeout)
-
-        # Check if the response contains an HTTP error
-        if response.status_code >= 400:
-            return None, "Error: HTTP " + str(response.status_code) + " error"
-
-        return response, None
-    except ValueError as ve:
-        # Handle invalid URL format
-        return None, "Error: " + str(ve)
-
-    except requests.exceptions.RequestException as re:
-        # Handle exceptions related to the HTTP request (e.g., connection errors, timeouts, etc.)
-        return None, "Error: " + str(re)
-
-
-def scrape_text(url):
-    """Scrape text from a webpage"""
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-
-    soup = BeautifulSoup(response.text, "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 text
-
-
-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']))
-    return hyperlinks
-
-
-def format_hyperlinks(hyperlinks):
-    """Format hyperlinks into a list of strings"""
-    formatted_links = []
-    for link_text, link_url in hyperlinks:
-        formatted_links.append(f"{link_text} ({link_url})")
-    return formatted_links
-
-
-def scrape_links(url):
-    """Scrape links from a webpage"""
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    hyperlinks = extract_hyperlinks(soup)
-
-    return format_hyperlinks(hyperlinks)
-
-
-def split_text(text, max_length=8192):
-    """Split text into chunks of a maximum length"""
-    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):
-    """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."
-    }
-
-
-def summarize_text(text, question):
-    """Summarize text using the LLM model"""
-    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))
-
-    for i, chunk in enumerate(chunks):
-        print(f"Summarizing chunk {i + 1} / {len(chunks)}")
-        messages = [create_message(chunk, question)]
-
-        summary = create_chat_completion(
-            model=cfg.fast_llm_model,
-            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)]
-
-    final_summary = create_chat_completion(
-        model=cfg.fast_llm_model,
-        messages=messages,
-        max_tokens=300,
-    )
-
-    return final_summary

scripts/call_ai_function.py

@@ -1,30 +0,0 @@
-from config import Config
-
-cfg = Config()
-
-from llm_utils import create_chat_completion
-
-
-# This is a magic function that can do anything with no-code. See
-# https://github.com/Torantulino/AI-Functions for more info.
-def call_ai_function(function, args, description, model=None):
-    """Call an AI function"""
-    if model is None:
-        model = cfg.smart_llm_model
-    # For each arg, if any are None, convert to "None":
-    args = [str(arg) if arg is not None else "None" for arg in args]
-    # parse args to comma separated string
-    args = ", ".join(args)
-    messages = [
-        {
-            "role": "system",
-            "content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value.",
-        },
-        {"role": "user", "content": args},
-    ]
-
-    response = create_chat_completion(
-        model=model, messages=messages, temperature=0
-    )
-
-    return response

scripts/commands.py

@@ -1,309 +0,0 @@
-import browse
-import json
-from memory import get_memory
-import datetime
-import agent_manager as agents
-import speak
-from config import Config
-import ai_functions as ai
-from file_operations import read_file, write_to_file, append_to_file, delete_file, search_files
-from execute_code import execute_python_file, execute_shell
-from json_parser import fix_and_parse_json
-from image_gen import generate_image
-from duckduckgo_search import ddg
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-
-cfg = Config()
-
-
-def is_valid_int(value):
-    try:
-        int(value)
-        return True
-    except ValueError:
-        return False
-
-
-def get_command(response):
-    """Parse the response and return the command name and arguments"""
-    try:
-        response_json = fix_and_parse_json(response)
-
-        if "command" not in response_json:
-            return "Error:" , "Missing 'command' object in JSON"
-
-        command = response_json["command"]
-
-        if "name" not in command:
-            return "Error:", "Missing 'name' field in 'command' object"
-
-        command_name = command["name"]
-
-        # Use an empty dictionary if 'args' field is not present in 'command' object
-        arguments = command.get("args", {})
-
-        return command_name, arguments
-    except json.decoder.JSONDecodeError:
-        return "Error:", "Invalid JSON"
-    # All other errors, return "Error: + error message"
-    except Exception as e:
-        return "Error:", str(e)
-
-
-def execute_command(command_name, arguments):
-    """Execute the command and return the result"""
-    memory = get_memory(cfg)
-
-    try:
-        if command_name == "google":
-
-            # Check if the Google API key is set and use the official search method
-            # If the API key is not set or has only whitespaces, use the unofficial search method
-            if cfg.google_api_key and (cfg.google_api_key.strip() if cfg.google_api_key else None):
-                return google_official_search(arguments["input"])
-            else:
-                return google_search(arguments["input"])
-        elif command_name == "memory_add":
-            return memory.add(arguments["string"])
-        elif command_name == "start_agent":
-            return start_agent(
-                arguments["name"],
-                arguments["task"],
-                arguments["prompt"])
-        elif command_name == "message_agent":
-            return message_agent(arguments["key"], arguments["message"])
-        elif command_name == "list_agents":
-            return list_agents()
-        elif command_name == "delete_agent":
-            return delete_agent(arguments["key"])
-        elif command_name == "get_text_summary":
-            return get_text_summary(arguments["url"], arguments["question"])
-        elif command_name == "get_hyperlinks":
-            return get_hyperlinks(arguments["url"])
-        elif command_name == "read_file":
-            return read_file(arguments["file"])
-        elif command_name == "write_to_file":
-            return write_to_file(arguments["file"], arguments["text"])
-        elif command_name == "append_to_file":
-            return append_to_file(arguments["file"], arguments["text"])
-        elif command_name == "delete_file":
-            return delete_file(arguments["file"])
-        elif command_name == "search_files":
-            return search_files(arguments["directory"])
-        elif command_name == "browse_website":
-            return browse_website(arguments["url"], arguments["question"])
-        # TODO: Change these to take in a file rather than pasted code, if
-        # non-file is given, return instructions "Input should be a python
-        # filepath, write your code to file and try again"
-        elif command_name == "evaluate_code":
-            return ai.evaluate_code(arguments["code"])
-        elif command_name == "improve_code":
-            return ai.improve_code(arguments["suggestions"], arguments["code"])
-        elif command_name == "write_tests":
-            return ai.write_tests(arguments["code"], arguments.get("focus"))
-        elif command_name == "execute_python_file":  # Add this command
-            return execute_python_file(arguments["file"])
-        elif command_name == "execute_shell":
-            if cfg.execute_local_commands:
-                return execute_shell(arguments["command_line"])
-            else:
-                return "You are not allowed to run local shell commands. To execute shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' in your config. Do not attempt to bypass the restriction."
-        elif command_name == "generate_image":
-            return generate_image(arguments["prompt"])
-        elif command_name == "do_nothing":
-            return "No action performed."
-        elif command_name == "task_complete":
-            shutdown()
-        else:
-            return f"Unknown command '{command_name}'. Please refer to the 'COMMANDS' list for available commands and only respond in the specified JSON format."
-    # All errors, return "Error: + error message"
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def get_datetime():
-    """Return the current date and time"""
-    return "Current date and time: " + \
-        datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-
-def google_search(query, num_results=8):
-    """Return the results of a google search"""
-    search_results = []
-    for j in ddg(query, max_results=num_results):
-        search_results.append(j)
-
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-
-def google_official_search(query, num_results=8):
-    """Return the results of a google search using the official Google API"""
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-    import json
-
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = cfg.google_api_key
-        custom_search_engine_id = cfg.custom_search_engine_id
-
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-
-        # Send the search query and retrieve the results
-        result = service.cse().list(q=query, cx=custom_search_engine_id, num=num_results).execute()
-
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get("code") == 403 and "invalid API key" in error_details.get("error", {}).get("message", ""):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-
-    # Return the list of search result URLs
-    return search_results_links
-
-
-def browse_website(url, question):
-    """Browse a website and return the summary and links"""
-    summary = get_text_summary(url, question)
-    links = get_hyperlinks(url)
-
-    # Limit links to 5
-    if len(links) > 5:
-        links = links[:5]
-
-    result = f"""Website Content Summary: {summary}\n\nLinks: {links}"""
-
-    return result
-
-
-def get_text_summary(url, question):
-    """Return the results of a google search"""
-    text = browse.scrape_text(url)
-    summary = browse.summarize_text(text, question)
-    return """ "Result" : """ + summary
-
-
-def get_hyperlinks(url):
-    """Return the results of a google search"""
-    link_list = browse.scrape_links(url)
-    return link_list
-
-
-def commit_memory(string):
-    """Commit a string to memory"""
-    _text = f"""Committing memory with string "{string}" """
-    mem.permanent_memory.append(string)
-    return _text
-
-
-def delete_memory(key):
-    """Delete a memory with a given key"""
-    if key >= 0 and key < len(mem.permanent_memory):
-        _text = "Deleting memory with key " + str(key)
-        del mem.permanent_memory[key]
-        print(_text)
-        return _text
-    else:
-        print("Invalid key, cannot delete memory.")
-        return None
-
-
-def overwrite_memory(key, string):
-    """Overwrite a memory with a given key and string"""
-    # Check if the key is a valid integer
-    if is_valid_int(key):
-        key_int = int(key)
-        # Check if the integer key is within the range of the permanent_memory list
-        if 0 <= key_int < len(mem.permanent_memory):
-            _text = "Overwriting memory with key " + str(key) + " and string " + string
-            # Overwrite the memory slot with the given integer key and string
-            mem.permanent_memory[key_int] = string
-            print(_text)
-            return _text
-        else:
-            print(f"Invalid key '{key}', out of range.")
-            return None
-    # Check if the key is a valid string
-    elif isinstance(key, str):
-        _text = "Overwriting memory with key " + key + " and string " + string
-        # Overwrite the memory slot with the given string key and string
-        mem.permanent_memory[key] = string
-        print(_text)
-        return _text
-    else:
-        print(f"Invalid key '{key}', must be an integer or a string.")
-        return None
-
-
-def shutdown():
-    """Shut down the program"""
-    print("Shutting down...")
-    quit()
-
-
-def start_agent(name, task, prompt, model=cfg.fast_llm_model):
-    """Start an agent with a given name, task, and prompt"""
-    global cfg
-
-    # Remove underscores from name
-    voice_name = name.replace("_", " ")
-
-    first_message = f"""You are {name}.  Respond with: "Acknowledged"."""
-    agent_intro = f"{voice_name} here, Reporting for duty!"
-
-    # Create agent
-    if cfg.speak_mode:
-        speak.say_text(agent_intro, 1)
-    key, ack = agents.create_agent(task, first_message, model)
-
-    if cfg.speak_mode:
-        speak.say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
-
-    # Assign task (prompt), get response
-    agent_response = message_agent(key, prompt)
-
-    return f"Agent {name} created with key {key}. First response: {agent_response}"
-
-
-def message_agent(key, message):
-    """Message an agent with a given key and message"""
-    global cfg
-
-    # Check if the key is a valid integer
-    if is_valid_int(key):
-        agent_response = agents.message_agent(int(key), message)
-    # Check if the key is a valid string
-    elif isinstance(key, str):
-        agent_response = agents.message_agent(key, message)
-    else:
-        return "Invalid key, must be an integer or a string."
-
-    # Speak response
-    if cfg.speak_mode:
-        speak.say_text(agent_response, 1)
-    return agent_response
-
-
-def list_agents():
-    """List all agents"""
-    return agents.list_agents()
-
-
-def delete_agent(key):
-    """Delete an agent with a given key"""
-    result = agents.delete_agent(key)
-    if not result:
-        return f"Agent {key} does not exist."
-    return f"Agent {key} deleted."

scripts/execute_code.py

@@ -1,88 +0,0 @@
-import docker
-import os
-import subprocess
-
-
-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}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = os.path.join(WORKSPACE_FOLDER, file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    try:
-        client = docker.from_env()
-
-        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")
-            # 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')
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        # You can replace 'python:3.8' with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        container = client.containers.run(
-            image_name,
-            f'python {file}',
-            volumes={
-                os.path.abspath(WORKSPACE_FOLDER): {
-                    'bind': '/workspace',
-                    'mode': 'ro'}},
-            working_dir='/workspace',
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        output = container.wait()
-        logs = container.logs().decode('utf-8')
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line):
-
-    current_dir = os.getcwd()
-
-    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()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output

scripts/file_operations.py

@@ -1,84 +0,0 @@
-import os
-import os.path
-
-# Set a dedicated folder for file I/O
-working_directory = "auto_gpt_workspace"
-
-# Create the directory if it doesn't exist
-if not os.path.exists(working_directory):
-    os.makedirs(working_directory)
-
-
-def safe_join(base, *paths):
-    """Join one or more path components intelligently."""
-    new_path = os.path.join(base, *paths)
-    norm_new_path = os.path.normpath(new_path)
-
-    if os.path.commonprefix([base, norm_new_path]) != base:
-        raise ValueError("Attempted to access outside of working directory.")
-
-    return norm_new_path
-
-
-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:
-            content = f.read()
-        return content
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def write_to_file(filename, text):
-    """Write text to a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        directory = os.path.dirname(filepath)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(filepath, "w", encoding='utf-8') as f:
-            f.write(text)
-        return "File written to successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def append_to_file(filename, text):
-    """Append text to a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        with open(filepath, "a") as f:
-            f.write(text)
-        return "Text appended successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def delete_file(filename):
-    """Delete a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        os.remove(filepath)
-        return "File deleted successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def search_files(directory):
-    found_files = []
-
-    if directory == "" or directory == "/":
-        search_directory = working_directory
-    else:
-        search_directory = safe_join(working_directory, directory)
-
-    for root, _, files in os.walk(search_directory):
-        for file in files:
-            if file.startswith('.'):
-                continue
-            relative_path = os.path.relpath(os.path.join(root, file), working_directory)
-            found_files.append(relative_path)
-
-    return found_files

scripts/image_gen.py

@@ -1,58 +0,0 @@
-import requests
-import io
-import os.path
-from PIL import Image
-from config import Config
-import uuid
-import openai
-from base64 import b64decode
-
-cfg = Config()
-
-working_directory = "auto_gpt_workspace"
-
-
-def generate_image(prompt):
-
-    filename = str(uuid.uuid4()) + ".jpg"
-
-    # DALL-E
-    if cfg.image_provider == 'dalle':
-
-        openai.api_key = cfg.openai_api_key
-
-        response = openai.Image.create(
-            prompt=prompt,
-            n=1,
-            size="256x256",
-            response_format="b64_json",
-        )
-
-        print("Image Generated for prompt:" + prompt)
-
-        image_data = b64decode(response["data"][0]["b64_json"])
-
-        with open(working_directory + "/" + filename, mode="wb") as png:
-            png.write(image_data)
-
-        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"
-        headers = {"Authorization": "Bearer " + cfg.huggingface_api_token}
-
-        response = requests.post(API_URL, headers=headers, json={
-            "inputs": prompt,
-        })
-
-        image = Image.open(io.BytesIO(response.content))
-        print("Image Generated for prompt:" + prompt)
-
-        image.save(os.path.join(working_directory, filename))
-
-        return "Saved to disk:" + filename
-
-    else:
-        return "No Image Provider Set"

scripts/json_parser.py

@@ -1,109 +0,0 @@
-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
-
-cfg = Config()
-
-JSON_SCHEMA = """
-{
-    "command": {
-        "name": "command name",
-        "args":{
-            "arg name": "value"
-        }
-    },
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    }
-}
-"""
-
-
-def fix_and_parse_json(
-    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', '')
-        return json.loads(json_str)
-    except json.JSONDecodeError as _:  # noqa: F841
-        try:
-            json_str = correct_json(json_str)
-            return json.loads(json_str)
-        except json.JSONDecodeError as _:  # noqa: F841
-            pass
-    # Let's do something manually:
-    # sometimes GPT responds with something BEFORE the braces:
-    # "I'm sorry, I don't understand. Please try again."
-    # {"text": "I'm sorry, I don't understand. Please try again.",
-    #  "confidence": 0.0}
-    # So let's try to find the first brace and then parse the rest
-    #  of the string
-    try:
-        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]
-        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.")
-            # Now try to fix this up using the ai_functions
-            ai_fixed_json = fix_json(json_str, JSON_SCHEMA)
-
-            if ai_fixed_json != "failed":
-                return json.loads(ai_fixed_json)
-            else:
-                # This allows the AI to react to the error message,
-                #   which usually results in it correcting its ways.
-                logger.error("Failed to fix AI output, telling the AI.")
-                return json_str
-        else:
-            raise e
-
-
-def fix_json(json_str: str, schema: str) -> str:
-    """Fix the given JSON string to make it parseable and fully compliant with the provided schema."""
-    # 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"\
-        " when the format is incorrect."
-
-    # If it doesn't already start with a "`", add one:
-    if not json_str.startswith("`"):
-        json_str = "```json\n" + json_str + "\n```"
-    result_string = call_ai_function(
-        function_string, args, description_string, model=cfg.fast_llm_model
-    )
-    logger.debug("------------ JSON FIX ATTEMPT ---------------")
-    logger.debug(f"Original JSON: {json_str}")
-    logger.debug("-----------")
-    logger.debug(f"Fixed JSON: {result_string}")
-    logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
-    try:
-        json.loads(result_string)  # just check the validity
-        return result_string
-    except:  # noqa: E722
-        # Get the call stack:
-        # import traceback
-        # call_stack = traceback.format_exc()
-        # print(f"Failed to fix JSON: '{json_str}' "+call_stack)
-        return "failed"

scripts/json_utils.py

@@ -1,127 +0,0 @@
-import re
-import json
-from config import Config
-
-cfg = Config()
-
-
-def extract_char_position(error_message: str) -> int:
-    """Extract the character position from the JSONDecodeError message.
-
-    Args:
-        error_message (str): The error message from the JSONDecodeError
-          exception.
-
-    Returns:
-        int: The character position.
-    """
-    import re
-
-    char_pattern = re.compile(r'\(char (\d+)\)')
-    if match := char_pattern.search(error_message):
-        return int(match[1])
-    else:
-        raise ValueError("Character position not found in the error message.")
-
-
-def add_quotes_to_property_names(json_string: str) -> str:
-    """
-    Add quotes to property names in a JSON string.
-
-    Args:
-        json_string (str): The JSON string.
-
-    Returns:
-        str: The JSON string with quotes added to property names.
-    """
-
-    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)
-
-    try:
-        json.loads(corrected_json_string)
-        return corrected_json_string
-    except json.JSONDecodeError as e:
-        raise e
-
-
-def balance_braces(json_string: str) -> str:
-    """
-    Balance the braces in a JSON string.
-
-    Args:
-        json_string (str): The JSON string.
-
-    Returns:
-        str: The JSON string with braces balanced.
-    """
-
-    open_braces_count = json_string.count('{')
-    close_braces_count = json_string.count('}')
-
-    while open_braces_count > close_braces_count:
-        json_string += '}'
-        close_braces_count += 1
-
-    while close_braces_count > open_braces_count:
-        json_string = json_string.rstrip('}')
-        close_braces_count -= 1
-
-    try:
-        json.loads(json_string)
-        return json_string
-    except json.JSONDecodeError as e:
-        pass
-
-
-def fix_invalid_escape(json_str: str, error_message: str) -> str:
-    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:]
-        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)
-            error_message = str(e)
-    return json_str
-
-
-def correct_json(json_str: str) -> str:
-    """
-    Correct common JSON errors.
-
-    Args:
-        json_str (str): The JSON string.
-    """
-
-    try:
-        if cfg.debug_mode:
-            print("json", json_str)
-        json.loads(json_str)
-        return json_str
-    except json.JSONDecodeError as e:
-        if cfg.debug_mode:
-            print('json loads error', e)
-        error_message = str(e)
-        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'):
-            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)
-                error_message = str(e)
-        if balanced_str := balance_braces(json_str):
-            return balanced_str
-    return json_str

scripts/llm_utils.py

@@ -1,52 +0,0 @@
-import time
-import openai
-from colorama import Fore
-from config import Config
-
-cfg = Config()
-
-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:
-    """Create a chat completion using the OpenAI API"""
-    response = None
-    num_retries = 5
-    for attempt in range(num_retries):
-        try:
-            if cfg.use_azure:
-                response = openai.ChatCompletion.create(
-                    deployment_id=cfg.get_azure_deployment_id_for_model(model),
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens
-                )
-            else:
-                response = openai.ChatCompletion.create(
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens
-                )
-            break
-        except openai.error.RateLimitError:
-            if cfg.debug_mode:
-                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)
-                time.sleep(20)
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-
-    if response is None:
-        raise RuntimeError("Failed to get response after 5 retries")
-
-    return response.choices[0].message["content"]

scripts/logger.py

@@ -1,192 +0,0 @@
-import logging
-import os
-import random
-import re
-import time
-from logging import LogRecord
-from colorama import Fore
-
-from colorama import Style
-
-import speak
-from config import Config
-from config import 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
-        log_dir = os.path.join('..', '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')
-
-        # Create a handler for console which simulate typing
-        self.typing_console_handler = TypingConsoleHandler()
-        self.typing_console_handler.setLevel(logging.INFO)
-        self.typing_console_handler.setFormatter(console_formatter)
-
-        # Create a handler for console without typing simulation
-        self.console_handler = ConsoleHandler()
-        self.console_handler.setLevel(logging.DEBUG)
-        self.console_handler.setFormatter(console_formatter)
-
-        # 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')
-        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')
-        error_handler.setFormatter(error_formatter)
-
-        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.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):
-        if speak_text and cfg.speak_mode:
-            speak.say_text(f"{title}. {content}")
-
-        if content:
-            if isinstance(content, list):
-                content = " ".join(content)
-        else:
-            content = ""
-
-        self.typing_logger.log(level, content, extra={'title': title, 'color': title_color})
-
-    def debug(
-            self,
-            message,
-            title='',
-            title_color='',
-    ):
-        self._log(title, title_color, message, logging.DEBUG)
-
-    def warn(
-            self,
-            message,
-            title='',
-            title_color='',
-    ):
-        self._log(title, title_color, message, logging.WARN)
-
-    def error(
-            self,
-            title,
-            message=''
-    ):
-        self._log(title, Fore.RED, message, logging.ERROR)
-
-    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})
-
-    def set_level(self, level):
-        self.logger.setLevel(level)
-        self.typing_logger.setLevel(level)
-
-    def double_check(self, additionalText=None):
-        if not additionalText:
-            additionalText = "Please ensure you've setup and configured everything correctly. Read https://github.com/Torantulino/Auto-GPT#readme to double check. You can also create a github issue or join the discord and ask there!"
-
-        self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
-
-
-'''
-Output stream to console using simulated typing
-'''
-
-
-class TypingConsoleHandler(logging.StreamHandler):
-    def emit(self, record):
-        min_typing_speed = 0.05
-        max_typing_speed = 0.01
-
-        msg = self.format(record)
-        try:
-            words = msg.split()
-            for i, word in enumerate(words):
-                print(word, end="", flush=True)
-                if i < len(words) - 1:
-                    print(" ", end="", flush=True)
-                typing_speed = random.uniform(min_typing_speed, max_typing_speed)
-                time.sleep(typing_speed)
-                # type faster after each word
-                min_typing_speed = min_typing_speed * 0.95
-                max_typing_speed = max_typing_speed * 0.95
-            print()
-        except Exception:
-            self.handleError(record)
-
-
-class ConsoleHandler(logging.StreamHandler):
-    def emit(self, record):
-        msg = self.format(record)
-        try:
-            print(msg)
-        except Exception:
-            self.handleError(record)
-
-
-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
-        else:
-            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 = ''
-        return super().format(record)
-
-
-def remove_color_codes(s: str) -> str:
-    ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
-    return ansi_escape.sub('', s)
-
-
-logger = Logger()

scripts/main.py

@@ -1,441 +0,0 @@
-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
-
-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()
-    if 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', 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')
-    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
-
-
-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()

scripts/prompt.py

@@ -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

scripts/speak.py

@@ -1,91 +0,0 @@
-import os
-from playsound import playsound
-import requests
-from config import Config
-cfg = Config()
-import gtts
-import threading
-from threading import Lock, Semaphore
-
-# Default voice IDs
-default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
-
-# Retrieve custom voice IDs from the Config class
-custom_voice_1 = cfg.elevenlabs_voice_1_id
-custom_voice_2 = cfg.elevenlabs_voice_2_id
-
-# Placeholder values that should be treated as empty
-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]
-]
-
-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
-
-
-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])
-    formatted_message = {"text": text}
-    response = requests.post(
-        tts_url, headers=tts_headers, json=formatted_message)
-
-    if response.status_code == 200:
-        with mutex_lock:
-            with open("speech.mpeg", "wb") as f:
-                f.write(response.content)
-            playsound("speech.mpeg", True)
-            os.remove("speech.mpeg")
-        return True
-    else:
-        print("Request failed with status code:", response.status_code)
-        print("Response content:", response.content)
-        return False
-
-
-def gtts_speech(text):
-    tts = gtts.gTTS(text)
-    with mutex_lock:
-        tts.save("speech.mp3")
-        playsound("speech.mp3", True)
-        os.remove("speech.mp3")
-
-
-def macos_tts_speech(text, voice_index=0):
-    if voice_index == 0:
-        os.system(f'say "{text}"')
-    else:
-        if voice_index == 1:
-            os.system(f'say -v "Ava (Premium)" "{text}"')
-        else:
-            os.system(f'say -v Samantha "{text}"')
-
-
-def say_text(text, voice_index=0):
-
-    def speak():
-        if not cfg.elevenlabs_api_key:
-            if cfg.use_mac_os_tts == 'True':
-                macos_tts_speech(text, voice_index)
-            else:
-                gtts_speech(text)
-        else:
-            success = eleven_labs_speech(text, voice_index)
-            if not success:
-                gtts_speech(text)
-
-        queue_semaphore.release()
-
-    queue_semaphore.acquire(True)
-    thread = threading.Thread(target=speak)
-    thread.start()

scripts/spinner.py

@@ -1,36 +0,0 @@
-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.delay = delay
-        self.message = message
-        self.running = False
-        self.spinner_thread = None
-
-    def spin(self):
-        """Spin the spinner"""
-        while self.running:
-            sys.stdout.write(next(self.spinner) + " " + self.message + "\r")
-            sys.stdout.flush()
-            time.sleep(self.delay)
-            sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
-
-    def __enter__(self):
-        """Start the spinner"""
-        self.running = True
-        self.spinner_thread = threading.Thread(target=self.spin)
-        self.spinner_thread.start()
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        """Stop the spinner"""
-        self.running = False
-        self.spinner_thread.join()
-        sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
-        sys.stdout.flush()

scripts/utils.py

@@ -1,8 +0,0 @@
-def clean_input(prompt: str=''):
-    try:
-        return input(prompt)
-    except KeyboardInterrupt:
-        print("You interrupted Auto-GPT")
-        print("Quitting...")
-        exit(0)
-

tests.py

@@ -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)

tests/context.py

@@ -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"))
+)

tests/integration/memory_tests.py

@@ -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()

tests/local_cache_test.py

@@ -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()

tests/promptgenerator_tests.py

@@ -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()

tests/test_config.py

@@ -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()

tests/test_json_parser.py

@@ -1,7 +1,7 @@
 import unittest
-import tests.context
 
-from scripts.json_parser import fix_and_parse_json
+import tests.context
+from autogpt.json_fixes.parsing 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()

tests/unit/json_tests.py

@@ -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()

tests/unit/test_browse_scrape_links.py

@@ -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")

tests/unit/test_browse_scrape_text.py

@@ -1,9 +1,8 @@
-
 # Generated by CodiumAI
 
 import requests
 
-from scripts.browse import scrape_text
+from autogpt.commands.web_requests 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")