Push stable to huggingface

.devcontainer/Dockerfile

@@ -0,0 +1,23 @@
+# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster
+ARG VARIANT=3-bullseye
+FROM python:3.8
+
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    # Remove imagemagick due to https://security-tracker.debian.org/tracker/CVE-2019-10131
+    && apt-get purge -y imagemagick imagemagick-6-common 
+
+# Temporary: Upgrade python packages due to https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-40897
+# They are installed by the base image (python) which does not have the patch.
+RUN python3 -m pip install --upgrade setuptools
+
+# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
+# COPY requirements.txt /tmp/pip-tmp/
+# RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
+#    && rm -rf /tmp/pip-tmp
+
+# [Optional] Uncomment this section to install additional OS packages.
+# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+#     && apt-get -y install --no-install-recommends <your-package-list-here>
+
+# [Optional] Uncomment this line to install global node packages.
+# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

.devcontainer/devcontainer.json

@@ -0,0 +1,39 @@
+{
+  "build": {
+    "dockerfile": "./Dockerfile",
+    "context": "."
+  },
+  "features": {
+    "ghcr.io/devcontainers/features/common-utils:2": {
+      "installZsh": "true",
+      "username": "vscode",
+      "userUid": "1000",
+      "userGid": "1000",
+      "upgradePackages": "true"
+    },
+    "ghcr.io/devcontainers/features/python:1": "none",
+    "ghcr.io/devcontainers/features/node:1": "none",
+    "ghcr.io/devcontainers/features/git:1": {
+      "version": "latest",
+      "ppa": "false"
+    }
+  },
+  // Configure tool-specific properties.
+  "customizations": {
+    // Configure properties specific to VS Code.
+    "vscode": {
+      // Set *default* container specific settings.json values on container create.
+      "settings": {
+        "python.defaultInterpreterPath": "/usr/local/bin/python"
+      }
+    }
+  },
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "forwardPorts": [],
+
+  // Use 'postCreateCommand' to run commands after the container is created.
+  // "postCreateCommand": "pip3 install --user -r requirements.txt",
+
+  // Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
+  "remoteUser": "vscode"
+}

.env.template

@@ -0,0 +1,123 @@
+################################################################################
+### AUTO-GPT - GENERAL SETTINGS
+################################################################################
+# 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
+################################################################################
+
+### OPENAI
+# OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
+# TEMPERATURE - Sets temperature in OpenAI (Default: 1)
+# USE_AZURE - Use Azure OpenAI or not (Default: False)
+OPENAI_API_KEY=your-openai-api-key
+TEMPERATURE=1
+USE_AZURE=False
+
+### AZURE
+# OPENAI_AZURE_API_BASE - OpenAI API base URL for Azure (Example: https://my-azure-openai-url.com)
+# OPENAI_AZURE_API_VERSION - OpenAI API version for Azure (Example: v1)
+# OPENAI_AZURE_DEPLOYMENT_ID - OpenAI deployment ID for Azure (Example: my-deployment-id)
+# OPENAI_AZURE_CHAT_DEPLOYMENT_ID - OpenAI deployment ID for Azure Chat (Example: my-deployment-id-for-azure-chat)
+# OPENAI_AZURE_EMBEDDINGS_DEPLOYMENT_ID - OpenAI deployment ID for Embedding (Example: my-deployment-id-for-azure-embeddigs)
+OPENAI_AZURE_API_BASE=your-base-url-for-azure
+OPENAI_AZURE_API_VERSION=api-version-for-azure
+OPENAI_AZURE_DEPLOYMENT_ID=deployment-id-for-azure
+OPENAI_AZURE_CHAT_DEPLOYMENT_ID=deployment-id-for-azure-chat
+OPENAI_AZURE_EMBEDDINGS_DEPLOYMENT_ID=deployment-id-for-azure-embeddigs
+
+################################################################################
+### LLM MODELS
+################################################################################
+
+# SMART_LLM_MODEL - Smart language model (Default: gpt-4)
+# FAST_LLM_MODEL - Fast language model (Default: gpt-3.5-turbo)
+SMART_LLM_MODEL=gpt-4
+FAST_LLM_MODEL=gpt-3.5-turbo
+
+### LLM MODEL SETTINGS
+# FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
+# SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
+# When using --gpt3onlythis needs to be set to 4000.
+FAST_TOKEN_LIMIT=4000
+SMART_TOKEN_LIMIT=8000
+
+################################################################################
+### MEMORY
+################################################################################
+
+# MEMORY_BACKEND - Memory backend type (Default: local)
+MEMORY_BACKEND=local
+
+### PINECONE
+# PINECONE_API_KEY - Pinecone API Key (Example: my-pinecone-api-key)
+# PINECONE_ENV - Pinecone environment (region) (Example: us-west-2)
+PINECONE_API_KEY=your-pinecone-api-key
+PINECONE_ENV=your-pinecone-region
+
+### REDIS
+# REDIS_HOST - Redis host (Default: localhost)
+# REDIS_PORT - Redis port (Default: 6379)
+# REDIS_PASSWORD - Redis password (Default: "")
+# WIPE_REDIS_ON_START - Wipes data / index on start (Default: False)
+# MEMORY_INDEX - Name of index created in Redis database (Default: auto-gpt)
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_PASSWORD=
+WIPE_REDIS_ON_START=False
+MEMORY_INDEX=auto-gpt
+
+################################################################################
+### IMAGE GENERATION PROVIDER
+################################################################################
+
+### OPEN AI
+# IMAGE_PROVIDER - Image provider (Example: dalle)
+IMAGE_PROVIDER=dalle
+
+### HUGGINGFACE
+# STABLE DIFFUSION
+# (Default URL: https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4)
+# Set in image_gen.py)
+# HUGGINGFACE_API_TOKEN - HuggingFace API token (Example: my-huggingface-api-token)
+HUGGINGFACE_API_TOKEN=your-huggingface-api-token
+
+################################################################################
+### SEARCH PROVIDER
+################################################################################
+
+### GOOGLE
+# GOOGLE_API_KEY - Google API key (Example: my-google-api-key)
+# CUSTOM_SEARCH_ENGINE_ID - Custom search engine ID (Example: my-custom-search-engine-id)
+GOOGLE_API_KEY=your-google-api-key
+CUSTOM_SEARCH_ENGINE_ID=your-custom-search-engine-id
+
+################################################################################
+### TTS PROVIDER
+################################################################################
+
+### MAC OS
+# 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)
+# ELEVENLABS_VOICE_2_ID - Eleven Labs voice 2 ID (Example: my-voice-id-2)
+ELEVENLABS_API_KEY=your-elevenlabs-api-key
+ELEVENLABS_VOICE_1_ID=your-voice-id-1
+ELEVENLABS_VOICE_2_ID=your-voice-id-2

.flake8

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

.gitignore

@@ -0,0 +1,154 @@
+## Original ignores
+autogpt/keys.py
+autogpt/*json
+autogpt/node_modules/
+autogpt/__pycache__/keys.cpython-310.pyc
+package-lock.json
+*.pyc
+auto_gpt_workspace/*
+*.mpeg
+.env
+azure.yaml
+*venv/*
+outputs/*
+ai_settings.yaml
+last_run_ai_settings.yaml
+.vscode
+.idea/*
+auto-gpt.json
+log.txt
+log-ingestion.txt
+logs
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# 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

@@ -0,0 +1,64 @@
+
+To contribute to this GitHub project, you can follow these steps:
+
+1. Fork the repository you want to contribute to by clicking the "Fork" button on the project page.
+
+2. Clone the repository to your local machine using the following command:
+
+```
+git clone https://github.com/<YOUR-GITHUB-USERNAME>/Auto-GPT
+```
+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"
+```
+6. Make your changes to the code or documentation.
+- Example: Improve User Interface or Add Documentation.
+
+
+7. Add the changes to the staging area using the following command:
+```
+git add .
+```
+
+8. Commit the changes with a meaningful commit message using the following command:
+```
+git commit -m "your commit message"
+```
+9. Push the changes to your forked repository using the following command:
+```
+git push origin branch-name
+```
+10. Go to the GitHub website and navigate to your forked repository.
+
+11. Click the "New pull request" button.
+
+12. Select the branch you just pushed to and the branch you want to merge into on the original repository.
+
+13. Add a description of your changes and click the "Create pull request" button.
+
+14. Wait for the project maintainer to review your changes and provide feedback.
+
+15. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
+
+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
+git checkout master
+git merge upstream/master
+```
+Finally, delete the branch you created with the following command:
+```
+git branch -d branch-name
+```
+That's it you made it 🐣⭐⭐

Dockerfile

@@ -0,0 +1,23 @@
+# Use an official Python base image from the Docker Hub
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PIP_NO_CACHE_DIR=yes \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# Create a non-root user and set permissions
+RUN useradd --create-home appuser
+WORKDIR /home/appuser
+RUN chown appuser:appuser /home/appuser
+USER appuser
+
+# Copy the requirements.txt file and install the requirements
+COPY --chown=appuser:appuser requirements.txt .
+RUN pip install --no-cache-dir --user -r requirements.txt
+
+# Copy the application files
+COPY --chown=appuser:appuser autogpt/ .
+
+# Set the entrypoint
+ENTRYPOINT ["python", "-m", "autogpt"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Toran Bruce Richards
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,22 @@
 ---
-title: Bluesix
-emoji: 🐨
-colorFrom: pink
-colorTo: purple
+emoji: 🤖
+colorFrom: blue
+colorTo: indigo
 sdk: streamlit
-sdk_version: 1.17.0
+python_version: 3.9
+sdk_version: 1.0.0
 app_file: app.py
-pinned: false
+app_port: 7860
+fullWidth: false
+models:
+  - openai/gpt-3
+datasets:
+  - wikitext
+tags:
+  - gpt
+  - nlp
+  - text-generation
+pinned: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

autogpt/__main__.py

@@ -0,0 +1,572 @@
+import argparse
+import json
+import logging
+import traceback
+
+from colorama import Fore, Style
+
+from autogpt import chat
+from autogpt import commands as cmd
+from autogpt import speak, utils
+from autogpt.ai_config import AIConfig
+from autogpt.config import Config
+from autogpt.json_parser import fix_and_parse_json
+from autogpt.logger import logger
+from autogpt.memory import get_memory, get_supported_memory_backends
+from autogpt.spinner import Spinner
+
+cfg = Config()
+config = None
+
+
+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.debug_mode:
+            logger.error("Error: Invalid JSON: %s\n", json_string)
+        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, 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:
+            speak.say_text(assistant_thoughts_speak)
+
+        return assistant_reply_json
+    except json.decoder.JSONDecodeError:
+        call_stack = traceback.format_exc()
+        logger.error("Error: Invalid JSON\n", assistant_reply)
+        logger.error("Traceback: \n", call_stack)
+        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:
+        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 = 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 = 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
+
+    return config.construct_full_prompt()
+
+
+def prompt_user():
+    """Prompt the user for input"""
+    ai_name = ""
+    # Construct the prompt
+    logger.typewriter_log(
+        "Welcome to Auto-GPT! ",
+        Fore.GREEN,
+        "Enter the name of your AI and its role below. Entering nothing will load"
+        " defaults.",
+        speak_text=True,
+    )
+
+    # Get AI Name from User
+    logger.typewriter_log(
+        "Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
+    )
+    ai_name = utils.clean_input("AI Name: ")
+    if ai_name == "":
+        ai_name = "Entrepreneur-GPT"
+
+    logger.typewriter_log(
+        f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
+    )
+
+    # Get AI Role from User
+    logger.typewriter_log(
+        "Describe your AI's role: ",
+        Fore.GREEN,
+        "For example, 'an AI designed to autonomously develop and run businesses with"
+        " the sole goal of increasing your net worth.'",
+    )
+    ai_role = utils.clean_input(f"{ai_name} is: ")
+    if ai_role == "":
+        ai_role = "an AI designed to autonomously develop and run businesses with the"
+        " sole goal of increasing your net worth."
+
+    # Enter up to 5 goals for the AI
+    logger.typewriter_log(
+        "Enter up to 5 goals for your AI: ",
+        Fore.GREEN,
+        "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
+        " multiple businesses autonomously'",
+    )
+    print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
+    ai_goals = []
+    for i in range(5):
+        ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
+        if ai_goal == "":
+            break
+        ai_goals.append(ai_goal)
+    if len(ai_goals) == 0:
+        ai_goals = [
+            "Increase net worth",
+            "Grow Twitter Account",
+            "Develop and manage multiple businesses autonomously",
+        ]
+
+    config = AIConfig(ai_name, ai_role, ai_goals)
+    return config
+
+
+def parse_arguments():
+    """Parses the arguments passed to the script"""
+    global cfg
+    cfg.set_debug_mode(False)
+    cfg.set_continuous_mode(False)
+    cfg.set_speak_mode(False)
+
+    parser = argparse.ArgumentParser(description="Process arguments.")
+    parser.add_argument(
+        "--continuous", "-c", action="store_true", help="Enable Continuous Mode"
+    )
+    parser.add_argument(
+        "--continuous-limit",
+        "-l",
+        type=int,
+        dest="continuous_limit",
+        help="Defines the number of times to run in continuous mode",
+    )
+    parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
+    parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
+    parser.add_argument(
+        "--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
+    )
+    parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
+    parser.add_argument(
+        "--use-memory",
+        "-m",
+        dest="memory_type",
+        help="Defines which Memory backend to use",
+    )
+    parser.add_argument(
+        "--skip-reprompt",
+        "-y",
+        dest="skip_reprompt",
+        action="store_true",
+        help="Skips the re-prompting messages at the beginning of the script",
+    )
+    parser.add_argument(
+        "--ai-settings",
+        "-C",
+        dest="ai_settings_file",
+        help="Specifies which ai_settings.yaml file to use, will also automatically"
+        " skip the re-prompt.",
+    )
+    args = parser.parse_args()
+
+    if args.debug:
+        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_debug_mode(True)
+
+    if args.continuous:
+        logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
+        logger.typewriter_log(
+            "WARNING: ",
+            Fore.RED,
+            "Continuous mode is not recommended. It is potentially dangerous and may"
+            " cause your AI to run forever or carry out actions you would not usually"
+            " authorise. Use at your own risk.",
+        )
+        cfg.set_continuous_mode(True)
+
+        if args.continuous_limit:
+            logger.typewriter_log(
+                "Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
+            )
+            cfg.set_continuous_limit(args.continuous_limit)
+
+    # Check if continuous limit is used without continuous mode
+    if args.continuous_limit and not args.continuous:
+        parser.error("--continuous-limit can only be used with --continuous")
+
+    if args.speak:
+        logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_speak_mode(True)
+
+    if args.gpt3only:
+        logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_smart_llm_model(cfg.fast_llm_model)
+
+    if args.gpt4only:
+        logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_fast_llm_model(cfg.smart_llm_model)
+
+    if args.memory_type:
+        supported_memory = get_supported_memory_backends()
+        chosen = args.memory_type
+        if not chosen in supported_memory:
+            logger.typewriter_log(
+                "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
+                Fore.RED,
+                f"{supported_memory}",
+            )
+            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
+        else:
+            cfg.memory_backend = chosen
+
+    if args.skip_reprompt:
+        logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
+        cfg.skip_reprompt = True
+
+    if args.ai_settings_file:
+        file = args.ai_settings_file
+
+        # Validate file
+        (validated, message) = utils.validate_yaml_file(file)
+        if not validated:
+            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
+            logger.double_check()
+            exit(1)
+
+        logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
+        cfg.ai_settings_file = file
+        cfg.skip_reprompt = True
+
+
+def main():
+    global ai_name, memory
+    # TODO: fill in llm values here
+    check_openai_api_key()
+    parse_arguments()
+    logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
+    ai_name = ""
+    prompt = construct_prompt()
+    # print(prompt)
+    # Initialize variables
+    full_message_history = []
+    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()
+
+
+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
+        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.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}"
+                    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 = 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}"
+                    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} "
+                    f"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()

autogpt/agent.py

@@ -0,0 +1,304 @@
+import json
+import regex
+import traceback
+
+from colorama import Fore, Style
+
+from autogpt.chat import chat_with_ai, create_chat_message
+import autogpt.commands as cmd
+from autogpt.config import Config
+from autogpt.json_parser import fix_and_parse_json
+from autogpt.logger import logger
+from autogpt.speak 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 = cmd.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"{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(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"
+                )
+
+
+def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
+    cfg = Config()
+    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.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 print_assistant_thoughts(ai_name, assistant_reply):
+    """Prints the assistant's thoughts to the console"""
+    cfg = Config()
+    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/agent_manager.py

@@ -0,0 +1,75 @@
+from autogpt.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

autogpt/ai_config.py

@@ -0,0 +1,114 @@
+import os
+from typing import Type
+import yaml
+
+from autogpt.prompt import get_prompt
+
+
+class AIConfig:
+    """
+    A class object that contains the configuration information for the AI
+
+    Attributes:
+        ai_name (str): The name of the AI.
+        ai_role (str): The description of the AI's role.
+        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:
+        """
+        Initialize a class instance
+
+        Parameters:
+            ai_name (str): The name of the AI.
+            ai_role (str): The description of the AI's role.
+            ai_goals (list): The list of objectives the AI is supposed to complete.
+        Returns:
+            None
+        """
+
+        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")
+
+    @classmethod
+    def load(cls: "Type[AIConfig]", config_file: str = SAVE_FILE) -> "Type[AIConfig]":
+        """
+        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"
+
+        Returns:
+            cls (object): An instance of given cls object
+        """
+
+        try:
+            with open(config_file, encoding="utf-8") as file:
+                config_params = yaml.load(file, Loader=yaml.FullLoader)
+        except FileNotFoundError:
+            config_params = {}
+
+        ai_name = config_params.get("ai_name", "")
+        ai_role = config_params.get("ai_role", "")
+        ai_goals = config_params.get("ai_goals", [])
+        # type: Type[AIConfig]
+        return cls(ai_name, ai_role, ai_goals)
+
+    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"
+
+        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:
+            yaml.dump(config, file, allow_unicode=True)
+
+    def construct_full_prompt(self) -> str:
+        """
+        Returns a prompt to the user with the class information in an organized fashion.
+
+        Parameters:
+            None
+
+        Returns:
+            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."
+            ""
+        )
+
+        # Construct full prompt
+        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"
+
+        full_prompt += f"\n\n{get_prompt()}"
+        return full_prompt

autogpt/ai_functions.py

@@ -0,0 +1,77 @@
+import json
+from typing import List
+
+from autogpt.call_ai_function import call_ai_function
+from autogpt.config import Config
+
+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."
+    )
+
+    return call_ai_function(function_string, args, description_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."
+    )
+
+    return call_ai_function(function_string, args, description_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."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/browse.py

@@ -0,0 +1,198 @@
+from urllib.parse import urljoin, urlparse
+
+import requests
+from bs4 import BeautifulSoup
+
+from autogpt.config import Config
+from autogpt.llm_utils import create_chat_completion
+from autogpt.memory import get_memory
+
+cfg = Config()
+memory = get_memory(cfg)
+
+session = requests.Session()
+session.headers.update({"User-Agent": cfg.user_agent})
+
+
+# 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, 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 = session.get(sanitized_url, 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
+    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):
+    """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
+    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 split_text(text, max_length=cfg.browse_chunk_max_length):
+    """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'
+        f' question: "{question}" -- if the question cannot be answered using the'
+        " text, please summarize the text.",
+    }
+
+
+def summarize_text(url, 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"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)]
+
+    final_summary = create_chat_completion(
+        model=cfg.fast_llm_model,
+        messages=messages,
+        max_tokens=cfg.browse_summary_max_token,
+    )
+
+    return final_summary

autogpt/call_ai_function.py

@@ -0,0 +1,26 @@
+from autogpt.config import Config
+from autogpt.llm_utils import create_chat_completion
+
+cfg = Config()
+
+
+# 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) -> str:
+    """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}"
+            f"\n{function}```\n\nOnly respond with your `return` value.",
+        },
+        {"role": "user", "content": args},
+    ]
+
+    return create_chat_completion(model=model, messages=messages, temperature=0)

autogpt/chat.py

@@ -0,0 +1,175 @@
+import time
+
+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.logger import logger
+
+cfg = Config()
+
+
+def create_chat_message(role, content):
+    """
+    Create a chat message with the given role and content.
+
+    Args:
+    role (str): The role of the message sender, e.g., "system", "user", or "assistant".
+    content (str): The content of the message.
+
+    Returns:
+    dict: A dictionary containing the role and content of the message.
+    """
+    return {"role": role, "content": content}
+
+
+def generate_context(prompt, relevant_memory, full_message_history, model):
+    current_context = [
+        create_chat_message("system", prompt),
+        create_chat_message(
+            "system", f"The current time and date is {time.strftime('%c')}"
+        ),
+        create_chat_message(
+            "system",
+            f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
+        ),
+    ]
+
+    # Add messages from the full message history until we reach the token limit
+    next_message_to_add_index = len(full_message_history) - 1
+    insertion_index = len(current_context)
+    # Count the currently used tokens
+    current_tokens_used = token_counter.count_message_tokens(current_context, model)
+    return (
+        next_message_to_add_index,
+        current_tokens_used,
+        insertion_index,
+        current_context,
+    )
+
+
+# TODO: Change debug from hardcode to argument
+def chat_with_ai(
+    prompt, user_input, full_message_history, permanent_memory, token_limit
+):
+    """Interact with the OpenAI API, sending the prompt, user input, message history,
+    and permanent memory."""
+    while True:
+        try:
+            """
+            Interact with the OpenAI API, sending the prompt, user input,
+                message history, and permanent memory.
+
+            Args:
+                prompt (str): The prompt explaining the rules to the AI.
+                user_input (str): The input from the user.
+                full_message_history (list): The list of all messages sent between the
+                    user and the AI.
+                permanent_memory (Obj): The memory object containing the permanent
+                  memory.
+                token_limit (int): The maximum number of tokens allowed in the API call.
+
+            Returns:
+            str: The AI's response.
+            """
+            model = cfg.fast_llm_model  # TODO: Change model from hardcode to argument
+            # Reserve 1000 tokens for the response
+
+            logger.debug(f"Token limit: {token_limit}")
+            send_token_limit = token_limit - 1000
+
+            relevant_memory = (
+                ""
+                if len(full_message_history) == 0
+                else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
+            )
+
+            logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
+
+            (
+                next_message_to_add_index,
+                current_tokens_used,
+                insertion_index,
+                current_context,
+            ) = generate_context(prompt, relevant_memory, full_message_history, model)
+
+            while current_tokens_used > 2500:
+                # remove memories until we are under 2500 tokens
+                relevant_memory = relevant_memory[1:]
+                (
+                    next_message_to_add_index,
+                    current_tokens_used,
+                    insertion_index,
+                    current_context,
+                ) = generate_context(
+                    prompt, relevant_memory, full_message_history, model
+                )
+
+            current_tokens_used += token_counter.count_message_tokens(
+                [create_chat_message("user", user_input)], model
+            )  # Account for user input (appended later)
+
+            while next_message_to_add_index >= 0:
+                # print (f"CURRENT TOKENS USED: {current_tokens_used}")
+                message_to_add = full_message_history[next_message_to_add_index]
+
+                tokens_to_add = token_counter.count_message_tokens(
+                    [message_to_add], model
+                )
+                if current_tokens_used + tokens_to_add > send_token_limit:
+                    break
+
+                # Add the most recent message to the start of the current context,
+                #  after the two system prompts.
+                current_context.insert(
+                    insertion_index, full_message_history[next_message_to_add_index]
+                )
+
+                # Count the currently used tokens
+                current_tokens_used += tokens_to_add
+
+                # Move to the next most recent message in the full message history
+                next_message_to_add_index -= 1
+
+            # Append user input, the length of this is accounted for above
+            current_context.extend([create_chat_message("user", user_input)])
+
+            # Calculate remaining tokens
+            tokens_remaining = token_limit - current_tokens_used
+            # assert tokens_remaining >= 0, "Tokens remaining is negative.
+            # This should never happen, please submit a bug report at
+            #  https://www.github.com/Torantulino/Auto-GPT"
+
+            # Debug print the current context
+            logger.debug(f"Token limit: {token_limit}")
+            logger.debug(f"Send Token Count: {current_tokens_used}")
+            logger.debug(f"Tokens remaining for response: {tokens_remaining}")
+            logger.debug("------------ CONTEXT SENT TO AI ---------------")
+            for message in current_context:
+                # Skip printing the prompt
+                if message["role"] == "system" and message["content"] == prompt:
+                    continue
+                logger.debug(f"{message['role'].capitalize()}: {message['content']}")
+                logger.debug("")
+            logger.debug("----------- END OF CONTEXT ----------------")
+
+            # TODO: use a model defined elsewhere, so that model can contain
+            # temperature and other settings we care about
+            assistant_reply = create_chat_completion(
+                model=model,
+                messages=current_context,
+                max_tokens=tokens_remaining,
+            )
+
+            # Update full message history
+            full_message_history.append(create_chat_message("user", user_input))
+            full_message_history.append(
+                create_chat_message("assistant", assistant_reply)
+            )
+
+            return assistant_reply
+        except RateLimitError:
+            # TODO: When we switch to langchain, this is built in
+            print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
+            time.sleep(10)

autogpt/commands.py

@@ -0,0 +1,274 @@
+import json
+import datetime
+import autogpt.agent_manager as agents
+from autogpt.config import Config
+from autogpt.json_parser import fix_and_parse_json
+from autogpt.image_gen import generate_image
+from duckduckgo_search import ddg
+from autogpt.ai_functions import evaluate_code, improve_code, write_tests
+from autogpt.browse import scrape_links, scrape_text, summarize_text
+from autogpt.execute_code import execute_python_file, execute_shell
+from autogpt.file_operations import (
+    append_to_file,
+    delete_file,
+    read_file,
+    search_files,
+    write_to_file,
+)
+from autogpt.memory import get_memory
+from autogpt.speak import say_text
+from autogpt.web import browse_website
+
+
+cfg = Config()
+
+
+def is_valid_int(value) -> bool:
+    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"
+
+        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, 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
+            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."
+            )
+    # 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 = []
+    if not query:
+        return json.dumps(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"""
+    import json
+
+    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
+
+
+def get_text_summary(url, question):
+    """Return the results of a google search"""
+    text = scrape_text(url)
+    summary = summarize_text(url, text, question)
+    return """ "Result" : """ + summary
+
+
+def get_hyperlinks(url):
+    """Return the results of a google search"""
+    return scrape_links(url)
+
+
+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"""
+    # 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 = agents.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 = agents.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"""
+    # 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:
+        say_text(agent_response, 1)
+    return agent_response
+
+
+def list_agents():
+    """List all agents"""
+    return list_agents()
+
+
+def delete_agent(key):
+    """Delete an agent with a given key"""
+    result = agents.delete_agent(key)
+    return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

autogpt/config.py

@@ -0,0 +1,221 @@
+import abc
+import os
+
+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
+
+
+class Config(metaclass=Singleton):
+    """
+    Configuration class to store the state of bools for different scripts access.
+    """
+
+    def __init__(self):
+        """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"
+        )
+
+        if self.use_azure:
+            self.load_azure_config()
+            openai.api_type = self.openai_api_type
+            openai.api_base = self.openai_api_base
+            openai.api_version = self.openai_api_version
+
+        self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
+        self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
+        self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
+
+        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")
+
+        self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
+        self.pinecone_region = os.getenv("PINECONE_ENV")
+
+        self.image_provider = os.getenv("IMAGE_PROVIDER")
+        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 = 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")
+        # Note that indexes must be created on db 0 in redis, this is not configurable.
+
+        self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
+        # Initialize the OpenAI API client
+        openai.api_key = self.openai_api_key
+
+    def get_azure_deployment_id_for_model(self, model: str) -> str:
+        """
+        Returns the relevant deployment id for the model specified.
+
+        Parameters:
+            model(str): The model to map to the deployment id.
+
+        Returns:
+            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"]
+        elif model == self.smart_llm_model:
+            return self.azure_model_to_deployment_id_map[
+                "smart_llm_model_deployment_id"
+            ]
+        elif model == "text-embedding-ada-002":
+            return self.azure_model_to_deployment_id_map[
+                "embedding_model_deployment_id"
+            ]
+        else:
+            return ""
+
+    AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "..", "azure.yaml")
+
+    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.
+
+        Parameters:
+            config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
+
+        Returns:
+            None
+        """
+        try:
+            with open(config_file) as file:
+                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.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
+
+    def set_continuous_mode(self, value: bool):
+        """Set the continuous mode value."""
+        self.continuous_mode = value
+
+    def set_continuous_limit(self, value: int):
+        """Set the continuous limit value."""
+        self.continuous_limit = value
+
+    def set_speak_mode(self, value: bool):
+        """Set the speak mode value."""
+        self.speak_mode = value
+
+    def set_fast_llm_model(self, value: str):
+        """Set the fast LLM model value."""
+        self.fast_llm_model = value
+
+    def set_smart_llm_model(self, value: str):
+        """Set the smart LLM model value."""
+        self.smart_llm_model = value
+
+    def set_fast_token_limit(self, value: int):
+        """Set the fast token limit value."""
+        self.fast_token_limit = value
+
+    def set_smart_token_limit(self, value: int):
+        """Set the smart token limit value."""
+        self.smart_token_limit = value
+
+    def set_browse_chunk_max_length(self, value: int):
+        """Set the browse_website command chunk max length value."""
+        self.browse_chunk_max_length = value
+
+    def set_browse_summary_max_token(self, value: int):
+        """Set the browse_website command summary max token value."""
+        self.browse_summary_max_token = value
+
+    def set_openai_api_key(self, value: str):
+        """Set the OpenAI API key value."""
+        self.openai_api_key = value
+
+    def set_elevenlabs_api_key(self, value: str):
+        """Set the ElevenLabs API key value."""
+        self.elevenlabs_api_key = value
+
+    def set_elevenlabs_voice_1_id(self, value: str):
+        """Set the ElevenLabs Voice 1 ID value."""
+        self.elevenlabs_voice_1_id = value
+
+    def set_elevenlabs_voice_2_id(self, value: str):
+        """Set the ElevenLabs Voice 2 ID value."""
+        self.elevenlabs_voice_2_id = value
+
+    def set_google_api_key(self, value: str):
+        """Set the Google API key value."""
+        self.google_api_key = value
+
+    def set_custom_search_engine_id(self, value: str):
+        """Set the custom search engine id value."""
+        self.custom_search_engine_id = value
+
+    def set_pinecone_api_key(self, value: str):
+        """Set the Pinecone API key value."""
+        self.pinecone_api_key = value
+
+    def set_pinecone_region(self, value: str):
+        """Set the Pinecone region value."""
+        self.pinecone_region = value
+
+    def set_debug_mode(self, value: bool):
+        """Set the debug mode value."""
+        self.debug_mode = value

autogpt/data_ingestion.py

@@ -0,0 +1,95 @@
+import argparse
+import logging
+
+from autogpt.config import Config
+from autogpt.file_operations import ingest_file, search_files
+from autogpt.memory import get_memory
+
+cfg = Config()
+
+
+def configure_logging():
+    logging.basicConfig(
+        filename="log-ingestion.txt",
+        filemode="a",
+        format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
+        datefmt="%H:%M:%S",
+        level=logging.DEBUG,
+    )
+    return logging.getLogger("AutoGPT-Ingestion")
+
+
+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/execute_code.py

@@ -0,0 +1,105 @@
+import os
+import subprocess
+
+import docker
+from docker.errors import ImageNotFound
+
+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."
+
+    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}"
+    else:
+        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(WORKSPACE_FOLDER): {
+                        "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):
+    current_dir = os.getcwd()
+
+    if WORKSPACE_FOLDER not 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
+
+
+def we_are_running_in_a_docker_container():
+    os.path.exists("/.dockerenv")

autogpt/file_operations.py

@@ -0,0 +1,141 @@
+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 split_file(content, max_length=4000, overlap=0):
+    """
+    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:
+    """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 f"Error: {str(e)}"
+
+
+def ingest_file(filename, memory, max_length=4000, overlap=200):
+    """
+    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, 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

autogpt/image_gen.py

@@ -0,0 +1,67 @@
+import io
+import os.path
+import uuid
+from base64 import b64decode
+
+import openai
+import requests
+from PIL import Image
+
+from autogpt.config import Config
+
+cfg = Config()
+
+working_directory = "auto_gpt_workspace"
+
+
+def generate_image(prompt):
+    filename = str(uuid.uuid4()) + ".jpg"
+
+    # DALL-E
+    if cfg.image_provider == "dalle":
+        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"
+        )
+        if cfg.huggingface_api_token is None:
+            raise ValueError(
+                "You need to set your Hugging Face API token in the config file."
+            )
+        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"

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_parser.py

@@ -0,0 +1,113 @@
+import json
+from typing import Any, Dict, Union
+
+from autogpt.call_ai_function import call_ai_function
+from autogpt.config import Config
+from autogpt.json_utils import correct_json
+from autogpt.logger import logger
+
+cfg = Config()
+
+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"

autogpt/json_utils.py

@@ -0,0 +1,128 @@
+import json
+import re
+from typing import Optional
+
+from autogpt.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) -> 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
+
+    try:
+        json.loads(json_string)
+        return json_string
+    except json.JSONDecodeError:
+        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

autogpt/llm_utils.py

@@ -0,0 +1,69 @@
+import time
+
+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
+
+
+# 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
+    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):
+        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:
+            if cfg.debug_mode:
+                print(
+                    Fore.RED + "Error: ",
+                    "API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET,
+                )
+            time.sleep(20)
+        except 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"]

autogpt/logger.py

@@ -0,0 +1,195 @@
+import logging
+import os
+import random
+import re
+import time
+from logging import LogRecord
+
+from colorama import Fore, Style
+
+from autogpt import speak
+from autogpt.config import Config, Singleton
+
+cfg = Config()
+
+"""
+Logger that handle titles in different colors.
+Outputs logs in console, activity.log, and errors.log
+For console handler: simulates typing
+"""
+
+
+class Logger(metaclass=Singleton):
+    def __init__(self):
+        # create log directory if it doesn't exist
+        this_files_dir_path = os.path.dirname(__file__)
+        log_dir = os.path.join(this_files_dir_path, "../logs")
+        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) -> 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()

autogpt/memory/__init__.py

@@ -0,0 +1,59 @@
+from autogpt.memory.local import LocalCache
+from autogpt.memory.no_memory import NoMemory
+
+# List of supported memory backends
+# Add a backend to this list if the import attempt is successful
+supported_memory = ["local", "no_memory"]
+
+try:
+    from autogpt.memory.redismem import RedisMemory
+
+    supported_memory.append("redis")
+except ImportError:
+    print("Redis not installed. Skipping import.")
+    RedisMemory = None
+
+try:
+    from autogpt.memory.pinecone import PineconeMemory
+
+    supported_memory.append("pinecone")
+except ImportError:
+    print("Pinecone not installed. Skipping import.")
+    PineconeMemory = None
+
+
+def get_memory(cfg, init=False):
+    memory = None
+    if cfg.memory_backend == "pinecone":
+        if not PineconeMemory:
+            print(
+                "Error: Pinecone is not installed. Please install pinecone"
+                " to use Pinecone as a memory backend."
+            )
+        else:
+            memory = PineconeMemory(cfg)
+            if init:
+                memory.clear()
+    elif cfg.memory_backend == "redis":
+        if not RedisMemory:
+            print(
+                "Error: Redis is not installed. Please install redis-py to"
+                " use Redis as a memory backend."
+            )
+        else:
+            memory = RedisMemory(cfg)
+    elif cfg.memory_backend == "no_memory":
+        memory = NoMemory(cfg)
+
+    if memory is None:
+        memory = LocalCache(cfg)
+        if init:
+            memory.clear()
+    return memory
+
+
+def get_supported_memory_backends():
+    return supported_memory
+
+
+__all__ = ["get_memory", "LocalCache", "RedisMemory", "PineconeMemory", "NoMemory"]

autogpt/memory/base.py

@@ -0,0 +1,43 @@
+"""Base class for memory providers."""
+import abc
+
+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"]
+    else:
+        return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[
+            "data"
+        ][0]["embedding"]
+
+
+class MemoryProviderSingleton(AbstractSingleton):
+    @abc.abstractmethod
+    def add(self, data):
+        pass
+
+    @abc.abstractmethod
+    def get(self, data):
+        pass
+
+    @abc.abstractmethod
+    def clear(self):
+        pass
+
+    @abc.abstractmethod
+    def get_relevant(self, data, num_relevant=5):
+        pass
+
+    @abc.abstractmethod
+    def get_stats(self):
+        pass

autogpt/memory/local.py

@@ -0,0 +1,123 @@
+import dataclasses
+import os
+from typing import Any, List, Optional
+
+import numpy as np
+import orjson
+
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+
+EMBED_DIM = 1536
+SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
+
+
+def create_default_embeddings():
+    return np.zeros((0, EMBED_DIM)).astype(np.float32)
+
+
+@dataclasses.dataclass
+class CacheContent:
+    texts: List[str] = dataclasses.field(default_factory=list)
+    embeddings: np.ndarray = dataclasses.field(
+        default_factory=create_default_embeddings
+    )
+
+
+class LocalCache(MemoryProviderSingleton):
+    # on load, load our database
+    def __init__(self, cfg) -> None:
+        self.filename = f"{cfg.memory_index}.json"
+        if os.path.exists(self.filename):
+            try:
+                with open(self.filename, "w+b") as f:
+                    file_content = f.read()
+                    if not file_content.strip():
+                        file_content = b"{}"
+                        f.write(file_content)
+
+                    loaded = orjson.loads(file_content)
+                    self.data = CacheContent(**loaded)
+            except orjson.JSONDecodeError:
+                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."
+            )
+            self.data = CacheContent()
+
+    def add(self, text: str):
+        """
+        Add text to our list of texts, add embedding as row to our
+            embeddings-matrix
+
+        Args:
+            text: str
+
+        Returns: None
+        """
+        if "Command Error:" in text:
+            return ""
+        self.data.texts.append(text)
+
+        embedding = get_ada_embedding(text)
+
+        vector = np.array(embedding).astype(np.float32)
+        vector = vector[np.newaxis, :]
+        self.data.embeddings = np.concatenate(
+            [
+                self.data.embeddings,
+                vector,
+            ],
+            axis=0,
+        )
+
+        with open(self.filename, "wb") as f:
+            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
+            f.write(out)
+        return text
+
+    def clear(self) -> str:
+        """
+        Clears the redis server.
+
+        Returns: A message indicating that the memory has been cleared.
+        """
+        self.data = CacheContent()
+        return "Obliviated"
+
+    def get(self, data: str) -> Optional[List[Any]]:
+        """
+        Gets the data from the memory that is most relevant to the given data.
+
+        Args:
+            data: The data to compare to.
+
+        Returns: The most relevant data.
+        """
+        return self.get_relevant(data, 1)
+
+    def get_relevant(self, text: str, k: int) -> List[Any]:
+        """ "
+        matrix-vector mult to find score-for-each-row-of-matrix
+         get indices for top-k winning scores
+         return texts for those indices
+        Args:
+            text: str
+            k: int
+
+        Returns: List[str]
+        """
+        embedding = get_ada_embedding(text)
+
+        scores = np.dot(self.data.embeddings, embedding)
+
+        top_k_indices = np.argsort(scores)[-k:][::-1]
+
+        return [self.data.texts[i] for i in top_k_indices]
+
+    def get_stats(self):
+        """
+        Returns: The stats of the local cache.
+        """
+        return len(self.data.texts), self.data.embeddings.shape

autogpt/memory/no_memory.py

@@ -0,0 +1,66 @@
+from typing import Optional, List, Any
+
+from autogpt.memory.base import MemoryProviderSingleton
+
+
+class NoMemory(MemoryProviderSingleton):
+    def __init__(self, cfg):
+        """
+        Initializes the NoMemory provider.
+
+        Args:
+            cfg: The config object.
+
+        Returns: None
+        """
+        pass
+
+    def add(self, data: str) -> str:
+        """
+        Adds a data point to the memory. No action is taken in NoMemory.
+
+        Args:
+            data: The data to add.
+
+        Returns: An empty string.
+        """
+        return ""
+
+    def get(self, data: str) -> Optional[List[Any]]:
+        """
+        Gets the data from the memory that is most relevant to the given data.
+        NoMemory always returns None.
+
+        Args:
+            data: The data to compare to.
+
+        Returns: None
+        """
+        return None
+
+    def clear(self) -> str:
+        """
+        Clears the memory. No action is taken in NoMemory.
+
+        Returns: An empty string.
+        """
+        return ""
+
+    def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
+        """
+        Returns all the data in the memory that is relevant to the given data.
+        NoMemory always returns None.
+
+        Args:
+            data: The data to compare to.
+            num_relevant: The number of relevant data to return.
+
+        Returns: None
+        """
+        return None
+
+    def get_stats(self):
+        """
+        Returns: An empty dictionary as there are no stats in NoMemory.
+        """
+        return {}

autogpt/memory/pinecone.py

@@ -0,0 +1,71 @@
+import pinecone
+from colorama import Fore, Style
+
+from autogpt.logger import logger
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+
+
+class PineconeMemory(MemoryProviderSingleton):
+    def __init__(self, cfg):
+        pinecone_api_key = cfg.pinecone_api_key
+        pinecone_region = cfg.pinecone_region
+        pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
+        dimension = 1536
+        metric = "cosine"
+        pod_type = "p1"
+        table_name = "auto-gpt"
+        # this assumes we don't start with memory.
+        # for now this works.
+        # we'll need a more complicated and robust system if we want to start with memory.
+        self.vec_num = 0
+
+        try:
+            pinecone.whoami()
+        except Exception as e:
+            logger.typewriter_log(
+                "FAILED TO CONNECT TO PINECONE",
+                Fore.RED,
+                Style.BRIGHT + str(e) + Style.RESET_ALL,
+            )
+            logger.double_check(
+                "Please ensure you have setup and configured Pinecone properly for use. "
+                + f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup{Style.RESET_ALL} to ensure you've set up everything correctly."
+            )
+            exit(1)
+
+        if table_name not in pinecone.list_indexes():
+            pinecone.create_index(
+                table_name, dimension=dimension, metric=metric, pod_type=pod_type
+            )
+        self.index = pinecone.Index(table_name)
+
+    def add(self, data):
+        vector = 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})])
+        _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
+        self.vec_num += 1
+        return _text
+
+    def get(self, data):
+        return self.get_relevant(data, 1)
+
+    def clear(self):
+        self.index.delete(deleteAll=True)
+        return "Obliviated"
+
+    def get_relevant(self, data, num_relevant=5):
+        """
+        Returns all the data in the memory that is relevant to the given data.
+        :param data: The data to compare to.
+        :param num_relevant: The number of relevant data to return. Defaults to 5
+        """
+        query_embedding = get_ada_embedding(data)
+        results = self.index.query(
+            query_embedding, top_k=num_relevant, include_metadata=True
+        )
+        sorted_results = sorted(results.matches, key=lambda x: x.score)
+        return [str(item["metadata"]["raw_text"]) for item in sorted_results]
+
+    def get_stats(self):
+        return self.index.describe_index_stats()

autogpt/memory/redismem.py

@@ -0,0 +1,151 @@
+"""Redis memory provider."""
+from typing import Any, List, Optional
+
+import numpy as np
+import redis
+from colorama import Fore, Style
+from redis.commands.search.field import TextField, VectorField
+from redis.commands.search.indexDefinition import IndexDefinition, IndexType
+from redis.commands.search.query import Query
+
+from autogpt.logger import logger
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+
+SCHEMA = [
+    TextField("data"),
+    VectorField(
+        "embedding",
+        "HNSW",
+        {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
+    ),
+]
+
+
+class RedisMemory(MemoryProviderSingleton):
+    def __init__(self, cfg):
+        """
+        Initializes the Redis memory provider.
+
+        Args:
+            cfg: The config object.
+
+        Returns: None
+        """
+        redis_host = cfg.redis_host
+        redis_port = cfg.redis_port
+        redis_password = cfg.redis_password
+        self.dimension = 1536
+        self.redis = redis.Redis(
+            host=redis_host,
+            port=redis_port,
+            password=redis_password,
+            db=0,  # Cannot be changed
+        )
+        self.cfg = cfg
+
+        # Check redis connection
+        try:
+            self.redis.ping()
+        except redis.ConnectionError as e:
+            logger.typewriter_log(
+                "FAILED TO CONNECT TO REDIS",
+                Fore.RED,
+                Style.BRIGHT + str(e) + Style.RESET_ALL,
+            )
+            logger.double_check(
+                "Please ensure you have setup and configured Redis properly for use. "
+                + f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL} to ensure you've set up everything correctly."
+            )
+            exit(1)
+
+        if cfg.wipe_redis_on_start:
+            self.redis.flushall()
+        try:
+            self.redis.ft(f"{cfg.memory_index}").create_index(
+                fields=SCHEMA,
+                definition=IndexDefinition(
+                    prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
+                ),
+            )
+        except Exception as e:
+            print("Error creating Redis search index: ", e)
+        existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
+        self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
+
+    def add(self, data: str) -> str:
+        """
+        Adds a data point to the memory.
+
+        Args:
+            data: The data to add.
+
+        Returns: Message indicating that the data has been added.
+        """
+        if "Command Error:" in data:
+            return ""
+        vector = get_ada_embedding(data)
+        vector = np.array(vector).astype(np.float32).tobytes()
+        data_dict = {b"data": data, "embedding": vector}
+        pipe = self.redis.pipeline()
+        pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
+        _text = (
+            f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
+        )
+        self.vec_num += 1
+        pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
+        pipe.execute()
+        return _text
+
+    def get(self, data: str) -> Optional[List[Any]]:
+        """
+        Gets the data from the memory that is most relevant to the given data.
+
+        Args:
+            data: The data to compare to.
+
+        Returns: The most relevant data.
+        """
+        return self.get_relevant(data, 1)
+
+    def clear(self) -> str:
+        """
+        Clears the redis server.
+
+        Returns: A message indicating that the memory has been cleared.
+        """
+        self.redis.flushall()
+        return "Obliviated"
+
+    def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
+        """
+        Returns all the data in the memory that is relevant to the given data.
+        Args:
+            data: The data to compare to.
+            num_relevant: The number of relevant data to return.
+
+        Returns: A list of the most relevant data.
+        """
+        query_embedding = 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_vector = np.array(query_embedding).astype(np.float32).tobytes()
+
+        try:
+            results = self.redis.ft(f"{self.cfg.memory_index}").search(
+                query, query_params={"vector": query_vector}
+            )
+        except Exception as e:
+            print("Error calling Redis search: ", e)
+            return None
+        return [result.data for result in results.docs]
+
+    def get_stats(self):
+        """
+        Returns: The stats of the memory index.
+        """
+        return self.redis.ft(f"{self.cfg.memory_index}").info()

autogpt/prompt.py

@@ -0,0 +1,108 @@
+from autogpt.promptgenerator import PromptGenerator
+
+
+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()

autogpt/promptgenerator.py

@@ -0,0 +1,134 @@
+import json
+
+
+class PromptGenerator:
+    """
+    A class for generating custom prompt strings based on constraints, commands,
+        resources, and performance evaluations.
+    """
+
+    def __init__(self):
+        """
+        Initialize the PromptGenerator object with empty lists of constraints,
+            commands, resources, and performance evaluations.
+        """
+        self.constraints = []
+        self.commands = []
+        self.resources = []
+        self.performance_evaluation = []
+        self.response_format = {
+            "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",
+            },
+            "command": {"name": "command name", "args": {"arg name": "value"}},
+        }
+
+    def add_constraint(self, constraint):
+        """
+        Add a constraint to the constraints list.
+
+        Args:
+            constraint (str): The constraint to be added.
+        """
+        self.constraints.append(constraint)
+
+    def add_command(self, command_label, command_name, args=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.
+        """
+        if args is None:
+            args = {}
+
+        command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
+
+        command = {
+            "label": command_label,
+            "name": command_name,
+            "args": command_args,
+        }
+
+        self.commands.append(command)
+
+    def _generate_command_string(self, command):
+        """
+        Generate a formatted string representation of a command.
+
+        Args:
+            command (dict): A dictionary containing command information.
+
+        Returns:
+            str: The formatted command string.
+        """
+        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: str) -> None:
+        """
+        Add a resource to the resources list.
+
+        Args:
+            resource (str): The resource to be added.
+        """
+        self.resources.append(resource)
+
+    def add_performance_evaluation(self, evaluation: str) -> None:
+        """
+        Add a performance evaluation item to the performance_evaluation list.
+
+        Args:
+            evaluation (str): The evaluation item to be added.
+        """
+        self.performance_evaluation.append(evaluation)
+
+    def _generate_numbered_list(self, items, 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'.
+
+        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)
+            )
+        else:
+            return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
+
+    def generate_prompt_string(self) -> str:
+        """
+        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)
+        return (
+            f"Constraints:\n{self._generate_numbered_list(self.constraints)}\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"
+            "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"
+        )

autogpt/speak.py

@@ -0,0 +1,120 @@
+import os
+
+import requests
+from playsound import playsound
+
+from autogpt.config import Config
+
+import threading
+from threading import Lock, Semaphore
+
+import gtts
+
+cfg = Config()
+
+# 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 brian_speech(text):
+    """Speak text using Brian with the streamelements API"""
+    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 mutex_lock:
+            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
+
+
+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)
+            elif cfg.use_brian_tts == "True":
+                success = brian_speech(text)
+                if not success:
+                    gtts_speech(text)
+            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()

autogpt/spinner.py

@@ -0,0 +1,38 @@
+import itertools
+import sys
+import threading
+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(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):
+        """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
+        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/summary.py

@@ -0,0 +1,69 @@
+from autogpt.llm_utils import create_chat_completion
+
+
+def summarize_text(driver, text, question):
+    if not text:
+        return "Error: No text to summarize"
+
+    text_length = len(text)
+    print(f"Text length: {text_length} characters")
+
+    summaries = []
+    chunks = list(split_text(text))
+
+    scroll_ratio = 1 / len(chunks)
+    for i, chunk in enumerate(chunks):
+        scroll_to_percentage(driver, scroll_ratio * i)
+        print(f"Summarizing chunk {i + 1} / {len(chunks)}")
+        messages = [create_message(chunk, question)]
+
+        summary = create_chat_completion(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=300,
+        )
+        summaries.append(summary)
+
+    print(f"Summarized {len(chunks)} chunks.")
+
+    combined_summary = "\n".join(summaries)
+    messages = [create_message(combined_summary, question)]
+
+    return create_chat_completion(
+        model="gpt-3.5-turbo",
+        messages=messages,
+        max_tokens=300,
+    )
+
+
+def split_text(text, max_length=8192):
+    paragraphs = text.split("\n")
+    current_length = 0
+    current_chunk = []
+
+    for paragraph in paragraphs:
+        if current_length + len(paragraph) + 1 <= max_length:
+            current_chunk.append(paragraph)
+            current_length += len(paragraph) + 1
+        else:
+            yield "\n".join(current_chunk)
+            current_chunk = [paragraph]
+            current_length = len(paragraph) + 1
+
+    if current_chunk:
+        yield "\n".join(current_chunk)
+
+
+def create_message(chunk, question):
+    return {
+        "role": "user",
+        "content": f'"""{chunk}""" Using the above text, please answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the text,'
+        " please summarize the text.",
+    }
+
+
+def scroll_to_percentage(driver, ratio):
+    if ratio < 0 or ratio > 1:
+        raise ValueError("Percentage should be between 0 and 1")
+    driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")

autogpt/token_counter.py

@@ -0,0 +1,72 @@
+from typing import Dict, List
+
+import tiktoken
+
+from autogpt.logger import logger
+
+
+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".
+
+    Returns:
+    int: The number of tokens used by the list of messages.
+    """
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        logger.warn("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model == "gpt-3.5-turbo":
+        # !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_name = -1  # if there's a name, the role is omitted
+    elif model == "gpt-4-0314":
+        tokens_per_message = 3
+        tokens_per_name = 1
+    else:
+        raise NotImplementedError(
+            f"num_tokens_from_messages() is not implemented for model {model}.\n"
+            " See https://github.com/openai/openai-python/blob/main/chatml.md for"
+            " information on how messages are converted to tokens."
+        )
+    num_tokens = 0
+    for message in messages:
+        num_tokens += tokens_per_message
+        for key, value in message.items():
+            num_tokens += len(encoding.encode(value))
+            if key == "name":
+                num_tokens += tokens_per_name
+    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+    return num_tokens
+
+
+def count_string_tokens(string: str, model_name: str) -> int:
+    """
+    Returns the number of tokens in a text string.
+
+    Args:
+    string (str): The text string.
+    model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
+
+    Returns:
+    int: The number of tokens in the text string.
+    """
+    encoding = tiktoken.encoding_for_model(model_name)
+    return len(encoding.encode(string))

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}!")

autogpt/web.py

@@ -0,0 +1,85 @@
+from selenium import webdriver
+import autogpt.summary as summary
+from bs4 import BeautifulSoup
+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
+cfg = Config()
+
+
+def browse_website(url, question):
+    driver, text = scrape_text_with_selenium(url)
+    add_header(driver)
+    summary_text = summary.summarize_text(driver, text, question)
+    links = scrape_links_with_selenium(driver)
+
+    # Limit links to 5
+    if len(links) > 5:
+        links = links[:5]
+    close_browser(driver)
+    return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
+
+
+def scrape_text_with_selenium(url):
+    logging.getLogger("selenium").setLevel(logging.CRITICAL)
+
+    options = Options()
+    options.add_argument(
+        "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
+    )
+    driver = webdriver.Chrome(
+        executable_path=ChromeDriverManager().install(), options=options
+    )
+    driver.get(url)
+
+    WebDriverWait(driver, 10).until(
+        EC.presence_of_element_located((By.TAG_NAME, "body"))
+    )
+
+    # Get the HTML content directly from the browser's DOM
+    page_source = driver.execute_script("return document.body.outerHTML;")
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+    return driver, text
+
+
+def scrape_links_with_selenium(driver):
+    page_source = driver.page_source
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    hyperlinks = extract_hyperlinks(soup)
+
+    return format_hyperlinks(hyperlinks)
+
+
+def close_browser(driver):
+    driver.quit()
+
+
+def extract_hyperlinks(soup):
+    return [(link.text, link["href"]) for link in soup.find_all("a", href=True)]
+
+
+def format_hyperlinks(hyperlinks):
+    return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
+
+
+def add_header(driver):
+    driver.execute_script(open(f"{file_dir}/js/overlay.js", "r").read())

azure.yaml.template

@@ -0,0 +1,7 @@
+azure_api_type: azure_ad
+azure_api_base: your-base-url-for-azure
+azure_api_version: api-version-for-azure
+azure_model_map:
+    fast_llm_model_deployment_id: gpt35-deployment-id-for-azure
+    smart_llm_model_deployment_id: gpt4-deployment-id-for-azure 
+    embedding_model_deployment_id: embedding-deployment-id-for-azure

docker-compose.yml

@@ -0,0 +1,16 @@
+# To boot the app run the following:
+# docker-compose run auto-gpt
+version: "3.9"
+
+services:
+  auto-gpt:
+    depends_on:
+      - redis
+    build: ./
+    volumes:
+      - "./autogpt:/app"
+      - ".env:/app/.env"
+    profiles: ["exclude-from-up"]
+
+  redis:
+    image: "redis/redis-stack-server:latest"

main.py

@@ -0,0 +1 @@
+from autogpt import main

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 = ""