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Merge pull request #1365 from Torantulino/master

Browse Source 
Merge into Stable for PR batch 4 v0.1.3
This commit is contained in:
Richard Beales
2023-04-14 18:18:06 +01:00
committed by GitHub
parent 2a7bc5cb5c a17a850b25
commit 638c956f72
29 changed files with 1076 additions and 344 deletions
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23
.devcontainer/Dockerfile Normal file
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@@ -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

39
.devcontainer/devcontainer.json Normal file
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@@ -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"
}

130
.env.template
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@@ -1,17 +1,123 @@
PINECONE_API_KEY=your-pinecone-api-key
PINECONE_ENV=your-pinecone-region
################################################################################
### 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
ELEVENLABS_API_KEY=your-elevenlabs-api-key
ELEVENLABS_VOICE_1_ID=your-voice-id
ELEVENLABS_VOICE_2_ID=your-voice-id
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
GOOGLE_API_KEY=
CUSTOM_SEARCH_ENGINE_ID=
USE_AZURE=False
EXECUTE_LOCAL_COMMANDS=False
IMAGE_PROVIDER=dalle
HUGGINGFACE_API_TOKEN=
USE_MAC_OS_TTS=False
### 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

2
.gitignore vendored
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@@ -16,6 +16,8 @@ last_run_ai_settings.yaml
.idea/*
auto-gpt.json
log.txt
log-ingestion.txt
logs
# Coverage reports
.coverage

28
Dockerfile
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@@ -1,7 +1,23 @@
# Use an official Python base image from the Docker Hub
FROM python:3.11-slim
ENV PIP_NO_CACHE_DIR=yes
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY scripts/ .
ENTRYPOINT ["python", "main.py"]
# 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 scripts/ .
# Set the entrypoint
ENTRYPOINT ["python", "main.py"]

90
README.md
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@@ -1,9 +1,9 @@
# Auto-GPT: An Autonomous GPT-4 Experiment
![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)
[![Discord Follow](https://dcbadge.vercel.app/api/server/PQ7VX6TY4t?style=flat)](https://discord.gg/PQ7VX6TY4t)
[![Unit Tests](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml/badge.svg)](https://github.com/Torantulino/Auto-GPT/actions/workflows/unit_tests.yml)
[![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)](https://twitter.com/SigGravitas)
[![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](https://discord.gg/autogpt)
[![Unit Tests](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml/badge.svg)](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml)
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
@@ -46,6 +46,7 @@ Your support is greatly appreciated
- [Setting up environment variables](#setting-up-environment-variables-1)
- [Setting Your Cache Type](#setting-your-cache-type)
- [View Memory Usage](#view-memory-usage)
- [🧠 Memory pre-seeding](#memory-pre-seeding)
- [💀 Continuous Mode ⚠️](#-continuous-mode-️)
- [GPT3.5 ONLY Mode](#gpt35-only-mode)
- [🖼 Image Generation](#-image-generation)
@@ -65,12 +66,15 @@ Your support is greatly appreciated
## 📋 Requirements
- [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
- environments(just choose one)
- [vscode + devcontainer](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers): It has been configured in the .devcontainer folder and can be used directly
- [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
- [OpenAI API key](https://platform.openai.com/account/api-keys)
- [PINECONE API key](https://www.pinecone.io/)
Optional:
- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
- ElevenLabs Key (If you want the AI to speak)
## 💾 Installation
@@ -122,8 +126,8 @@ pip install -r requirements.txt
python scripts/main.py
```
2. After each of AUTO-GPT's actions, type "NEXT COMMAND" to authorise them to continue.
3. To exit the program, type "exit" and press Enter.
2. After each of action, enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter additional feedback for the AI.
### Logs
@@ -134,6 +138,14 @@ To output debug logs:
```
python scripts/main.py --debug
```
### Command Line Arguments
Here are some common arguments you can use when running Auto-GPT:
> Replace anything in angled brackets (<>) to a value you want to specify
* `python scripts/main.py --help` to see a list of all available command line arguments.
* `python scripts/main.py --ai-settings <filename>` to run Auto-GPT with a different AI Settings file.
* `python scripts/main.py --use-memory <memory-backend>` to specify one of 3 memory backends: `local`, `redis`, `pinecone` or 'no_memory'.
> **NOTE**: There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `python scripts/main.py --help` for more information
## 🗣️ Speech Mode
@@ -154,9 +166,10 @@ To use the `google_official_search` command, you need to set up your Google API
4. Go to the [APIs & Services Dashboard](https://console.cloud.google.com/apis/dashboard) and click "Enable APIs and Services". Search for "Custom Search API" and click on it, then click "Enable".
5. Go to the [Credentials](https://console.cloud.google.com/apis/credentials) page and click "Create Credentials". Choose "API Key".
6. Copy the API key and set it as an environment variable named `GOOGLE_API_KEY` on your machine. See setting up environment variables below.
7. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
9. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
7. [Enable](https://console.developers.google.com/apis/api/customsearch.googleapis.com) the Custom Search API on your project. (Might need to wait few minutes to propagate)
8. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
9. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
10. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
_Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches._
@@ -225,7 +238,10 @@ Pinecone enables the storage of vast amounts of vector-based memory, allowing fo
### Setting up environment variables
Simply set them in the `.env` file.
In the `.env` file set:
- `PINECONE_API_KEY`
- `PINECONE_ENV` (something like: us-east4-gcp)
- `MEMORY_BACKEND=pinecone`
Alternatively, you can set them from the command line (advanced):
@@ -234,7 +250,7 @@ For Windows Users:
```
setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
setx MEMORY_BACKEND "pinecone"
```
For macOS and Linux users:
@@ -242,7 +258,7 @@ For macOS and Linux users:
```
export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
export MEMORY_BACKEND="pinecone"
```
## Setting Your Cache Type
@@ -259,6 +275,52 @@ To switch to either, change the `MEMORY_BACKEND` env variable to the value that
1. View memory usage by using the `--debug` flag :)
## 🧠 Memory pre-seeding
```
# python scripts/data_ingestion.py -h
usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH]
Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.
options:
-h, --help show this help message and exit
--file FILE The file to ingest.
--dir DIR The directory containing the files to ingest.
--init Init the memory and wipe its content (default: False)
--overlap OVERLAP The overlap size between chunks when ingesting files (default: 200)
--max_length MAX_LENGTH The max_length of each chunk when ingesting files (default: 4000
# python scripts/data_ingestion.py --dir seed_data --init --overlap 200 --max_length 1000
```
This script located at scripts/data_ingestion.py, allows you to ingest files into memory and pre-seed it before running Auto-GPT.
Memory pre-seeding is a technique that involves ingesting relevant documents or data into the AI's memory so that it can use this information to generate more informed and accurate responses.
To pre-seed the memory, the content of each document is split into chunks of a specified maximum length with a specified overlap between chunks, and then each chunk is added to the memory backend set in the .env file. When the AI is prompted to recall information, it can then access those pre-seeded memories to generate more informed and accurate responses.
This technique is particularly useful when working with large amounts of data or when there is specific information that the AI needs to be able to access quickly.
By pre-seeding the memory, the AI can retrieve and use this information more efficiently, saving time, API call and improving the accuracy of its responses.
You could for example download the documentation of an API, a Github repository, etc. and ingest it into memory before running Auto-GPT.
⚠️ If you use Redis as your memory, make sure to run Auto-GPT with the WIPE_REDIS_ON_START set to False in your .env file.
⚠️For other memory backend, we currently forcefully wipe the memory when starting Auto-GPT. To ingest data with those memory backend, you can call the data_ingestion.py script anytime during an Auto-GPT run.
Memories will be available to the AI immediately as they are ingested, even if ingested while Auto-GPT is running.
In the example above, the script initializes the memory, ingests all files within the seed_data directory into memory with an overlap between chunks of 200 and a maximum length of each chunk of 4000.
Note that you can also use the --file argument to ingest a single file into memory and that the script will only ingest files within the auto_gpt_workspace directory.
You can adjust the max_length and overlap parameters to fine-tune the way the docuents are presented to the AI when it "recall" that memory:
- Adjusting the overlap value allows the AI to access more contextual information from each chunk when recalling information, but will result in more chunks being created and therefore increase memory backend usage and OpenAI API requests.
- Reducing the max_length value will create more chunks, which can save prompt tokens by allowing for more message history in the context, but will also increase the number of chunks.
- Increasing the max_length value will provide the AI with more contextual information from each chunk, reducing the number of chunks created and saving on OpenAI API requests. However, this may also use more prompt tokens and decrease the overall context available to the AI.
## 💀 Continuous Mode ⚠️
Run the AI **without** user authorisation, 100% automated.
@@ -357,4 +419,4 @@ flake8 scripts/ tests/
# Or, if you want to run flake8 with the same configuration as the CI:
flake8 scripts/ tests/ --select E303,W293,W291,W292,E305,E231,E302
```
```

16
docker-compose.yml Normal file
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@@ -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:
- "./scripts:/app"
- ".env:/app/.env"
profiles: ["exclude-from-up"]
redis:
image: "redis/redis-stack-server:latest"

10
scripts/ai_config.py
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@@ -1,6 +1,6 @@
import yaml
import data
import os
from prompt import get_prompt
class AIConfig:
@@ -47,7 +47,7 @@ class AIConfig:
"""
try:
with open(config_file) as file:
with open(config_file, encoding='utf-8') as file:
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
@@ -70,8 +70,8 @@ class AIConfig:
"""
config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
with open(config_file, "w") as file:
yaml.dump(config, file)
with open(config_file, "w", encoding='utf-8') as file:
yaml.dump(config, file, allow_unicode=True)
def construct_full_prompt(self) -> str:
"""
@@ -91,5 +91,5 @@ class AIConfig:
for i, goal in enumerate(self.ai_goals):
full_prompt += f"{i+1}. {goal}\n"
full_prompt += f"\n\n{data.load_prompt()}"
full_prompt += f"\n\n{get_prompt()}"
return full_prompt

3
scripts/ai_functions.py
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@@ -1,8 +1,7 @@
from typing import List, Optional
from typing import List
import json
from config import Config
from call_ai_function import call_ai_function
from json_parser import fix_and_parse_json
cfg = Config()

30
scripts/browse.py
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@@ -1,10 +1,15 @@
import requests
from bs4 import BeautifulSoup
from memory import get_memory
from config import Config
from llm_utils import create_chat_completion
from urllib.parse import urlparse, urljoin
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
@@ -27,7 +32,7 @@ def check_local_file_access(url):
return any(url.startswith(prefix) for prefix in local_prefixes)
def get_response(url, headers=cfg.user_agent_header, timeout=10):
def get_response(url, timeout=10):
try:
# Restrict access to local files
if check_local_file_access(url):
@@ -39,7 +44,7 @@ def get_response(url, headers=cfg.user_agent_header, timeout=10):
sanitized_url = sanitize_url(url)
response = requests.get(sanitized_url, headers=headers, timeout=timeout)
response = session.get(sanitized_url, timeout=timeout)
# Check if the response contains an HTTP error
if response.status_code >= 400:
@@ -106,7 +111,7 @@ def scrape_links(url):
return format_hyperlinks(hyperlinks)
def split_text(text, max_length=8192):
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
@@ -133,7 +138,7 @@ def create_message(chunk, question):
}
def summarize_text(text, question):
def summarize_text(url, text, question):
"""Summarize text using the LLM model"""
if not text:
return "Error: No text to summarize"
@@ -145,15 +150,28 @@ def summarize_text(text, question):
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=300,
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.")
@@ -163,7 +181,7 @@ def summarize_text(text, question):
final_summary = create_chat_completion(
model=cfg.fast_llm_model,
messages=messages,
max_tokens=300,
max_tokens=cfg.browse_summary_max_token,
)
return final_summary

2
scripts/call_ai_function.py
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@@ -13,7 +13,7 @@ def call_ai_function(function, args, description, model=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 seperated string
# parse args to comma separated string
args = ", ".join(args)
messages = [
{

2
scripts/chat.py
View File

@@ -70,7 +70,7 @@ def chat_with_ai(
logger.debug(f"Token limit: {token_limit}")
send_token_limit = token_limit - 1000
relevant_memory = permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
relevant_memory = '' if len(full_message_history) ==0 else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
logger.debug(f'Memory Stats: {permanent_memory.get_stats()}')

2
scripts/commands.py
View File

@@ -191,7 +191,7 @@ def browse_website(url, question):
def get_text_summary(url, question):
"""Return the results of a google search"""
text = browse.scrape_text(url)
summary = browse.summarize_text(text, question)
summary = browse.summarize_text(url, text, question)
return """ "Result" : """ + summary

22
scripts/config.py
View File

@@ -36,12 +36,17 @@ class Config(metaclass=Singleton):
"""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"))
@@ -61,6 +66,9 @@ class Config(metaclass=Singleton):
self.use_mac_os_tts = False
self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
self.use_brian_tts = False
self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
self.google_api_key = os.getenv("GOOGLE_API_KEY")
self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
@@ -72,7 +80,7 @@ class Config(metaclass=Singleton):
# User agent headers to use when browsing web
# Some websites might just completely deny request with an error code if no user agent was found.
self.user_agent_header = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
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", "")
@@ -129,6 +137,10 @@ class Config(metaclass=Singleton):
"""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
@@ -149,6 +161,14 @@ class Config(metaclass=Singleton):
"""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

19
scripts/data.py
View File

@@ -1,19 +0,0 @@
import os
from pathlib import Path
def load_prompt():
"""Load the prompt from data/prompt.txt"""
try:
# get directory of this file:
file_dir = Path(__file__).parent
prompt_file_path = file_dir / "data" / "prompt.txt"
# Load the prompt from data/prompt.txt
with open(prompt_file_path, "r") as prompt_file:
prompt = prompt_file.read()
return prompt
except FileNotFoundError:
print("Error: Prompt file not found", flush=True)
return ""

64
scripts/data/prompt.txt
View File

@@ -1,64 +0,0 @@
CONSTRAINTS:
1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
5. Browse Website: "browse_website", args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"
6. Start GPT Agent: "start_agent", args: "name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"
7. Message GPT Agent: "message_agent", args: "key": "<key>", "message": "<message>"
8. List GPT Agents: "list_agents", args: ""
9. Delete GPT Agent: "delete_agent", args: "key": "<key>"
10. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
11. Read file: "read_file", args: "file": "<file>"
12. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
13. Delete file: "delete_file", args: "file": "<file>"
14. Search Files: "search_files", args: "directory": "<directory>"
15. Evaluate Code: "evaluate_code", args: "code": "<full_code_string>"
16. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
17. Write Tests: "write_tests", args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"
18. Execute Python File: "execute_python_file", args: "file": "<file>"
19. Execute Shell Command, non-interactive commands only: "execute_shell", args: "command_line": "<command_line>".
20. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
21. Generate Image: "generate_image", args: "prompt": "<prompt>"
22. Do Nothing: "do_nothing", args: ""
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-3.5 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behavior constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
You should only respond in JSON format as described below
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"
}
}
}
Ensure the response can be parsed by Python json.loads

70
scripts/data_ingestion.py Normal file
View File

@@ -0,0 +1,70 @@
import argparse
import logging
from config import Config
from memory import get_memory
from file_operations import ingest_file, search_files
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():
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()

93
scripts/execute_code.py
View File

@@ -19,53 +19,60 @@ def execute_python_file(file):
if not os.path.isfile(file_path):
return f"Error: File '{file}' does not exist."
try:
client = docker.from_env()
image_name = 'python:3.10'
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.images.get(image_name)
print(f"Image '{image_name}' found locally")
except docker.errors.ImageNotFound:
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get('status')
progress = line.get('progress')
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
client = docker.from_env()
# 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,
)
image_name = 'python:3.10'
try:
client.images.get(image_name)
print(f"Image '{image_name}' found locally")
except docker.errors.ImageNotFound:
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get('status')
progress = line.get('progress')
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
output = container.wait()
logs = container.logs().decode('utf-8')
container.remove()
# 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,
)
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
output = container.wait()
logs = container.logs().decode('utf-8')
container.remove()
return logs
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
except Exception as e:
return f"Error: {str(e)}"
return logs
except Exception as e:
return f"Error: {str(e)}"
def execute_shell(command_line):
@@ -86,3 +93,7 @@ def execute_shell(command_line):
os.chdir(current_dir)
return output
def we_are_running_in_a_docker_container():
os.path.exists('/.dockerenv')

54
scripts/file_operations.py
View File

@@ -20,6 +20,29 @@ def safe_join(base, *paths):
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):
"""Read a file and return the contents"""
try:
@@ -31,6 +54,37 @@ def read_file(filename):
return "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:

55
scripts/llm_utils.py
View File

@@ -1,27 +1,52 @@
import time
import openai
from colorama import Fore
from config import Config
cfg = Config()
openai.api_key = cfg.openai_api_key
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
def create_chat_completion(messages, model=None, temperature=cfg.temperature, max_tokens=None)->str:
"""Create a chat completion using the OpenAI API"""
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
)
response = None
num_retries = 5
for attempt in range(num_retries):
try:
if cfg.use_azure:
response = openai.ChatCompletion.create(
deployment_id=cfg.get_azure_deployment_id_for_model(model),
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
)
break
except openai.error.RateLimitError:
if cfg.debug_mode:
print(Fore.RED + "Error: ", "API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET)
time.sleep(20)
except openai.error.APIError as e:
if e.http_status == 502:
if cfg.debug_mode:
print(Fore.RED + "Error: ", "API Bad gateway. Waiting 20 seconds..." + Fore.RESET)
time.sleep(20)
else:
raise
if attempt == num_retries - 1:
raise
if response is None:
raise RuntimeError("Failed to get response after 5 retries")
return response.choices[0].message["content"]

3
scripts/logger.py
View File

@@ -24,7 +24,8 @@ For console handler: simulates typing
class Logger(metaclass=Singleton):
def __init__(self):
# create log directory if it doesn't exist
log_dir = os.path.join('..', 'logs')
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)

323
scripts/main.py
View File

@@ -3,7 +3,6 @@ import random
import commands as cmd
import utils
from memory import get_memory, get_supported_memory_backends
import data
import chat
from colorama import Fore, Style
from spinner import Spinner
@@ -17,6 +16,7 @@ import yaml
import argparse
from logger import logger
import logging
from prompt import get_prompt
cfg = Config()
@@ -129,64 +129,14 @@ def print_assistant_thoughts(assistant_reply):
logger.error("Error: \n", call_stack)
def load_variables(config_file="config.yaml"):
"""Load variables from yaml file if it exists, otherwise prompt the user for input"""
try:
with open(config_file) as file:
config = yaml.load(file, Loader=yaml.FullLoader)
ai_name = config.get("ai_name")
ai_role = config.get("ai_role")
ai_goals = config.get("ai_goals")
except FileNotFoundError:
ai_name = ""
ai_role = ""
ai_goals = []
# Prompt the user for input if config file is missing or empty values
if not ai_name:
ai_name = utils.clean_input("Name your AI: ")
if ai_name == "":
ai_name = "Entrepreneur-GPT"
if not ai_role:
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."
if not ai_goals:
print("Enter up to 5 goals for your AI: ")
print("For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'")
print("Enter nothing to load defaults, enter nothing when finished.")
ai_goals = []
for i in range(5):
ai_goal = utils.clean_input(f"Goal {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"]
# Save variables to yaml file
config = {"ai_name": ai_name, "ai_role": ai_role, "ai_goals": ai_goals}
with open(config_file, "w") as file:
documents = yaml.dump(config, file)
prompt = data.load_prompt()
prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as a LLM and pursue simple strategies with no legal complications."""
# Construct full prompt
full_prompt = f"You are {ai_name}, {ai_role}\n{prompt_start}\n\nGOALS:\n\n"
for i, goal in enumerate(ai_goals):
full_prompt += f"{i+1}. {goal}\n"
full_prompt += f"\n\n{prompt}"
return full_prompt
def construct_prompt():
"""Construct the prompt for the AI to respond to"""
config = AIConfig.load()
if config.ai_name:
config = AIConfig.load(cfg.ai_settings_file)
if cfg.skip_reprompt and config.ai_name:
logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
logger.typewriter_log("Goals:", Fore.GREEN, config.ai_goals)
elif config.ai_name:
logger.typewriter_log(
f"Welcome back! ",
Fore.GREEN,
@@ -274,12 +224,15 @@ def parse_arguments():
cfg.set_speak_mode(False)
parser = argparse.ArgumentParser(description='Process arguments.')
parser.add_argument('--continuous', action='store_true', help='Enable Continuous Mode')
parser.add_argument('--continuous', '-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:
@@ -294,6 +247,17 @@ def parse_arguments():
"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)
@@ -306,10 +270,6 @@ def parse_arguments():
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_fast_llm_model(cfg.smart_llm_model)
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
cfg.set_debug_mode(True)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
@@ -319,6 +279,24 @@ def parse_arguments():
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
@@ -339,103 +317,148 @@ def main():
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
print('Using memory of type: ' + memory.__class__.__name__)
# Interaction Loop
while True:
# Send message to AI, get response
with Spinner("Thinking... "):
assistant_reply = chat.chat_with_ai(
prompt,
user_input,
full_message_history,
memory,
cfg.fast_token_limit) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
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()
# 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))
class Agent:
"""Agent class for interacting with Auto-GPT.
if not cfg.continuous_mode and 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
user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {ai_name}...",
flush=True)
while True:
console_input = utils.clean_input(Fore.MAGENTA + "Input:" + Style.RESET_ALL)
if console_input.lower().rstrip() == "y":
user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
next_action_count = abs(int(console_input.split(" ")[1]))
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":
user_input = "EXIT"
break
else:
user_input = console_input
command_name = "human_feedback"
break
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.
if user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"")
elif user_input == "EXIT":
print("Exiting...", flush=True)
"""
def __init__(self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if cfg.continuous_mode and cfg.continuous_limit > 0 and loop_count > cfg.continuous_limit:
logger.typewriter_log("Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}")
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = f"Command {command_name} threw the following error: " + arguments
elif command_name == "human_feedback":
result = f"Human feedback: {user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if next_action_count > 0:
next_action_count -= 1
# 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
memory_to_add = f"Assistant Reply: {assistant_reply} " \
f"\nResult: {result} " \
f"\nHuman Feedback: {user_input} "
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
memory.add(memory_to_add)
# 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))
# Check if there's a result from the command append it to the message
# history
if result is not None:
full_message_history.append(chat.create_chat_message("system", result))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
full_message_history.append(
chat.create_chat_message(
"system", "Unable to execute command"))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, "Unable to execute command")
if not cfg.continuous_mode and self.next_action_count == 0:
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True)
while True:
console_input = utils.clean_input(Fore.MAGENTA + "Input:" + Style.RESET_ALL)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(int(console_input.split(" ")[1]))
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print("Invalid input format. Please enter 'y -n' where n is the number of continuous tasks.")
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"")
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = f"Command {command_name} threw the following error: " + arguments
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = f"Assistant Reply: {assistant_reply} " \
f"\nResult: {result} " \
f"\nHuman Feedback: {self.user_input} "
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(chat.create_chat_message("system", result))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message(
"system", "Unable to execute command"))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, "Unable to execute command")
if __name__ == "__main__":

2
scripts/memory/__init__.py
View File

@@ -3,7 +3,7 @@ from memory.no_memory import NoMemory
# List of supported memory backends
# Add a backend to this list if the import attempt is successful
supported_memory = ['local']
supported_memory = ['local', 'no_memory']
try:
from memory.redismem import RedisMemory

63
scripts/prompt.py Normal file
View File

@@ -0,0 +1,63 @@
from promptgenerator import PromptGenerator
def get_prompt():
"""
This function generates a prompt string that includes various constraints, commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the PromptGenerator object
prompt_generator = PromptGenerator()
# Add constraints to the PromptGenerator object
prompt_generator.add_constraint("~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.")
prompt_generator.add_constraint("If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.")
prompt_generator.add_constraint("No user assistance")
prompt_generator.add_constraint('Exclusively use the commands listed in double quotes e.g. "command name"')
# Define the command list
commands = [
("Google Search", "google", {"input": "<search>"}),
("Browse Website", "browse_website", {"url": "<url>", "question": "<what_you_want_to_find_on_website>"}),
("Start GPT Agent", "start_agent", {"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"}),
("Message GPT Agent", "message_agent", {"key": "<key>", "message": "<message>"}),
("List GPT Agents", "list_agents", {}),
("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
("Write to file", "write_to_file", {"file": "<file>", "text": "<text>"}),
("Read file", "read_file", {"file": "<file>"}),
("Append to file", "append_to_file", {"file": "<file>", "text": "<text>"}),
("Delete file", "delete_file", {"file": "<file>"}),
("Search Files", "search_files", {"directory": "<directory>"}),
("Evaluate Code", "evaluate_code", {"code": "<full_code_string>"}),
("Get Improved Code", "improve_code", {"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"}),
("Write Tests", "write_tests", {"code": "<full_code_string>", "focus": "<list_of_focus_areas>"}),
("Execute Python File", "execute_python_file", {"file": "<file>"}),
("Execute Shell Command, non-interactive commands only", "execute_shell", { "command_line": "<command_line>"}),
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
("Generate Image", "generate_image", {"prompt": "<prompt>"}),
("Do Nothing", "do_nothing", {}),
]
# Add commands to the PromptGenerator object
for command_label, command_name, args in commands:
prompt_generator.add_command(command_label, command_name, args)
# Add resources to the PromptGenerator object
prompt_generator.add_resource("Internet access for searches and information gathering.")
prompt_generator.add_resource("Long Term memory management.")
prompt_generator.add_resource("GPT-3.5 powered Agents for delegation of simple tasks.")
prompt_generator.add_resource("File output.")
# Add performance evaluations to the PromptGenerator object
prompt_generator.add_performance_evaluation("Continuously review and analyze your actions to ensure you are performing to the best of your abilities.")
prompt_generator.add_performance_evaluation("Constructively self-criticize your big-picture behavior constantly.")
prompt_generator.add_performance_evaluation("Reflect on past decisions and strategies to refine your approach.")
prompt_generator.add_performance_evaluation("Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.")
# Generate the prompt string
prompt_string = prompt_generator.generate_prompt_string()
return prompt_string

129
scripts/promptgenerator.py Normal file
View File

@@ -0,0 +1,129 @@
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):
"""
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):
"""
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'):
"""
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):
"""
Generate a prompt string based on the constraints, commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
formatted_response_format = json.dumps(self.response_format, indent=4)
prompt_string = (
f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
f"Commands:\n{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
f"Performance Evaluation:\n{self._generate_numbered_list(self.performance_evaluation)}\n\n"
f"You should only respond in JSON format as described below \nResponse Format: \n{formatted_response_format} \nEnsure the response can be parsed by Python json.loads"
)
return prompt_string

24
scripts/speak.py
View File

@@ -53,6 +53,24 @@ def eleven_labs_speech(text, voice_index=0):
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:
@@ -76,7 +94,11 @@ def say_text(text, voice_index=0):
def speak():
if not cfg.elevenlabs_api_key:
if cfg.use_mac_os_tts == 'True':
macos_tts_speech(text, voice_index)
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:

6
scripts/spinner.py
View File

@@ -17,10 +17,10 @@ class Spinner:
def spin(self):
"""Spin the spinner"""
while self.running:
sys.stdout.write(next(self.spinner) + " " + self.message + "\r")
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
sys.stdout.flush()
time.sleep(self.delay)
sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
def __enter__(self):
"""Start the spinner"""
@@ -32,5 +32,5 @@ class Spinner:
"""Stop the spinner"""
self.running = False
self.spinner_thread.join()
sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
sys.stdout.flush()

15
scripts/utils.py
View File

@@ -1,3 +1,7 @@
import yaml
from colorama import Fore
def clean_input(prompt: str=''):
try:
return input(prompt)
@@ -6,3 +10,14 @@ def clean_input(prompt: str=''):
print("Quitting...")
exit(0)
def validate_yaml_file(file: str):
try:
with open(file) as file:
yaml.load(file, 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}!")

101
tests/promptgenerator_tests.py Normal file
View File

@@ -0,0 +1,101 @@
# Import the required libraries for unit testing
import unittest
import sys
import os
# Add the path to the "scripts" directory to import the PromptGenerator module
sys.path.append(os.path.abspath("../scripts"))
from promptgenerator import PromptGenerator
# Create a test class for the PromptGenerator, subclassed from unittest.TestCase
class promptgenerator_tests(unittest.TestCase):
# Set up the initial state for each test method by creating an instance of PromptGenerator
def setUp(self):
self.generator = PromptGenerator()
# Test whether the add_constraint() method adds a constraint to the generator's constraints list
def test_add_constraint(self):
constraint = "Constraint1"
self.generator.add_constraint(constraint)
self.assertIn(constraint, self.generator.constraints)
# Test whether the add_command() method adds a command to the generator's commands list
def test_add_command(self):
command_label = "Command Label"
command_name = "command_name"
args = {"arg1": "value1", "arg2": "value2"}
self.generator.add_command(command_label, command_name, args)
command = {
"label": command_label,
"name": command_name,
"args": args,
}
self.assertIn(command, self.generator.commands)
# Test whether the add_resource() method adds a resource to the generator's resources list
def test_add_resource(self):
resource = "Resource1"
self.generator.add_resource(resource)
self.assertIn(resource, self.generator.resources)
# Test whether the add_performance_evaluation() method adds an evaluation to the generator's performance_evaluation list
def test_add_performance_evaluation(self):
evaluation = "Evaluation1"
self.generator.add_performance_evaluation(evaluation)
self.assertIn(evaluation, self.generator.performance_evaluation)
# Test whether the generate_prompt_string() method generates a prompt string with all the added constraints, commands, resources and evaluations
def test_generate_prompt_string(self):
constraints = ["Constraint1", "Constraint2"]
commands = [
{
"label": "Command1",
"name": "command_name1",
"args": {"arg1": "value1"},
},
{
"label": "Command2",
"name": "command_name2",
"args": {},
},
]
resources = ["Resource1", "Resource2"]
evaluations = ["Evaluation1", "Evaluation2"]
# Add all the constraints, commands, resources, and evaluations to the generator
for constraint in constraints:
self.generator.add_constraint(constraint)
for command in commands:
self.generator.add_command(
command["label"], command["name"], command["args"])
for resource in resources:
self.generator.add_resource(resource)
for evaluation in evaluations:
self.generator.add_performance_evaluation(evaluation)
# Generate the prompt string and verify its correctness
prompt_string = self.generator.generate_prompt_string()
self.assertIsNotNone(prompt_string)
for constraint in constraints:
self.assertIn(constraint, prompt_string)
for command in commands:
self.assertIn(command["name"], prompt_string)
# Check for each key-value pair in the command args dictionary
for key, value in command["args"].items():
self.assertIn(f'"{key}": "{value}"', prompt_string)
for resource in resources:
self.assertIn(resource, prompt_string)
for evaluation in evaluations:
self.assertIn(evaluation, prompt_string)
self.assertIn("constraints", prompt_string.lower())
self.assertIn("commands", prompt_string.lower())
self.assertIn("resources", prompt_string.lower())
self.assertIn("performance evaluation", prompt_string.lower())
# Run the tests when this script is executed
if __name__ == '__main__':
unittest.main()