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Merge branch 'master' of https://github.com/Significant-Gravitas/Auto-GPT into pr/1436

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BillSchumacher
2023-04-16 01:32:18 -05:00
parent 16c0dc9267 f048f88337
commit 0d1fd4fcf0
100 changed files with 4281 additions and 2667 deletions
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56
.env.template
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@@ -11,6 +11,9 @@ BROWSE_SUMMARY_MAX_TOKEN=300
# 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
# USE_WEB_BROWSER - Sets the web-browser drivers to use with selenium (defaults to chrome).
# Note: set this to either 'chrome', 'firefox', or 'safari' depending on your current browser
# USE_WEB_BROWSER=chrome
################################################################################
### LLM PROVIDER
@@ -21,20 +24,11 @@ AI_SETTINGS_FILE=ai_settings.yaml
# 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
TEMPERATURE=0
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
# cleanup azure env as already moved to `azure.yaml.template`
################################################################################
### LLM MODELS
@@ -48,7 +42,7 @@ 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.
# When using --gpt3only this needs to be set to 4000.
FAST_TOKEN_LIMIT=4000
SMART_TOKEN_LIMIT=8000
@@ -56,7 +50,10 @@ SMART_TOKEN_LIMIT=8000
### MEMORY
################################################################################
# MEMORY_BACKEND - Memory backend type (Default: local)
### MEMORY_BACKEND - Memory backend type
# local - Default
# pinecone - Pinecone (if configured)
# redis - Redis (if configured)
MEMORY_BACKEND=local
### PINECONE
@@ -77,6 +74,13 @@ REDIS_PASSWORD=
WIPE_REDIS_ON_START=False
MEMORY_INDEX=auto-gpt
### MILVUS
# MILVUS_ADDR - Milvus remote address (e.g. localhost:19530)
# MILVUS_COLLECTION - Milvus collection,
# change it if you want to start a new memory and retain the old memory.
MILVUS_ADDR=your-milvus-cluster-host-port
MILVUS_COLLECTION=autogpt
################################################################################
### IMAGE GENERATION PROVIDER
################################################################################
@@ -92,6 +96,23 @@ IMAGE_PROVIDER=dalle
# HUGGINGFACE_API_TOKEN - HuggingFace API token (Example: my-huggingface-api-token)
HUGGINGFACE_API_TOKEN=your-huggingface-api-token
################################################################################
### AUDIO TO TEXT PROVIDER
################################################################################
### HUGGINGFACE
HUGGINGFACE_AUDIO_TO_TEXT_MODEL=facebook/wav2vec2-base-960h
################################################################################
### GIT Provider for repository actions
################################################################################
### GITHUB
# GITHUB_API_KEY - Github API key / PAT (Example: github_pat_123)
# GITHUB_USERNAME - Github username
GITHUB_API_KEY=github_pat_123
GITHUB_USERNAME=your-github-username
################################################################################
### SEARCH PROVIDER
################################################################################
@@ -121,3 +142,12 @@ USE_BRIAN_TTS=False
ELEVENLABS_API_KEY=your-elevenlabs-api-key
ELEVENLABS_VOICE_1_ID=your-voice-id-1
ELEVENLABS_VOICE_2_ID=your-voice-id-2
################################################################################
### TWITTER API
################################################################################
TW_CONSUMER_KEY=
TW_CONSUMER_SECRET=
TW_ACCESS_TOKEN=
TW_ACCESS_TOKEN_SECRET=

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.gitignore vendored
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@@ -19,6 +19,8 @@ auto-gpt.json
log.txt
log-ingestion.txt
logs
*.log
*.mp3
# Byte-compiled / optimized / DLL files
__pycache__/
@@ -151,4 +153,7 @@ dmypy.json
# Pyre type checker
.pyre/
llama-*
vicuna-*
vicuna-*
# mac
.DS_Store

10
Dockerfile
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@@ -1,6 +1,10 @@
# Use an official Python base image from the Docker Hub
FROM python:3.11-slim
# Install git
RUN apt-get -y update
RUN apt-get -y install git
# Set environment variables
ENV PIP_NO_CACHE_DIR=yes \
PYTHONUNBUFFERED=1 \
@@ -13,11 +17,11 @@ 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 --chown=appuser:appuser requirements-docker.txt .
RUN pip install --no-cache-dir --user -r requirements-docker.txt
# Copy the application files
COPY --chown=appuser:appuser autogpt/ .
COPY --chown=appuser:appuser autogpt/ ./autogpt
# Set the entrypoint
ENTRYPOINT ["python", "-m", "autogpt"]

262
README.md
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@@ -1,8 +1,10 @@
# Auto-GPT: An Autonomous GPT-4 Experiment
### 🔴 🔴 🔴 Urgent: USE `stable` not `master` 🔴 🔴 🔴
We've improved our workflow. `master` will often be **in a broken state**.
Download the latest `stable` release here: https://github.com/Torantulino/Auto-GPT/releases/latest
This information SUPERCEDES any following information. Takes precedence. Do this not that.
> This information takes precedence over any other information provided and should be followed accordingly.
Our workflow has been improved, but please note that `master` branch may often be in a **broken** state.
Please download the latest `stable` release from here: https://github.com/Torantulino/Auto-GPT/releases/latest.
![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
[![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)](https://twitter.com/SigGravitas)
@@ -35,22 +37,27 @@ Your support is greatly appreciated
## Table of Contents
- [Auto-GPT: An Autonomous GPT-4 Experiment](#auto-gpt-an-autonomous-gpt-4-experiment)
- [Demo (30/03/2023):](#demo-30032023)
- [🔴 🔴 🔴 Urgent: USE `stable` not `master` 🔴 🔴 🔴](#----urgent-use-stable-not-master----)
- [Demo (30/03/2023):](#demo-30032023)
- [Table of Contents](#table-of-contents)
- [🚀 Features](#-features)
- [📋 Requirements](#-requirements)
- [💾 Installation](#-installation)
- [🔧 Usage](#-usage)
- [Logs](#logs)
- [Docker](#docker)
- [Command Line Arguments](#command-line-arguments)
- [🗣️ Speech Mode](#️-speech-mode)
- [🔍 Google API Keys Configuration](#-google-api-keys-configuration)
- [Setting up environment variables](#setting-up-environment-variables)
- [Redis Setup](#redis-setup)
- [🌲 Pinecone API Key Setup](#-pinecone-api-key-setup)
- [Memory Backend Setup](#memory-backend-setup)
- [Redis Setup](#redis-setup)
- [🌲 Pinecone API Key Setup](#-pinecone-api-key-setup)
- [Milvus Setup](#milvus-setup)
- [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)
- [🧠 Memory pre-seeding](#-memory-pre-seeding)
- [💀 Continuous Mode ⚠️](#-continuous-mode-️)
- [GPT3.5 ONLY Mode](#gpt35-only-mode)
- [🖼 Image Generation](#-image-generation)
@@ -75,69 +82,84 @@ Your support is greatly appreciated
- [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)
Optional:
- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
- Memory backend
- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
- [Milvus](https://milvus.io/) (If you want Milvus as memory backend)
- ElevenLabs Key (If you want the AI to speak)
## 💾 Installation
To install Auto-GPT, follow these steps:
1. Make sure you have all the **requirements** above, if not, install/get them.
1. Make sure you have all the **requirements** listed above, if not, install/get them
_The following commands should be executed in a CMD, Bash or Powershell window. To do this, go to a folder on your computer, click in the folder path at the top and type CMD, then press enter._
_To execute the following commands, open a CMD, Bash, or Powershell window by navigating to a folder on your computer and typing `CMD` in the folder path at the top, then press enter._
2. Clone the repository:
For this step you need Git installed, but you can just download the zip file instead by clicking the button at the top of this page ☝️
2. Clone the repository: For this step, you need Git installed. Alternatively, you can download the zip file by clicking the button at the top of this page ☝️
```
```bash
git clone https://github.com/Torantulino/Auto-GPT.git
```
3. Navigate to the project directory:
_(Type this into your CMD window, you're aiming to navigate the CMD window to the repository you just downloaded)_
3. Navigate to the directory where the repository was downloaded
```
cd 'Auto-GPT'
```bash
cd Auto-GPT
```
4. Install the required dependencies:
_(Again, type this into your CMD window)_
4. Install the required dependencies
```
```bash
pip install -r requirements.txt
```
5. Rename `.env.template` to `.env` and fill in your `OPENAI_API_KEY`. If you plan to use Speech Mode, fill in your `ELEVEN_LABS_API_KEY` as well.
- Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
5. Locate the file named `.env.template` in the main `/Auto-GPT` folder.
Create a copy of this file, called `.env` by removing the `template` extension. The easiest way is to do this in a command prompt/terminal window `cp .env.template .env`
Open the `.env` file in a text editor. Note: Files starting with a dot might be hidden by your Operating System.
Find the line that says `OPENAI_API_KEY=`.
After the `"="`, enter your unique OpenAI API Key (without any quotes or spaces).
Enter any other API keys or Tokens for services you would like to utilize.
Save and close the `".env"` file.
By completing these steps, you have properly configured the API Keys for your project.
- See [OpenAI API Keys Configuration](#openai-api-keys-configuration) to obtain your OpenAI API key.
- Obtain your ElevenLabs API key from: https://elevenlabs.io. You can view your xi-api-key using the "Profile" tab on the website.
- If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and then:
- Rename `azure.yaml.template` to `azure.yaml` and provide the relevant `azure_api_base`, `azure_api_version` and all of the deployment ids for the relevant models in the `azure_model_map` section:
- `fast_llm_model_deployment_id` - your gpt-3.5-turbo or gpt-4 deployment id
- `smart_llm_model_deployment_id` - your gpt-4 deployment id
- `embedding_model_deployment_id` - your text-embedding-ada-002 v2 deployment id
- Please specify all of these values as double quoted strings
- details can be found here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.
- If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and then follow these steps:
- Rename `azure.yaml.template` to `azure.yaml` and provide the relevant `azure_api_base`, `azure_api_version` and all the deployment IDs for the relevant models in the `azure_model_map` section:
- `fast_llm_model_deployment_id` - your gpt-3.5-turbo or gpt-4 deployment ID
- `smart_llm_model_deployment_id` - your gpt-4 deployment ID
- `embedding_model_deployment_id` - your text-embedding-ada-002 v2 deployment ID
- Please specify all of these values as double-quoted strings
```yaml
# Replace string in angled brackets (<>) to your own ID
azure_model_map:
fast_llm_model_deployment_id: "<my-fast-llm-deployment-id>"
...
```
- Details can be found here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.
## 🔧 Usage
1. Run the `autogpt` Python module in your terminal:
_(Type this into your CMD window)_
1. Run `autogpt` Python module in your terminal
```
python -m autogpt
```
2. After each of action, enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter additional feedback for the AI.
2. After each action, choose from options to authorize command(s),
exit the program, or provide feedback to the AI.
1. Authorize a single command, enter `y`
2. Authorize a series of _N_ continuous commands, enter `y -N`
3. Exit the program, enter `n`
### Logs
You will find activity and error logs in the folder `./output/logs`
Activity and error logs are located in the `./output/logs`
To output debug logs:
To print out debug logs:
```
python -m autogpt --debug
@@ -159,20 +181,53 @@ docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_worksp
### 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'.
* View all available command line arguments
```bash
python scripts/main.py --help
```
* Run Auto-GPT with a different AI Settings file
```bash
python scripts/main.py --ai-settings <filename>
```
* Specify one of 3 memory backends: `local`, `redis`, `pinecone` or `no_memory`
```bash
python scripts/main.py --use-memory <memory-backend>
```
> **NOTE**: There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `python scripts/main.py --help` for more information
## 🗣️ Speech Mode
Use this to use TTS for Auto-GPT
Use this to use TTS _(Text-to-Speech)_ for Auto-GPT
```
```bash
python -m autogpt --speak
```
### List of IDs with names from eleven labs, you can use the name or ID:
- Rachel : 21m00Tcm4TlvDq8ikWAM
- Domi : AZnzlk1XvdvUeBnXmlld
- Bella : EXAVITQu4vr4xnSDxMaL
- Antoni : ErXwobaYiN019PkySvjV
- Elli : MF3mGyEYCl7XYWbV9V6O
- Josh : TxGEqnHWrfWFTfGW9XjX
- Arnold : VR6AewLTigWG4xSOukaG
- Adam : pNInz6obpgDQGcFmaJgB
- Sam : yoZ06aMxZJJ28mfd3POQ
## OpenAI API Keys Configuration
Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
To use OpenAI API key for Auto-GPT, you NEED to have billing set up (AKA paid account).
You can set up paid account at https://platform.openai.com/account/billing/overview.
![For OpenAI API key to work, set up paid account at OpenAI API > Billing](./docs/imgs/openai-api-key-billing-paid-account.png)
## 🔍 Google API Keys Configuration
This section is optional, use the official google api if you are having issues with error 429 when running a google search.
@@ -195,58 +250,50 @@ _Remember that your free daily custom search quota allows only up to 100 searche
For Windows Users:
```
```bash
setx GOOGLE_API_KEY "YOUR_GOOGLE_API_KEY"
setx CUSTOM_SEARCH_ENGINE_ID "YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
For macOS and Linux users:
```
```bash
export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"
```
## Redis Setup
Install docker desktop.
Run:
```
> _**CAUTION**_ \
This is not intended to be publicly accessible and lacks security measures. Therefore, avoid exposing Redis to the internet without a password or at all
1. Install docker desktop
```bash
docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
```
> See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
Set the following environment variables:
```
2. Set the following environment variables
> Replace **PASSWORD** in angled brackets (<>)
```bash
MEMORY_BACKEND=redis
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=
REDIS_PASSWORD=<PASSWORD>
```
Note that this is not intended to be run facing the internet and is not secure, do not expose redis to the internet without a password or at all really.
You can optionally set
```
```bash
WIPE_REDIS_ON_START=False
```
To persist memory stored in Redis.
To persist memory stored in Redis
You can specify the memory index for redis using the following:
```
MEMORY_INDEX=whatever
```bash
MEMORY_INDEX=<WHATEVER>
```
## 🌲 Pinecone API Key Setup
### 🌲 Pinecone API Key Setup
Pinecone enables the storage of vast amounts of vector-based memory, allowing for only relevant memories to be loaded for the agent at any given time.
@@ -254,40 +301,52 @@ Pinecone enables the storage of vast amounts of vector-based memory, allowing fo
2. Choose the `Starter` plan to avoid being charged.
3. Find your API key and region under the default project in the left sidebar.
### Milvus Setup
[Milvus](https://milvus.io/) is a open-source, high scalable vector database to storage huge amount of vector-based memory and provide fast relevant search.
- setup milvus database, keep your pymilvus version and milvus version same to avoid compatible issues.
- setup by open source [Install Milvus](https://milvus.io/docs/install_standalone-operator.md)
- or setup by [Zilliz Cloud](https://zilliz.com/cloud)
- set `MILVUS_ADDR` in `.env` to your milvus address `host:ip`.
- set `MEMORY_BACKEND` in `.env` to `milvus` to enable milvus as backend.
- optional
- set `MILVUS_COLLECTION` in `.env` to change milvus collection name as you want, `autogpt` is the default name.
### Setting up environment variables
In the `.env` file set:
- `PINECONE_API_KEY`
- `PINECONE_ENV` (something like: us-east4-gcp)
- `PINECONE_ENV` (example: _"us-east4-gcp"_)
- `MEMORY_BACKEND=pinecone`
Alternatively, you can set them from the command line (advanced):
For Windows Users:
```
setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
```bash
setx PINECONE_API_KEY "<YOUR_PINECONE_API_KEY>"
setx PINECONE_ENV "<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
setx MEMORY_BACKEND "pinecone"
```
For macOS and Linux users:
```
export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
```bash
export PINECONE_API_KEY="<YOUR_PINECONE_API_KEY>"
export PINECONE_ENV="<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
export MEMORY_BACKEND="pinecone"
```
## Setting Your Cache Type
By default Auto-GPT is going to use LocalCache instead of redis or Pinecone.
By default, Auto-GPT is going to use LocalCache instead of redis or Pinecone.
To switch to either, change the `MEMORY_BACKEND` env variable to the value that you want:
`local` (default) uses a local JSON cache file
`pinecone` uses the Pinecone.io account you configured in your ENV settings
`redis` will use the redis cache that you configured
* `local` (default) uses a local JSON cache file
* `pinecone` uses the Pinecone.io account you configured in your ENV settings
* `redis` will use the redis cache that you configured
## View Memory Usage
@@ -296,8 +355,7 @@ To switch to either, change the `MEMORY_BACKEND` env variable to the value that
## 🧠 Memory pre-seeding
```
# python scripts/data_ingestion.py -h
# python autogpt/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.
@@ -308,12 +366,10 @@ options:
--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
--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.
# python autogpt/data_ingestion.py --dir seed_data --init --overlap 200 --max_length 1000
This script located at autogpt/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.
@@ -322,35 +378,34 @@ To pre-seed the memory, the content of each document is split into chunks of a s
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.
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.
⚠️ 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.
⚠️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.
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:
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.
- 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.
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.
Run the AI **without** user authorization, 100% automated.
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 authorize.
Use at your own risk.
1. Run the `autogpt` python module in your terminal:
```
```bash
python -m autogpt --speak --continuous
```
2. To exit the program, press Ctrl + C
@@ -359,7 +414,7 @@ python -m autogpt --speak --continuous
If you don't have access to the GPT4 api, this mode will allow you to use Auto-GPT!
```
```bash
python -m autogpt --speak --gpt3only
```
@@ -367,15 +422,20 @@ It is recommended to use a virtual machine for tasks that require high security
## 🖼 Image Generation
By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a [HuggingFace API Token](https://huggingface.co/settings/tokens) is required.
By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a [Hugging Face API Token](https://huggingface.co/settings/tokens) is required.
Once you have a token, set these variables in your `.env`:
```
```bash
IMAGE_PROVIDER=sd
HUGGINGFACE_API_TOKEN="YOUR_HUGGINGFACE_API_TOKEN"
```
## Selenium
```bash
sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 <YOUR_CLIENT>
```
## ⚠️ Limitations
This experiment aims to showcase the potential of GPT-4 but comes with some limitations:
@@ -416,13 +476,13 @@ We look forward to connecting with you and hearing your thoughts, ideas, and exp
To run tests, run the following command:
```
```bash
python -m unittest discover tests
```
To run tests and see coverage, run the following command:
```
```bash
coverage run -m unittest discover tests
```
@@ -432,7 +492,7 @@ This project uses [flake8](https://flake8.pycqa.org/en/latest/) for linting. We
To run the linter, run the following command:
```
```bash
flake8 autogpt/ tests/
# Or, if you want to run flake8 with the same configuration as the CI:

533
autogpt/__main__.py
View File

@@ -1,357 +1,21 @@
import argparse
import json
"""Main script for the autogpt package."""
import logging
import random
import time
import traceback
from colorama import Fore
from autogpt.agent.agent import Agent
from autogpt.args import parse_arguments
import yaml
from colorama import Fore, Style
from autogpt.config import Config, check_openai_api_key
from autogpt.logs import logger
from autogpt.memory import get_memory
from autogpt import chat
from autogpt import commands as cmd
from autogpt import speak, utils
from autogpt.ai_config import AIConfig
from autogpt.config import Config
from autogpt.json_parser import fix_and_parse_json
from autogpt.logger import logger
from autogpt.memory import get_memory, get_supported_memory_backends
from autogpt.prompt import get_prompt
from autogpt.spinner import Spinner
from autogpt.prompt import construct_prompt
cfg = Config()
# Load environment variables from .env file
def check_openai_api_key():
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
if not cfg.openai_api_key:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)
def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
if cfg.speak_mode and cfg.debug_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
# Use regex to search for JSON objects
import regex
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError) as e:
if cfg.speak_mode:
speak.say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, assistant_thoughts_text
)
logger.typewriter_log("REASONING:", Fore.YELLOW, assistant_thoughts_reasoning)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log("CRITICISM:", Fore.YELLOW, assistant_thoughts_criticism)
# Speak the assistant's thoughts
if cfg.speak_mode and assistant_thoughts_speak:
speak.say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
if cfg.speak_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception as e:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)
def construct_prompt():
"""Construct the prompt for the AI to respond to"""
config = AIConfig.load(cfg.ai_settings_file)
if cfg.skip_reprompt and config.ai_name:
logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
logger.typewriter_log("Goals:", Fore.GREEN, config.ai_goals)
elif config.ai_name:
logger.typewriter_log(
f"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True,
)
should_continue = utils.clean_input(
f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
Continue (y/n): """
)
if should_continue.lower() == "n":
config = AIConfig()
if not config.ai_name:
config = prompt_user()
config.save()
# Get rid of this global:
global ai_name
ai_name = config.ai_name
full_prompt = config.construct_full_prompt()
return full_prompt
def prompt_user():
"""Prompt the user for input"""
ai_name = ""
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load defaults.",
speak_text=True,
)
# Get AI Name from User
logger.typewriter_log(
"Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
)
ai_name = utils.clean_input("AI Name: ")
if ai_name == "":
ai_name = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
)
# Get AI Role from User
logger.typewriter_log(
"Describe your AI's role: ",
Fore.GREEN,
"For example, 'an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.'",
)
ai_role = utils.clean_input(f"{ai_name} is: ")
if ai_role == "":
ai_role = "an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth."
# Enter up to 5 goals for the AI
logger.typewriter_log(
"Enter up to 5 goals for your AI: ",
Fore.GREEN,
"For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'",
)
print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
ai_goals = []
for i in range(5):
ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
if ai_goal == "":
break
ai_goals.append(ai_goal)
if len(ai_goals) == 0:
ai_goals = [
"Increase net worth",
"Grow Twitter Account",
"Develop and manage multiple businesses autonomously",
]
config = AIConfig(ai_name, ai_role, ai_goals)
return config
def parse_arguments():
"""Parses the arguments passed to the script"""
global cfg
cfg.set_debug_mode(False)
cfg.set_continuous_mode(False)
cfg.set_speak_mode(False)
parser = argparse.ArgumentParser(description="Process arguments.")
parser.add_argument(
"--continuous", "-c", action="store_true", help="Enable Continuous Mode"
)
parser.add_argument(
"--continuous-limit",
"-l",
type=int,
dest="continuous_limit",
help="Defines the number of times to run in continuous mode",
)
parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
parser.add_argument(
"--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
)
parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
parser.add_argument(
"--use-memory",
"-m",
dest="memory_type",
help="Defines which Memory backend to use",
)
parser.add_argument(
"--skip-reprompt",
"-y",
dest="skip_reprompt",
action="store_true",
help="Skips the re-prompting messages at the beginning of the script",
)
parser.add_argument(
"--ai-settings",
"-C",
dest="ai_settings_file",
help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
)
args = parser.parse_args()
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
cfg.set_debug_mode(True)
if args.continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.",
)
cfg.set_continuous_mode(True)
if args.continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
)
cfg.set_continuous_limit(args.continuous_limit)
# Check if continuous limit is used without continuous mode
if args.continuous_limit and not args.continuous:
parser.error("--continuous-limit can only be used with --continuous")
if args.speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
cfg.set_speak_mode(True)
if args.gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_smart_llm_model(cfg.fast_llm_model)
if args.gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
cfg.set_fast_llm_model(cfg.smart_llm_model)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
if not chosen in supported_memory:
logger.typewriter_log(
"ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log(f"Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
else:
cfg.memory_backend = chosen
if args.skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
cfg.skip_reprompt = True
if args.ai_settings_file:
file = args.ai_settings_file
# Validate file
(validated, message) = utils.validate_yaml_file(file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
cfg.ai_settings_file = file
cfg.skip_reprompt = True
def main():
global ai_name, memory
def main() -> None:
"""Main function for the script"""
cfg = Config()
# TODO: fill in llm values here
check_openai_api_key()
parse_arguments()
@@ -361,14 +25,19 @@ def main():
# print(prompt)
# Initialize variables
full_message_history = []
result = None
next_action_count = 0
# Make a constant:
user_input = "Determine which next command to use, and respond using the format specified above:"
user_input = (
"Determine which next command to use, and respond using the"
" format specified above:"
)
# Initialize memory and make sure it is empty.
# this is particularly important for indexing and referencing pinecone memory
memory = get_memory(cfg, init=True)
print("Using memory of type: " + memory.__class__.__name__)
logger.typewriter_log(
f"Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}"
)
logger.typewriter_log(f"Using Browser:", Fore.GREEN, cfg.selenium_web_browser)
agent = Agent(
ai_name=ai_name,
memory=memory,
@@ -380,167 +49,5 @@ def main():
agent.start_interaction_loop()
class Agent:
"""Agent class for interacting with Auto-GPT.
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_count > cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
)
break
# Send message to AI, get response
with Spinner("Thinking... "):
assistant_reply = chat.chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
# Get command name and arguments
try:
command_name, arguments = cmd.get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
speak.say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
if not cfg.continuous_mode and self.next_action_count == 0:
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True,
)
while True:
console_input = utils.clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is the number of continuous tasks."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: " + arguments
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(
chat.create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
if __name__ == "__main__":
main()

289
autogpt/agent.py
View File

@@ -1,289 +0,0 @@
import json
import traceback
from tkinter.ttk import Style
from colorama import Fore
import autogpt.chat
import autogpt.commands as cmd
import autogpt.speak
from autogpt.config import Config
from autogpt.logger import logger
from autogpt.spinner import Spinner
class Agent:
"""Agent class for interacting with Auto-GPT.
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
cfg = Config()
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_count > cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
)
break
# Send message to AI, get response
with Spinner("Thinking... "):
assistant_reply = chat.chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(assistant_reply)
# Get command name and arguments
try:
command_name, arguments = cmd.get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
speak.say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
if not cfg.continuous_mode and self.next_action_count == 0:
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {self.ai_name}...",
flush=True,
)
while True:
console_input = utils.clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is the number of continuous tasks."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: " + arguments
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(
chat.create_chat_message("system", result)
)
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
chat.create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)
def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
cfg = Config()
if cfg.speak_mode and cfg.debug_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
# Use regex to search for JSON objects
import regex
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if cfg.speak_mode and cfg.debug_mode:
speak.say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError) as e:
if cfg.speak_mode:
speak.say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def print_assistant_thoughts(assistant_reply):
"""Prints the assistant's thoughts to the console"""
global ai_name
global cfg
cfg = Config()
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, assistant_thoughts_text
)
logger.typewriter_log("REASONING:", Fore.YELLOW, assistant_thoughts_reasoning)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log("CRITICISM:", Fore.YELLOW, assistant_thoughts_criticism)
# Speak the assistant's thoughts
if cfg.speak_mode and assistant_thoughts_speak:
speak.say_text(assistant_thoughts_speak)
return assistant_reply_json
except json.decoder.JSONDecodeError as e:
logger.error("Error: Invalid JSON\n", assistant_reply)
if cfg.speak_mode:
speak.say_text(
"I have received an invalid JSON response from the OpenAI API. I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception as e:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)

4
autogpt/agent/__init__.py Normal file
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from autogpt.agent.agent import Agent
from autogpt.agent.agent_manager import AgentManager
__all__ = ["Agent", "AgentManager"]

183
autogpt/agent/agent.py Normal file
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from colorama import Fore, Style
from autogpt.app import execute_command, get_command
from autogpt.chat import chat_with_ai, create_chat_message
from autogpt.config import Config
from autogpt.json_fixes.bracket_termination import (
attempt_to_fix_json_by_finding_outermost_brackets,
)
from autogpt.logs import logger, print_assistant_thoughts
from autogpt.speech import say_text
from autogpt.spinner import Spinner
from autogpt.utils import clean_input
class Agent:
"""Agent class for interacting with Auto-GPT.
Attributes:
ai_name: The name of the agent.
memory: The memory object to use.
full_message_history: The full message history.
next_action_count: The number of actions to execute.
prompt: The prompt to use.
user_input: The user input.
"""
def __init__(
self,
ai_name,
memory,
full_message_history,
next_action_count,
prompt,
user_input,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history = full_message_history
self.next_action_count = next_action_count
self.prompt = prompt
self.user_input = user_input
def start_interaction_loop(self):
# Interaction Loop
cfg = Config()
loop_count = 0
command_name = None
arguments = None
while True:
# Discontinue if continuous limit is reached
loop_count += 1
if (
cfg.continuous_mode
and cfg.continuous_limit > 0
and loop_count > cfg.continuous_limit
):
logger.typewriter_log(
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
)
break
# Send message to AI, get response
with Spinner("Thinking... "):
assistant_reply = chat_with_ai(
self.prompt,
self.user_input,
self.full_message_history,
self.memory,
cfg.fast_token_limit,
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
# Print Assistant thoughts
print_assistant_thoughts(self.ai_name, assistant_reply)
# Get command name and arguments
try:
command_name, arguments = get_command(
attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
)
if cfg.speak_mode:
say_text(f"I want to execute {command_name}")
except Exception as e:
logger.error("Error: \n", str(e))
if not cfg.continuous_mode and self.next_action_count == 0:
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
# Get key press: Prompt the user to press enter to continue or escape
# to exit
self.user_input = ""
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} "
f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
print(
"Enter 'y' to authorise command, 'y -N' to run N continuous "
"commands, 'n' to exit program, or enter feedback for "
f"{self.ai_name}...",
flush=True,
)
while True:
console_input = clean_input(
Fore.MAGENTA + "Input:" + Style.RESET_ALL
)
if console_input.lower().rstrip() == "y":
self.user_input = "GENERATE NEXT COMMAND JSON"
break
elif console_input.lower().startswith("y -"):
try:
self.next_action_count = abs(
int(console_input.split(" ")[1])
)
self.user_input = "GENERATE NEXT COMMAND JSON"
except ValueError:
print(
"Invalid input format. Please enter 'y -n' where n is"
" the number of continuous tasks."
)
continue
break
elif console_input.lower() == "n":
self.user_input = "EXIT"
break
else:
self.user_input = console_input
command_name = "human_feedback"
break
if self.user_input == "GENERATE NEXT COMMAND JSON":
logger.typewriter_log(
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
Fore.MAGENTA,
"",
)
elif self.user_input == "EXIT":
print("Exiting...", flush=True)
break
else:
# Print command
logger.typewriter_log(
"NEXT ACTION: ",
Fore.CYAN,
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
f" ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
)
# Execute command
if command_name is not None and command_name.lower().startswith("error"):
result = (
f"Command {command_name} threw the following error: {arguments}"
)
elif command_name == "human_feedback":
result = f"Human feedback: {self.user_input}"
else:
result = (
f"Command {command_name} returned: "
f"{execute_command(command_name, arguments)}"
)
if self.next_action_count > 0:
self.next_action_count -= 1
memory_to_add = (
f"Assistant Reply: {assistant_reply} "
f"\nResult: {result} "
f"\nHuman Feedback: {self.user_input} "
)
self.memory.add(memory_to_add)
# Check if there's a result from the command append it to the message
# history
if result is not None:
self.full_message_history.append(create_chat_message("system", result))
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
else:
self.full_message_history.append(
create_chat_message("system", "Unable to execute command")
)
logger.typewriter_log(
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
)

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

75
autogpt/agent_manager.py
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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

68
autogpt/ai_functions.py
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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."""
result_string = call_ai_function(function_string, args, description_string)
return result_string
def improve_code(suggestions: List[str], code: str) -> str:
"""
A function that takes in code and suggestions and returns a response from create chat completion api call.
Parameters:
suggestions (List): A list of suggestions around what needs to be improved.
code (str): Code to be improved.
Returns:
A result string from create chat completion. Improved code in response.
"""
function_string = (
"def generate_improved_code(suggestions: List[str], code: str) -> str:"
)
args = [json.dumps(suggestions), code]
description_string = """Improves the provided code based on the suggestions provided, making no other changes."""
result_string = call_ai_function(function_string, args, description_string)
return result_string
def write_tests(code: str, focus: List[str]) -> str:
"""
A function that takes in code and focus topics and returns a response from create chat completion api call.
Parameters:
focus (List): A list of suggestions around what needs to be improved.
code (str): Code for test cases to be generated against.
Returns:
A result string from create chat completion. Test cases for the submitted code in response.
"""
function_string = (
"def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
)
args = [code, json.dumps(focus)]
description_string = """Generates test cases for the existing code, focusing on specific areas if required."""
result_string = call_ai_function(function_string, args, description_string)
return result_string

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autogpt/app.py Normal file
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""" Command and Control """
import json
from typing import List, NoReturn, Union
from autogpt.agent.agent_manager import AgentManager
from autogpt.commands.evaluate_code import evaluate_code
from autogpt.commands.google_search import google_official_search, google_search
from autogpt.commands.improve_code import improve_code
from autogpt.commands.write_tests import write_tests
from autogpt.config import Config
from autogpt.commands.image_gen import generate_image
from autogpt.commands.audio_text import read_audio_from_file
from autogpt.commands.web_requests import scrape_links, scrape_text
from autogpt.commands.execute_code import execute_python_file, execute_shell
from autogpt.commands.file_operations import (
append_to_file,
delete_file,
read_file,
search_files,
write_to_file,
)
from autogpt.json_fixes.parsing import fix_and_parse_json
from autogpt.memory import get_memory
from autogpt.processing.text import summarize_text
from autogpt.speech import say_text
from autogpt.commands.web_selenium import browse_website
from autogpt.commands.git_operations import clone_repository
from autogpt.commands.twitter import send_tweet
CFG = Config()
AGENT_MANAGER = AgentManager()
def is_valid_int(value: str) -> bool:
"""Check if the value is a valid integer
Args:
value (str): The value to check
Returns:
bool: True if the value is a valid integer, False otherwise
"""
try:
int(value)
return True
except ValueError:
return False
def get_command(response: str):
"""Parse the response and return the command name and arguments
Args:
response (str): The response from the user
Returns:
tuple: The command name and arguments
Raises:
json.decoder.JSONDecodeError: If the response is not valid JSON
Exception: If any other error occurs
"""
try:
response_json = fix_and_parse_json(response)
if "command" not in response_json:
return "Error:", "Missing 'command' object in JSON"
if not isinstance(response_json, dict):
return "Error:", f"'response_json' object is not dictionary {response_json}"
command = response_json["command"]
if not isinstance(command, dict):
return "Error:", "'command' object is not a dictionary"
if "name" not in command:
return "Error:", "Missing 'name' field in 'command' object"
command_name = command["name"]
# Use an empty dictionary if 'args' field is not present in 'command' object
arguments = command.get("args", {})
return command_name, arguments
except json.decoder.JSONDecodeError:
return "Error:", "Invalid JSON"
# All other errors, return "Error: + error message"
except Exception as e:
return "Error:", str(e)
def map_command_synonyms(command_name: str):
"""Takes the original command name given by the AI, and checks if the
string matches a list of common/known hallucinations
"""
synonyms = [
("write_file", "write_to_file"),
("create_file", "write_to_file"),
("search", "google"),
]
for seen_command, actual_command_name in synonyms:
if command_name == seen_command:
return actual_command_name
return command_name
def execute_command(command_name: str, arguments):
"""Execute the command and return the result
Args:
command_name (str): The name of the command to execute
arguments (dict): The arguments for the command
Returns:
str: The result of the command"""
memory = get_memory(CFG)
try:
command_name = map_command_synonyms(command_name)
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":
google_result = google_official_search(arguments["input"])
else:
google_result = google_search(arguments["input"])
safe_message = google_result.encode("utf-8", "ignore")
return str(safe_message)
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 == "clone_repository":
return clone_repository(
arguments["repository_url"], arguments["clone_path"]
)
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 == "read_audio_from_file":
return read_audio_from_file(arguments["file"])
elif command_name == "generate_image":
return generate_image(arguments["prompt"])
elif command_name == "send_tweet":
return send_tweet(arguments["text"])
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
return (
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
" list for available commands and only respond in the specified JSON"
" format."
)
except Exception as e:
return f"Error: {str(e)}"
def get_text_summary(url: str, question: str) -> str:
"""Return the results of a google search
Args:
url (str): The url to scrape
question (str): The question to summarize the text for
Returns:
str: The summary of the text
"""
text = scrape_text(url)
summary = summarize_text(url, text, question)
return f""" "Result" : {summary}"""
def get_hyperlinks(url: str) -> Union[str, List[str]]:
"""Return the results of a google search
Args:
url (str): The url to scrape
Returns:
str or list: The hyperlinks on the page
"""
return scrape_links(url)
def shutdown() -> NoReturn:
"""Shut down the program"""
print("Shutting down...")
quit()
def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
"""Start an agent with a given name, task, and prompt
Args:
name (str): The name of the agent
task (str): The task of the agent
prompt (str): The prompt for the agent
model (str): The model to use for the agent
Returns:
str: The response of the agent
"""
# Remove underscores from name
voice_name = name.replace("_", " ")
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if CFG.speak_mode:
say_text(agent_intro, 1)
key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
if CFG.speak_mode:
say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = AGENT_MANAGER.message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
def message_agent(key: str, message: str) -> str:
"""Message an agent with a given key and message"""
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = AGENT_MANAGER.message_agent(int(key), message)
else:
return "Invalid key, must be an integer."
# Speak response
if CFG.speak_mode:
say_text(agent_response, 1)
return agent_response
def list_agents():
"""List all agents
Returns:
str: A list of all agents
"""
return "List of agents:\n" + "\n".join(
[str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
)
def delete_agent(key: str) -> str:
"""Delete an agent with a given key
Args:
key (str): The key of the agent to delete
Returns:
str: A message indicating whether the agent was deleted or not
"""
result = AGENT_MANAGER.delete_agent(key)
return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

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autogpt/args.py Normal file
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"""This module contains the argument parsing logic for the script."""
import argparse
from colorama import Fore
from autogpt import utils
from autogpt.config import Config
from autogpt.logs import logger
from autogpt.memory import get_supported_memory_backends
CFG = Config()
def parse_arguments() -> None:
"""Parses the arguments passed to the script
Returns:
None
"""
CFG.set_debug_mode(False)
CFG.set_continuous_mode(False)
CFG.set_speak_mode(False)
parser = argparse.ArgumentParser(description="Process arguments.")
parser.add_argument(
"--continuous", "-c", action="store_true", help="Enable Continuous Mode"
)
parser.add_argument(
"--continuous-limit",
"-l",
type=int,
dest="continuous_limit",
help="Defines the number of times to run in continuous mode",
)
parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
parser.add_argument(
"--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
)
parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
parser.add_argument(
"--use-memory",
"-m",
dest="memory_type",
help="Defines which Memory backend to use",
)
parser.add_argument(
"--skip-reprompt",
"-y",
dest="skip_reprompt",
action="store_true",
help="Skips the re-prompting messages at the beginning of the script",
)
parser.add_argument(
"--use-browser",
"-b",
dest="browser_name",
help="Specifies which web-browser to use when using selenium to scrape the web.",
)
parser.add_argument(
"--ai-settings",
"-C",
dest="ai_settings_file",
help="Specifies which ai_settings.yaml file to use, will also automatically"
" skip the re-prompt.",
)
args = parser.parse_args()
if args.debug:
logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
CFG.set_debug_mode(True)
if args.continuous:
logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
logger.typewriter_log(
"WARNING: ",
Fore.RED,
"Continuous mode is not recommended. It is potentially dangerous and may"
" cause your AI to run forever or carry out actions you would not usually"
" authorise. Use at your own risk.",
)
CFG.set_continuous_mode(True)
if args.continuous_limit:
logger.typewriter_log(
"Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
)
CFG.set_continuous_limit(args.continuous_limit)
# Check if continuous limit is used without continuous mode
if args.continuous_limit and not args.continuous:
parser.error("--continuous-limit can only be used with --continuous")
if args.speak:
logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
CFG.set_speak_mode(True)
if args.gpt3only:
logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_smart_llm_model(CFG.fast_llm_model)
if args.gpt4only:
logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
CFG.set_fast_llm_model(CFG.smart_llm_model)
if args.memory_type:
supported_memory = get_supported_memory_backends()
chosen = args.memory_type
if chosen not in supported_memory:
logger.typewriter_log(
"ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
Fore.RED,
f"{supported_memory}",
)
logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
else:
CFG.memory_backend = chosen
if args.skip_reprompt:
logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
CFG.skip_reprompt = True
if args.ai_settings_file:
file = args.ai_settings_file
# Validate file
(validated, message) = utils.validate_yaml_file(file)
if not validated:
logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
logger.double_check()
exit(1)
logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
CFG.ai_settings_file = file
CFG.skip_reprompt = True
if args.browser_name:
CFG.selenium_web_browser = args.browser_name

192
autogpt/browse.py
View File

@@ -1,192 +0,0 @@
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
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return text
def extract_hyperlinks(soup):
"""Extract hyperlinks from a BeautifulSoup object"""
hyperlinks = []
for link in soup.find_all("a", href=True):
hyperlinks.append((link.text, link["href"]))
return hyperlinks
def format_hyperlinks(hyperlinks):
"""Format hyperlinks into a list of strings"""
formatted_links = []
for link_text, link_url in hyperlinks:
formatted_links.append(f"{link_text} ({link_url})")
return formatted_links
def scrape_links(url):
"""Scrape links from a webpage"""
response, error_message = get_response(url)
if error_message:
return error_message
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup)
return format_hyperlinks(hyperlinks)
def split_text(text, max_length=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 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

28
autogpt/call_ai_function.py
View File

@@ -1,28 +0,0 @@
from autogpt.config import Config
cfg = Config()
from autogpt.llm_utils import create_chat_completion
# This is a magic function that can do anything with no-code. See
# https://github.com/Torantulino/AI-Functions for more info.
def call_ai_function(function, args, description, model=None):
"""Call an AI function"""
if model is None:
model = cfg.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
args = ", ".join(args)
messages = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
response = create_chat_completion(model=model, messages=messages, temperature=0)
return response

36
autogpt/chat.py
View File

@@ -1,13 +1,11 @@
import logging
import time
import openai
from dotenv import load_dotenv
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
from autogpt.logs import logger
cfg = Config()
@@ -55,18 +53,22 @@ def generate_context(prompt, relevant_memory, full_message_history, model):
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."""
"""Interact with the OpenAI API, sending the prompt, user input, message history,
and permanent memory."""
while True:
try:
"""
Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory.
Interact with the OpenAI API, sending the prompt, user input,
message history, and permanent memory.
Args:
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the user and the AI.
permanent_memory (Obj): The memory object containing the permanent memory.
token_limit (int): The maximum number of tokens allowed in the API call.
prompt (str): The prompt explaining the rules to the AI.
user_input (str): The input from the user.
full_message_history (list): The list of all messages sent between the
user and the AI.
permanent_memory (Obj): The memory object containing the permanent
memory.
token_limit (int): The maximum number of tokens allowed in the API call.
Returns:
str: The AI's response.
@@ -118,7 +120,8 @@ def chat_with_ai(
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.
# 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]
)
@@ -134,7 +137,9 @@ def chat_with_ai(
# Calculate remaining tokens
tokens_remaining = token_limit - current_tokens_used
# assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
# assert tokens_remaining >= 0, "Tokens remaining is negative.
# This should never happen, please submit a bug report at
# https://www.github.com/Torantulino/Auto-GPT"
# Debug print the current context
logger.debug(f"Token limit: {token_limit}")
@@ -149,7 +154,8 @@ def chat_with_ai(
logger.debug("")
logger.debug("----------- END OF CONTEXT ----------------")
# TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
# TODO: use a model defined elsewhere, so that model can contain
# temperature and other settings we care about
assistant_reply = create_chat_completion(
model=model,
messages=current_context,
@@ -163,7 +169,7 @@ def chat_with_ai(
)
return assistant_reply
except openai.error.RateLimitError:
except RateLimitError:
# TODO: When we switch to langchain, this is built in
print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
time.sleep(10)

304
autogpt/commands.py
View File

@@ -1,304 +0,0 @@
from autogpt import browse
import json
from autogpt.memory import get_memory
import datetime
import autogpt.agent_manager as agents
from autogpt import speak
from autogpt.config import Config
import autogpt.ai_functions as ai
from autogpt.file_operations import read_file, write_to_file, append_to_file, delete_file, search_files
from autogpt.execute_code import execute_python_file, execute_shell
from autogpt.json_parser import fix_and_parse_json
from autogpt.image_gen import generate_image
from duckduckgo_search import ddg
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from autogpt.web import browse_website
cfg = Config()
def is_valid_int(value):
try:
int(value)
return True
except ValueError:
return False
def get_command(response):
"""Parse the response and return the command name and arguments"""
try:
response_json = fix_and_parse_json(response)
if "command" not in response_json:
return "Error:", "Missing 'command' object in JSON"
command = response_json["command"]
if "name" not in command:
return "Error:", "Missing 'name' field in 'command' object"
command_name = command["name"]
# Use an empty dictionary if 'args' field is not present in 'command' object
arguments = command.get("args", {})
return command_name, arguments
except json.decoder.JSONDecodeError:
return "Error:", "Invalid JSON"
# All other errors, return "Error: + error message"
except Exception as e:
return "Error:", str(e)
def execute_command(command_name, arguments):
"""Execute the command and return the result"""
memory = get_memory(cfg)
try:
if command_name == "google":
# Check if the Google API key is set and use the official search method
# If the API key is not set or has only whitespaces, use the unofficial search method
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 ai.evaluate_code(arguments["code"])
elif command_name == "improve_code":
return ai.improve_code(arguments["suggestions"], arguments["code"])
elif command_name == "write_tests":
return ai.write_tests(arguments["code"], arguments.get("focus"))
elif command_name == "execute_python_file": # Add this command
return execute_python_file(arguments["file"])
elif command_name == "execute_shell":
if cfg.execute_local_commands:
return execute_shell(arguments["command_line"])
else:
return "You are not allowed to run local shell commands. To execute shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' in your config. Do not attempt to bypass the restriction."
elif command_name == "generate_image":
return generate_image(arguments["prompt"])
elif command_name == "do_nothing":
return "No action performed."
elif command_name == "task_complete":
shutdown()
else:
return f"Unknown command '{command_name}'. Please refer to the 'COMMANDS' list for available commands and only respond in the specified JSON format."
# All errors, return "Error: + error message"
except Exception as e:
return "Error: " + str(e)
def get_datetime():
"""Return the current date and time"""
return "Current date and time: " + datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S"
)
def google_search(query, num_results=8):
"""Return the results of a google search"""
search_results = []
for j in ddg(query, max_results=num_results):
search_results.append(j)
return json.dumps(search_results, ensure_ascii=False, indent=4)
def google_official_search(query, num_results=8):
"""Return the results of a google search using the official Google API"""
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 = browse.scrape_text(url)
summary = browse.summarize_text(url, text, question)
return """ "Result" : """ + summary
def get_hyperlinks(url):
"""Return the results of a google search"""
link_list = browse.scrape_links(url)
return link_list
def commit_memory(string):
"""Commit a string to memory"""
_text = f"""Committing memory with string "{string}" """
mem.permanent_memory.append(string)
return _text
def delete_memory(key):
"""Delete a memory with a given key"""
if key >= 0 and key < len(mem.permanent_memory):
_text = "Deleting memory with key " + str(key)
del mem.permanent_memory[key]
print(_text)
return _text
else:
print("Invalid key, cannot delete memory.")
return None
def overwrite_memory(key, string):
"""Overwrite a memory with a given key and string"""
# Check if the key is a valid integer
if is_valid_int(key):
key_int = int(key)
# Check if the integer key is within the range of the permanent_memory list
if 0 <= key_int < len(mem.permanent_memory):
_text = "Overwriting memory with key " + str(key) + " and string " + string
# Overwrite the memory slot with the given integer key and string
mem.permanent_memory[key_int] = string
print(_text)
return _text
else:
print(f"Invalid key '{key}', out of range.")
return None
# Check if the key is a valid string
elif isinstance(key, str):
_text = "Overwriting memory with key " + key + " and string " + string
# Overwrite the memory slot with the given string key and string
mem.permanent_memory[key] = string
print(_text)
return _text
else:
print(f"Invalid key '{key}', must be an integer or a string.")
return None
def shutdown():
"""Shut down the program"""
print("Shutting down...")
quit()
def start_agent(name, task, prompt, model=cfg.fast_llm_model):
"""Start an agent with a given name, task, and prompt"""
global cfg
# Remove underscores from name
voice_name = name.replace("_", " ")
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
agent_intro = f"{voice_name} here, Reporting for duty!"
# Create agent
if cfg.speak_mode:
speak.say_text(agent_intro, 1)
key, ack = agents.create_agent(task, first_message, model)
if cfg.speak_mode:
speak.say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
# Assign task (prompt), get response
agent_response = message_agent(key, prompt)
return f"Agent {name} created with key {key}. First response: {agent_response}"
def message_agent(key, message):
"""Message an agent with a given key and message"""
global cfg
# Check if the key is a valid integer
if is_valid_int(key):
agent_response = agents.message_agent(int(key), message)
# Check if the key is a valid string
elif isinstance(key, str):
agent_response = agents.message_agent(key, message)
else:
return "Invalid key, must be an integer or a string."
# Speak response
if cfg.speak_mode:
speak.say_text(agent_response, 1)
return agent_response
def list_agents():
"""List all agents"""
return agents.list_agents()
def delete_agent(key):
"""Delete an agent with a given key"""
result = agents.delete_agent(key)
if not result:
return f"Agent {key} does not exist."
return f"Agent {key} deleted."

0
autogpt/commands/__init__.py Normal file
View File

37
autogpt/commands/audio_text.py Normal file
View File

@@ -0,0 +1,37 @@
import requests
import json
from autogpt.config import Config
from autogpt.commands.file_operations import safe_join
cfg = Config()
working_directory = "auto_gpt_workspace"
def read_audio_from_file(audio_path):
audio_path = safe_join(working_directory, audio_path)
with open(audio_path, "rb") as audio_file:
audio = audio_file.read()
return read_audio(audio)
def read_audio(audio):
model = cfg.huggingface_audio_to_text_model
api_url = f"https://api-inference.huggingface.co/models/{model}"
api_token = cfg.huggingface_api_token
headers = {"Authorization": f"Bearer {api_token}"}
if api_token is None:
raise ValueError(
"You need to set your Hugging Face API token in the config file."
)
response = requests.post(
api_url,
headers=headers,
data=audio,
)
text = json.loads(response.content.decode("utf-8"))["text"]
return "The audio says: " + text

25
autogpt/commands/evaluate_code.py Normal file
View File

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

125
autogpt/commands/execute_code.py Normal file
View File

@@ -0,0 +1,125 @@
"""Execute code in a Docker container"""
import os
from pathlib import Path
import subprocess
import docker
from docker.errors import ImageNotFound
WORKING_DIRECTORY = Path(__file__).parent.parent / "auto_gpt_workspace"
def execute_python_file(file: str):
"""Execute a Python file in a Docker container and return the output
Args:
file (str): The name of the file to execute
Returns:
str: The output of the file
"""
print(f"Executing file '{file}' in workspace '{WORKING_DIRECTORY}'")
if not file.endswith(".py"):
return "Error: Invalid file type. Only .py files are allowed."
file_path = os.path.join(WORKING_DIRECTORY, file)
if not os.path.isfile(file_path):
return f"Error: File '{file}' does not exist."
if we_are_running_in_a_docker_container():
result = subprocess.run(
f"python {file_path}", capture_output=True, encoding="utf8", shell=True
)
if result.returncode == 0:
return result.stdout
else:
return f"Error: {result.stderr}"
try:
client = docker.from_env()
image_name = "python:3.10"
try:
client.images.get(image_name)
print(f"Image '{image_name}' found locally")
except ImageNotFound:
print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get("status")
progress = line.get("progress")
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
# You can replace 'python:3.8' with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
container = client.containers.run(
image_name,
f"python {file}",
volumes={
os.path.abspath(WORKING_DIRECTORY): {
"bind": "/workspace",
"mode": "ro",
}
},
working_dir="/workspace",
stderr=True,
stdout=True,
detach=True,
)
container.wait()
logs = container.logs().decode("utf-8")
container.remove()
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
return logs
except Exception as e:
return f"Error: {str(e)}"
def execute_shell(command_line: str) -> str:
"""Execute a shell command and return the output
Args:
command_line (str): The command line to execute
Returns:
str: The output of the command
"""
current_dir = os.getcwd()
# Change dir into workspace if necessary
if str(WORKING_DIRECTORY) not in current_dir:
work_dir = os.path.join(os.getcwd(), WORKING_DIRECTORY)
os.chdir(work_dir)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
result = subprocess.run(command_line, capture_output=True, shell=True)
output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
# Change back to whatever the prior working dir was
os.chdir(current_dir)
return output
def we_are_running_in_a_docker_container() -> bool:
"""Check if we are running in a Docker container
Returns:
bool: True if we are running in a Docker container, False otherwise
"""
return os.path.exists("/.dockerenv")

170
autogpt/file_operations.py → autogpt/commands/file_operations.py
View File

@@ -1,40 +1,63 @@
"""File operations for AutoGPT"""
import os
import os.path
from pathlib import Path
from typing import Generator, List
# Set a dedicated folder for file I/O
working_directory = "auto_gpt_workspace"
log_file = "file_logger.txt"
WORKING_DIRECTORY = Path(os.getcwd()) / "auto_gpt_workspace"
# Create the directory if it doesn't exist
if not os.path.exists(working_directory):
os.makedirs(working_directory)
if not os.path.exists(WORKING_DIRECTORY):
os.makedirs(WORKING_DIRECTORY)
LOG_FILE = "file_logger.txt"
LOG_FILE_PATH = WORKING_DIRECTORY / LOG_FILE
WORKING_DIRECTORY = str(WORKING_DIRECTORY)
def check_duplicate_operation(operation: str, filename: str) -> bool:
"""Check if the operation has already been performed on the given file
def log_operation(operation, filename):
"""Log the file operation to the file_logger.txt"""
log_entry = f"{operation}: {filename}\n"
Args:
operation (str): The operation to check for
filename (str): The name of the file to check for
log_filepath = safe_join(working_directory, log_file)
# Create the log file if it doesn't exist
if not os.path.exists(log_filepath):
with open(log_filepath, "w", encoding="utf-8") as f:
f.write("File Operation Logger ")
append_to_file(log_file, log_entry)
def check_duplicate_operation(operation, filename):
"""Check if the operation has already been performed on the given file"""
log_content = read_file(log_file)
Returns:
bool: True if the operation has already been performed on the file
"""
log_content = read_file(LOG_FILE)
log_entry = f"{operation}: {filename}\n"
return log_entry in log_content
def safe_join(base, *paths):
"""Join one or more path components intelligently."""
def log_operation(operation: str, filename: str) -> None:
"""Log the file operation to the file_logger.txt
Args:
operation (str): The operation to log
filename (str): The name of the file the operation was performed on
"""
log_entry = f"{operation}: {filename}\n"
# Create the log file if it doesn't exist
if not os.path.exists(LOG_FILE_PATH):
with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
f.write("File Operation Logger ")
append_to_file(LOG_FILE, log_entry)
def safe_join(base: str, *paths) -> str:
"""Join one or more path components intelligently.
Args:
base (str): The base path
*paths (str): The paths to join to the base path
Returns:
str: The joined path
"""
new_path = os.path.join(base, *paths)
norm_new_path = os.path.normpath(new_path)
@@ -44,14 +67,18 @@ def safe_join(base, *paths):
return norm_new_path
def split_file(content, max_length=4000, overlap=0):
def split_file(
content: str, max_length: int = 4000, overlap: int = 0
) -> Generator[str, None, None]:
"""
Split text into chunks of a specified maximum length with a specified overlap
between chunks.
:param text: The input text to be split into chunks
:param max_length: The maximum length of each chunk, default is 4000 (about 1k token)
:param overlap: The number of overlapping characters between chunks, default is no overlap
:param content: 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
@@ -67,18 +94,27 @@ def split_file(content, max_length=4000, overlap=0):
start += max_length - overlap
def read_file(filename):
"""Read a file and return the contents"""
def read_file(filename: str) -> str:
"""Read a file and return the contents
Args:
filename (str): The name of the file to read
Returns:
str: The contents of the file
"""
try:
filepath = safe_join(working_directory, filename)
filepath = safe_join(WORKING_DIRECTORY, filename)
with open(filepath, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return "Error: " + str(e)
return f"Error: {str(e)}"
def ingest_file(filename, memory, max_length=4000, overlap=200):
def ingest_file(
filename: str, memory, max_length: int = 4000, overlap: int = 200
) -> None:
"""
Ingest a file by reading its content, splitting it into chunks with a specified
maximum length and overlap, and adding the chunks to the memory storage.
@@ -110,13 +146,20 @@ def ingest_file(filename, memory, max_length=4000, overlap=200):
print(f"Error while ingesting file '{filename}': {str(e)}")
def write_to_file(filename, text):
"""Write text to a file and log the operation"""
def write_to_file(filename: str, text: str) -> str:
"""Write text to a file
Args:
filename (str): The name of the file to write to
text (str): The text to write to the file
Returns:
str: A message indicating success or failure
"""
if check_duplicate_operation("write", filename):
return "Error: File has already been updated."
try:
filepath = safe_join(working_directory, filename)
filepath = safe_join(WORKING_DIRECTORY, filename)
directory = os.path.dirname(filepath)
if not os.path.exists(directory):
os.makedirs(directory)
@@ -125,47 +168,70 @@ def write_to_file(filename, text):
log_operation("write", filename)
return "File written to successfully."
except Exception as e:
return "Error: " + str(e)
return f"Error: {str(e)}"
def append_to_file(filename, text):
"""Append text to a file"""
def append_to_file(filename: str, text: str) -> str:
"""Append text to a file
Args:
filename (str): The name of the file to append to
text (str): The text to append to the file
Returns:
str: A message indicating success or failure
"""
try:
filepath = safe_join(working_directory, filename)
filepath = safe_join(WORKING_DIRECTORY, filename)
with open(filepath, "a") as f:
f.write(text)
log_operation("append", filename)
return "Text appended successfully."
except Exception as e:
return "Error: " + str(e)
return f"Error: {str(e)}"
def delete_file(filename):
"""Delete a file and log the operation"""
def delete_file(filename: str) -> str:
"""Delete a file
Args:
filename (str): The name of the file to delete
Returns:
str: A message indicating success or failure
"""
if check_duplicate_operation("delete", filename):
return "Error: File has already been deleted."
try:
filepath = safe_join(working_directory, filename)
filepath = safe_join(WORKING_DIRECTORY, filename)
os.remove(filepath)
log_operation("delete", filename)
return "File deleted successfully."
except Exception as e:
return "Error: " + str(e)
return f"Error: {str(e)}"
def search_files(directory):
def search_files(directory: str) -> List[str]:
"""Search for files in a directory
Args:
directory (str): The directory to search in
Returns:
List[str]: A list of files found in the directory
"""
found_files = []
if directory == "" or directory == "/":
search_directory = working_directory
if directory in {"", "/"}:
search_directory = WORKING_DIRECTORY
else:
search_directory = safe_join(working_directory, directory)
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)
relative_path = os.path.relpath(os.path.join(root, file), WORKING_DIRECTORY)
found_files.append(relative_path)
return found_files

23
autogpt/commands/git_operations.py Normal file
View File

@@ -0,0 +1,23 @@
"""Git operations for autogpt"""
import git
from autogpt.config import Config
CFG = Config()
def clone_repository(repo_url: str, clone_path: str) -> str:
"""Clone a github repository locally
Args:
repo_url (str): The URL of the repository to clone
clone_path (str): The path to clone the repository to
Returns:
str: The result of the clone operation"""
split_url = repo_url.split("//")
auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url)
try:
git.Repo.clone_from(auth_repo_url, clone_path)
return f"""Cloned {repo_url} to {clone_path}"""
except Exception as e:
return f"Error: {str(e)}"

86
autogpt/commands/google_search.py Normal file
View File

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

99
autogpt/commands/image_gen.py Normal file
View File

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

28
autogpt/commands/improve_code.py Normal file
View File

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

10
autogpt/commands/times.py Normal file
View File

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

25
autogpt/commands/twitter.py Normal file
View File

@@ -0,0 +1,25 @@
import tweepy
import os
from dotenv import load_dotenv
load_dotenv()
def send_tweet(tweet_text):
consumer_key = os.environ.get("TW_CONSUMER_KEY")
consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
access_token = os.environ.get("TW_ACCESS_TOKEN")
access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
# Send tweet
try:
api.update_status(tweet_text)
print("Tweet sent successfully!")
except tweepy.TweepyException as e:
print("Error sending tweet: {}".format(e.reason))

78
autogpt/commands/web_playwright.py Normal file
View File

@@ -0,0 +1,78 @@
"""Web scraping commands using Playwright"""
try:
from playwright.sync_api import sync_playwright
except ImportError:
print(
"Playwright not installed. Please install it with 'pip install playwright' to use."
)
from bs4 import BeautifulSoup
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
from typing import List, Union
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "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)
except Exception as e:
text = f"Error: {str(e)}"
finally:
browser.close()
return text
def scrape_links(url: str) -> Union[str, List[str]]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
Union[str, List[str]]: The scraped links
"""
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
try:
page.goto(url)
html_content = page.content()
soup = BeautifulSoup(html_content, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
formatted_links = format_hyperlinks(hyperlinks)
except Exception as e:
formatted_links = f"Error: {str(e)}"
finally:
browser.close()
return formatted_links

170
autogpt/commands/web_requests.py Normal file
View File

@@ -0,0 +1,170 @@
"""Browse a webpage and summarize it using the LLM model"""
from typing import List, Tuple, Union
from urllib.parse import urljoin, urlparse
import requests
from requests.compat import urljoin
from requests import Response
from bs4 import BeautifulSoup
from autogpt.config import Config
from autogpt.memory import get_memory
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
CFG = Config()
memory = get_memory(CFG)
session = requests.Session()
session.headers.update({"User-Agent": CFG.user_agent})
def is_valid_url(url: str) -> bool:
"""Check if the URL is valid
Args:
url (str): The URL to check
Returns:
bool: True if the URL is valid, False otherwise
"""
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except ValueError:
return False
def sanitize_url(url: str) -> str:
"""Sanitize the URL
Args:
url (str): The URL to sanitize
Returns:
str: The sanitized URL
"""
return urljoin(url, urlparse(url).path)
def check_local_file_access(url: str) -> bool:
"""Check if the URL is a local file
Args:
url (str): The URL to check
Returns:
bool: True if the URL is a local file, False otherwise
"""
local_prefixes = [
"file:///",
"file://localhost",
"http://localhost",
"https://localhost",
]
return any(url.startswith(prefix) for prefix in local_prefixes)
def get_response(
url: str, timeout: int = 10
) -> Union[Tuple[None, str], Tuple[Response, None]]:
"""Get the response from a URL
Args:
url (str): The URL to get the response from
timeout (int): The timeout for the HTTP request
Returns:
Tuple[None, str] | Tuple[Response, None]: The response and error message
Raises:
ValueError: If the URL is invalid
requests.exceptions.RequestException: If the HTTP request fails
"""
try:
# Restrict access to local files
if check_local_file_access(url):
raise ValueError("Access to local files is restricted")
# Most basic check if the URL is valid:
if not url.startswith("http://") and not url.startswith("https://"):
raise ValueError("Invalid URL format")
sanitized_url = sanitize_url(url)
response = session.get(sanitized_url, timeout=timeout)
# Check if the response contains an HTTP error
if response.status_code >= 400:
return None, f"Error: HTTP {str(response.status_code)} error"
return response, None
except ValueError as ve:
# Handle invalid URL format
return None, f"Error: {str(ve)}"
except requests.exceptions.RequestException as re:
# Handle exceptions related to the HTTP request
# (e.g., connection errors, timeouts, etc.)
return None, f"Error: {str(re)}"
def scrape_text(url: str) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return text
def scrape_links(url: str) -> Union[str, List[str]]:
"""Scrape links from a webpage
Args:
url (str): The URL to scrape links from
Returns:
Union[str, List[str]]: The scraped links
"""
response, error_message = get_response(url)
if error_message:
return error_message
if not response:
return "Error: Could not get response"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def create_message(chunk, question):
"""Create a message for the user to summarize a chunk of text"""
return {
"role": "user",
"content": f'"""{chunk}""" Using the above text, answer the following'
f' question: "{question}" -- if the question cannot be answered using the'
" text, summarize the text.",
}

141
autogpt/commands/web_selenium.py Normal file
View File

@@ -0,0 +1,141 @@
"""Selenium web scraping module."""
from selenium import webdriver
from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
import autogpt.processing.text as summary
from bs4 import BeautifulSoup
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
from webdriver_manager.firefox import GeckoDriverManager
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.safari.options import Options as SafariOptions
import logging
from pathlib import Path
from autogpt.config import Config
from typing import List, Tuple, Union
FILE_DIR = Path(__file__).parent.parent
CFG = Config()
def browse_website(url: str, question: str) -> Tuple[str, WebDriver]:
"""Browse a website and return the answer and links to the user
Args:
url (str): The url of the website to browse
question (str): The question asked by the user
Returns:
Tuple[str, WebDriver]: The answer and links to the user and the webdriver
"""
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary_text = summary.summarize_text(url, text, question, driver)
links = scrape_links_with_selenium(driver, url)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url: str) -> Tuple[WebDriver, str]:
"""Scrape text from a website using selenium
Args:
url (str): The url of the website to scrape
Returns:
Tuple[WebDriver, str]: The webdriver and the text scraped from the website
"""
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options_available = {
"chrome": ChromeOptions,
"safari": SafariOptions,
"firefox": FirefoxOptions,
}
options = options_available[CFG.selenium_web_browser]()
options.add_argument(
"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
)
if CFG.selenium_web_browser == "firefox":
driver = webdriver.Firefox(
executable_path=GeckoDriverManager().install(), options=options
)
elif CFG.selenium_web_browser == "safari":
# Requires a bit more setup on the users end
# See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
driver = webdriver.Safari(options=options)
else:
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
# Get the HTML content directly from the browser's DOM
page_source = driver.execute_script("return document.body.outerHTML;")
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return driver, text
def scrape_links_with_selenium(driver: WebDriver, url: str) -> List[str]:
"""Scrape links from a website using selenium
Args:
driver (WebDriver): The webdriver to use to scrape the links
Returns:
List[str]: The links scraped from the website
"""
page_source = driver.page_source
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup, url)
return format_hyperlinks(hyperlinks)
def close_browser(driver: WebDriver) -> None:
"""Close the browser
Args:
driver (WebDriver): The webdriver to close
Returns:
None
"""
driver.quit()
def add_header(driver: WebDriver) -> None:
"""Add a header to the website
Args:
driver (WebDriver): The webdriver to use to add the header
Returns:
None
"""
driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

29
autogpt/commands/write_tests.py Normal file
View File

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

14
autogpt/config/__init__.py Normal file
View File

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

43
autogpt/ai_config.py → autogpt/config/ai_config.py
View File

@@ -1,9 +1,11 @@
# sourcery skip: do-not-use-staticmethod
"""
A module that contains the AIConfig class object that contains the configuration
"""
import os
from typing import List, Optional, Type
import yaml
from autogpt.prompt import get_prompt
class AIConfig:
"""
@@ -16,7 +18,7 @@ class AIConfig:
"""
def __init__(
self, ai_name: str = "", ai_role: str = "", ai_goals: list = []
self, ai_name: str = "", ai_role: str = "", ai_goals: Optional[List] = None
) -> None:
"""
Initialize a class instance
@@ -28,7 +30,8 @@ class AIConfig:
Returns:
None
"""
if ai_goals is None:
ai_goals = []
self.ai_name = ai_name
self.ai_role = ai_role
self.ai_goals = ai_goals
@@ -36,15 +39,16 @@ class AIConfig:
# 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: object, config_file: str = SAVE_FILE) -> object:
@staticmethod
def load(config_file: str = SAVE_FILE) -> "AIConfig":
"""
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from
yaml file if yaml file exists,
else returns class with no parameters.
Parameters:
cls (class object): An AIConfig Class object.
config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
config_file (int): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
cls (object): An instance of given cls object
@@ -59,15 +63,16 @@ class AIConfig:
ai_name = config_params.get("ai_name", "")
ai_role = config_params.get("ai_role", "")
ai_goals = config_params.get("ai_goals", [])
return cls(ai_name, ai_role, ai_goals)
# type: Type[AIConfig]
return AIConfig(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"
config_file(str): The path to the config yaml file.
DEFAULT: "../ai_settings.yaml"
Returns:
None
@@ -89,10 +94,18 @@ class AIConfig:
None
Returns:
full_prompt (str): A string containing the initial prompt for the user including the ai_name, ai_role and ai_goals.
full_prompt (str): A string containing the initial prompt for the user
including the ai_name, ai_role and ai_goals.
"""
prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications."""
prompt_start = (
"Your decisions must always be made independently without"
" seeking user assistance. Play to your strengths as an LLM and pursue"
" simple strategies with no legal complications."
""
)
from autogpt.prompt import get_prompt
# Construct full prompt
full_prompt = (

117
autogpt/config.py → autogpt/config/config.py
View File

@@ -1,30 +1,15 @@
import abc
"""Configuration class to store the state of bools for different scripts access."""
import os
from colorama import Fore
from autogpt.config.singleton import Singleton
import openai
import yaml
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
class Singleton(abc.ABCMeta, type):
"""
Singleton metaclass for ensuring only one instance of a class.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
"""Call method for the singleton metaclass."""
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class AbstractSingleton(abc.ABC, metaclass=Singleton):
pass
load_dotenv(verbose=True)
class Config(metaclass=Singleton):
@@ -32,7 +17,7 @@ class Config(metaclass=Singleton):
Configuration class to store the state of bools for different scripts access.
"""
def __init__(self):
def __init__(self) -> None:
"""Initialize the Config class"""
self.debug_mode = False
self.continuous_mode = False
@@ -40,6 +25,7 @@ class Config(metaclass=Singleton):
self.speak_mode = False
self.skip_reprompt = False
self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome")
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")
@@ -71,20 +57,32 @@ class Config(metaclass=Singleton):
self.use_brian_tts = False
self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
self.github_api_key = os.getenv("GITHUB_API_KEY")
self.github_username = os.getenv("GITHUB_USERNAME")
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")
# milvus configuration, e.g., localhost:19530.
self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")
self.image_provider = os.getenv("IMAGE_PROVIDER")
self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
self.huggingface_audio_to_text_model = os.getenv(
"HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
)
# 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.
# 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",
"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")
@@ -108,15 +106,17 @@ class Config(metaclass=Singleton):
The matching deployment id if found, otherwise an empty string.
"""
if model == self.fast_llm_model:
return self.azure_model_to_deployment_id_map["fast_llm_model_deployment_id"]
return self.azure_model_to_deployment_id_map[
"fast_llm_model_deployment_id"
] # type: ignore
elif model == self.smart_llm_model:
return self.azure_model_to_deployment_id_map[
"smart_llm_model_deployment_id"
]
] # type: ignore
elif model == "text-embedding-ada-002":
return self.azure_model_to_deployment_id_map[
"embedding_model_deployment_id"
]
] # type: ignore
else:
return ""
@@ -124,7 +124,8 @@ class Config(metaclass=Singleton):
def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
"""
Loads the configuration parameters for Azure hosting from the specified file path as a yaml file.
Loads the configuration parameters for Azure hosting from the specified file
path as a yaml file.
Parameters:
config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
@@ -137,85 +138,93 @@ class Config(metaclass=Singleton):
config_params = yaml.load(file, Loader=yaml.FullLoader)
except FileNotFoundError:
config_params = {}
self.openai_api_type = os.getenv(
"OPENAI_API_TYPE", config_params.get("azure_api_type", "azure")
)
self.openai_api_base = os.getenv(
"OPENAI_AZURE_API_BASE", config_params.get("azure_api_base", "")
)
self.openai_api_version = os.getenv(
"OPENAI_AZURE_API_VERSION", config_params.get("azure_api_version", "")
self.openai_api_type = config_params.get("azure_api_type") or "azure"
self.openai_api_base = config_params.get("azure_api_base") or ""
self.openai_api_version = (
config_params.get("azure_api_version") or "2023-03-15-preview"
)
self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
def set_continuous_mode(self, value: bool):
def set_continuous_mode(self, value: bool) -> None:
"""Set the continuous mode value."""
self.continuous_mode = value
def set_continuous_limit(self, value: int):
def set_continuous_limit(self, value: int) -> None:
"""Set the continuous limit value."""
self.continuous_limit = value
def set_speak_mode(self, value: bool):
def set_speak_mode(self, value: bool) -> None:
"""Set the speak mode value."""
self.speak_mode = value
def set_fast_llm_model(self, value: str):
def set_fast_llm_model(self, value: str) -> None:
"""Set the fast LLM model value."""
self.fast_llm_model = value
def set_smart_llm_model(self, value: str):
def set_smart_llm_model(self, value: str) -> None:
"""Set the smart LLM model value."""
self.smart_llm_model = value
def set_fast_token_limit(self, value: int):
def set_fast_token_limit(self, value: int) -> None:
"""Set the fast token limit value."""
self.fast_token_limit = value
def set_smart_token_limit(self, value: int):
def set_smart_token_limit(self, value: int) -> None:
"""Set the smart token limit value."""
self.smart_token_limit = value
def set_browse_chunk_max_length(self, value: int):
def set_browse_chunk_max_length(self, value: int) -> None:
"""Set the browse_website command chunk max length value."""
self.browse_chunk_max_length = value
def set_browse_summary_max_token(self, value: int):
def set_browse_summary_max_token(self, value: int) -> None:
"""Set the browse_website command summary max token value."""
self.browse_summary_max_token = value
def set_openai_api_key(self, value: str):
def set_openai_api_key(self, value: str) -> None:
"""Set the OpenAI API key value."""
self.openai_api_key = value
def set_elevenlabs_api_key(self, value: str):
def set_elevenlabs_api_key(self, value: str) -> None:
"""Set the ElevenLabs API key value."""
self.elevenlabs_api_key = value
def set_elevenlabs_voice_1_id(self, value: str):
def set_elevenlabs_voice_1_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 1 ID value."""
self.elevenlabs_voice_1_id = value
def set_elevenlabs_voice_2_id(self, value: str):
def set_elevenlabs_voice_2_id(self, value: str) -> None:
"""Set the ElevenLabs Voice 2 ID value."""
self.elevenlabs_voice_2_id = value
def set_google_api_key(self, value: str):
def set_google_api_key(self, value: str) -> None:
"""Set the Google API key value."""
self.google_api_key = value
def set_custom_search_engine_id(self, value: str):
def set_custom_search_engine_id(self, value: str) -> None:
"""Set the custom search engine id value."""
self.custom_search_engine_id = value
def set_pinecone_api_key(self, value: str):
def set_pinecone_api_key(self, value: str) -> None:
"""Set the Pinecone API key value."""
self.pinecone_api_key = value
def set_pinecone_region(self, value: str):
def set_pinecone_region(self, value: str) -> None:
"""Set the Pinecone region value."""
self.pinecone_region = value
def set_debug_mode(self, value: bool):
def set_debug_mode(self, value: bool) -> None:
"""Set the debug mode value."""
self.debug_mode = value
def check_openai_api_key() -> None:
"""Check if the OpenAI API key is set in config.py or as an environment variable."""
cfg = Config()
if not cfg.openai_api_key:
print(
Fore.RED
+ "Please set your OpenAI API key in .env or as an environment variable."
)
print("You can get your key from https://beta.openai.com/account/api-keys")
exit(1)

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

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

10
autogpt/data_ingestion.py
View File

@@ -2,7 +2,7 @@ import argparse
import logging
from autogpt.config import Config
from autogpt.file_operations import ingest_file, search_files
from autogpt.commands.file_operations import ingest_file, search_files
from autogpt.memory import get_memory
cfg = Config()
@@ -34,11 +34,12 @@ def ingest_directory(directory, memory, args):
print(f"Error while ingesting directory '{directory}': {str(e)}")
def main():
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."
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.")
@@ -86,7 +87,8 @@ def main():
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."
"Please provide either a file path (--file) or a directory name (--dir)"
" inside the auto_gpt_workspace directory as input."
)

104
autogpt/execute_code.py
View File

@@ -1,104 +0,0 @@
import os
import subprocess
import docker
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 docker.errors.ImageNotFound:
print(
f"Image '{image_name}' not found locally, pulling from Docker Hub"
)
# Use the low-level API to stream the pull response
low_level_client = docker.APIClient()
for line in low_level_client.pull(image_name, stream=True, decode=True):
# Print the status and progress, if available
status = line.get("status")
progress = line.get("progress")
if status and progress:
print(f"{status}: {progress}")
elif status:
print(status)
# You can replace 'python:3.8' with the desired Python image/version
# You can find available Python images on Docker Hub:
# https://hub.docker.com/_/python
container = client.containers.run(
image_name,
f"python {file}",
volumes={
os.path.abspath(WORKSPACE_FOLDER): {
"bind": "/workspace",
"mode": "ro",
}
},
working_dir="/workspace",
stderr=True,
stdout=True,
detach=True,
)
output = container.wait()
logs = container.logs().decode("utf-8")
container.remove()
# print(f"Execution complete. Output: {output}")
# print(f"Logs: {logs}")
return logs
except Exception as e:
return f"Error: {str(e)}"
def execute_shell(command_line):
current_dir = os.getcwd()
if not WORKSPACE_FOLDER in current_dir: # Change dir into workspace if necessary
work_dir = os.path.join(os.getcwd(), WORKSPACE_FOLDER)
os.chdir(work_dir)
print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
result = subprocess.run(command_line, capture_output=True, shell=True)
output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
# Change back to whatever the prior working dir was
os.chdir(current_dir)
return output
def we_are_running_in_a_docker_container():
os.path.exists("/.dockerenv")

63
autogpt/image_gen.py
View File

@@ -1,63 +0,0 @@
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"
)
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"

0
autogpt/json_fixes/__init__.py Normal file
View File

53
autogpt/json_fixes/auto_fix.py Normal file
View File

@@ -0,0 +1,53 @@
"""This module contains the function to fix JSON strings using GPT-3."""
import json
from autogpt.llm_utils import call_ai_function
from autogpt.logs import logger
from autogpt.config import Config
CFG = Config()
def fix_json(json_string: str, schema: str) -> str:
"""Fix the given JSON string to make it parseable and fully compliant with
the provided schema.
Args:
json_string (str): The JSON string to fix.
schema (str): The schema to use to fix the JSON.
Returns:
str: The fixed JSON string.
"""
# Try to fix the JSON using GPT:
function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
args = [f"'''{json_string}'''", f"'''{schema}'''"]
description_string = (
"This function takes a JSON string and ensures that it"
" is parseable and fully compliant with the provided schema. If an object"
" or field specified in the schema isn't contained within the correct JSON,"
" it is omitted. The function also escapes any double quotes within JSON"
" string values to ensure that they are valid. If the JSON string contains"
" any None or NaN values, they are replaced with null before being parsed."
)
# If it doesn't already start with a "`", add one:
if not json_string.startswith("`"):
json_string = "```json\n" + json_string + "\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_string}")
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 json.JSONDecodeError: # noqa: E722
# Get the call stack:
# import traceback
# call_stack = traceback.format_exc()
# print(f"Failed to fix JSON: '{json_string}' "+call_stack)
return "failed"

73
autogpt/json_fixes/bracket_termination.py Normal file
View File

@@ -0,0 +1,73 @@
"""Fix JSON brackets."""
import contextlib
import json
from typing import Optional
import regex
from colorama import Fore
from autogpt.logs import logger
from autogpt.config import Config
from autogpt.speech import say_text
CFG = Config()
def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
if CFG.speak_mode and CFG.debug_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API. "
"Trying to fix it now."
)
logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
try:
json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
json_match = json_pattern.search(json_string)
if json_match:
# Extract the valid JSON object from the string
json_string = json_match.group(0)
logger.typewriter_log(
title="Apparently json was fixed.", title_color=Fore.GREEN
)
if CFG.speak_mode and CFG.debug_mode:
say_text("Apparently json was fixed.")
else:
raise ValueError("No valid JSON object found")
except (json.JSONDecodeError, ValueError):
if CFG.debug_mode:
logger.error(f"Error: Invalid JSON: {json_string}\n")
if CFG.speak_mode:
say_text("Didn't work. I will have to ignore this response then.")
logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
json_string = {}
return json_string
def balance_braces(json_string: str) -> Optional[str]:
"""
Balance the braces in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with braces balanced.
"""
open_braces_count = json_string.count("{")
close_braces_count = json_string.count("}")
while open_braces_count > close_braces_count:
json_string += "}"
close_braces_count += 1
while close_braces_count > open_braces_count:
json_string = json_string.rstrip("}")
close_braces_count -= 1
with contextlib.suppress(json.JSONDecodeError):
json.loads(json_string)
return json_string

33
autogpt/json_fixes/escaping.py Normal file
View File

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

27
autogpt/json_fixes/missing_quotes.py Normal file
View File

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

143
autogpt/json_fixes/parsing.py Normal file
View File

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

20
autogpt/json_fixes/utilities.py Normal file
View File

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

113
autogpt/json_parser.py
View File

@@ -1,113 +0,0 @@
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"

127
autogpt/json_utils.py
View File

@@ -1,127 +0,0 @@
import json
import re
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) -> str:
"""
Balance the braces in a JSON string.
Args:
json_string (str): The JSON string.
Returns:
str: The JSON string with braces balanced.
"""
open_braces_count = json_string.count("{")
close_braces_count = json_string.count("}")
while open_braces_count > close_braces_count:
json_string += "}"
close_braces_count += 1
while close_braces_count > open_braces_count:
json_string = json_string.rstrip("}")
close_braces_count -= 1
try:
json.loads(json_string)
return json_string
except json.JSONDecodeError as e:
pass
def fix_invalid_escape(json_str: str, error_message: str) -> str:
while error_message.startswith("Invalid \\escape"):
bad_escape_location = extract_char_position(error_message)
json_str = json_str[:bad_escape_location] + json_str[bad_escape_location + 1 :]
try:
json.loads(json_str)
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print("json loads error - fix invalid escape", e)
error_message = str(e)
return json_str
def correct_json(json_str: str) -> str:
"""
Correct common JSON errors.
Args:
json_str (str): The JSON string.
"""
try:
if cfg.debug_mode:
print("json", json_str)
json.loads(json_str)
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print("json loads error", e)
error_message = str(e)
if error_message.startswith("Invalid \\escape"):
json_str = fix_invalid_escape(json_str, error_message)
if error_message.startswith(
"Expecting property name enclosed in double quotes"
):
json_str = add_quotes_to_property_names(json_str)
try:
json.loads(json_str)
return json_str
except json.JSONDecodeError as e:
if cfg.debug_mode:
print("json loads error - add quotes", e)
error_message = str(e)
if balanced_str := balance_braces(json_str):
return balanced_str
return json_str

131
autogpt/llm_utils.py
View File

@@ -1,28 +1,86 @@
from ast import List
import time
from typing import Dict, Optional
import openai
from openai.error import APIError, RateLimitError
from colorama import Fore
from autogpt.config import Config
cfg = Config()
CFG = Config()
openai.api_key = cfg.openai_api_key
openai.api_key = CFG.openai_api_key
def call_ai_function(
function: str, args: List, description: str, model: Optional[str] = None
) -> str:
"""Call an AI function
This is a magic function that can do anything with no-code. See
https://github.com/Torantulino/AI-Functions for more info.
Args:
function (str): The function to call
args (list): The arguments to pass to the function
description (str): The description of the function
model (str, optional): The model to use. Defaults to None.
Returns:
str: The response from the function
"""
if model is None:
model = CFG.smart_llm_model
# For each arg, if any are None, convert to "None":
args = [str(arg) if arg is not None else "None" for arg in args]
# parse args to comma separated string
args = ", ".join(args)
messages = [
{
"role": "system",
"content": f"You are now the following python function: ```# {description}"
f"\n{function}```\n\nOnly respond with your `return` value.",
},
{"role": "user", "content": args},
]
return create_chat_completion(model=model, messages=messages, temperature=0)
# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
def create_chat_completion(
messages, model=None, temperature=cfg.temperature, max_tokens=None
messages: List, # type: ignore
model: Optional[str] = None,
temperature: float = CFG.temperature,
max_tokens: Optional[int] = None,
) -> str:
"""Create a chat completion using the OpenAI API"""
"""Create a chat completion using the OpenAI API
Args:
messages (List[Dict[str, str]]): The messages to send to the chat completion
model (str, optional): The model to use. Defaults to None.
temperature (float, optional): The temperature to use. Defaults to 0.9.
max_tokens (int, optional): The max tokens to use. Defaults to None.
Returns:
str: The response from the chat completion
"""
response = None
num_retries = 5
num_retries = 10
if CFG.debug_mode:
print(
Fore.GREEN
+ f"Creating chat completion with model {model}, temperature {temperature},"
f" max_tokens {max_tokens}" + Fore.RESET
)
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
if cfg.use_azure:
if CFG.use_azure:
response = openai.ChatCompletion.create(
deployment_id=cfg.get_azure_deployment_id_for_model(model),
deployment_id=CFG.get_azure_deployment_id_for_model(model),
model=model,
messages=messages,
temperature=temperature,
@@ -36,27 +94,60 @@ def create_chat_completion(
max_tokens=max_tokens,
)
break
except openai.error.RateLimitError:
if cfg.debug_mode:
except RateLimitError:
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
"API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET,
f"Reached rate limit, passing..." + Fore.RESET,
)
time.sleep(20)
except openai.error.APIError as e:
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)
pass
else:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
)
time.sleep(backoff)
if response is None:
raise RuntimeError("Failed to get response after 5 retries")
raise RuntimeError(f"Failed to get response after {num_retries} retries")
return response.choices[0].message["content"]
def create_embedding_with_ada(text) -> list:
"""Create a embedding with text-ada-002 using the OpenAI SDK"""
num_retries = 10
for attempt in range(num_retries):
backoff = 2 ** (attempt + 2)
try:
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"]
except RateLimitError:
pass
except APIError as e:
if e.http_status == 502:
pass
else:
raise
if attempt == num_retries - 1:
raise
if CFG.debug_mode:
print(
Fore.RED + "Error: ",
f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
)
time.sleep(backoff)

127
autogpt/logger.py → autogpt/logs.py
View File

@@ -1,25 +1,28 @@
"""Logging module for Auto-GPT."""
import json
import logging
import os
import random
import re
import time
from logging import LogRecord
import traceback
from colorama import Fore, Style
from autogpt import speak
from autogpt.speech import say_text
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
"""
CFG = Config()
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
@@ -54,7 +57,8 @@ class Logger(metaclass=Singleton):
error_handler = logging.FileHandler(os.path.join(log_dir, error_file))
error_handler.setLevel(logging.ERROR)
error_formatter = AutoGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s %(message_no_color)s"
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
@@ -73,8 +77,8 @@ class Logger(metaclass=Singleton):
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 speak_text and CFG.speak_mode:
say_text(f"{title}. {content}")
if content:
if isinstance(content, list):
@@ -117,7 +121,12 @@ class Logger(metaclass=Singleton):
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!"
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)
@@ -150,7 +159,7 @@ class TypingConsoleHandler(logging.StreamHandler):
class ConsoleHandler(logging.StreamHandler):
def emit(self, record):
def emit(self, record) -> None:
msg = self.format(record)
try:
print(msg)
@@ -187,3 +196,95 @@ def remove_color_codes(s: str) -> str:
logger = Logger()
def print_assistant_thoughts(ai_name, assistant_reply):
"""Prints the assistant's thoughts to the console"""
from autogpt.json_fixes.bracket_termination import (
attempt_to_fix_json_by_finding_outermost_brackets,
)
from autogpt.json_fixes.parsing import fix_and_parse_json
try:
try:
# Parse and print Assistant response
assistant_reply_json = fix_and_parse_json(assistant_reply)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply
)
if isinstance(assistant_reply_json, str):
assistant_reply_json = fix_and_parse_json(assistant_reply_json)
# Check if assistant_reply_json is a string and attempt to parse
# it into a JSON object
if isinstance(assistant_reply_json, str):
try:
assistant_reply_json = json.loads(assistant_reply_json)
except json.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
assistant_reply_json = (
attempt_to_fix_json_by_finding_outermost_brackets(
assistant_reply_json
)
)
assistant_thoughts_reasoning = None
assistant_thoughts_plan = None
assistant_thoughts_speak = None
assistant_thoughts_criticism = None
if not isinstance(assistant_reply_json, dict):
assistant_reply_json = {}
assistant_thoughts = assistant_reply_json.get("thoughts", {})
assistant_thoughts_text = assistant_thoughts.get("text")
if assistant_thoughts:
assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
assistant_thoughts_plan = assistant_thoughts.get("plan")
assistant_thoughts_criticism = assistant_thoughts.get("criticism")
assistant_thoughts_speak = assistant_thoughts.get("speak")
logger.typewriter_log(
f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
)
logger.typewriter_log(
"REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
)
if assistant_thoughts_plan:
logger.typewriter_log("PLAN:", Fore.YELLOW, "")
# If it's a list, join it into a string
if isinstance(assistant_thoughts_plan, list):
assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
elif isinstance(assistant_thoughts_plan, dict):
assistant_thoughts_plan = str(assistant_thoughts_plan)
# Split the input_string using the newline character and dashes
lines = assistant_thoughts_plan.split("\n")
for line in lines:
line = line.lstrip("- ")
logger.typewriter_log("- ", Fore.GREEN, line.strip())
logger.typewriter_log(
"CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
)
# Speak the assistant's thoughts
if CFG.speak_mode and assistant_thoughts_speak:
say_text(assistant_thoughts_speak)
else:
logger.typewriter_log("SPEAK:", Fore.YELLOW, f"{assistant_thoughts_speak}")
return assistant_reply_json
except json.decoder.JSONDecodeError:
logger.error("Error: Invalid JSON\n", assistant_reply)
if CFG.speak_mode:
say_text(
"I have received an invalid JSON response from the OpenAI API."
" I cannot ignore this response."
)
# All other errors, return "Error: + error message"
except Exception:
call_stack = traceback.format_exc()
logger.error("Error: \n", call_stack)

23
autogpt/memory/__init__.py
View File

@@ -21,6 +21,12 @@ except ImportError:
print("Pinecone not installed. Skipping import.")
PineconeMemory = None
try:
from autogpt.memory.milvus import MilvusMemory
except ImportError:
print("pymilvus not installed. Skipping import.")
MilvusMemory = None
def get_memory(cfg, init=False):
memory = None
@@ -42,6 +48,14 @@ def get_memory(cfg, init=False):
)
else:
memory = RedisMemory(cfg)
elif cfg.memory_backend == "milvus":
if not MilvusMemory:
print(
"Error: Milvus sdk is not installed."
"Please install pymilvus to use Milvus as memory backend."
)
else:
memory = MilvusMemory(cfg)
elif cfg.memory_backend == "no_memory":
memory = NoMemory(cfg)
@@ -56,4 +70,11 @@ def get_supported_memory_backends():
return supported_memory
__all__ = ["get_memory", "LocalCache", "RedisMemory", "PineconeMemory", "NoMemory"]
__all__ = [
"get_memory",
"LocalCache",
"RedisMemory",
"PineconeMemory",
"NoMemory",
"MilvusMemory",
]

25
autogpt/memory/local.py
View File

@@ -1,11 +1,12 @@
import dataclasses
import os
from typing import Any, List, Optional
from typing import Any, List, Optional, Tuple
import numpy as np
import orjson
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
EMBED_DIM = 1536
SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
@@ -24,8 +25,17 @@ class CacheContent:
class LocalCache(MemoryProviderSingleton):
# on load, load our database
"""A class that stores the memory in a local file"""
def __init__(self, cfg) -> None:
"""Initialize a class instance
Args:
cfg: Config object
Returns:
None
"""
self.filename = f"{cfg.memory_index}.json"
if os.path.exists(self.filename):
try:
@@ -42,7 +52,8 @@ class LocalCache(MemoryProviderSingleton):
self.data = CacheContent()
else:
print(
f"Warning: The file '{self.filename}' does not exist. Local memory would not be saved to a file."
f"Warning: The file '{self.filename}' does not exist."
"Local memory would not be saved to a file."
)
self.data = CacheContent()
@@ -60,7 +71,7 @@ class LocalCache(MemoryProviderSingleton):
return ""
self.data.texts.append(text)
embedding = get_ada_embedding(text)
embedding = create_embedding_with_ada(text)
vector = np.array(embedding).astype(np.float32)
vector = vector[np.newaxis, :]
@@ -108,7 +119,7 @@ class LocalCache(MemoryProviderSingleton):
Returns: List[str]
"""
embedding = get_ada_embedding(text)
embedding = create_embedding_with_ada(text)
scores = np.dot(self.data.embeddings, embedding)
@@ -116,7 +127,7 @@ class LocalCache(MemoryProviderSingleton):
return [self.data.texts[i] for i in top_k_indices]
def get_stats(self):
def get_stats(self) -> Tuple[int, Tuple[int, ...]]:
"""
Returns: The stats of the local cache.
"""

121
autogpt/memory/milvus.py Normal file
View File

@@ -0,0 +1,121 @@
""" Milvus memory storage provider."""
from pymilvus import (
connections,
FieldSchema,
CollectionSchema,
DataType,
Collection,
)
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
class MilvusMemory(MemoryProviderSingleton):
"""Milvus memory storage provider."""
def __init__(self, cfg) -> None:
"""Construct a milvus memory storage connection.
Args:
cfg (Config): Auto-GPT global config.
"""
# connect to milvus server.
connections.connect(address=cfg.milvus_addr)
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
]
# create collection if not exist and load it.
self.milvus_collection = cfg.milvus_collection
self.schema = CollectionSchema(fields, "auto-gpt memory storage")
self.collection = Collection(self.milvus_collection, self.schema)
# create index if not exist.
if not self.collection.has_index():
self.collection.release()
self.collection.create_index(
"embeddings",
{
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
},
index_name="embeddings",
)
self.collection.load()
def add(self, data) -> str:
"""Add a embedding of data into memory.
Args:
data (str): The raw text to construct embedding index.
Returns:
str: log.
"""
embedding = get_ada_embedding(data)
result = self.collection.insert([[embedding], [data]])
_text = (
"Inserting data into memory at primary key: "
f"{result.primary_keys[0]}:\n data: {data}"
)
return _text
def get(self, data):
"""Return the most relevant data in memory.
Args:
data: The data to compare to.
"""
return self.get_relevant(data, 1)
def clear(self) -> str:
"""Drop the index in memory.
Returns:
str: log.
"""
self.collection.drop()
self.collection = Collection(self.milvus_collection, self.schema)
self.collection.create_index(
"embeddings",
{
"metric_type": "IP",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
},
index_name="embeddings",
)
self.collection.load()
return "Obliviated"
def get_relevant(self, data: str, num_relevant: int = 5):
"""Return the top-k relevant data in memory.
Args:
data: The data to compare to.
num_relevant (int, optional): The max number of relevant data.
Defaults to 5.
Returns:
list: The top-k relevant data.
"""
# search the embedding and return the most relevant text.
embedding = get_ada_embedding(data)
search_params = {
"metrics_type": "IP",
"params": {"nprobe": 8},
}
result = self.collection.search(
[embedding],
"embeddings",
search_params,
num_relevant,
output_fields=["raw_text"],
)
return [item.entity.value_of_field("raw_text") for item in result[0]]
def get_stats(self) -> str:
"""
Returns: The stats of the milvus cache.
"""
return f"Entities num: {self.collection.num_entities}"

5
autogpt/memory/no_memory.py
View File

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

21
autogpt/memory/pinecone.py
View File

@@ -1,9 +1,9 @@
import pinecone
from colorama import Fore, Style
from autogpt.config import Config, Singleton
from autogpt.logger import logger
from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
class PineconeMemory(MemoryProviderSingleton):
@@ -17,7 +17,8 @@ class PineconeMemory(MemoryProviderSingleton):
table_name = "auto-gpt"
# this assumes we don't start with memory.
# for now this works.
# we'll need a more complicated and robust system if we want to start with memory.
# we'll need a more complicated and robust system if we want to start with
# memory.
self.vec_num = 0
try:
@@ -29,8 +30,10 @@ class PineconeMemory(MemoryProviderSingleton):
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."
"Please ensure you have setup and configured Pinecone properly for use."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
"https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
f"{Style.RESET_ALL} to ensure you've set up everything correctly."
)
exit(1)
@@ -41,9 +44,9 @@ class PineconeMemory(MemoryProviderSingleton):
self.index = pinecone.Index(table_name)
def add(self, data):
vector = get_ada_embedding(data)
vector = create_embedding_with_ada(data)
# no metadata here. We may wish to change that long term.
resp = self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
_text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
self.vec_num += 1
return _text
@@ -61,7 +64,7 @@ class PineconeMemory(MemoryProviderSingleton):
: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)
query_embedding = create_embedding_with_ada(data)
results = self.index.query(
query_embedding, top_k=num_relevant, include_metadata=True
)

13
autogpt/memory/redismem.py
View File

@@ -8,8 +8,9 @@ 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
from autogpt.logs import logger
from autogpt.memory.base import MemoryProviderSingleton
from autogpt.llm_utils import create_embedding_with_ada
SCHEMA = [
TextField("data"),
@@ -54,7 +55,9 @@ class RedisMemory(MemoryProviderSingleton):
)
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."
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
" to ensure you've set up everything correctly."
)
exit(1)
@@ -83,7 +86,7 @@ class RedisMemory(MemoryProviderSingleton):
"""
if "Command Error:" in data:
return ""
vector = get_ada_embedding(data)
vector = create_embedding_with_ada(data)
vector = np.array(vector).astype(np.float32).tobytes()
data_dict = {b"data": data, "embedding": vector}
pipe = self.redis.pipeline()
@@ -125,7 +128,7 @@ class RedisMemory(MemoryProviderSingleton):
Returns: A list of the most relevant data.
"""
query_embedding = get_ada_embedding(data)
query_embedding = create_embedding_with_ada(data)
base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
query = (
Query(base_query)

0
autogpt/permanent_memory/__init__.py Normal file
View File

123
autogpt/permanent_memory/sqlite3_store.py Normal file
View File

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

0
autogpt/processing/__init__.py Normal file
View File

32
autogpt/processing/html.py Normal file
View File

@@ -0,0 +1,32 @@
"""HTML processing functions"""
from requests.compat import urljoin
from typing import List, Tuple
from bs4 import BeautifulSoup
def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> List[Tuple[str, str]]:
"""Extract hyperlinks from a BeautifulSoup object
Args:
soup (BeautifulSoup): The BeautifulSoup object
base_url (str): The base URL
Returns:
List[Tuple[str, str]]: The extracted hyperlinks
"""
return [
(link.text, urljoin(base_url, link["href"]))
for link in soup.find_all("a", href=True)
]
def format_hyperlinks(hyperlinks: List[Tuple[str, str]]) -> List[str]:
"""Format hyperlinks to be displayed to the user
Args:
hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
Returns:
List[str]: The formatted hyperlinks
"""
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]

132
autogpt/processing/text.py Normal file
View File

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

102
autogpt/prompt.py
View File

@@ -1,23 +1,38 @@
from colorama import Fore
from autogpt.config.ai_config import AIConfig
from autogpt.config.config import Config
from autogpt.logs import logger
from autogpt.promptgenerator import PromptGenerator
from autogpt.config import Config
from autogpt.setup import prompt_user
from autogpt.utils import clean_input
CFG = Config()
def get_prompt():
def get_prompt() -> str:
"""
This function generates a prompt string that includes various constraints, commands, resources, and performance evaluations.
This function generates a prompt string that includes various constraints,
commands, resources, and performance evaluations.
Returns:
str: The generated prompt string.
"""
# Initialize the Config object
cfg = Config()
# 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."
"~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."
"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(
@@ -44,6 +59,11 @@ def get_prompt():
),
("List GPT Agents", "list_agents", {}),
("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
(
"Clone Repository",
"clone_repository",
{"repository_url": "<url>", "clone_path": "<directory>"},
),
("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>"}),
@@ -61,16 +81,29 @@ def get_prompt():
{"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", {}),
("Convert Audio to text", "read_audio_from_file", {"file": "<file>"}),
("Send Tweet", "send_tweet", {"text": "<text>"}),
]
# Only add shell command to the prompt if the AI is allowed to execute it
if cfg.execute_local_commands:
commands.append(
(
"Execute Shell Command, non-interactive commands only",
"execute_shell",
{"command_line": "<command_line>"},
),
)
# Add these command last.
commands.append(
("Do Nothing", "do_nothing", {}),
)
commands.append(
("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
)
# Add commands to the PromptGenerator object
for command_label, command_name, args in commands:
prompt_generator.add_command(command_label, command_name, args)
@@ -87,7 +120,8 @@ def get_prompt():
# 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."
"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."
@@ -96,10 +130,48 @@ def get_prompt():
"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."
"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_generator.generate_prompt_string()
return prompt_string
def construct_prompt() -> str:
"""Construct the prompt for the AI to respond to
Returns:
str: The prompt string
"""
config = AIConfig.load(CFG.ai_settings_file)
if CFG.skip_reprompt and config.ai_name:
logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
elif config.ai_name:
logger.typewriter_log(
"Welcome back! ",
Fore.GREEN,
f"Would you like me to return to being {config.ai_name}?",
speak_text=True,
)
should_continue = clean_input(
f"""Continue with the last settings?
Name: {config.ai_name}
Role: {config.ai_role}
Goals: {config.ai_goals}
Continue (y/n): """
)
if should_continue.lower() == "n":
config = AIConfig()
if not config.ai_name:
config = prompt_user()
config.save()
# Get rid of this global:
global ai_name
ai_name = config.ai_name
return config.construct_full_prompt()

47
autogpt/promptgenerator.py
View File

@@ -1,14 +1,18 @@
""" A module for generating custom prompt strings."""
import json
from typing import Any, Dict, List
class PromptGenerator:
"""
A class for generating custom prompt strings based on constraints, commands, resources, and performance evaluations.
A class for generating custom prompt strings based on constraints, commands,
resources, and performance evaluations.
"""
def __init__(self):
def __init__(self) -> None:
"""
Initialize the PromptGenerator object with empty lists of constraints, commands, resources, and performance evaluations.
Initialize the PromptGenerator object with empty lists of constraints,
commands, resources, and performance evaluations.
"""
self.constraints = []
self.commands = []
@@ -25,7 +29,7 @@ class PromptGenerator:
"command": {"name": "command name", "args": {"arg name": "value"}},
}
def add_constraint(self, constraint):
def add_constraint(self, constraint: str) -> None:
"""
Add a constraint to the constraints list.
@@ -34,14 +38,15 @@ class PromptGenerator:
"""
self.constraints.append(constraint)
def add_command(self, command_label, command_name, args=None):
def add_command(self, command_label: str, command_name: str, args=None) -> None:
"""
Add a command to the commands list with a label, name, and optional arguments.
Args:
command_label (str): The label of the command.
command_name (str): The name of the command.
args (dict, optional): A dictionary containing argument names and their values. Defaults to None.
args (dict, optional): A dictionary containing argument names and their
values. Defaults to None.
"""
if args is None:
args = {}
@@ -56,7 +61,7 @@ class PromptGenerator:
self.commands.append(command)
def _generate_command_string(self, command):
def _generate_command_string(self, command: Dict[str, Any]) -> str:
"""
Generate a formatted string representation of a command.
@@ -71,7 +76,7 @@ class PromptGenerator:
)
return f'{command["label"]}: "{command["name"]}", args: {args_string}'
def add_resource(self, resource):
def add_resource(self, resource: str) -> None:
"""
Add a resource to the resources list.
@@ -80,7 +85,7 @@ class PromptGenerator:
"""
self.resources.append(resource)
def add_performance_evaluation(self, evaluation):
def add_performance_evaluation(self, evaluation: str) -> None:
"""
Add a performance evaluation item to the performance_evaluation list.
@@ -89,13 +94,14 @@ class PromptGenerator:
"""
self.performance_evaluation.append(evaluation)
def _generate_numbered_list(self, items, item_type="list"):
def _generate_numbered_list(self, items: List[Any], item_type="list") -> str:
"""
Generate a numbered list from given items based on the item_type.
Args:
items (list): A list of items to be numbered.
item_type (str, optional): The type of items in the list. Defaults to 'list'.
item_type (str, optional): The type of items in the list.
Defaults to 'list'.
Returns:
str: The formatted numbered list.
@@ -108,20 +114,23 @@ class PromptGenerator:
else:
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
def generate_prompt_string(self):
def generate_prompt_string(self) -> str:
"""
Generate a prompt string based on the constraints, commands, resources, and performance evaluations.
Generate a prompt string based on the constraints, commands, resources,
and performance evaluations.
Returns:
str: The generated prompt string.
"""
formatted_response_format = json.dumps(self.response_format, indent=4)
prompt_string = (
return (
f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
f"Commands:\n{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
"Commands:\n"
f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
f"Performance Evaluation:\n{self._generate_numbered_list(self.performance_evaluation)}\n\n"
f"You should only respond in JSON format as described below \nResponse Format: \n{formatted_response_format} \nEnsure the response can be parsed by Python json.loads"
"Performance Evaluation:\n"
f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
"You should only respond in JSON format as described below \nResponse"
f" Format: \n{formatted_response_format} \nEnsure the response can be"
"parsed by Python json.loads"
)
return prompt_string

69
autogpt/setup.py Normal file
View File

@@ -0,0 +1,69 @@
"""Setup the AI and its goals"""
from colorama import Fore, Style
from autogpt import utils
from autogpt.config.ai_config import AIConfig
from autogpt.logs import logger
def prompt_user() -> AIConfig:
"""Prompt the user for input
Returns:
AIConfig: The AIConfig object containing the user's input
"""
ai_name = ""
# Construct the prompt
logger.typewriter_log(
"Welcome to Auto-GPT! ",
Fore.GREEN,
"Enter the name of your AI and its role below. Entering nothing will load"
" defaults.",
speak_text=True,
)
# Get AI Name from User
logger.typewriter_log(
"Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
)
ai_name = utils.clean_input("AI Name: ")
if ai_name == "":
ai_name = "Entrepreneur-GPT"
logger.typewriter_log(
f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
)
# Get AI Role from User
logger.typewriter_log(
"Describe your AI's role: ",
Fore.GREEN,
"For example, 'an AI designed to autonomously develop and run businesses with"
" the sole goal of increasing your net worth.'",
)
ai_role = utils.clean_input(f"{ai_name} is: ")
if ai_role == "":
ai_role = "an AI designed to autonomously develop and run businesses with the"
" sole goal of increasing your net worth."
# Enter up to 5 goals for the AI
logger.typewriter_log(
"Enter up to 5 goals for your AI: ",
Fore.GREEN,
"For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
" multiple businesses autonomously'",
)
print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
ai_goals = []
for i in range(5):
ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
if ai_goal == "":
break
ai_goals.append(ai_goal)
if not ai_goals:
ai_goals = [
"Increase net worth",
"Grow Twitter Account",
"Develop and manage multiple businesses autonomously",
]
return AIConfig(ai_name, ai_role, ai_goals)

119
autogpt/speak.py
View File

@@ -1,119 +0,0 @@
import os
import requests
from playsound import playsound
from autogpt.config import Config
cfg = Config()
import threading
from threading import Lock, Semaphore
import gtts
# 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()

4
autogpt/speech/__init__.py Normal file
View File

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

50
autogpt/speech/base.py Normal file
View File

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

39
autogpt/speech/brian.py Normal file
View File

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

86
autogpt/speech/eleven_labs.py Normal file
View File

@@ -0,0 +1,86 @@
"""ElevenLabs speech module"""
import os
from playsound import playsound
import requests
from autogpt.config import Config
from autogpt.speech.base import VoiceBase
PLACEHOLDERS = {"your-voice-id"}
class ElevenLabsSpeech(VoiceBase):
"""ElevenLabs speech class"""
def _setup(self) -> None:
"""Setup the voices, API key, etc.
Returns:
None: None
"""
cfg = Config()
default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
voice_options = {
"Rachel": "21m00Tcm4TlvDq8ikWAM",
"Domi": "AZnzlk1XvdvUeBnXmlld",
"Bella": "EXAVITQu4vr4xnSDxMaL",
"Antoni": "ErXwobaYiN019PkySvjV",
"Elli": "MF3mGyEYCl7XYWbV9V6O",
"Josh": "TxGEqnHWrfWFTfGW9XjX",
"Arnold": "VR6AewLTigWG4xSOukaG",
"Adam": "pNInz6obpgDQGcFmaJgB",
"Sam": "yoZ06aMxZJJ28mfd3POQ",
}
self._headers = {
"Content-Type": "application/json",
"xi-api-key": cfg.elevenlabs_api_key,
}
self._voices = default_voices.copy()
if cfg.elevenlabs_voice_1_id in voice_options:
cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id]
if cfg.elevenlabs_voice_2_id in voice_options:
cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id]
self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0)
self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1)
def _use_custom_voice(self, voice, voice_index) -> None:
"""Use a custom voice if provided and not a placeholder
Args:
voice (str): The voice ID
voice_index (int): The voice index
Returns:
None: None
"""
# Placeholder values that should be treated as empty
if voice and voice not in PLACEHOLDERS:
self._voices[voice_index] = voice
def _speech(self, text: str, voice_index: int = 0) -> bool:
"""Speak text using elevenlabs.io's API
Args:
text (str): The text to speak
voice_index (int, optional): The voice to use. Defaults to 0.
Returns:
bool: True if the request was successful, False otherwise
"""
tts_url = (
f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
)
response = requests.post(tts_url, headers=self._headers, json={"text": text})
if response.status_code == 200:
with open("speech.mpeg", "wb") as f:
f.write(response.content)
playsound("speech.mpeg", True)
os.remove("speech.mpeg")
return True
else:
print("Request failed with status code:", response.status_code)
print("Response content:", response.content)
return False

21
autogpt/speech/gtts.py Normal file
View File

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

21
autogpt/speech/macos_tts.py Normal file
View File

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

42
autogpt/speech/say.py Normal file
View File

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

27
autogpt/spinner.py
View File

@@ -1,3 +1,4 @@
"""A simple spinner module"""
import itertools
import sys
import threading
@@ -7,15 +8,20 @@ import time
class Spinner:
"""A simple spinner class"""
def __init__(self, message="Loading...", delay=0.1):
"""Initialize the spinner class"""
def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
"""Initialize the spinner class
Args:
message (str): The message to display.
delay (float): The delay between each spinner update.
"""
self.spinner = itertools.cycle(["-", "/", "|", "\\"])
self.delay = delay
self.message = message
self.running = False
self.spinner_thread = None
def spin(self):
def spin(self) -> None:
"""Spin the spinner"""
while self.running:
sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
@@ -23,15 +29,22 @@ class Spinner:
time.sleep(self.delay)
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
def __enter__(self):
def __enter__(self) -> None:
"""Start the spinner"""
self.running = True
self.spinner_thread = threading.Thread(target=self.spin)
self.spinner_thread.start()
def __exit__(self, exc_type, exc_value, exc_traceback):
"""Stop the spinner"""
def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
"""Stop the spinner
Args:
exc_type (Exception): The exception type.
exc_value (Exception): The exception value.
exc_traceback (Exception): The exception traceback.
"""
self.running = False
self.spinner_thread.join()
if self.spinner_thread is not None:
self.spinner_thread.join()
sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
sys.stdout.flush()

67
autogpt/summary.py
View File

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

27
autogpt/token_counter.py
View File

@@ -1,7 +1,10 @@
"""Functions for counting the number of tokens in a message or string."""
from typing import Dict, List
import tiktoken
from autogpt.logs import logger
def count_message_tokens(
messages: List[Dict[str, str]], model: str = "gpt-3.5-turbo-0301"
@@ -10,11 +13,13 @@ def count_message_tokens(
Returns the number of tokens used by a list of messages.
Args:
messages (list): A list of messages, each of which is a dictionary containing the role and content of the message.
model (str): The name of the model to use for tokenization. Defaults to "gpt-3.5-turbo-0301".
messages (list): A list of messages, each of which is a dictionary
containing the role and content of the message.
model (str): The name of the model to use for tokenization.
Defaults to "gpt-3.5-turbo-0301".
Returns:
int: The number of tokens used by the list of messages.
int: The number of tokens used by the list of messages.
"""
try:
encoding = tiktoken.encoding_for_model(model)
@@ -22,7 +27,8 @@ def count_message_tokens(
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.")
# !Note: gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.")
return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
# !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
@@ -37,7 +43,9 @@ def count_message_tokens(
tokens_per_name = 1
else:
raise NotImplementedError(
f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
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:
@@ -55,12 +63,11 @@ def count_string_tokens(string: str, model_name: str) -> int:
Returns the number of tokens in a text string.
Args:
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
string (str): The text string.
model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
Returns:
int: The number of tokens in the text string.
int: The number of tokens in the text string.
"""
encoding = tiktoken.encoding_for_model(model_name)
num_tokens = len(encoding.encode(string))
return num_tokens
return len(encoding.encode(string))

4
autogpt/utils.py
View File

@@ -13,8 +13,8 @@ def clean_input(prompt: str = ""):
def validate_yaml_file(file: str):
try:
with open(file) as file:
yaml.load(file, Loader=yaml.FullLoader)
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:

91
autogpt/web.py
View File

@@ -1,91 +0,0 @@
from duckduckgo_search import ddg
from selenium import webdriver
import autogpt.summary as summary
from bs4 import BeautifulSoup
import json
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.chrome.service import Service as ChromeService
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import os
import logging
from pathlib import Path
from autogpt.config import Config
file_dir = Path(__file__).parent
cfg = Config()
def browse_website(url, question):
driver, text = scrape_text_with_selenium(url)
add_header(driver)
summary_text = summary.summarize_text(driver, text, question)
links = scrape_links_with_selenium(driver)
# Limit links to 5
if len(links) > 5:
links = links[:5]
close_browser(driver)
return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
def scrape_text_with_selenium(url):
logging.getLogger("selenium").setLevel(logging.CRITICAL)
options = Options()
options.add_argument(
"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
)
driver = webdriver.Chrome(
executable_path=ChromeDriverManager().install(), options=options
)
driver.get(url)
WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.TAG_NAME, "body"))
)
# Get the HTML content directly from the browser's DOM
page_source = driver.execute_script("return document.body.outerHTML;")
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return driver, text
def scrape_links_with_selenium(driver):
page_source = driver.page_source
soup = BeautifulSoup(page_source, "html.parser")
for script in soup(["script", "style"]):
script.extract()
hyperlinks = extract_hyperlinks(soup)
return format_hyperlinks(hyperlinks)
def close_browser(driver):
driver.quit()
def extract_hyperlinks(soup):
return [(link.text, link["href"]) for link in soup.find_all("a", href=True)]
def format_hyperlinks(hyperlinks):
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
def add_header(driver):
driver.execute_script(open(f"{file_dir}/js/overlay.js", "r").read())

2
docker-compose.yml
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@@ -7,6 +7,8 @@ services:
depends_on:
- redis
build: ./
env_file:
- .env
volumes:
- "./autogpt:/app"
- ".env:/app/.env"

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130
outputs/logs/message-log-3.txt
View File

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

27
requirements-docker.txt Normal file
View File

@@ -0,0 +1,27 @@
beautifulsoup4
colorama==0.4.6
openai==0.27.2
playsound==1.2.2
python-dotenv==1.0.0
pyyaml==6.0
readability-lxml==0.8.1
requests
tiktoken==0.3.3
gTTS==2.3.1
docker
duckduckgo-search
google-api-python-client #(https://developers.google.com/custom-search/v1/overview)
pinecone-client==2.2.1
redis
orjson
Pillow
selenium
webdriver-manager
coverage
flake8
numpy
pre-commit
black
isort
gitpython==3.1.31
tweepy

4
requirements.txt
View File

@@ -24,3 +24,7 @@ pre-commit
black
sourcery
isort
gitpython==3.1.31
pytest
pytest-mock
tweepy

8
run.bat Normal file
View File

@@ -0,0 +1,8 @@
@echo off
python scripts/check_requirements.py requirements.txt
if errorlevel 1 (
echo Installing missing packages...
pip install -r requirements.txt
)
python -m autogpt %*
pause

3
run_continuous.bat Normal file
View File

@@ -0,0 +1,3 @@
@echo off
set argument=--continuous
call run.bat %argument%

31
scripts/check_requirements.py Normal file
View File

@@ -0,0 +1,31 @@
import pkg_resources
import sys
def main():
requirements_file = sys.argv[1]
with open(requirements_file, "r") as f:
required_packages = [
line.strip().split("#")[0].strip() for line in f.readlines()
]
installed_packages = [package.key for package in pkg_resources.working_set]
missing_packages = []
for package in required_packages:
if not package: # Skip empty lines
continue
package_name = package.strip().split("==")[0]
if package_name.lower() not in installed_packages:
missing_packages.append(package_name)
if missing_packages:
print("Missing packages:")
print(", ".join(missing_packages))
sys.exit(1)
else:
print("All packages are installed.")
if __name__ == "__main__":
main()

11
scripts/main.py
View File

@@ -1,11 +0,0 @@
from colorama import Style, init
# Initialize colorama
init(autoreset=True)
# Use the bold ANSI style
print(
f"""{Style.BRIGHT}Please run:
python -m autogpt
"""
)

14
tests.py
View File

@@ -1,8 +1,20 @@
import unittest
import coverage
if __name__ == "__main__":
# Start coverage collection
cov = coverage.Coverage()
cov.start()
# Load all tests from the 'autogpt/tests' package
suite = unittest.defaultTestLoader.discover("autogpt/tests")
suite = unittest.defaultTestLoader.discover("./tests")
# Run the tests
unittest.TextTestRunner().run(suite)
# Stop coverage collection
cov.stop()
cov.save()
# Report the coverage
cov.report(show_missing=True)

26
tests/browse_tests.py Normal file
View File

@@ -0,0 +1,26 @@
import unittest
import os
import sys
from bs4 import BeautifulSoup
sys.path.append(os.path.abspath("../scripts"))
from browse import extract_hyperlinks
class TestBrowseLinks(unittest.TestCase):
def test_extract_hyperlinks(self):
body = """
<body>
<a href="https://google.com">Google</a>
<a href="foo.html">Foo</a>
<div>Some other crap</div>
</body>
"""
soup = BeautifulSoup(body, "html.parser")
links = extract_hyperlinks(soup, "http://example.com")
self.assertEqual(
links,
[("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
)

48
tests/integration/milvus_memory_tests.py Normal file
View File

@@ -0,0 +1,48 @@
import random
import string
import unittest
from autogpt.config import Config
from autogpt.memory.milvus import MilvusMemory
class TestMilvusMemory(unittest.TestCase):
def random_string(self, length):
return "".join(random.choice(string.ascii_letters) for _ in range(length))
def setUp(self):
cfg = Config()
cfg.milvus_addr = "localhost:19530"
self.memory = MilvusMemory(cfg)
self.memory.clear()
# Add example texts to the cache
self.example_texts = [
"The quick brown fox jumps over the lazy dog",
"I love machine learning and natural language processing",
"The cake is a lie, but the pie is always true",
"ChatGPT is an advanced AI model for conversation",
]
for text in self.example_texts:
self.memory.add(text)
# Add some random strings to test noise
for _ in range(5):
self.memory.add(self.random_string(10))
def test_get_relevant(self):
query = "I'm interested in artificial intelligence and NLP"
k = 3
relevant_texts = self.memory.get_relevant(query, k)
print(f"Top {k} relevant texts for the query '{query}':")
for i, text in enumerate(relevant_texts, start=1):
print(f"{i}. {text}")
self.assertEqual(len(relevant_texts), k)
self.assertIn(self.example_texts[1], relevant_texts)
if __name__ == "__main__":
unittest.main()

1
tests/local_cache_test.py
View File

@@ -1,5 +1,6 @@
import os
import sys
import unittest
from autogpt.memory.local import LocalCache

63
tests/milvus_memory_test.py Normal file
View File

@@ -0,0 +1,63 @@
import os
import sys
import unittest
from autogpt.memory.milvus import MilvusMemory
def MockConfig():
return type(
"MockConfig",
(object,),
{
"debug_mode": False,
"continuous_mode": False,
"speak_mode": False,
"milvus_collection": "autogpt",
"milvus_addr": "localhost:19530",
},
)
class TestMilvusMemory(unittest.TestCase):
def setUp(self):
self.cfg = MockConfig()
self.memory = MilvusMemory(self.cfg)
def test_add(self):
text = "Sample text"
self.memory.clear()
self.memory.add(text)
result = self.memory.get(text)
self.assertEqual([text], result)
def test_clear(self):
self.memory.clear()
self.assertEqual(self.memory.collection.num_entities, 0)
def test_get(self):
text = "Sample text"
self.memory.clear()
self.memory.add(text)
result = self.memory.get(text)
self.assertEqual(result, [text])
def test_get_relevant(self):
text1 = "Sample text 1"
text2 = "Sample text 2"
self.memory.clear()
self.memory.add(text1)
self.memory.add(text2)
result = self.memory.get_relevant(text1, 1)
self.assertEqual(result, [text1])
def test_get_stats(self):
text = "Sample text"
self.memory.clear()
self.memory.add(text)
stats = self.memory.get_stats()
self.assertEqual(15, len(stats))
if __name__ == "__main__":
unittest.main()

63
tests/smoke_test.py Normal file
View File

@@ -0,0 +1,63 @@
import os
import subprocess
import sys
import unittest
from autogpt.commands.file_operations import delete_file, read_file
env_vars = {"MEMORY_BACKEND": "no_memory", "TEMPERATURE": "0"}
class TestCommands(unittest.TestCase):
def test_write_file(self):
# Test case to check if the write_file command can successfully write 'Hello World' to a file
# named 'hello_world.txt'.
# Read the current ai_settings.yaml file and store its content.
ai_settings = None
if os.path.exists("ai_settings.yaml"):
with open("ai_settings.yaml", "r") as f:
ai_settings = f.read()
os.remove("ai_settings.yaml")
try:
if os.path.exists("hello_world.txt"):
# Clean up any existing 'hello_world.txt' file before testing.
delete_file("hello_world.txt")
# Prepare input data for the test.
input_data = """write_file-GPT
an AI designed to use the write_file command to write 'Hello World' into a file named "hello_world.txt" and then use the task_complete command to complete the task.
Use the write_file command to write 'Hello World' into a file named "hello_world.txt".
Use the task_complete command to complete the task.
Do not use any other commands.
y -5
EOF"""
command = f"{sys.executable} -m autogpt"
# Execute the script with the input data.
process = subprocess.Popen(
command,
stdin=subprocess.PIPE,
shell=True,
env={**os.environ, **env_vars},
)
process.communicate(input_data.encode())
# Read the content of the 'hello_world.txt' file created during the test.
content = read_file("hello_world.txt")
finally:
if ai_settings:
# Restore the original ai_settings.yaml file.
with open("ai_settings.yaml", "w") as f:
f.write(ai_settings)
# Check if the content of the 'hello_world.txt' file is equal to 'Hello World'.
self.assertEqual(
content, "Hello World", f"Expected 'Hello World', got {content}"
)
# Run the test case.
if __name__ == "__main__":
unittest.main()

99
tests/test_config.py
View File

@@ -1,59 +1,84 @@
import unittest
from unittest import TestCase
from autogpt.config import Config
class TestConfig(unittest.TestCase):
class TestConfig(TestCase):
"""
Test cases for the Config class, which handles the configuration settings
for the AI and ensures it behaves as a singleton.
"""
def setUp(self):
"""
Set up the test environment by creating an instance of the Config class.
"""
self.config = Config()
def test_singleton(self):
config1 = Config()
"""
Test if the Config class behaves as a singleton by ensuring that two instances are the same.
"""
config2 = Config()
self.assertIs(config1, config2)
self.assertIs(self.config, config2)
def test_initial_values(self):
config = Config()
self.assertFalse(config.debug_mode)
self.assertFalse(config.continuous_mode)
self.assertFalse(config.speak_mode)
self.assertEqual(config.fast_llm_model, "gpt-3.5-turbo")
self.assertEqual(config.smart_llm_model, "gpt-4")
self.assertEqual(config.fast_token_limit, 4000)
self.assertEqual(config.smart_token_limit, 8000)
"""
Test if the initial values of the Config class attributes are set correctly.
"""
self.assertFalse(self.config.debug_mode)
self.assertFalse(self.config.continuous_mode)
self.assertFalse(self.config.speak_mode)
self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
self.assertEqual(self.config.smart_llm_model, "gpt-4")
self.assertEqual(self.config.fast_token_limit, 4000)
self.assertEqual(self.config.smart_token_limit, 8000)
def test_set_continuous_mode(self):
config = Config()
config.set_continuous_mode(True)
self.assertTrue(config.continuous_mode)
"""
Test if the set_continuous_mode() method updates the continuous_mode attribute.
"""
self.config.set_continuous_mode(True)
self.assertTrue(self.config.continuous_mode)
def test_set_speak_mode(self):
config = Config()
config.set_speak_mode(True)
self.assertTrue(config.speak_mode)
"""
Test if the set_speak_mode() method updates the speak_mode attribute.
"""
self.config.set_speak_mode(True)
self.assertTrue(self.config.speak_mode)
def test_set_fast_llm_model(self):
config = Config()
config.set_fast_llm_model("gpt-3.5-turbo-test")
self.assertEqual(config.fast_llm_model, "gpt-3.5-turbo-test")
"""
Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
"""
self.config.set_fast_llm_model("gpt-3.5-turbo-test")
self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
def test_set_smart_llm_model(self):
config = Config()
config.set_smart_llm_model("gpt-4-test")
self.assertEqual(config.smart_llm_model, "gpt-4-test")
"""
Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
"""
self.config.set_smart_llm_model("gpt-4-test")
self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
def test_set_fast_token_limit(self):
config = Config()
config.set_fast_token_limit(5000)
self.assertEqual(config.fast_token_limit, 5000)
"""
Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
"""
self.config.set_fast_token_limit(5000)
self.assertEqual(self.config.fast_token_limit, 5000)
def test_set_smart_token_limit(self):
config = Config()
config.set_smart_token_limit(9000)
self.assertEqual(config.smart_token_limit, 9000)
"""
Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
"""
self.config.set_smart_token_limit(9000)
self.assertEqual(self.config.smart_token_limit, 9000)
def test_set_debug_mode(self):
config = Config()
config.set_debug_mode(True)
self.assertTrue(config.debug_mode)
if __name__ == "__main__":
unittest.main()
"""
Test if the set_debug_mode() method updates the debug_mode attribute.
"""
self.config.set_debug_mode(True)
self.assertTrue(self.config.debug_mode)

2
tests/test_json_parser.py
View File

@@ -1,7 +1,7 @@
import unittest
import tests.context
from autogpt.json_parser import fix_and_parse_json
from autogpt.json_fixes.parsing import fix_and_parse_json
class TestParseJson(unittest.TestCase):

59
tests/promptgenerator_tests.py → tests/test_prompt_generator.py
View File

@@ -1,25 +1,35 @@
# Import the required libraries for unit testing
import os
import sys
import unittest
from unittest import TestCase
from autogpt.promptgenerator import PromptGenerator
# Create a test class for the PromptGenerator, subclassed from unittest.TestCase
class promptgenerator_tests(unittest.TestCase):
# Set up the initial state for each test method by creating an instance of PromptGenerator
def setUp(self):
self.generator = PromptGenerator()
class TestPromptGenerator(TestCase):
"""
Test cases for the PromptGenerator class, which is responsible for generating
prompts for the AI with constraints, commands, resources, and performance evaluations.
"""
@classmethod
def setUpClass(cls):
"""
Set up the initial state for each test method by creating an instance of PromptGenerator.
"""
cls.generator = PromptGenerator()
# Test whether the add_constraint() method adds a constraint to the generator's constraints list
def test_add_constraint(self):
"""
Test if the add_constraint() method adds a constraint to the generator's constraints list.
"""
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):
"""
Test if the add_command() method adds a command to the generator's commands list.
"""
command_label = "Command Label"
command_name = "command_name"
args = {"arg1": "value1", "arg2": "value2"}
@@ -31,20 +41,29 @@ class promptgenerator_tests(unittest.TestCase):
}
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):
"""
Test if the add_resource() method adds a resource to the generator's resources list.
"""
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):
"""
Test if the add_performance_evaluation() method adds an evaluation to the generator's
performance_evaluation list.
"""
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):
"""
Test if the generate_prompt_string() method generates a prompt string with all the added
constraints, commands, resources, and evaluations.
"""
# Define the test data
constraints = ["Constraint1", "Constraint2"]
commands = [
{
@@ -61,7 +80,7 @@ class promptgenerator_tests(unittest.TestCase):
resources = ["Resource1", "Resource2"]
evaluations = ["Evaluation1", "Evaluation2"]
# Add all the constraints, commands, resources, and evaluations to the generator
# Add test data to the generator
for constraint in constraints:
self.generator.add_constraint(constraint)
for command in commands:
@@ -76,24 +95,20 @@ class promptgenerator_tests(unittest.TestCase):
# Generate the prompt string and verify its correctness
prompt_string = self.generator.generate_prompt_string()
self.assertIsNotNone(prompt_string)
# Check if all constraints, commands, resources, and evaluations are present in the 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 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()

62
tests/test_token_counter.py Normal file
View File

@@ -0,0 +1,62 @@
import unittest
import tests.context
from autogpt.token_counter import count_message_tokens, count_string_tokens
class TestTokenCounter(unittest.TestCase):
def test_count_message_tokens(self):
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
]
self.assertEqual(count_message_tokens(messages), 17)
def test_count_message_tokens_with_name(self):
messages = [
{"role": "user", "content": "Hello", "name": "John"},
{"role": "assistant", "content": "Hi there!"},
]
self.assertEqual(count_message_tokens(messages), 17)
def test_count_message_tokens_empty_input(self):
self.assertEqual(count_message_tokens([]), 3)
def test_count_message_tokens_invalid_model(self):
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
]
with self.assertRaises(KeyError):
count_message_tokens(messages, model="invalid_model")
def test_count_message_tokens_gpt_4(self):
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
]
self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15)
def test_count_string_tokens(self):
string = "Hello, world!"
self.assertEqual(
count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4
)
def test_count_string_tokens_empty_input(self):
self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0)
def test_count_message_tokens_invalid_model(self):
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
]
with self.assertRaises(NotImplementedError):
count_message_tokens(messages, model="invalid_model")
def test_count_string_tokens_gpt_4(self):
string = "Hello, world!"
self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4)
if __name__ == "__main__":
unittest.main()

10
tests/unit/test_browse_scrape_links.py
View File

@@ -4,7 +4,7 @@
# pip install pytest-mock
import pytest
from scripts.browse import scrape_links
from autogpt.commands.web_requests import scrape_links
"""
Code Analysis
@@ -55,7 +55,7 @@ class TestScrapeLinks:
mock_response.text = (
"<html><body><a href='https://www.google.com'>Google</a></body></html>"
)
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with a valid URL
result = scrape_links("https://www.example.com")
@@ -68,7 +68,7 @@ class TestScrapeLinks:
# Mock the requests.get() function to return an HTTP error response
mock_response = mocker.Mock()
mock_response.status_code = 404
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with an invalid URL
result = scrape_links("https://www.invalidurl.com")
@@ -82,7 +82,7 @@ class TestScrapeLinks:
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = "<html><body><p>No hyperlinks here</p></body></html>"
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with a URL containing no hyperlinks
result = scrape_links("https://www.example.com")
@@ -105,7 +105,7 @@ class TestScrapeLinks:
</body>
</html>
"""
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function being tested
result = scrape_links("https://www.example.com")

14
tests/unit/test_browse_scrape_text.py
View File

@@ -2,7 +2,7 @@
import requests
from autogpt.browse import scrape_text
from autogpt.commands.web_requests import scrape_text
"""
Code Analysis
@@ -41,7 +41,7 @@ class TestScrapeText:
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = f"<html><body><div><p style='color: blue;'>{expected_text}</p></div></body></html>"
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with a valid URL and assert that it returns the expected text
url = "http://www.example.com"
@@ -50,7 +50,9 @@ class TestScrapeText:
# Tests that the function returns an error message when an invalid or unreachable url is provided.
def test_invalid_url(self, mocker):
# Mock the requests.get() method to raise an exception
mocker.patch("requests.get", side_effect=requests.exceptions.RequestException)
mocker.patch(
"requests.Session.get", side_effect=requests.exceptions.RequestException
)
# Call the function with an invalid URL and assert that it returns an error message
url = "http://www.invalidurl.com"
@@ -63,7 +65,7 @@ class TestScrapeText:
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = "<html><body></body></html>"
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with a valid URL and assert that it returns an empty string
url = "http://www.example.com"
@@ -72,7 +74,7 @@ class TestScrapeText:
# Tests that the function returns an error message when the response status code is an http error (>=400).
def test_http_error(self, mocker):
# Mock the requests.get() method to return a response with a 404 status code
mocker.patch("requests.get", return_value=mocker.Mock(status_code=404))
mocker.patch("requests.Session.get", return_value=mocker.Mock(status_code=404))
# Call the function with a URL
result = scrape_text("https://www.example.com")
@@ -87,7 +89,7 @@ class TestScrapeText:
mock_response = mocker.Mock()
mock_response.status_code = 200
mock_response.text = html
mocker.patch("requests.get", return_value=mock_response)
mocker.patch("requests.Session.get", return_value=mock_response)
# Call the function with a URL
result = scrape_text("https://www.example.com")

86
tests/unit/test_chat.py Normal file
View File

@@ -0,0 +1,86 @@
# Generated by CodiumAI
import unittest
import time
from unittest.mock import patch
from autogpt.chat import create_chat_message, generate_context
class TestChat(unittest.TestCase):
# Tests that the function returns a dictionary with the correct keys and values when valid strings are provided for role and content.
def test_happy_path_role_content(self):
result = create_chat_message("system", "Hello, world!")
self.assertEqual(result, {"role": "system", "content": "Hello, world!"})
# Tests that the function returns a dictionary with the correct keys and values when empty strings are provided for role and content.
def test_empty_role_content(self):
result = create_chat_message("", "")
self.assertEqual(result, {"role": "", "content": ""})
# Tests the behavior of the generate_context function when all input parameters are empty.
@patch("time.strftime")
def test_generate_context_empty_inputs(self, mock_strftime):
# Mock the time.strftime function to return a fixed value
mock_strftime.return_value = "Sat Apr 15 00:00:00 2023"
# Arrange
prompt = ""
relevant_memory = ""
full_message_history = []
model = "gpt-3.5-turbo-0301"
# Act
result = generate_context(prompt, relevant_memory, full_message_history, model)
# Assert
expected_result = (
-1,
47,
3,
[
{"role": "system", "content": ""},
{
"role": "system",
"content": f"The current time and date is {time.strftime('%c')}",
},
{
"role": "system",
"content": f"This reminds you of these events from your past:\n\n\n",
},
],
)
self.assertEqual(result, expected_result)
# Tests that the function successfully generates a current_context given valid inputs.
def test_generate_context_valid_inputs(self):
# Given
prompt = "What is your favorite color?"
relevant_memory = "You once painted your room blue."
full_message_history = [
create_chat_message("user", "Hi there!"),
create_chat_message("assistant", "Hello! How can I assist you today?"),
create_chat_message("user", "Can you tell me a joke?"),
create_chat_message(
"assistant",
"Why did the tomato turn red? Because it saw the salad dressing!",
),
create_chat_message("user", "Haha, that's funny."),
]
model = "gpt-3.5-turbo-0301"
# When
result = generate_context(prompt, relevant_memory, full_message_history, model)
# Then
self.assertIsInstance(result[0], int)
self.assertIsInstance(result[1], int)
self.assertIsInstance(result[2], int)
self.assertIsInstance(result[3], list)
self.assertGreaterEqual(result[0], 0)
self.assertGreaterEqual(result[1], 0)
self.assertGreaterEqual(result[2], 0)
self.assertGreaterEqual(
len(result[3]), 3
) # current_context should have at least 3 messages
self.assertLessEqual(
result[1], 2048
) # token limit for GPT-3.5-turbo-0301 is 2048 tokens

18
tests/unit/test_commands.py Normal file
View File

@@ -0,0 +1,18 @@
import autogpt.agent.agent_manager as agent_manager
from autogpt.app import start_agent, list_agents, execute_command
import unittest
from unittest.mock import patch, MagicMock
class TestCommands(unittest.TestCase):
def test_make_agent(self):
with patch("openai.ChatCompletion.create") as mock:
obj = MagicMock()
obj.response.choices[0].messages[0].content = "Test message"
mock.return_value = obj
start_agent("Test Agent", "chat", "Hello, how are you?", "gpt2")
agents = list_agents()
self.assertEqual("List of agents:\n0: chat", agents)
start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt2")
agents = list_agents()
self.assertEqual("List of agents:\n0: chat\n1: write", agents)