This package contains ROS Nodes related to the open source project GPT4ALL.
It integrates a GPT chat into the ROS framework. It enables to communicate via
topics or UNIX socket with a running GPT4ALL chat session.
Additionally a custom configurable terminal window ROS node is available to
enter chat messages and display the GPT response similar to the existing
GPT4ALL GUI but with reduced features.
This package is not another GPT4all variant. It just make use of it and can be seen as a wrapper for ROS.
Related links:
# install gpt4all
pip install gpt4all
# if using embedding
# use Qdrant
pip install 'qdrant-client[fastembed]'
# or Chroma DB
# install chroma
sudo apt install chroma
# install chromadb
pip install chromadb
pip install unstructured# for the following commands to work, an installed ROS2 distribution is required
# run in your ros2_ws/src folder
git clone https://gitlab.com/bob-ros2/rosgpt4all.git
cd ..
colcon build
. install/setup.bash# run as server ROS node
ros2 run rosgpt4all gpt.py
# run with redirect produced output
ros2 run rosgpt4all gpt.py --ros-args -r gpt_out:=/speak
# send message to gpt4all via command line
ros2 topic pub --once /gpt_in std_msgs/msg/String "{data: 'Hello'}"~allow_download
Type: string
Allow API to download models from gpt4all.io.
Default: true
~device
Type: string
The processing unit on which the GPT4All model will run. It can be set to:
"cpu": Model will run on the central processing unit.
"gpu": Model will run on the best available graphics processing unit, irrespective of its vendor.
"amd", "nvidia", "intel": Model will run on the best available GPU from the specified vendor.
Alternatively, a specific GPU name can also be provided, and the model will run on the GPU that matches the name if it's available.
Default: cpu
~eof_indicator
Type: string
End of file indicator. It will be send when the generator task was done. (only gpt_generator and gpt_sentence topics). Leave blank if no EOF should be send.
Default: EOF
~max_tokens
Type: integer
The maximum number of tokens to generate.
Default: 200
~model_name
Type: string
GPT4ALL model to be used. On the node very first start it will be dowloaded if it does not exist in ~model_path. Auto download depends on ~allow_download.
Default: orca-mini-3b.ggmlv3.q4_0.bin
~model_path
Type: string
GPT4ALL model path to be used.
Default: ~/.local/share/nomic.ai/GPT4All
~n_batch
Type: integer
Number of prompt tokens processed in parallel. Larger values decrease latency but increase resource requirements.
Default: 8
~prompt
Type: string
Promp format string to be used.
Default: > {0}\n\n
~repeat_last_n
Type: integer
How far in the models generation history to apply the repeat penalty.
Default: 64
~repeat_penalty
Type: double
Penalize the model for repetition. Higher values result in less repetition.
Default: 1.18
~socket_path
Type: string
Unix socket where to write the output.
Default: /tmp/gpt.sock
~temp
Type: double
The model temperature. Larger values increase creativity but decrease factuality.
Default: 0.7
~top_k
Type: integer
Randomly sample from the top_k most likely tokens at each generation step. Set this to 1 for greedy decoding.
Default: 40
~top_p
Type: double
Randomly sample at each generation step from the top most likely tokens whose probabilities add up to top_p.
Default: 0.4
~gpt_in (std_msgs/String)
GPT input.
~gpt_out (std_msgs/String)
GPT ouput. The whole message will be published when the generator has finished.
~gpt_generator (std_msgs/String)
GPT generator ouput. Each single token is published to the topic.
~gpt_sentence (std_msgs/String)
GPT ouput aggregated as sentence or sub sentence. A message with content EOF indicates the end. of the generator output.
The GPT node also creates a socket where the generator output will be streamed in realtime. Multiple clients can connect.
# the generator output can be received as followed
netcat -v -U /tmp/gpt.sock With this Ros Node the generator output of GPT4ALL node can be received and displayed in realtime. This works as well with any other stdout stream. Also a topic input subscriber is available. An input field can optionally be turned on which can be used to publish messages to another topic.
The required QT5 libraries should already exist if ROS is installed. If missing use below installation to get them.
sudo apt-get install python3-pyqt5# connect terminal with GPT node socket stream, use one of the existing QT5 arguments
netcat -v -U /tmp/gpt.sock | python3 terminal.py -qwindowgeometry 640x480+600+300
# run frameless terminal window using ROS parameter
netcat -v -U /tmp/gpt.sock | ros2 run rosgpt4all terminal.py --ros-args -p frameless:=true -p geometry:=[300,300,600,480]
# show window on another display
netcat -U /tmp/gpt.sock | ros2 run rosgpt4all terminal.py --ros-args -p display:=1 -p geometry:=[300,300,600,480]~display
Type: integer
Display number where to show the window. -1 = default display.
Default: -1
~fontname
Type: string
Window fontname.
Default: courier
~fontsize
Type: integer
Window fontsize.
Default: 12
~frameless
Type: boolean
Switch off window caption.
Default: false
~geometry
Type: integer array
Window geometry. [x, y, with, height]
~margin
Type: integer array
Window inner margin. [left, top, right, bottom]
Default: [10,0,0,0]
~input
Type: boolean
Enables or disables the text input field.
Default: true
~opacity
Type: double
Window opacity. 1.0 = fully visible.
Default: 1.0
~stylesheet
Type: string
Stylesheet qss of PlainText area.
Default: background-color: #000000; color: #f0f0f0;
~title
Type: string
Title of window.
Default: GPT4ALL Terminal
~line_count
Type: string
Maximum line count in the text area. 0 = unlimited.
If the number exceeds the lines are removed from the top. Default: 0
~input (std_msgs/String)
Read input data from topic in addition to be able to read data from stdin.
~gpt_in (std_msgs/String)
Publish to GPT4ALL input.
Basic Embedder ROS Node. This ROS node subscribes to an /embed String topic to receive JSON data with embedding messages. It embeds the delivered data into the configured Vector DB. A Qdrant Vector DB is the default DB. To embed into a Chroma DB set the environment variable EMBED_CHROMADB, it's enough if it just exists. See ROS parameter for further configuration.
Related Qdrant links:
- https://qdrant.tech/
- https://github.com/qdrant/fastembed
- https://qdrant.tech/documentation/embeddings/nomic/
By default model nomic-ai/nomic-embed-text-v1 will be used to create
embeddings for Qdrant.
Related Chroma links:
By default Sentence Transformers all-MiniLM-L6-v2 model will be use to
create embeddings for Chroma.
The JSON data which is received by the String topic has to contain the following fields. (for Qdrant the ids are optional)
{
"collection": "xyz",
"documents": ["hey ya","jo do"],
"metadatas": [{"name":"works"}, {"name":"nut"}],
"ids": ["id1","id2"]
}# in order to work below examples expects a running Qdrant or Chroma Vector DB
# start embedder node, by default use Qdrant Vector DB running on localhost
ros2 run rosgpt4all embed
# start embedder node using Chroma Vector DB running on localhost
EMBED_CHROMADB= ros2 run rosgpt4all embedder
# run in debug mode, this will also set Python logging to level DEBUG
ros2 run rosgpt4all embed --ros-args --log-level DEBUG~model
Modelname to be used to produce the embedding. It will be downloaded if it does not yet exists locally.
Default: ''
~host
Host name or ip.
Default: 'localhost'
~port
To be used port for the Vector DB API.
Default: (6333 or 8000, this depends from used Vector DB)
~location
Location of Vector DB. Can also be an URL. This parameter is only used by Qdrant.
Default: ''
~path
Path where to store the Vector DB data.
Default: ''
~embed (std_msgs/String)
Incoming JSON string with the embedding data.
Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.
Please make sure to update tests as appropriate.