diff --git a/.gitignore b/.gitignore index dd230dd..f2cb1a2 100644 --- a/.gitignore +++ b/.gitignore @@ -176,6 +176,8 @@ rlhf_tests test_data example/flex/docs example/flex/flex.ipynb +example/flex/flex_sample/ +example/flex/vectordb/ # mac .DS_Store @@ -189,3 +191,6 @@ models/ # retrieval temp/ + +# poetry +poetry.lock \ No newline at end of file diff --git a/README.md b/README.md index 6b89c6d..f81e069 100644 --- a/README.md +++ b/README.md @@ -1,75 +1,97 @@ -# 🎏 pykoi: Active learning in one unified interface +# 🎏 pykoi: RLHF/RLAIF in one unified interface +

+ License Apache-2.0 + pypi_status + Commit activity + Slack +

+ +[pykoi](https://www.cambioml.com/pykoi/) is an open-source python library for improving LLMs with RLHF. We provide a unified interface including RLHF/RLAIF data and feedback collection, finetuning with reinforcement learning and reward modeling, and LLM comparisons. + +## Features + +`pykoi` let you easily get real-time user feedback and continuously improve your models. Here are some common applications: + +### Sharable UI + +Do you want to store your chat history with LLMs from OpenAI, Amazon Bedrock(:woman_technologist: building now :man_technologist:), or Huggingface? With just three lines of code, pykoi lets you to store them locally, ensuring 100% privacy. This includes launching a chatbot UI, automatically saving your chat history in your compute instance (cpu or gpu), and visualizing it on a dashboard. Explore the demos below: + +- If you're using a CPU instance, check out [demo_launch_app_cpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_launch_app_cpu_openai.ipynb) +- If you're using a GPU instance, check out [demo_launch_app_gpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_launch_app_gpu_huggingface.ipynb) +- Alternatively, read our [blog](https://www.cambioml.com/docs/data_collection_feedback.html) for more information! + +![Watch the video](example/image/pykoi_demo_rlaif_data_collection.gif) + + +### Model comparison + +Comparing models is a difficult task. `pykoi` makes it easy by allowing one to directly compare the performance of multiple models to each other, with just a few lines of code. If you have multiple language models that you’d like to compare to each other on a set of prompts or via an interactive session, you can use `pk.Compare`. Check out any of the demo below: + +- If you're using a CPU instance, check out [demo_launch_app_cpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_openai.ipynb) +- If you're using a GPU instance, check out [demo_launch_app_gpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_gpu_huggingface.ipynb) +- Alternatively, read our [blog](https://www.cambioml.com/docs/model_comparison.html) for more information! + + ![Watch the video](example/image/pykoi_demo_model_comparison.gif) + +### RLHF + +Reinforcement Learning with Human Feedback (RLHF) is a unique training paradigm that blends reinforcement learning with human-in-the-loop training. The central idea is to use human evaluative feedback to refine a model's decision-making ability and guide the learning process towards desired outcomes. Researchers from [Deepmind](https://www.deepmind.com/blog/learning-through-human-feedback), [OpenAI](https://openai.com/research/learning-from-human-preferences) and [Meta Llama2](https://arxiv.org/pdf/2307.09288.pdf) have all demonstrated that RLHF is a game changer for large language models (LLMs) training. + +`pykoi` allows you to easily fine-tune your model on the datasets you've collected via your `pykoi` chat or rank databases. Check our [blog](https://www.cambioml.com/docs/rlhf.html) for detailed instructions on how to use it. -[pykoi](https://www.cambioml.com/pykoi/) is an open-source python library for LLMs providing a unified interface for data & feedback collection, RLHF, and model comparisons. ## Installation -To get started with `pykoi`, you can choose to one of following compute options: CPU (e.g. your laptop) or GPU (e.g. EC2). +To get started with pykoi, you can choose from any of the installation options. The choice should be based on the features you need (e.g., RAG, RLHF or all) and the compute resources you have, such as a CPU (e.g., your laptop) or GPU (e.g., AWS EC2 or SageMaker). -### Option 1: CPU (e.g. your laptop) -Installation on a CPU is simple if you have conda. If not, install [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) for your operating system. +### Option 1: RAG (CPU) +This option allows you to run RAG on a CPU using either the OpenAI API or the Anthropic Claude2 API. Installation of RAG (CPU) is simple if you have conda. If not, install [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) for your operating system. First, create a conda environment on your terminal using: ``` conda create -n pykoi python=3.10 -y -conda activate pykoi +conda activate pykoi # some OS requires `source activate pykoi` ``` -Then install `pykoi` and the compatible torch version. +Then install `pykoi` and the compatible [pytorch based on your os](https://pytorch.org/get-started) ``` -pip3 install pykoi +pip3 install pykoi[rag] +pip3 install torch ``` -### Option 2: GPU (e.g. EC2) +### Option 2: RAG (GPU) +This option allows you to run RAG on a GPU using an open-source LLM from HuggingFace. Here's a quick [tutorial](#ec2-dev-setup) on setting up an EC2 GPU instance for the installation below. -If you are on EC2, you can launch a GPU instance with the following config: -- EC2 `g4dn.xlarge` (if you want to run a pretrained model with 7B parameters) -- Deep Learning AMI PyTorch GPU 2.0.1 (Ubuntu 20.04) - Alt text -- EBS: at least 100G - Alt text - -Next, on your GPU instance terminal, create a conda environment using: +On your GPU instance terminal, create a conda environment using: ``` conda create -n pykoi python=3.10 -y && source activate pykoi ``` -Then install `pykoi` and the correlated torch version. +Then install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started). You can find your CUDA version via `nvcc -V`. ``` -pip3 install pykoi && pip3 install torch --index-url https://download.pytorch.org/whl/cu118 -``` - - -Congrats you have finished installation, +pip3 install pykoi[huggingface] -## How do I use `pykoi`? - -`pykoi` is a python interface to unify your ML model development and production. You can easily get real-time user feedback and continuously improve your model. - -Here are some common applications: - -### Sharable Chatbot UI with Database - -Are you concerned about storing your chat history with chatbots from OpenAI, Amazon Bedrock(:woman_technologist: building now :man_technologist:), or Huggingface? With just three lines of code, we enable you to store it locally, ensuring 100% privacy. This includes launching a chatbot UI, automatically saving your chat history, and visualizing it on a dashboard. Explore the demos below: - -- If you're using a CPU instance, check out [demo_launch_app_api.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_launch_app_api.ipynb) -- If you're using a GPU instance, check out [demo_launch_app_gpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_launch_app_gpu.ipynb) -- Alternatively, read our [blog](https://www.cambioml.com/docs/data_collection_feedback.html) for more information! - -### Model comparison +# install torch based on cuda (e.g. cu118 means cuda 11.8) +pip3 install torch --index-url https://download.pytorch.org/whl/cu118 +``` -Comparing models is a difficult task. `pykoi` makes it easy by allowing one to directly compare the performance of multiple models to each other, with just 1 line of code. If you have multiple language models that you’d like to compare to each other on a set of prompts or via an interactive session, you can use `pk.Compare`. Check out any of the demo below: +### Option 3: RLHF (GPU) +This option allows you to train LLM via RLHF on a GPU. Here's a quick [tutorial](#ec2-dev-setup) on setting up an EC2 GPU instance for the installation below. -- If you're using a CPU instance, check out [demo_launch_app_api.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_openai.ipynb) -- If you're using a GPU instance, check out [demo_launch_app_gpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_hf.ipynb) -- Alternatively, read our [blog](https://www.cambioml.com/docs/model_comparison.html) for more information! - -### RLHF +On your GPU instance terminal, create a conda environment using: +``` +conda create -n pykoi python=3.10 -y && source activate pykoi +``` -Reinforcement Learning with Human Feedback (RLHF) is a unique training paradigm that blends reinforcement learning with human-in-the-loop training. The central idea is to use human evaluative feedback to refine a model's decision-making ability and guide the learning process towards desired outcomes. Researchers from [Deepmind](https://www.deepmind.com/blog/learning-through-human-feedback), [OpenAI](https://openai.com/research/learning-from-human-preferences) and [Meta Llama2](https://arxiv.org/pdf/2307.09288.pdf) have all demonstrated that RLHF is a game changer for large language models (LLMs) training. +Then install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started). You can find your CUDA version via `nvcc -V`. +``` +pip3 install pykoi[rlhf] -`pykoi` allows you to easily fine-tune your model on the datasets you've collected via your `pykoi` chat or rank databases. Check our [blog](https://www.cambioml.com/docs/rlhf.html) for detailed instructions on how to use it. +# install torch based on cuda (e.g. cu118 means cuda 11.8) +pip3 install torch --index-url https://download.pytorch.org/whl/cu118 +``` +Congrats you have finished the installation! ## Dev Setup If you are interested to contribute to us, here are the preliminary development setup. @@ -80,13 +102,44 @@ conda create -n pykoi python=3.10 conda activate pykoi cd pykoi pip3 install poetry -poetry install --no-root ``` +Then, based the feature you need to develop, run one or more installation options below. We recommend install all the options below although it may take ~3 minutes longer. + +- Option 1: RAG (CPU) + ``` + poetry install --no-root --extras rag + ``` +- Option 2: RAG (GPU) + ``` + poetry install --no-root --extras huggingface + ``` +- Option 3: RLHF (GPU) + ``` + poetry install --no-root --extras rlhf + ``` + +Finally, if you are on a GPU, install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started). You can find your CUDA version via `nvcc -V`. +``` +pip3 install pykoi[huggingface] + +# install torch based on cuda (e.g. cu118 means cuda 11.8) +pip3 install torch --index-url https://download.pytorch.org/whl/cu118 +``` + + ### Frontend Dev Setup -Frontend: ``` cd pykoi/pykoi/frontend npm install npm run build ``` + +### EC2 Dev Setup +If you are on EC2, you can launch a GPU instance with the following config: +- EC2 `g4dn.xlarge` (if you want to run a pretrained LLM with 7B parameters) +- Deep Learning AMI PyTorch GPU 2.0.1 (Ubuntu 20.04) + Alt text +- EBS: at least 100G + Alt text + diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..a2bb3a4 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,14 @@ +# Overview +In this folder, we create the different dockerfiles for using pykoi. + +1. `pykoi-cpu`: The base image for the cpu-based usage. +2. `pykoi-cpu-custom`: When you run this docker image, try to modify the `app.py` and mount it when running the docker container. + +To run a docker container, we can use the following command: +```bash +docker run -dp 5000:5000 -v $(pwd)/app.py:/app/app.py \ + --name alex_test \ + pykoi/pykoi-cpu:app +``` + +Note that we need to keep the exposed port as 5000 (default value) to make the server work. \ No newline at end of file diff --git a/docker/pykoi-cpu-custom/Dockerfile b/docker/pykoi-cpu-custom/Dockerfile new file mode 100644 index 0000000..f4431fa --- /dev/null +++ b/docker/pykoi-cpu-custom/Dockerfile @@ -0,0 +1,13 @@ +FROM pykoi/pykoi-cpu:base + +# set the working directory in the container +WORKDIR /app + +# copy the content +COPY . /app + +# Expose the port on which the app will be running +EXPOSE 5000 + +# For dev +CMD ["python", "app.py"] diff --git a/example/chatbot/openai_model_demo.py b/docker/pykoi-cpu-custom/app.py similarity index 89% rename from example/chatbot/openai_model_demo.py rename to docker/pykoi-cpu-custom/app.py index 733acac..6f46654 100644 --- a/example/chatbot/openai_model_demo.py +++ b/docker/pykoi-cpu-custom/app.py @@ -5,7 +5,7 @@ # Creating an OpenAI model (requires an OpenAI API key) # ########################################################## # enter openai api key here -api_key = "" +api_key = "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" # Creating an OpenAI model model = pykoi.ModelFactory.create_model( @@ -25,7 +25,9 @@ ########################################################### # Create the application # app = pykoi.Application(debug=False, share=True) -app = pykoi.Application(debug=False, share=False) +app = pykoi.Application( + debug=False, + share=True) app.add_component(chatbot) app.add_component(dashboard) app.run() diff --git a/docker/pykoi-cpu/Dockerfile b/docker/pykoi-cpu/Dockerfile new file mode 100644 index 0000000..6dfed62 --- /dev/null +++ b/docker/pykoi-cpu/Dockerfile @@ -0,0 +1,27 @@ +FROM python:3.10 +LABEL maintainer="Pykio Team" +LABEL repository="Pykoi" + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt update && \ + apt install -y bash \ + build-essential \ + git \ + git-lfs \ + curl \ + ca-certificates \ + libsndfile1-dev + +# make sure to use venv +RUN python -m venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py) +# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container +RUN python -m pip install --no-cache-dir --upgrade pip && \ + python -m pip install --upgrade --no-cache-dir \ + pykoi + +CMD ["/bin/bash"] + diff --git a/docker/pykoi-gpu/Dockerfile b/docker/pykoi-gpu/Dockerfile new file mode 100644 index 0000000..8e07f3c --- /dev/null +++ b/docker/pykoi-gpu/Dockerfile @@ -0,0 +1,31 @@ +FROM python:3.10 +LABEL maintainer="Pykio Team" +LABEL repository="Pykoi" + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt update && \ + apt install -y bash \ + build-essential \ + git \ + git-lfs \ + curl \ + ca-certificates \ + libsndfile1-dev \ + libgl1 + +# make sure to use venv +RUN python3 -m venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py) +RUN python3 -m pip install --no-cache-dir --upgrade pip && \ + python3 -m pip install --no-cache-dir \ + torch \ + torchvision \ + torchaudio && \ + python3 -m pip install --no-cache-dir \ + pykoi + +CMD ["/bin/bash"] + diff --git a/example/chatbot/chatbot_in_jupyter.ipynb b/example/chatbot/chatbot_in_jupyter.ipynb index 72234c4..8b7acbc 100644 --- a/example/chatbot/chatbot_in_jupyter.ipynb +++ b/example/chatbot/chatbot_in_jupyter.ipynb @@ -16,9 +16,20 @@ "current_directory = os.getcwd()\n", "sys.path.append(\".\")\n", "sys.path.append(\"..\")\n", - "sys.path.append(\"../..\")\n", - "\n", - "import pykoi as pk" + "sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a907bb3", + "metadata": {}, + "outputs": [], + "source": [ + "from pykoi import Application\n", + "from pykoi.chat import ModelFactory\n", + "from pykoi.chat import QuestionAnswerDatabase\n", + "from pykoi.component import Chatbot" ] }, { @@ -31,7 +42,7 @@ "api_key = \"\"\n", "\n", "# Creating an OpenAI model\n", - "model = pk.ModelFactory.create_model(model_source=\"openai\", api_key=api_key)" + "model = ModelFactory.create_model(model_source=\"openai\", api_key=api_key)" ] }, { @@ -41,10 +52,10 @@ "metadata": {}, "outputs": [], "source": [ - "database = pk.QuestionAnswerDatabase(debug=True)\n", - "chatbot = pk.Chatbot(model=model, feedback=\"vote\")\n", + "database = QuestionAnswerDatabase(debug=True)\n", + "chatbot = Chatbot(model=model, feedback=\"vote\")\n", "\n", - "app = pk.Application(debug=False, share=False)\n", + "app = Application(debug=False, share=False)\n", "app.add_component(chatbot)" ] }, @@ -76,7 +87,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/example/chatbot/demo_launch_app_api.ipynb b/example/chatbot/demo_launch_app_api.ipynb deleted file mode 100644 index 57b934d..0000000 --- a/example/chatbot/demo_launch_app_api.ipynb +++ /dev/null @@ -1,292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/goldpiggy/anaconda3/envs/0826/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/goldpiggy/anaconda3/envs/0826/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n", - " warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'NoneType' object has no attribute 'cadam32bit_grad_fp32'\n" - ] - } - ], - "source": [ - "## pip install ipykernel\n", - "import pykoi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating an OpenAI model (requires an OpenAI API key)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# enter openai api key here\n", - "api_key = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = pykoi.ModelFactory.create_model(\n", - " model_source=\"openai\",\n", - " api_key=api_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define a database, model and chatbot\n", - "\n", - "To a launch a pykoi App, you only need to customize 3 components: a model, a database and a chatbot. Let's try it out!" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "qa_database = pykoi.QuestionAnswerDatabase()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "chatbot = pykoi.Chatbot(model=model, feedback=\"vote\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Launch the App\n", - "\n", - "Now we can launch the chatbot using the model and database. Once you run the below line, you will see a Tunnel link which ends with `ngrok-free.app`. Click this link and you can see the below interface:\n", - "\n", - "

\n", - " \n", - "

\n", - "\n", - "\n", - "\n", - "You may need to set a ngrok token (one time) by uncomment the below line, add your [personal ngrok token](https://dashboard.ngrok.com/get-started/your-authtoken) and run this `ngrok config` line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !ngrok config add-authtoken xxx" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Public URL: NgrokTunnel: \"https://8101-76-21-76-39.ngrok.io\" -> \"http://127.0.0.1:5000\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Started server process [32123]\n", - "INFO: Waiting for application startup.\n", - "INFO: Application startup complete.\n", - "INFO: Uvicorn running on http://127.0.0.1:5000 (Press CTRL+C to quit)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: 127.0.0.1:52759 - \"GET / HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52759 - \"GET /assets/index-9d416840.js HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52760 - \"GET /assets/index-fd8bb80f.css HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52759 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52760 - \"GET /vite.svg HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52759 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 404 Not Found\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Shutting down\n", - "INFO: Waiting for application shutdown.\n", - "INFO: Application shutdown complete.\n", - "INFO: Finished server process [32123]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stopping server...\n" - ] - } - ], - "source": [ - "app = pykoi.Application(debug=False, share=True, username=\"rachel\", password=\"1234\")\n", - "app.add_component(chatbot)\n", - "app.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize your data\n", - "\n", - "Once you collect enough data via the chatbot app above, you can visualize your data via the dashboard." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "qa_dashboard = pykoi.Dashboard(database=qa_database)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Started server process [32123]\n", - "INFO: Waiting for application startup.\n", - "INFO: Application startup complete.\n", - "INFO: Uvicorn running on http://127.0.0.1:5000 (Press CTRL+C to quit)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: 127.0.0.1:52783 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52783 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52788 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52788 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n", - "INFO: 127.0.0.1:52788 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Shutting down\n", - "INFO: Waiting for application shutdown.\n", - "INFO: Application shutdown complete.\n", - "INFO: Finished server process [32123]\n" - ] - } - ], - "source": [ - "app = pykoi.Application(debug=False, share=False)\n", - "app.add_component(chatbot)\n", - "app.add_component(qa_dashboard)\n", - "app.run()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will only see one localhost link after you run the `app.run()` below. If you are on EC2 or a remote server, you can tunnel it back to your laptop via the following options:\n", - "\n", - "- If you are using VSCode, check [tunnel using VSCode](https://code.visualstudio.com/docs/remote/ssh#_forwarding-a-port-creating-ssh-tunnel);\n", - "- [Directly config via EC2](https://www.opensourceforu.com/2021/09/how-to-do-reverse-tunnelling-with-the-amazon-ec2-instance/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can launch the dashboard. Click this above link and you can see the below interface:\n", - "\n", - "

\n", - " \n", - "

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "0826", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/chatbot/demo_launch_app_cpu_openai.ipynb b/example/chatbot/demo_launch_app_cpu_openai.ipynb new file mode 100644 index 0000000..65c1060 --- /dev/null +++ b/example/chatbot/demo_launch_app_cpu_openai.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Launch a Chatbot UI (with Database) from an OpenAI model\n", + "\n", + "`pykoi` provides simple UI to launch a chatbot UI based on your LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs. This demo shows how to create and launch an LLM chatbot UI and database for OpenAI/Anthropic/Bedrock APIs. Let's get started!\n", + "\n", + "\n", + "### Prerequisites\n", + "To run this jupyter notebook, you need a `pykoi` environment with the `rag` option. You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-1-rag-cpu) to set up the environment. \n", + "\n", + "You may also need `pip install ipykernel` to run the kernel environment.\n", + "\n", + "\n", + "### (Optional) Developer setup\n", + "If you are a normal user of `pykoi`, you can skip this step. However, if you modify the pykoi code and want to test your changes, you can uncomment the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %reload_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "# import sys\n", + "\n", + "# sys.path.append(\".\")\n", + "# sys.path.append(\"..\")\n", + "# sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pykoi import Application\n", + "from pykoi.chat import ModelFactory\n", + "from pykoi.chat import QuestionAnswerDatabase\n", + "from pykoi.component import Chatbot, Dashboard" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating an OpenAI model (requires an OpenAI API key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter openai api key here\n", + "api_key = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = ModelFactory.create_model(\n", + " model_source=\"openai\",\n", + " api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a database, model and chatbot\n", + "\n", + "To a launch a pykoi App, you only need to customize 3 components: a model, a database and a chatbot. Let's try it out!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qa_database = QuestionAnswerDatabase()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chatbot = Chatbot(model=model, feedback=\"vote\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launch the App\n", + "\n", + "Now we can launch the chatbot using the model and database. Once you run the below line, you will see a Tunnel link which ends with `ngrok-free.app`. Click this link and you can see the below interface:\n", + "\n", + "

\n", + " \n", + "

\n", + "\n", + "\n", + "\n", + "You may need to set a ngrok token (one time) by uncomment the below line, add your [personal ngrok token](https://dashboard.ngrok.com/get-started/your-authtoken) and run this `ngrok config` line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !ngrok config add-authtoken xxx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app = Application(debug=False, share=True, username=\"rachel\", password=\"1234\")\n", + "app.add_component(chatbot)\n", + "app.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize your data\n", + "\n", + "Once you collect enough data via the chatbot app above, you can visualize your data via the dashboard." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qa_dashboard = Dashboard(database=qa_database)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "t=2023-09-12T01:18:52-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:51858->3.134.73.173:443: read: connection reset by peer\"\n", + "t=2023-09-12T01:32:52-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:53684->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T01:32:57-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=cc480ddd12ca clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T01:48:35-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:53813->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T02:04:07-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=04762d28f116 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T02:21:55-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:53905->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T02:39:34-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=15d86cc263ed clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T02:57:35-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54079->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T03:15:13-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=de851650ca25 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T03:30:48-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54292->3.134.73.173:443: read: connection reset by peer\"\n", + "t=2023-09-12T03:30:52-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=ee72220bc8ea clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T03:48:52-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54445->3.133.228.214:443: read: connection reset by peer\"\n", + "t=2023-09-12T04:05:34-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=6e8c1b39a7ed clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T04:07:35-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54579->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T04:24:16-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=1635c9fd0a95 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T04:40:32-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54645->3.16.250.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T04:40:36-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=7098a9514ade clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T04:57:38-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54759->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T05:12:59-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=17cd2e176a93 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T05:28:08-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:54905->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T05:44:11-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=c1f9867672ff clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T06:01:24-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55045->3.133.228.214:443: read: connection reset by peer\"\n", + "t=2023-09-12T06:01:28-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=d1b0adf825f4 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T06:17:57-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55197->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T06:33:59-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=045eb1bdd071 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T06:51:45-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55305->3.133.228.214:443: read: connection reset by peer\"\n", + "t=2023-09-12T07:08:47-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=ef02b407f58f clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T07:26:42-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55453->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T07:26:46-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=0ebf98c1b8d9 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T07:42:47-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55596->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T07:59:51-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to send authentication request: read tcp 192.168.1.14:55706->3.133.228.214:443: read: connection reset by peer\"\n", + "t=2023-09-12T07:59:54-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=9a7d09cfe70f clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T08:15:13-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55753->3.20.27.198:443: read: connection reset by peer\"\n", + "t=2023-09-12T08:30:30-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=4193bcab5d65 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T08:41:31-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"session closed\"\n", + "t=2023-09-12T08:41:35-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=68d49f4de50a clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T08:53:46-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:55974->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T09:13:38-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=ca4ad0efac68 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T09:29:33-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=27ed3a1045ee clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T09:29:33-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"session closed\"\n", + "t=2023-09-12T09:58:35-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56247->3.16.250.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T10:16:18-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56362->3.134.73.173:443: read: connection reset by peer\"\n", + "t=2023-09-12T10:32:26-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=40e8f70cb5f3 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T10:49:11-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56444->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T11:05:27-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=99a50827acf3 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T11:21:38-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56566->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T11:21:42-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=41878b24a2fb clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T11:38:57-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56777->3.134.73.173:443: read: connection reset by peer\"\n", + "t=2023-09-12T11:56:52-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56888->3.136.132.147:443: read: connection reset by peer\"\n", + "t=2023-09-12T11:56:54-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=f39f77e83e55 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T12:14:16-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:56940->3.16.250.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T12:31:34-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=1194c26a755c clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T12:46:44-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57062->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T13:00:58-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=45431debbd46 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T13:00:59-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=4ea8efaf157c clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T13:01:05-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57212->3.20.27.198:443: read: connection reset by peer\"\n", + "t=2023-09-12T13:05:13-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=1fa447c82069 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T13:23:09-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=0bd50258b330 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T13:57:11-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=2d8d7f46d1b3 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T13:57:11-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"session closed\"\n", + "t=2023-09-12T14:32:23-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57513->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T14:47:07-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57606->3.16.250.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T15:05:07-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=6d1d2a156588 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T15:21:19-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57717->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T15:21:23-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=f31d2ae7e439 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T15:32:55-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:57847->3.12.62.205:443: read: connection reset by peer\"\n", + "t=2023-09-12T15:33:01-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=e9d47c352d0a clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T15:33:18-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=f725fd685f71 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T15:43:14-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=35ca76001e49 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T15:43:14-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"session closed\"\n", + "t=2023-09-12T16:17:01-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:58013->3.20.27.198:443: read: connection reset by peer\"\n", + "t=2023-09-12T16:49:30-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"read tcp 192.168.1.14:58149->3.133.228.214:443: read: connection reset by peer\"\n", + "t=2023-09-12T17:00:22-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=15d8f761b1a0 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T17:00:33-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=a633c3aab1c2 clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T17:06:47-0700 lvl=eror msg=\"heartbeat timeout, terminating session\" obj=tunnels.session obj=csess id=844f0cac015e clientid=0e9d5d409373b0929f4cc030441823a8\n", + "t=2023-09-12T17:06:47-0700 lvl=eror msg=\"session closed, starting reconnect loop\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"session closed\"\n", + "t=2023-09-12T17:06:47-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:06:48-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:06:49-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:06:51-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:06:55-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:24:37-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T17:24:53-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:17:10-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:51:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:51:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:52:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:52:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:53:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:53:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:54:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:54:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:55:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:55:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:56:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:56:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:57:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:57:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:58:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:58:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:59:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T18:59:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:00:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:00:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:01:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:01:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:02:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:02:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:03:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:03:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:04:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:04:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:05:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:05:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:06:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:06:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:07:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:07:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:08:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:08:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:09:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:09:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:10:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:10:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:11:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:11:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:12:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:12:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:13:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:13:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:14:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:14:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:15:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:15:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:16:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:16:34-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n", + "t=2023-09-12T19:17:04-0700 lvl=eror msg=\"failed to reconnect session\" obj=tunnels.session obj=csess id=51b0fe54dcb1 err=\"failed to dial ngrok server with address \\\"tunnel.us.ngrok.com:443\\\": dial tcp: lookup tunnel.us.ngrok.com: no such host\"\n" + ] + } + ], + "source": [ + "app = Application(debug=False, share=False)\n", + "app.add_component(chatbot)\n", + "app.add_component(qa_dashboard)\n", + "app.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will only see one localhost link after you run the `app.run()` below. If you are on EC2 or a remote server, you can tunnel it back to your laptop via the following options:\n", + "\n", + "- If you are using VSCode, check [tunnel using VSCode](https://code.visualstudio.com/docs/remote/ssh#_forwarding-a-port-creating-ssh-tunnel);\n", + "- [Directly config via EC2](https://www.opensourceforu.com/2021/09/how-to-do-reverse-tunnelling-with-the-amazon-ec2-instance/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can launch the dashboard. Click this above link and you can see the below interface:\n", + "\n", + "

\n", + " \n", + "

" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "0826", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/chatbot/demo_launch_app_cpu_openai.py b/example/chatbot/demo_launch_app_cpu_openai.py new file mode 100644 index 0000000..4670ce2 --- /dev/null +++ b/example/chatbot/demo_launch_app_cpu_openai.py @@ -0,0 +1,49 @@ +""" +Demo for launching a chatbot UI (with database) from an OpenAI model. + +- Prerequisites: + To run this jupyter notebook, you need a `pykoi` environment with the `rag` option. + You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-1-rag-cpu) + to set up the environment. +- Run the demo: + 1. Enter your OpenAI API key in the `api_key` below. + 2. On terminal and `~/pykoi` directory, run + ``` + python -m example.chatbot.demo_launch_app_cpu_openai + ``` +""" + +from pykoi import Application +from pykoi.chat import ModelFactory +from pykoi.chat import QuestionAnswerDatabase +from pykoi.component import Chatbot, Dashboard + + +########################################################## +# Creating an OpenAI model (requires an OpenAI API key) # +########################################################## +# enter openai api key here +api_key = "" + +# Creating an OpenAI model +model = ModelFactory.create_model( + model_source="openai", + api_key=api_key) + +##################################### +# Creating a chatbot with the model # +##################################### +database = QuestionAnswerDatabase(debug=True) +chatbot = Chatbot(model=model, feedback="vote") +# chatbot = pykoi.Chatbot(model=model, feedback="rank") +dashboard = Dashboard(database=database) + +########################################################### +# Starting the application and add chatbot as a component # +########################################################### +# Create the application +# app = pykoi.Application(debug=False, share=True) +app = Application(debug=False, share=False) +app.add_component(chatbot) +app.add_component(dashboard) +app.run() diff --git a/example/chatbot/demo_launch_app_gpu.ipynb b/example/chatbot/demo_launch_app_gpu.ipynb deleted file mode 100644 index 03bcba0..0000000 --- a/example/chatbot/demo_launch_app_gpu.ipynb +++ /dev/null @@ -1,364 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/ec2-user/SageMaker/pykoi\n" - ] - } - ], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import sys\n", - "\n", - "# Add the root folder to the module search path\n", - "# Get the current directory\n", - "current_directory = os.getcwd()\n", - "\n", - "# Move two levels up (go to the parent directory of the parent directory)\n", - "two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", - "\n", - "print(two_levels_up_directory)\n", - "\n", - "sys.path.append(two_levels_up_directory)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ec2-user/anaconda3/envs/0731a/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "## pip install ipykernel\n", - "import pykoi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define a database, model and chatbot\n", - "\n", - "To a launch a pykoi App, you only need to customize 3 components: a model, a database and a chatbot. Let's try it out!" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "qa_database = pykoi.QuestionAnswerDatabase()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[HuggingfaceModel] loading model...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading checkpoint shards: 100%|██████████| 2/2 [00:23<00:00, 11.75s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[HuggingfaceModel] loading tokenizer...\n" - ] - } - ], - "source": [ - "model = pykoi.ModelFactory.create_model(\n", - " model_source=\"huggingface\", \n", - " pretrained_model_name_or_path=\"tiiuae/falcon-7b\",\n", - " trust_remote_code=True, ## TODO: set as default\n", - " load_in_8bit=True\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "chatbot = pykoi.Chatbot(model=model, feedback=\"vote\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Launch the App\n", - "\n", - "Now we can launch the chatbot using the model and database. Once you run the below line, you will see a Tunnel link which ends with `ngrok-free.app`. Click this link and you can see the below interface:\n", - "\n", - "

\n", - " \n", - "

\n", - "\n", - "\n", - "\n", - "You may need to set a ngrok token (one time) by uncomment the below line, add your [personal ngrok token](https://dashboard.ngrok.com/get-started/your-authtoken) and run this `ngrok config` line." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# !ngrok config add-authtoken xxx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add `nest_asyncio` \n", - "Add `nest_asyncio` to avoid error such as `asyncio.run() cannot be called from a running event loop`. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install -q nest_asyncio\n", - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "t=2023-08-01T04:26:32+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/home/ec2-user/.config/ngrok/ngrok.yml legacy_path=/home/ec2-user/.ngrok2/ngrok.yml\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Public URL: NgrokTunnel: \"https://d7cd-34-209-105-222.ngrok-free.app\" -> \"http://localhost:34349\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Started server process [21474]\n", - "INFO: Waiting for application startup.\n", - "INFO: Application startup complete.\n", - "INFO: Uvicorn running on http://127.0.0.1:34349 (Press CTRL+C to quit)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: 76.21.76.39:0 - \"GET / HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-fc1a91e8.js HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-f75c1c12.css HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /vite.svg HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 404 Not Found\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Shutting down\n", - "INFO: Waiting for application shutdown.\n", - "INFO: Application shutdown complete.\n", - "INFO: Finished server process [21474]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stopping server...\n" - ] - } - ], - "source": [ - "app = pykoi.Application(debug=False, share=True)\n", - "app.add_component(chatbot)\n", - "app.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize your data\n", - "\n", - "Once you collect enough data via the chatbot app above, you can visualize your data via the dashboard." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "qa_dashboard = pykoi.Dashboard(database=qa_database)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "t=2023-08-01T04:27:23+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/home/ec2-user/.config/ngrok/ngrok.yml legacy_path=/home/ec2-user/.ngrok2/ngrok.yml\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Public URL: NgrokTunnel: \"https://82f5-34-209-105-222.ngrok-free.app\" -> \"http://localhost:37933\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Started server process [21474]\n", - "INFO: Waiting for application startup.\n", - "INFO: Application startup complete.\n", - "INFO: Uvicorn running on http://127.0.0.1:37933 (Press CTRL+C to quit)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: 76.21.76.39:0 - \"GET / HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-fc1a91e8.js HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-f75c1c12.css HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /vite.svg HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /chat/qa_table/retrieve HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Shutting down\n", - "INFO: Waiting for application shutdown.\n", - "INFO: Application shutdown complete.\n", - "INFO: Finished server process [21474]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stopping server...\n" - ] - } - ], - "source": [ - "app = pykoi.Application(debug=False, share=True)\n", - "app.add_component(chatbot)\n", - "app.add_component(qa_dashboard)\n", - "app.run()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will only see one localhost link after you run the `app.run()` below. If you are on EC2 or a remote server, you can tunnel it back to your laptop via the following options:\n", - "\n", - "- If you are using VSCode, check [tunnel using VSCode](https://code.visualstudio.com/docs/remote/ssh#_forwarding-a-port-creating-ssh-tunnel);\n", - "- [Directly config via EC2](https://www.opensourceforu.com/2021/09/how-to-do-reverse-tunnelling-with-the-amazon-ec2-instance/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can launch the dashboard. Click this above link and you can see the below interface:\n", - "\n", - "

\n", - " \n", - "

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pykoi", - "language": "python", - "name": "0731a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/chatbot/demo_launch_app_gpu_huggingface.ipynb b/example/chatbot/demo_launch_app_gpu_huggingface.ipynb new file mode 100644 index 0000000..4b3246e --- /dev/null +++ b/example/chatbot/demo_launch_app_gpu_huggingface.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Launch a Chatbot UI (with Database) from Open-source LLMs\n", + "\n", + "`pykoi` provides simple UI to launch a chatbot UI based on your LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs. This demo shows how to create and launch an LLM chatbot UI and database (with Database) from Open-source LLMs from Huggingface. Let's get started!\n", + "\n", + "\n", + "### Prerequisites\n", + "To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) to set up the environment. \n", + "\n", + "You may also need `pip install ipykernel` to run the kernel environment.\n", + "\n", + "\n", + "### (Optional) Developer setup\n", + "If you are a normal user of `pykoi`, you can skip this step. However, if you modify the pykoi code and want to test your changes, you can uncomment the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %reload_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "# import sys\n", + "\n", + "# sys.path.append(\".\")\n", + "# sys.path.append(\"..\")\n", + "# sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## pip install ipykernel\n", + "from pykoi import Application\n", + "from pykoi.chat import ModelFactory\n", + "from pykoi.chat import QuestionAnswerDatabase\n", + "from pykoi.component import Chatbot, Dashboard" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a database, model and chatbot\n", + "\n", + "To a launch a pykoi App, you only need to customize 3 components: a model, a database and a chatbot. Let's try it out!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qa_database = QuestionAnswerDatabase()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = ModelFactory.create_model(\n", + " model_source=\"huggingface\", \n", + " pretrained_model_name_or_path=\"tiiuae/falcon-7b\",\n", + " trust_remote_code=True, ## TODO: set as default\n", + " load_in_8bit=True\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chatbot = Chatbot(model=model, feedback=\"vote\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launch the App\n", + "\n", + "Now we can launch the chatbot using the model and database. Once you run the below line, you will see a Tunnel link which ends with `ngrok-free.app`. Click this link and you can see the below interface:\n", + "\n", + "

\n", + " \n", + "

\n", + "\n", + "\n", + "\n", + "You may need to set a ngrok token (one time) by uncomment the below line, add your [personal ngrok token](https://dashboard.ngrok.com/get-started/your-authtoken) and run this `ngrok config` line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !ngrok config add-authtoken xxx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add `nest_asyncio` \n", + "Add `nest_asyncio` to avoid error such as `asyncio.run() cannot be called from a running event loop`. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install -q nest_asyncio\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app = Application(debug=False, share=True)\n", + "app.add_component(chatbot)\n", + "app.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize your data\n", + "\n", + "Once you collect enough data via the chatbot app above, you can visualize your data via the dashboard." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qa_dashboard = Dashboard(database=qa_database)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app = Application(debug=False, share=True)\n", + "app.add_component(chatbot)\n", + "app.add_component(qa_dashboard)\n", + "app.run()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will only see one localhost link after you run the `app.run()` below. If you are on EC2 or a remote server, you can tunnel it back to your laptop via the following options:\n", + "\n", + "- If you are using VSCode, check [tunnel using VSCode](https://code.visualstudio.com/docs/remote/ssh#_forwarding-a-port-creating-ssh-tunnel);\n", + "- [Directly config via EC2](https://www.opensourceforu.com/2021/09/how-to-do-reverse-tunnelling-with-the-amazon-ec2-instance/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can launch the dashboard. Click this above link and you can see the below interface:\n", + "\n", + "

\n", + " \n", + "

" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pykoi", + "language": "python", + "name": "0731a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/chatbot/demo_launch_app_gpu_huggingface.py b/example/chatbot/demo_launch_app_gpu_huggingface.py new file mode 100644 index 0000000..fe72694 --- /dev/null +++ b/example/chatbot/demo_launch_app_gpu_huggingface.py @@ -0,0 +1,44 @@ +""" +Demo for the chatbot application using open source LLMs from Huggingface. + +- Prerequisites: + To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. + You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) + to set up the environment. +- Run the demo: + 1. On terminal and `~/pykoi` directory, run + ``` + python -m example.chatbot.demo_launch_app_gpu_huggingface + ``` +""" +from pykoi import Application +from pykoi.chat import ModelFactory +from pykoi.chat import QuestionAnswerDatabase +from pykoi.component import Chatbot, Dashboard + + +################################################################################### +# Creating a Huggingface model tiiuae/falcon-7b (EC2 g5.4xlarge with 100GB space) # +################################################################################### +model = ModelFactory.create_model( + model_source="huggingface", + pretrained_model_name_or_path="tiiuae/falcon-7b", +) + +##################################### +# Creating a chatbot with the model # +##################################### +database = QuestionAnswerDatabase(debug=True) +chatbot = Chatbot(model=model, feedback="vote") +# chatbot = pykoi.Chatbot(model=model, feedback="rank") +dashboard = Dashboard(database=database) + +########################################################### +# Starting the application and add chatbot as a component # +########################################################### +# Create the application +# app = pykoi.Application(debug=False, share=True) +app = Application(debug=False, share=False) +app.add_component(chatbot) +app.add_component(dashboard) +app.run() diff --git a/example/chatbot/peft_huggingface_model_demo.py b/example/chatbot/demo_launch_app_gpu_huggingface_peft.py similarity index 52% rename from example/chatbot/peft_huggingface_model_demo.py rename to example/chatbot/demo_launch_app_gpu_huggingface_peft.py index 348e361..fe82e47 100644 --- a/example/chatbot/peft_huggingface_model_demo.py +++ b/example/chatbot/demo_launch_app_gpu_huggingface_peft.py @@ -1,12 +1,29 @@ -"""Demo for the chatbot application.""" -import pykoi +""" +Demo for the chatbot application using open source LLMs from Huggingface Peft. + +- Prerequisites: + To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. + You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) + to set up the environment. +- Run the demo: + 1. On terminal and `~/pykoi` directory, run + ``` + python -m example.chatbot.demo_launch_app_gpu_huggingface_peft + ``` +""" + +from pykoi import Application +from pykoi.chat import ModelFactory +from pykoi.chat import QuestionAnswerDatabase +from pykoi.component import Chatbot, Dashboard + ################################################################################### # Creating a Huggingface model tiiuae/falcon-7b (EC2 g5.4xlarge with 100GB space) # ################################################################################### # only run this after run rlfh/sft_demo.py to fine tune the model -model = pykoi.ModelFactory.create_model( +model = ModelFactory.create_model( model_source="peft_huggingface", base_model_path="elinas/llama-7b-hf-transformers-4.29", lora_model_path="/home/ubuntu/pykoi/models/rlhf_step1_sft", @@ -15,17 +32,17 @@ ##################################### # Creating a chatbot with the model # ##################################### -database = pykoi.QuestionAnswerDatabase(debug=True) -chatbot = pykoi.Chatbot(model=model, feedback="vote") +database = QuestionAnswerDatabase(debug=True) +chatbot = Chatbot(model=model, feedback="vote") # chatbot = pykoi.Chatbot(model=model, feedback="rank") -dashboard = pykoi.Dashboard(database=database) +dashboard = Dashboard(database=database) ########################################################### # Starting the application and add chatbot as a component # ########################################################### # Create the application # app = pykoi.Application(debug=False, share=True) -app = pykoi.Application(debug=False, share=False) +app = Application(debug=False, share=False) app.add_component(chatbot) app.add_component(dashboard) app.run() diff --git a/example/chatbot/demo_model_comparator_openai.ipynb b/example/chatbot/demo_model_comparator_openai.ipynb deleted file mode 100644 index 8d36a7a..0000000 --- a/example/chatbot/demo_model_comparator_openai.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/ec2-user/SageMaker/pykoi\n" - ] - } - ], - "source": [ - "# %reload_ext autoreload\n", - "# %autoreload 2\n", - "\n", - "# import os\n", - "# import sys\n", - "\n", - "# # Add the root folder to the module search path\n", - "# # Get the current directory\n", - "# current_directory = os.getcwd()\n", - "\n", - "# # Move two levels up (go to the parent directory of the parent directory)\n", - "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", - "\n", - "# print(two_levels_up_directory)\n", - "\n", - "# sys.path.append(two_levels_up_directory)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ec2-user/anaconda3/envs/0731a/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import pykoi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demo: LLMs Comparison\n", - "\n", - "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs.\n", - "\n", - "This demo shows how to create and launch an LLM comparison app. Let's get started!\n", - "\n", - "## Load LLMs\n", - "\n", - "#### 1. Creating an OpenAI model (requires an OpenAI API key)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# enter openai api key here\n", - "api_key = \"\"\n", - "\n", - "# Creating an OpenAI model\n", - "openai_model_1 = pykoi.ModelFactory.create_model(\n", - " model_source=\"openai\", api_key=api_key, engine=\"babbage\"\n", - ")\n", - "openai_model_2 = pykoi.ModelFactory.create_model(\n", - " model_source=\"openai\", api_key=api_key, engine=\"curie\"\n", - ")\n", - "openai_model_3 = pykoi.ModelFactory.create_model(\n", - " model_source=\"openai\", api_key=api_key, engine=\"davinci\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a chatbot comparator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# pass in a list of models to compare\n", - "chatbot_comparator = pykoi.Compare(models=[openai_model_1, openai_model_2])\n", - "chatbot_comparator.add(openai_model_3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add `nest_asyncio` \n", - "Add `nest_asyncio` to avoid error. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install -q nest_asyncio\n", - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "t=2023-08-01T04:09:42+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/home/ec2-user/.config/ngrok/ngrok.yml legacy_path=/home/ec2-user/.ngrok2/ngrok.yml\n", - "t=2023-08-01T04:09:42+0000 lvl=warn msg=\"can't bind default web address, trying alternatives\" obj=web addr=127.0.0.1:4040\n", - "INFO: Started server process [9949]\n", - "INFO: Waiting for application startup.\n", - "INFO: Application startup complete.\n", - "INFO: Uvicorn running on http://127.0.0.1:51117 (Press CTRL+C to quit)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Public URL: NgrokTunnel: \"https://9173-34-209-105-222.ngrok-free.app\" -> \"http://localhost:51117\"\n", - "INFO: 76.21.76.39:0 - \"GET / HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-fc1a91e8.js HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /assets/index-f75c1c12.css HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /components HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"GET /vite.svg HTTP/1.1\" 200 OK\n", - "INFO: 76.21.76.39:0 - \"POST /chat/comparator/who%20are%20the%20founders%20of%20YC HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Shutting down\n", - "INFO: Waiting for application shutdown.\n", - "INFO: Application shutdown complete.\n", - "INFO: Finished server process [9949]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stopping server...\n" - ] - } - ], - "source": [ - "app = pykoi.Application(debug=False, share=True)\n", - "app.add_component(chatbot_comparator)\n", - "app.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can launch the ranking chatbot. Click this above link and you can similar interface:\n", - "\n", - "

\n", - " \n", - "

\n", - "\n", - "\n", - "\n", - "You can also check the dashboard for your ranking of the model answers:\n", - "\n", - "

\n", - " \n", - "

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pykoi", - "language": "python", - "name": "0731a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/chatbot/huggingface_model_demo.py b/example/chatbot/huggingface_model_demo.py deleted file mode 100644 index b98cedb..0000000 --- a/example/chatbot/huggingface_model_demo.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Demo for the chatbot application.""" -import pykoi - -################################################################################### -# Creating a Huggingface model tiiuae/falcon-7b (EC2 g5.4xlarge with 100GB space) # -################################################################################### -model = pykoi.ModelFactory.create_model( - model_source="huggingface", - pretrained_model_name_or_path="tiiuae/falcon-7b", -) - -##################################### -# Creating a chatbot with the model # -##################################### -database = pykoi.QuestionAnswerDatabase(debug=True) -chatbot = pykoi.Chatbot(model=model, feedback="vote") -# chatbot = pykoi.Chatbot(model=model, feedback="rank") -dashboard = pykoi.Dashboard(database=database) - -########################################################### -# Starting the application and add chatbot as a component # -########################################################### -# Create the application -# app = pykoi.Application(debug=False, share=True) -app = pykoi.Application(debug=False, share=False) -app.add_component(chatbot) -app.add_component(dashboard) -app.run() diff --git a/example/comparator/demo_model_comparator_cpu_openai.ipynb b/example/comparator/demo_model_comparator_cpu_openai.ipynb new file mode 100644 index 0000000..e75aed6 --- /dev/null +++ b/example/comparator/demo_model_comparator_cpu_openai.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo: LLMs Comparison between OpenAI models\n", + "\n", + "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs. This demo shows how to create and launch an LLM comparison app for OpenAI/Anthropic/Bedrock APIs. Let's get started!\n", + "\n", + "### Prerequisites\n", + "To run this jupyter notebook, you need a `pykoi` environment with the `rag` option. You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-1-rag-cpu) to set up the environment. \n", + "\n", + "You may also need `pip install ipykernel` to run the kernel environment.\n", + "\n", + "\n", + "### (Optional) Developer setup\n", + "If you are a normal user of `pykoi`, you can skip this step. However, if you modify the pykoi code and want to test your changes, you can uncomment the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %reload_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "# import sys\n", + "\n", + "# sys.path.append(\".\")\n", + "# sys.path.append(\"..\")\n", + "# sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pykoi import Application\n", + "from pykoi.chat import ModelFactory\n", + "from pykoi.component import Compare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Load LLMs\n", + "\n", + "#### 1. Creating an OpenAI model (requires an OpenAI API key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enter openai api key here\n", + "api_key = \"\"\n", + "\n", + "# Creating an OpenAI model\n", + "openai_model_1 = ModelFactory.create_model(\n", + " model_source=\"openai\", api_key=api_key, engine=\"babbage\"\n", + ")\n", + "openai_model_2 = ModelFactory.create_model(\n", + " model_source=\"openai\", api_key=api_key, engine=\"curie\"\n", + ")\n", + "openai_model_3 = ModelFactory.create_model(\n", + " model_source=\"openai\", api_key=api_key, engine=\"davinci\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a chatbot comparator\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pass in a list of models to compare\n", + "chatbot_comparator = Compare(models=[openai_model_1, openai_model_2])\n", + "chatbot_comparator.add(openai_model_3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add `nest_asyncio` \n", + "Add `nest_asyncio` to avoid error. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install -q nest_asyncio\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launch the App" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app = Application(debug=False, share=False)\n", + "app.add_component(chatbot_comparator)\n", + "app.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can launch the ranking chatbot. Click this above link and you can similar interface:\n", + "\n", + "

\n", + " \n", + "

\n", + "\n", + "\n", + "\n", + "You can also check the dashboard for your ranking of the model answers:\n", + "\n", + "

\n", + " \n", + "

" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/chatbot/demo_model_comparator_openai.py b/example/comparator/demo_model_comparator_cpu_openai.py similarity index 67% rename from example/chatbot/demo_model_comparator_openai.py rename to example/comparator/demo_model_comparator_cpu_openai.py index 59c8db3..483c3f3 100644 --- a/example/chatbot/demo_model_comparator_openai.py +++ b/example/comparator/demo_model_comparator_cpu_openai.py @@ -1,6 +1,21 @@ -"""Demo for the chatbot application using multiple model endpoint.""" -import pykoi +""" +Demo for the chatbot application using multiple OpenAI models. +- Prerequisites: + To run this jupyter notebook, you need a `pykoi` environment with the `rag` option. + You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-1-rag-cpu) + to set up the environment. +- Run the demo: + 1. Enter your OpenAI API key in the `api_key` below. + 2. On terminal and `~/pykoi` directory, run + ``` + python -m example.comparator.demo_model_comparator_cpu_openai + ``` +""" + +from pykoi import Application +from pykoi.chat import ModelFactory +from pykoi.component import Compare ########################################################## # Creating an OpenAI model (requires an OpenAI API key) # @@ -9,13 +24,13 @@ api_key = "" # Creating an OpenAI model -openai_model_1 = pykoi.ModelFactory.create_model( +openai_model_1 = ModelFactory.create_model( model_source="openai", name="openai_babbage", api_key=api_key, engine="babbage" ) -openai_model_2 = pykoi.ModelFactory.create_model( +openai_model_2 = ModelFactory.create_model( model_source="openai", name="openai_curie", api_key=api_key, engine="curie" ) -openai_model_3 = pykoi.ModelFactory.create_model( +openai_model_3 = ModelFactory.create_model( model_source="openai", name="openai_davinci", api_key=api_key, engine="davinci" ) @@ -41,12 +56,12 @@ # Creating a chatbot comparator # ################################# # pass in a list of models to compare -chatbot_comparator = pykoi.Compare(models=[openai_model_1, openai_model_2]) +chatbot_comparator = Compare(models=[openai_model_1, openai_model_2]) chatbot_comparator.add(openai_model_3) # or add models later # chatbot_comparator.add(huggingface_model) # chatbot_comparator.add(peft_huggingface_model) -app = pykoi.Application(debug=False, share=True, username="rachel", password="rachel") +app = Application(debug=False, share=False) app.add_component(chatbot_comparator) app.run() diff --git a/example/chatbot/demo_model_comparator_hf.ipynb b/example/comparator/demo_model_comparator_gpu_huggingface.ipynb similarity index 67% rename from example/chatbot/demo_model_comparator_hf.ipynb rename to example/comparator/demo_model_comparator_gpu_huggingface.ipynb index 8869fbb..1799762 100644 --- a/example/chatbot/demo_model_comparator_hf.ipynb +++ b/example/comparator/demo_model_comparator_gpu_huggingface.ipynb @@ -1,67 +1,66 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/ec2-user/SageMaker/pykoi\n" - ] - } - ], "source": [ - "# %reload_ext autoreload\n", - "# %autoreload 2\n", + "# Demo: LLMs Comparison between Open-source LLMs\n", "\n", - "# import os\n", - "# import sys\n", + "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs. This demo shows how to create and launch an LLM comparison app between Open-source LLMs from huggingface. Let's get started!\n", "\n", - "# # Add the root folder to the module search path\n", - "# # Get the current directory\n", - "# current_directory = os.getcwd()\n", + "### Prerequisites\n", + "To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) to set up the environment. \n", "\n", - "# # Move two levels up (go to the parent directory of the parent directory)\n", - "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", + "You may also need `pip install ipykernel` to run the kernel environment.\n", "\n", - "# print(two_levels_up_directory)\n", "\n", - "# sys.path.append(two_levels_up_directory)" + "### (Optional) Developer setup\n", + "If you are a normal user of `pykoi`, you can skip this step. However, if you modify the pykoi code and want to test your changes, you can uncomment the code below." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ - "import pykoi" + "# %reload_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "# import sys\n", + "\n", + "# sys.path.append(\".\")\n", + "# sys.path.append(\"..\")\n", + "# sys.path.append(\"../..\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Demo: LLMs Comparison\n", - "\n", - "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs.\n", - "\n", - "This demo shows how to create and launch an LLM comparison app. Let's get started!\n", - "\n", - "## Load LLMs\n", - "\n" + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pykoi import Application\n", + "from pykoi.chat import ModelFactory\n", + "from pykoi.chat import QuestionAnswerDatabase\n", + "from pykoi.component import Chatbot, Dashboard, Compare" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Creating a Huggingface model (requires at least EC2 `g4.2xlarge` or GPU with 16G memory)" + "### Load LLMs\n", + "#### Creating a Huggingface model (requires at least EC2 `g4dn.xlarge` or GPU with at least 16G memory)" ] }, { @@ -70,8 +69,8 @@ "metadata": {}, "outputs": [], "source": [ - "## requires a GPU with at least 16GB memory (e.g. g4.2xlarge)\n", - "huggingface_model_1 = pykoi.ModelFactory.create_model(\n", + "## requires a GPU with at least 16GB memory (e.g. g4dn.xlarge)\n", + "huggingface_model_1 = ModelFactory.create_model(\n", " model_source=\"huggingface\",\n", " pretrained_model_name_or_path=\"tiiuae/falcon-rw-1b\",\n", ")" @@ -83,8 +82,8 @@ "metadata": {}, "outputs": [], "source": [ - "## requires a GPU with at least 16GB memory (e.g. g4.2xlarge)\n", - "huggingface_model_2 = pykoi.ModelFactory.create_model(\n", + "## requires a GPU with at least 16GB memory (e.g. g4dn.2xlarge)\n", + "huggingface_model_2 = ModelFactory.create_model(\n", " model_source=\"huggingface\",\n", " pretrained_model_name_or_path=\"databricks/dolly-v2-3b\",\n", ")" @@ -97,7 +96,7 @@ "outputs": [], "source": [ "## requires a GPU with at least 24GB memory (e.g. g5.2xlarge)\n", - "huggingface_model_3 = pykoi.ModelFactory.create_model(\n", + "huggingface_model_3 = ModelFactory.create_model(\n", " model_source=\"huggingface\",\n", " pretrained_model_name_or_path=\"tiiuae/falcon-7b\",\n", ")" @@ -107,9 +106,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create a chatbot comparator\n", + "### Create a chatbot comparator\n", "\n", - "### Add `nest_asyncio` \n", + "#### Add `nest_asyncio` \n", "Add `nest_asyncio` to avoid error. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)" ] }, @@ -131,7 +130,7 @@ "outputs": [], "source": [ "# pass in a list of models to compare\n", - "chatbot_comparator = pykoi.Compare(models=[huggingface_model_1, huggingface_model_2, huggingface_model_3])\n", + "chatbot_comparator = Compare(models=[huggingface_model_1, huggingface_model_2, huggingface_model_3])\n", "# chatbot_comparator.add(openai_model_3)" ] }, @@ -139,7 +138,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Add ngrok auth (TODO: change to bash file)" + "#### Add ngrok auth (TODO: change to bash file)" ] }, { @@ -157,7 +156,7 @@ "metadata": {}, "outputs": [], "source": [ - "app = pykoi.Application(debug=False, share=True, username=\"rachel\", password=\"rachel\")\n", + "app = Application(debug=False, share=False)\n", "app.add_component(chatbot_comparator)\n", "app.run()" ] @@ -180,13 +179,6 @@ " \n", "

" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/example/chatbot/demo_model_comparator_hf.py b/example/comparator/demo_model_comparator_gpu_huggingface.py similarity index 67% rename from example/chatbot/demo_model_comparator_hf.py rename to example/comparator/demo_model_comparator_gpu_huggingface.py index 1d77929..7ddec16 100644 --- a/example/chatbot/demo_model_comparator_hf.py +++ b/example/comparator/demo_model_comparator_gpu_huggingface.py @@ -1,10 +1,25 @@ -"""Demo for the chatbot application using multiple model endpoint.""" -import pykoi +""" +Demo for the chatbot application using multiple open source LLMs from Huggingface. + +- Prerequisites: + To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. + You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) + to set up the environment. +- Run the demo: + 1. On terminal and `~/pykoi` directory, run + ``` + python -m example.comparator.demo_model_comparator_gpu_huggingface + ``` +""" +from pykoi import Application +from pykoi.chat import ModelFactory +from pykoi.component import Compare + ################################################################################### # Creating a Huggingface model tiiuae/falcon-rw-1b (EC2 g4.2xlarge with 100GB space) # ################################################################################### -huggingface_model_1 = pykoi.ModelFactory.create_model( +huggingface_model_1 = ModelFactory.create_model( model_source="huggingface", pretrained_model_name_or_path="tiiuae/falcon-rw-1b", ) @@ -12,7 +27,7 @@ ################################################################################### # Creating a Huggingface model tiiuae/falcon-7b (EC2 g4.2xlarge with 100GB space) # ################################################################################### -huggingface_model_2 = pykoi.ModelFactory.create_model( +huggingface_model_2 = ModelFactory.create_model( model_source="huggingface", pretrained_model_name_or_path="tiiuae/falcon-7b", ) @@ -20,7 +35,7 @@ ################################################################################### # Creating a Huggingface model databricks/dolly-v2-3b (EC2 g4.2xlarge with 100GB space) # ################################################################################### -huggingface_model_3 = pykoi.ModelFactory.create_model( +huggingface_model_3 = ModelFactory.create_model( model_source="huggingface", pretrained_model_name_or_path="databricks/dolly-v2-3b", ) @@ -39,10 +54,10 @@ # Creating a chatbot comparator # ################################# # pass in a list of models to compare -chatbot_comparator = pykoi.Compare(models=[huggingface_model_1, huggingface_model_2]) +chatbot_comparator = Compare(models=[huggingface_model_1, huggingface_model_2]) # or add models later chatbot_comparator.add(huggingface_model_3) -app = pykoi.Application(debug=False, share=True, username="rachel", password="rachel") +app = Application(debug=False, share=True, username="rachel", password="rachel") app.add_component(chatbot_comparator) app.run() diff --git a/example/database/data_clean_votes.ipynb b/example/database/data_clean_votes.ipynb index 6f46d2f..c049205 100644 --- a/example/database/data_clean_votes.ipynb +++ b/example/database/data_clean_votes.ipynb @@ -1,12 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 1, @@ -31,7 +24,7 @@ } ], "source": [ - "import pykoi" + "from pykoi.chat import QuestionAnswerDatabase" ] }, { @@ -47,7 +40,7 @@ "metadata": {}, "outputs": [], "source": [ - "qa_database = pykoi.QuestionAnswerDatabase()" + "qa_database = QuestionAnswerDatabase()" ] }, { diff --git a/example/database/demo_qa_database.ipynb b/example/database/demo_qa_database.ipynb index 0ca1f1a..b51cbe9 100644 --- a/example/database/demo_qa_database.ipynb +++ b/example/database/demo_qa_database.ipynb @@ -837,7 +837,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pykoi.rlhf.rlhf import RLHFConfig\n", + "from pykoi.rlhf import RLHFConfig\n", "\n", "config = RLHFConfig(\n", " base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", ## \"distilroberta-base\", ##\n", @@ -888,9 +888,9 @@ } ], "source": [ - "from pykoi.rlhf.rlhf import SFT ##, RewardTrainer, RL\n", + "from pykoi.rlhf import SupervisedFinetuning ##, RewardTrainer, RL\n", "\n", - "rlhf_step1_sft = SFT(config)" + "rlhf_step1_sft = SupervisedFinetuning(config)" ] }, { diff --git a/example/database/demo_ranking_database.ipynb b/example/database/demo_ranking_database.ipynb index 75124c3..d339557 100644 --- a/example/database/demo_ranking_database.ipynb +++ b/example/database/demo_ranking_database.ipynb @@ -519,7 +519,7 @@ } ], "source": [ - "from pykoi.rlhf.rlhf import RLHFConfig\n", + "from pykoi.rlhf import RLHFConfig\n", "\n", "\n", "config = RLHFConfig(\n", @@ -582,9 +582,9 @@ } ], "source": [ - "from pykoi.rlhf.rlhf import RewardTrainer\n", + "from pykoi.rlhf import RewardFinetuning\n", "\n", - "rlhf_step2_reward_trainer = RewardTrainer(config)" + "rlhf_step2_reward_trainer = RewardFinetuning(config)" ] }, { diff --git a/example/flex/flex_demo.py b/example/flex/flex_demo.py index 6569454..20f6925 100644 --- a/example/flex/flex_demo.py +++ b/example/flex/flex_demo.py @@ -2,7 +2,16 @@ import os import argparse -import pykoi +from dotenv import load_dotenv +from pykoi import Application +from pykoi.chat import QuestionAnswerDatabase +from pykoi.retrieval import RetrievalFactory +from pykoi.retrieval import VectorDbFactory +from pykoi.component import Chatbot, Dashboard, RetrievalQA + + +load_dotenv() + def main(**kargs): os.environ["DOC_PATH"] = os.path.join(os.getcwd(), "temp/docs") @@ -13,12 +22,12 @@ def main(**kargs): # Creating a retrieval QA component # ##################################### # vector database - vector_db = pykoi.VectorDbFactory.create( + vector_db = VectorDbFactory.create( model_source=MODEL_SOURCE, vector_db_name=kargs.get("vectordb"), **kargs ) # retrieval model with vector database - retrieval_model = pykoi.RetrievalFactory.create( + retrieval_model = RetrievalFactory.create( model_source=MODEL_SOURCE, vector_db=vector_db ) @@ -31,7 +40,7 @@ def main(**kargs): # Starting the application and retrieval qa as a component # ############################################################ # Create the application - app = pykoi.Application(debug=False, share=False) + app = Application(debug=False, share=False) app.add_component(retriever) app.add_component(chatbot) app.add_component(dashboard) diff --git a/example/image/pykoi_demo_model_comparison.gif b/example/image/pykoi_demo_model_comparison.gif new file mode 100644 index 0000000..a1e6d6a Binary files /dev/null and b/example/image/pykoi_demo_model_comparison.gif differ diff --git a/example/image/pykoi_demo_rlaif_data_collection.gif b/example/image/pykoi_demo_rlaif_data_collection.gif new file mode 100644 index 0000000..a0759d9 Binary files /dev/null and b/example/image/pykoi_demo_rlaif_data_collection.gif differ diff --git a/example/mlu/demo_chat.py b/example/mlu/demo_chat.py index b6f10ee..78e14cf 100644 --- a/example/mlu/demo_chat.py +++ b/example/mlu/demo_chat.py @@ -2,8 +2,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer -import pykoi - +from pykoi import Application +from pykoi.chat import QuestionAnswerDatabase +from pykoi.chat.llm.huggingface import HuggingfaceModel +from pykoi.component import Chatbot, Dashboard ################################################################################### # Creating a Huggingface model tiiuae/falcon-7b (EC2 g5.4xlarge with 100GB space) # @@ -25,7 +27,7 @@ ) print("create pykoi model component for UI...") -model = pykoi.chat.llm.huggingface.HuggingfaceModel.create( +model = HuggingfaceModel.create( model=hf_model, tokenizer=hf_tokenizer, name="falcon-7b", @@ -35,15 +37,15 @@ ##################################### # Creating a chatbot with the model # ##################################### -database = pykoi.QuestionAnswerDatabase(debug=True) -chatbot = pykoi.Chatbot(model=model, feedback="vote") -dashboard = pykoi.Dashboard(database=database) +database = QuestionAnswerDatabase(debug=True) +chatbot = Chatbot(model=model, feedback="vote") +dashboard = Dashboard(database=database) ########################################################### # Starting the application and add chatbot as a component # ########################################################### # Create the application -app = pykoi.Application() +app = Application() app.add_component(chatbot) app.add_component(dashboard) app.run() diff --git a/example/mlu/demo_comparator.py b/example/mlu/demo_comparator.py index 1bf9d53..228a082 100644 --- a/example/mlu/demo_comparator.py +++ b/example/mlu/demo_comparator.py @@ -2,7 +2,9 @@ from transformers import AutoModelForCausalLM, AutoTokenizer -import pykoi +from pykoi import Application +from pykoi.chat.llm.huggingface import HuggingfaceModel +from pykoi.component import Compare ###################################################################################### @@ -71,13 +73,13 @@ tokenizers = [hf_tokenizer_1, hf_tokenizer_2, hf_tokenizer_3] models_list = [ - pykoi.chat.llm.huggingface.HuggingfaceModel.create( + HuggingfaceModel.create( model=model, tokenizer=tokenizer, name=name, max_length=100 ) for model, tokenizer, name in zip(models, tokenizers, model_name) ] -chatbot_comparator = pykoi.Compare(models=models_list) -app = pykoi.Application() +chatbot_comparator = Compare(models=models_list) +app = Application() app.add_component(chatbot_comparator) app.run() diff --git a/example/ops/demo_nvml.py b/example/ops/demo_nvml.py index 7724b69..181375c 100644 --- a/example/ops/demo_nvml.py +++ b/example/ops/demo_nvml.py @@ -1,16 +1,17 @@ """Demo for the chatbot application.""" -import pykoi +from pykoi import Application +from pykoi.component import Nvml ############################################## # Nvidia Management Library (NVML) Component # ############################################## -ops_nvidia_gpu = pykoi.Nvml() +ops_nvidia_gpu = Nvml() ######################################################## # Starting the application and add Nvml as a component # ######################################################## # Create the application # app = pykoi.Application(debug=False, share=True) -app = pykoi.Application(debug=False, share=False) +app = Application(debug=False, share=False) app.add_component(ops_nvidia_gpu) app.run() diff --git a/example/retrieval_qa/retrieval_qa_demo.py b/example/retrieval_qa/retrieval_qa_demo.py index bce0cc3..23d1c26 100644 --- a/example/retrieval_qa/retrieval_qa_demo.py +++ b/example/retrieval_qa/retrieval_qa_demo.py @@ -2,7 +2,11 @@ import os import argparse -import pykoi +from pykoi import Application +from pykoi.chat import QuestionAnswerDatabase +from pykoi.retrieval import RetrievalFactory +from pykoi.retrieval import VectorDbFactory +from pykoi.component import Chatbot, Dashboard, RetrievalQA def main(**kargs): @@ -16,25 +20,25 @@ def main(**kargs): # Creating a retrieval QA component # ##################################### # vector database - vector_db = pykoi.VectorDbFactory.create( + vector_db = VectorDbFactory.create( model_source=MODEL_SOURCE, vector_db_name=kargs.get("vectordb"), **kargs ) # retrieval model with vector database - retrieval_model = pykoi.RetrievalFactory.create( + retrieval_model = RetrievalFactory.create( model_source=MODEL_SOURCE, vector_db=vector_db ) # retrieval, chatbot, and dashboard pykoi components - retriever = pykoi.RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db) - chatbot = pykoi.Chatbot(None, feedback="vote", is_retrieval=True) - dashboard = pykoi.Dashboard(pykoi.QuestionAnswerDatabase()) + retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db) + chatbot = Chatbot(None, feedback="vote", is_retrieval=True) + dashboard = Dashboard(QuestionAnswerDatabase()) ############################################################ # Starting the application and retrieval qa as a component # ############################################################ # Create the application - app = pykoi.Application(debug=False, share=False) + app = Application(debug=False, share=False) app.add_component(retriever) app.add_component(chatbot) app.add_component(dashboard) diff --git a/example/retrieval_qa/retrieval_qa_huggingface_demo.py b/example/retrieval_qa/retrieval_qa_huggingface_demo.py index a5446bd..e42dcaf 100644 --- a/example/retrieval_qa/retrieval_qa_huggingface_demo.py +++ b/example/retrieval_qa/retrieval_qa_huggingface_demo.py @@ -2,7 +2,11 @@ import os import argparse -import pykoi +from pykoi import Application +from pykoi.chat import QuestionAnswerDatabase +from pykoi.retrieval import RetrievalFactory +from pykoi.retrieval import VectorDbFactory +from pykoi.component import Chatbot, Dashboard, RetrievalQA def main(**kwargs): @@ -14,7 +18,7 @@ def main(**kwargs): # Creating a retrieval QA component # ##################################### # vector database - vector_db = pykoi.VectorDbFactory.create( + vector_db = VectorDbFactory.create( model_source=MODEL_SOURCE, vector_db_name=kwargs.get("vectordb"), model_name="BAAI/bge-large-en", @@ -23,7 +27,7 @@ def main(**kwargs): ) # retrieval model with vector database - retrieval_model = pykoi.RetrievalFactory.create( + retrieval_model = RetrievalFactory.create( model_source=MODEL_SOURCE, vector_db=vector_db, model_name="databricks/dolly-v2-3b", @@ -32,15 +36,15 @@ def main(**kwargs): ) # retrieval, chatbot, and dashboard pykoi components - retriever = pykoi.RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db) - chatbot = pykoi.Chatbot(None, feedback="vote", is_retrieval=True) - dashboard = pykoi.Dashboard(pykoi.QuestionAnswerDatabase()) + retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db) + chatbot = Chatbot(None, feedback="vote", is_retrieval=True) + dashboard = Dashboard(QuestionAnswerDatabase()) ############################################################ # Starting the application and retrieval qa as a component # ############################################################ # Create the application - app = pykoi.Application(debug=False, share=False) + app = Application(debug=False, share=False) app.add_component(retriever) app.add_component(chatbot) app.add_component(dashboard) diff --git a/example/rlaif/llama2-70b.ipynb b/example/rlaif/llama2-70b.ipynb new file mode 100644 index 0000000..ddbe139 --- /dev/null +++ b/example/rlaif/llama2-70b.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LLaMA2 70B 4-bit Inference\n", + "\n", + "## Setup: \n", + "- EC2 `G5.12x` (96G GPU memory) instance\n", + "- Deep Learning AMI GPU PyTorch 2.0.1 (Ubuntu 20.04) 20230827\n", + " - `nvcc --version`: 12.1\n", + "- EBS: 300G\n", + "- Python: 3.10.12\n", + "- torch: 2.2.0.dev20230911+cu121\n", + "\n", + "## Installation\n", + "```\n", + "conda create -n 0911a python=3.10\n", + "source activate 0911a\n", + "pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121\n", + "pip3 install transformers bitsandbytes\n", + "huggingface-cli login\n", + "```\n", + "\n", + "## Inference script" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/0911a/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from transformers import (\n", + " AutoTokenizer,\n", + " AutoModelForCausalLM,\n", + " pipeline,\n", + " BitsAndBytesConfig,\n", + ")\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading (…)okenizer_config.json: 100%|██████████| 776/776 [00:00<00:00, 5.38MB/s]\n", + "Downloading tokenizer.model: 100%|██████████| 500k/500k [00:00<00:00, 16.3MB/s]\n", + "Downloading (…)/main/tokenizer.json: 100%|██████████| 1.84M/1.84M [00:00<00:00, 6.84MB/s]\n", + "Downloading (…)cial_tokens_map.json: 100%|██████████| 414/414 [00:00<00:00, 3.37MB/s]\n" + ] + } + ], + "source": [ + "name = \"meta-llama/Llama-2-70b-chat-hf\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(name)\n", + "tokenizer.pad_token_id = tokenizer.eos_token_id # for open-ended generation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading (…)lve/main/config.json: 100%|██████████| 614/614 [00:00<00:00, 4.70MB/s]\n", + "Downloading (…)fetensors.index.json: 100%|██████████| 66.7k/66.7k [00:00<00:00, 160MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.85G/9.85G [00:43<00:00, 225MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [00:43<00:00, 224MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.97G/9.97G [00:43<00:00, 231MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [00:41<00:00, 234MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [00:41<00:00, 235MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [00:44<00:00, 218MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.97G/9.97G [00:43<00:00, 229MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [01:00<00:00, 162MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [01:14<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [01:14<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.97G/9.97G [01:16<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [01:15<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.80G/9.80G [01:14<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 9.50G/9.50G [01:12<00:00, 131MB/s]\n", + "Downloading (…)of-00015.safetensors: 100%|██████████| 524M/524M [00:03<00:00, 137MB/s]\n", + "Downloading shards: 100%|██████████| 15/15 [13:37<00:00, 54.48s/it]\n", + "Loading checkpoint shards: 100%|██████████| 15/15 [00:18<00:00, 1.21s/it]\n", + "Downloading (…)neration_config.json: 100%|██████████| 188/188 [00:00<00:00, 1.32MB/s]\n" + ] + } + ], + "source": [ + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.float16,\n", + " bnb_4bit_use_double_quant=True,\n", + ")\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " name,\n", + " quantization_config=bnb_config,\n", + " device_map=\"auto\",\n", + " trust_remote_code=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n", + "pip install xformers.\n" + ] + } + ], + "source": [ + "generation_pipe = pipeline(\n", + " \"text-generation\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " trust_remote_code=True,\n", + " device_map=\"auto\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "who is jeff bezos?\n", + "Jeff Bezos is an American technology and retail entrepreneur, and the founder, chairman, and CEO of Amazon, the world's largest online retailer. He is widely recognized as one of the most successful entrepreneurs of our time, and has been named the richest person in the world by Forbes magazine for several years in a row.\n", + "Bezos was born in 1964 in Albuquerque, New Mexico, and grew up in Houston, Texas. He graduated from Princeton University in 1986 with a degree in electrical engineering and computer science. After working on Wall Street for several years, he left to start Amazon in 1994, initially operating the company out of his garage.\n", + "Under Bezos' leadership, Amazon has grown from a small online bookstore to a global retail giant, selling a wide range of products including electronics, clothing, home goods, and more. The company has also expanded into new areas such as cloud computing, advertising, and media production.\n", + "Bezos is known for his focus on customer satisfaction and his long-term approach to building and growing Amazon. He has also\n" + ] + } + ], + "source": [ + "text = \"who is jeff bezos?\" # prompt goes here\n", + "\n", + "sequences = generation_pipe(\n", + " text,\n", + " max_length=256,\n", + " pad_token_id=tokenizer.pad_token_id,\n", + " eos_token_id=tokenizer.eos_token_id,\n", + " do_sample=True,\n", + " top_k=10,\n", + " temperature=0.4,\n", + " top_p=0.9\n", + ")\n", + "\n", + "print(sequences[0][\"generated_text\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "0911a", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/rlhf/demo_reward.ipynb b/example/rlhf/demo_reward.ipynb index fd183cb..d84b90f 100644 --- a/example/rlhf/demo_reward.ipynb +++ b/example/rlhf/demo_reward.ipynb @@ -14,25 +14,22 @@ } ], "source": [ + "# %reload_ext autoreload\n", + "# %autoreload 2\n", "\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import sys\n", + "# import os\n", + "# import sys\n", "\n", "# # Add the root folder to the module search path\n", "# # Get the current directory\n", "# current_directory = os.getcwd()\n", "\n", "# # Move two levels up (go to the parent directory of the parent directory)\n", - "# # two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", - "# one_levels_up_directory = os.path.dirname(current_directory)\n", + "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", "\n", - "# print(one_levels_up_directory)\n", + "# print(two_levels_up_directory)\n", "\n", - "# sys.path.append(two_levels_up_directory)\n", - "sys.path.append(\"/home/ubuntu/git/pykoi\")\n" + "# sys.path.append(two_levels_up_directory)" ] }, { @@ -50,7 +47,10 @@ } ], "source": [ - "import pykoi" + "import pandas as pd\n", + "\n", + "from pykoi.rlhf import RLHFConfig\n", + "from pykoi.rlhf import SupervisedFinetuning" ] }, { @@ -306,11 +306,11 @@ ], "source": [ "# run supervised finetuning\n", - "config = pykoi.RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", \n", + "config = RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", \n", " dataset_type=\"local_db\",\n", " \n", " )\n", - "rlhf_step1_sft = pykoi.SupervisedFinetuning(config)\n", + "rlhf_step1_sft = SupervisedFinetuning(config)\n", "rlhf_step1_sft.train_and_save(\"./models/rlhf_step1_sft\")\n" ] }, diff --git a/example/rlhf/demo_rl.ipynb b/example/rlhf/demo_rl.ipynb index aae8e93..7eadde3 100644 --- a/example/rlhf/demo_rl.ipynb +++ b/example/rlhf/demo_rl.ipynb @@ -6,24 +6,22 @@ "metadata": {}, "outputs": [], "source": [ - "%reload_ext autoreload\n", - "%autoreload 2\n", + "# %reload_ext autoreload\n", + "# %autoreload 2\n", "\n", - "import os\n", - "import sys\n", + "# import os\n", + "# import sys\n", "\n", "# # Add the root folder to the module search path\n", "# # Get the current directory\n", "# current_directory = os.getcwd()\n", "\n", "# # Move two levels up (go to the parent directory of the parent directory)\n", - "# # two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", - "# one_levels_up_directory = os.path.dirname(current_directory)\n", + "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", "\n", - "# print(one_levels_up_directory)\n", + "# print(two_levels_up_directory)\n", "\n", - "# sys.path.append(two_levels_up_directory)\n", - "sys.path.append(\"/home/ubuntu/git/pykoi\")" + "# sys.path.append(two_levels_up_directory)" ] }, { @@ -56,8 +54,7 @@ "metadata": {}, "outputs": [], "source": [ - "import pykoi\n", - "from pykoi import RLHFConfig\n", + "from pykoi.rlhf import RLHFConfig\n", "\n", "from accelerate import notebook_launcher\n" ] @@ -367,7 +364,7 @@ "source": [ "from accelerate import notebook_launcher\n", "\n", - "config = pykoi.RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", # \"elinas/llama-7b-hf-transformers-4.29\", \n", + "config = RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", # \"elinas/llama-7b-hf-transformers-4.29\", \n", " dataset_type=\"local_db\",\n", " reward_model_path=\"goldmermaid/rlhf_reward_model\",\n", " save_freq=100,\n", diff --git a/example/rlhf/demo_rl.py b/example/rlhf/demo_rl.py index 64d7584..79e8d2f 100644 --- a/example/rlhf/demo_rl.py +++ b/example/rlhf/demo_rl.py @@ -1,7 +1,7 @@ """ accelerate config -LOCAL_DIR=/home/ubuntu/pykoi # change this to your local path +LOCAL_DIR=/home/ubuntu/pykoi/pykoi # change this to your local path export PYTHONPATH=$PYTHONPATH:${LOCAL_DIR} @@ -9,15 +9,17 @@ """ # accelerate launch --num_machines 1 --num_processes 1 --mixed_precision fp16 example/rlhf/demo_rl.py -import pykoi +from pykoi.rlhf import RLHFConfig +from pykoi.rlhf import RLFinetuning + # use huggingface sft and reward model -config = pykoi.RLHFConfig( - base_model_path="elinas/llama-7b-hf-transformers-4.29", # "elinas/llama-7b-hf-transformers-4.29", +config = RLHFConfig( + base_model_path="models/rlhf_step1_sft", #"elinas/llama-7b-hf-transformers-4.29", dataset_type="huggingface", dataset_name="goldmermaid/stack_exchange_rank_10k_dataset", dataset_subset_rl="data", - reward_model_path="cambioml/rlhf-reward-model", + reward_model_path="models/rlhf_step2_rw/", #"cambioml/rlhf_reward_model", save_freq=1, ppo_batch_size=32, ppo_epochs=4, @@ -25,5 +27,5 @@ output_dir="./models/rlhf_step3_rl", ) -rlhf_step3_rl = pykoi.RLFinetuning(config) +rlhf_step3_rl = RLFinetuning(config) rlhf_step3_rl.train_and_save("./models/rlhf_step3_rl") diff --git a/example/rlhf/demo_rw_finetuning.py b/example/rlhf/demo_rw_finetuning.py index 69cb541..fbec942 100644 --- a/example/rlhf/demo_rw_finetuning.py +++ b/example/rlhf/demo_rw_finetuning.py @@ -3,8 +3,9 @@ python -m example.rlhf.demo_rw_finetuning """ -import pykoi - +from pykoi.rlhf import RLHFConfig +from pykoi.rlhf import RewardFinetuning +from pykoi.chat import RankingDatabase from pykoi.chat.db.constants import ( RANKING_CSV_HEADER_ID, RANKING_CSV_HEADER_QUESTION, @@ -12,7 +13,7 @@ RANKING_CSV_HEADER_LOW_RANKING_ANSWER) # get data from local database -ranking_database = pykoi.RankingDatabase() +ranking_database = RankingDatabase() my_data_pd = ranking_database.retrieve_all_question_answers_as_pandas() my_data_pd = my_data_pd[[ RANKING_CSV_HEADER_ID, @@ -25,7 +26,7 @@ print("My local database has {} samples in total".format(my_data_pd.shape[0])) # run reward model finetuning -# config = pykoi.RLHFConfig(dataset_type="local_db") -config = pykoi.RLHFConfig() -rlhf_step2_rft = pykoi.RewardFinetuning(config) +# config = RLHFConfig(dataset_type="local_db") +config = RLHFConfig(reward_model_path="databricks/dolly-v2-3b") +rlhf_step2_rft = RewardFinetuning(config) rlhf_step2_rft.train_and_save("./models/rlhf_step2_rw") diff --git a/example/rlhf/demo_supervised_finetuning.ipynb b/example/rlhf/demo_supervised_finetuning.ipynb index f5532ba..476aa34 100644 --- a/example/rlhf/demo_supervised_finetuning.ipynb +++ b/example/rlhf/demo_supervised_finetuning.ipynb @@ -6,8 +6,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", "# %reload_ext autoreload\n", "# %autoreload 2\n", "\n", @@ -19,13 +17,11 @@ "# current_directory = os.getcwd()\n", "\n", "# # Move two levels up (go to the parent directory of the parent directory)\n", - "# # two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", - "# one_levels_up_directory = os.path.dirname(current_directory)\n", + "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n", "\n", - "# print(one_levels_up_directory)\n", + "# print(two_levels_up_directory)\n", "\n", - "# # sys.path.append(two_levels_up_directory)\n", - "# sys.path.append(\"/home/ubuntu/git/pykoi/pykoi\")\n" + "# sys.path.append(two_levels_up_directory)" ] }, { @@ -43,7 +39,9 @@ } ], "source": [ - "import pykoi" + "from pykoi.chat import QuestionAnswerDatabase\n", + "from pykoi.rlhf import RLHFConfig\n", + "from pykoi.rlhf import SupervisedFinetuning" ] }, { @@ -59,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "qa_database = pykoi.QuestionAnswerDatabase()" + "qa_database = QuestionAnswerDatabase()" ] }, { @@ -765,8 +763,8 @@ ], "source": [ "# run supervised finetuning\n", - "config = pykoi.RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", dataset_type=\"local_db\")\n", - "rlhf_step1_sft = pykoi.SupervisedFinetuning(config)\n", + "config = RLHFConfig(base_model_path=\"elinas/llama-7b-hf-transformers-4.29\", dataset_type=\"local_db\")\n", + "rlhf_step1_sft = SupervisedFinetuning(config)\n", "rlhf_step1_sft.train_and_save(\"./models/rlhf_step1_sft\")\n" ] }, diff --git a/example/rlhf/supervised_finetuning_demo.py b/example/rlhf/supervised_finetuning_demo.py index 8596e46..aba71e5 100644 --- a/example/rlhf/supervised_finetuning_demo.py +++ b/example/rlhf/supervised_finetuning_demo.py @@ -3,7 +3,9 @@ python -m example.rlhf.supervised_finetuning_demo """ -import pykoi +from pykoi.chat import QuestionAnswerDatabase +from pykoi.rlhf import RLHFConfig +from pykoi.rlhf import SupervisedFinetuning from pykoi.chat.db.constants import ( QA_CSV_HEADER_ID, @@ -12,7 +14,7 @@ QA_CSV_HEADER_VOTE_STATUS) # get data from local database -qa_database = pykoi.QuestionAnswerDatabase() +qa_database = QuestionAnswerDatabase() my_data_pd = qa_database.retrieve_all_question_answers_as_pandas() my_data_pd = my_data_pd[[ QA_CSV_HEADER_ID, @@ -25,6 +27,6 @@ print("My local database has {} samples in total".format(my_data_pd.shape[0])) # run supervised finetuning -config = pykoi.RLHFConfig(base_model_path="elinas/llama-7b-hf-transformers-4.29", dataset_type="local_db") -rlhf_step1_sft = pykoi.SupervisedFinetuning(config) +config = RLHFConfig(base_model_path="databricks/dolly-v2-3b", dataset_type="local_db") +rlhf_step1_sft = SupervisedFinetuning(config) rlhf_step1_sft.train_and_save("./models/rlhf_step1_sft") diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 5d7aef5..0000000 --- a/poetry.lock +++ /dev/null @@ -1,3793 +0,0 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. - 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-python-versions = ">=3.9,<3.13" -content-hash = "b735d716450f583775ffb22f0e8e2e3eefe59ccfbde87846220b838fbf4745b8" diff --git a/pykoi/__init__.py b/pykoi/__init__.py index 04a0168..16edacc 100644 --- a/pykoi/__init__.py +++ b/pykoi/__init__.py @@ -1,21 +1,3 @@ -from pykoi import chat - from pykoi.application import Application -from pykoi.chat.db.qa_database import QuestionAnswerDatabase -from pykoi.chat.db.rag_database import RAGDatabase -from pykoi.chat.db.ranking_database import RankingDatabase -from pykoi.chat.llm.abs_llm import AbsLlm -from pykoi.chat.llm.model_factory import ModelFactory -from pykoi.component.base import Chatbot, Dashboard, Dropdown -from pykoi.component.chatbot_comparator import Compare -from pykoi.component.retrieval_qa import RetrievalQA -from pykoi.component.nvml import Nvml -from pykoi.retrieval.llm.retrieval_factory import RetrievalFactory -from pykoi.retrieval.llm.embedding_factory import EmbeddingFactory -from pykoi.retrieval.vectordb.vectordb_factory import VectorDbFactory -from pykoi.rlhf.supervised_finetuning import SupervisedFinetuning -from pykoi.rlhf.rw_finetuning import RewardFinetuning -from pykoi.rlhf.rl_finetuning import RLFinetuning -from pykoi.rlhf.config import RLHFConfig __version__ = "0.0.6" diff --git a/pykoi/application.py b/pykoi/application.py index ac8953f..05f61ef 100644 --- a/pykoi/application.py +++ b/pykoi/application.py @@ -14,7 +14,6 @@ from pydantic import BaseModel from pyngrok import ngrok from starlette.middleware.cors import CORSMiddleware -from pykoi.component.base import Dropdown from pykoi.interactives.chatbot import Chatbot from pykoi.telemetry.telemetry import Telemetry from pykoi.telemetry.events import AppStartEvent, AppStopEvent @@ -169,6 +168,9 @@ def add_component(self, component: Any): Args: component (Any): The component to be added. """ + # TODO (Jojo): remove component Dropdown check + from pykoi.component.base import Dropdown + if component.data_source: self.data_sources[component.id] = component.data_source # set data_endpoint if it's a Dropdown component diff --git a/pykoi/chat/__init__.py b/pykoi/chat/__init__.py index 1c03ce2..cfdb23d 100644 --- a/pykoi/chat/__init__.py +++ b/pykoi/chat/__init__.py @@ -1 +1,5 @@ import pykoi.chat.llm as llm + +from pykoi.chat.llm.model_factory import ModelFactory +from pykoi.chat.db.qa_database import QuestionAnswerDatabase +from pykoi.chat.db.ranking_database import RankingDatabase diff --git a/pykoi/chat/llm/__init__.py b/pykoi/chat/llm/__init__.py index 69cd3d6..e69de29 100644 --- a/pykoi/chat/llm/__init__.py +++ b/pykoi/chat/llm/__init__.py @@ -1 +0,0 @@ -import pykoi.chat.llm.huggingface as huggingface diff --git a/pykoi/chat/llm/peft_huggingface.py b/pykoi/chat/llm/peft_huggingface.py index e2091ee..f0bbb33 100644 --- a/pykoi/chat/llm/peft_huggingface.py +++ b/pykoi/chat/llm/peft_huggingface.py @@ -67,7 +67,7 @@ def __init__( self._base_model_path = base_model_path self._lora_model_path = lora_model_path self._name = name - self._model.to("cuda") + self._model.to("cuda:0") self._model.eval() super().__init__() diff --git a/pykoi/component/__init__.py b/pykoi/component/__init__.py index e69de29..5a66798 100644 --- a/pykoi/component/__init__.py +++ b/pykoi/component/__init__.py @@ -0,0 +1,4 @@ +from pykoi.component.base import Chatbot, Dashboard, Dropdown +from pykoi.component.chatbot_comparator import Compare +from pykoi.component.retrieval_qa import RetrievalQA +from pykoi.component.nvml import Nvml diff --git a/pykoi/component/chatbot_comparator.py b/pykoi/component/chatbot_comparator.py index c75fe8d..09ffc64 100644 --- a/pykoi/component/chatbot_comparator.py +++ b/pykoi/component/chatbot_comparator.py @@ -47,7 +47,7 @@ def __init__(self, models: List[AbsLlm], **kwargs): "latency", "length_in_tokens", ] - ) # Added this line + ) def add(self, model: AbsLlm): """ @@ -118,5 +118,13 @@ def inference(self, questions: List[str]): return self.inference_results def visualize(self): + """ + Visualize the inference results using a bar chart. + + Returns + ------- + object + A Barchart visualization with the inference results. + """ records = df_to_js_array(self.inference_results) return Barchart()(data=records) diff --git a/pykoi/frontend/README.md b/pykoi/frontend/README.md index 382941e..d4bae9d 100644 --- a/pykoi/frontend/README.md +++ b/pykoi/frontend/README.md @@ -1,6 +1,8 @@ -# Svelte + Vite +# pykoi frontend -This template should help get you started developing with Svelte in Vite. +This is the frontend for the pykoi library. It includes interfaces and UI components for data collection, feedback, and visualization. + +To see it in action, check out the various examples in the `example/` repo. ## Recommended IDE Setup @@ -12,36 +14,13 @@ Check out [SvelteKit](https://github.com/sveltejs/kit#readme), which is also pow ## Technical considerations -**Why use this over SvelteKit?** - -- It brings its own routing solution which might not be preferable for some users. -- It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app. - -This template contains as little as possible to get started with Vite + Svelte, while taking into account the developer experience with regards to HMR and intellisense. It demonstrates capabilities on par with the other `create-vite` templates and is a good starting point for beginners dipping their toes into a Vite + Svelte project. - -Should you later need the extended capabilities and extensibility provided by SvelteKit, the template has been structured similarly to SvelteKit so that it is easy to migrate. - -**Why `global.d.ts` instead of `compilerOptions.types` inside `jsconfig.json` or `tsconfig.json`?** - -Setting `compilerOptions.types` shuts out all other types not explicitly listed in the configuration. Using triple-slash references keeps the default TypeScript setting of accepting type information from the entire workspace, while also adding `svelte` and `vite/client` type information. +All UI components are built using Svelte. -**Why include `.vscode/extensions.json`?** +## Development -Other templates indirectly recommend extensions via the README, but this file allows VS Code to prompt the user to install the recommended extension upon opening the project. +To develop new frontend components, you'll have to make sure everything is installed, then launch with npm: -**Why enable `checkJs` in the JS template?** - -It is likely that most cases of changing variable types in runtime are likely to be accidental, rather than deliberate. This provides advanced typechecking out of the box. Should you like to take advantage of the dynamically-typed nature of JavaScript, it is trivial to change the configuration. - -**Why is HMR not preserving my local component state?** - -HMR state preservation comes with a number of gotchas! It has been disabled by default in both `svelte-hmr` and `@sveltejs/vite-plugin-svelte` due to its often surprising behavior. You can read the details [here](https://github.com/sveltejs/svelte-hmr/tree/master/packages/svelte-hmr#preservation-of-local-state). - -If you have state that's important to retain within a component, consider creating an external store which would not be replaced by HMR. - -```js -// store.js -// An extremely simple external store -import { writable } from 'svelte/store' -export default writable(0) +``` +npm install +npm run dev ``` diff --git a/pykoi/ops/__init__.py b/pykoi/ops/__init__.py index e69de29..69f541b 100644 --- a/pykoi/ops/__init__.py +++ b/pykoi/ops/__init__.py @@ -0,0 +1 @@ +from pykoi.ops.nvml import Nvml \ No newline at end of file diff --git a/pykoi/retrieval/__init__.py b/pykoi/retrieval/__init__.py index e69de29..aa19812 100644 --- a/pykoi/retrieval/__init__.py +++ b/pykoi/retrieval/__init__.py @@ -0,0 +1,2 @@ +from pykoi.retrieval.llm.retrieval_factory import RetrievalFactory +from pykoi.retrieval.vectordb.vectordb_factory import VectorDbFactory diff --git a/pykoi/retrieval/llm/embedding_factory.py b/pykoi/retrieval/llm/embedding_factory.py index 5f66336..990384e 100644 --- a/pykoi/retrieval/llm/embedding_factory.py +++ b/pykoi/retrieval/llm/embedding_factory.py @@ -27,8 +27,10 @@ def create_embedding(model_source: Union[str, ModelSource], **kwargs) -> Embeddi try: model_source = ModelSource(model_source) if model_source == ModelSource.OPENAI: + from langchain.embeddings import OpenAIEmbeddings return OpenAIEmbeddings() elif model_source == ModelSource.HUGGINGFACE: + from langchain.embeddings import HuggingFaceEmbeddings return HuggingFaceEmbeddings( model_name=kwargs.get("model_name"), ) diff --git a/pykoi/retrieval/llm/openai.py b/pykoi/retrieval/llm/openai.py index dcb6112..90133b9 100644 --- a/pykoi/retrieval/llm/openai.py +++ b/pykoi/retrieval/llm/openai.py @@ -5,12 +5,10 @@ from langchain.chains import RetrievalQA from langchain.llms import OpenAI -from dotenv import load_dotenv from pykoi.retrieval.llm.abs_llm import AbsLlm from pykoi.retrieval.vectordb.abs_vectordb import AbsVectorDb -load_dotenv() OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") MIN_DOCS = 2 @@ -26,7 +24,10 @@ def __init__(self, vector_db: AbsVectorDb): Initializes the OpenAIModel class. """ try: - self._llm = OpenAI(temperature=0, max_tokens=500) + self._llm = OpenAI( + model_name="gpt-4", + temperature=0, + max_tokens=500) self._vector_db = vector_db.vector_db diff --git a/pykoi/retrieval/llm/retrieval_factory.py b/pykoi/retrieval/llm/retrieval_factory.py index 39ae633..dfca7db 100644 --- a/pykoi/retrieval/llm/retrieval_factory.py +++ b/pykoi/retrieval/llm/retrieval_factory.py @@ -2,8 +2,6 @@ from typing import Union from pykoi.retrieval.llm.abs_llm import AbsLlm -from pykoi.retrieval.llm.openai import OpenAIModel -from pykoi.retrieval.llm.huggingface import HuggingFaceModel from pykoi.retrieval.llm.constants import ModelSource from pykoi.retrieval.vectordb.abs_vectordb import AbsVectorDb @@ -29,8 +27,10 @@ def create( try: model_source = ModelSource(model_source) if model_source == ModelSource.OPENAI: + from pykoi.retrieval.llm.openai import OpenAIModel return OpenAIModel(vector_db) if model_source == ModelSource.HUGGINGFACE: + from pykoi.retrieval.llm.huggingface import HuggingFaceModel return HuggingFaceModel(vector_db, **kwargs) except Exception as ex: raise Exception(f"Unknown model: {model_source}") from ex diff --git a/pykoi/rlhf/__init__.py b/pykoi/rlhf/__init__.py index e69de29..affa11f 100644 --- a/pykoi/rlhf/__init__.py +++ b/pykoi/rlhf/__init__.py @@ -0,0 +1,4 @@ +from pykoi.rlhf.supervised_finetuning import SupervisedFinetuning +from pykoi.rlhf.rw_finetuning import RewardFinetuning +from pykoi.rlhf.rl_finetuning import RLFinetuning +from pykoi.rlhf.config import RLHFConfig \ No newline at end of file diff --git a/pykoi/rlhf/rl_finetuning.py b/pykoi/rlhf/rl_finetuning.py index d60eb56..bc6bbe3 100644 --- a/pykoi/rlhf/rl_finetuning.py +++ b/pykoi/rlhf/rl_finetuning.py @@ -1,4 +1,6 @@ """rl finetuning.""" +import time +from datetime import datetime from pykoi.rlhf.config import RLHFConfig from pykoi.chat.db.constants import ( QA_CSV_HEADER_ID, @@ -25,16 +27,27 @@ ) from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer from trl.core import LengthSampler +from huggingface_hub import hf_hub_download +from transformers import AutoModelForCausalLM +from peft import PeftModel, PeftConfig, AutoPeftModelForCausalLM +from pykoi.telemetry.telemetry import Telemetry +from pykoi.telemetry.events import ( + RLStartEvent, + RLStopEvent, +) class RLFinetuning(Trainer): - def __init__(self, rlhf_config: RLHFConfig): + def __init__(self, + rlhf_config: RLHFConfig, + enable_telemetry: bool = True) -> None: """ RLFinetuning class for finetuning a language model using reinforcement learning. Args: rlhf_config (RLHFConfig): Configuration object for RLHF. """ + self._telemetry = Telemetry(enable_telemetry) self._rlhf_config = rlhf_config self.accelerator = Accelerator() self.num_proc = ( @@ -66,12 +79,50 @@ def __init__(self, rlhf_config: RLHFConfig): rlhf_config.reward_model_path ) self.reward_dataset = self.create_dataset(self.reward_tokenizer) - self.reward_model = AutoModelForSequenceClassification.from_pretrained( - rlhf_config.reward_model_path, - num_labels=1, - load_in_8bit=True, - device_map={"": Accelerator().local_process_index}, + + reward_model_path = rlhf_config.reward_model_path + + try: + # If there is a trained peft adapter in the hub, load its config. + remote_adapter_config_reward = hf_hub_download(reward_model_path, "adapter_config.json") + except: + remote_adapter_config_reward = None + + + local_adapter_present_reward = os.path.exists( + os.path.join(reward_model_path, "adapter_config.json") + ) + + # # Load the trained peft adapter config + if local_adapter_present_reward: + trained_adapter_config_reward = PeftConfig.from_pretrained(reward_model_path) + else: + trained_adapter_config = PeftConfig.from_pretrained(remote_adapter_config_reward) + + ## Load the pretrained base model + pretrained_kwargs_reward = { + "num_labels": 1, + "load_in_8bit": False, #True, + "device_map": {"": Accelerator().local_process_index}, + } # TODO: ADD + pretrained_model_reward = AutoModelForSequenceClassification.from_pretrained( + trained_adapter_config_reward.base_model_name_or_path, + **pretrained_kwargs_reward ) + ## TODO: LOAD MERGED BASE MODEL FROM STEP 2 + + # Load the Peft model by combing the base model with the trained adapter + reward_model = PeftModel.from_pretrained(pretrained_model_reward, reward_model_path, is_trainable=False) # TODO: fix this. This should not be trainable. + self.reward_model = reward_model.merge_and_unload() + #pretrained_model.print_trainable_parameters() + print("\nTrained peft adapter loaded for reward model\n") + # have to specify the pad_token_id or will lead to error: "Cannot handle batch sizes > 1 if no padding token is defined" + # see https://stackoverflow.com/questions/68084302/assertionerror-cannot-handle-batch-sizes-1-if-no-padding-token-is-defined + self.reward_model.config.pad_token_id = self.reward_tokenizer.pad_token_id + + + + self.reward_kwargs = { "top_k": None, "function_to_apply": "none", @@ -92,12 +143,93 @@ def __init__(self, rlhf_config: RLHFConfig): ## Load the base model and tokenizer and define the PPO Trainer for RL self.base_tokenizer = self.create_tokenizer(rlhf_config.base_model_path) self.base_dataset = self.create_dataset(self.base_tokenizer) - self.base_model = AutoModelForCausalLMWithValueHead.from_pretrained( - rlhf_config.base_model_path, - load_in_8bit=rlhf_config.load_in_8bit, - device_map={"": Accelerator().local_process_index}, - peft_config=rlhf_config.lora_config_rl, + + pretrained_model_name_or_path = rlhf_config.base_model_path + # #NOTE: TODO: peft config will be directly inferred from the pre-trained model. rlhf_config.lora_config_rl will be ignored in previous implementation. Do we want to use it, in the flow of using merged model as base model and then add peft adapter again?? + + pretrained_kwargs = { + "load_in_8bit": rlhf_config.load_in_8bit, + "device_map": {"": Accelerator().local_process_index}, + } + + assert isinstance(pretrained_model_name_or_path, str), "The `pretrained_model_path` should be a string." + try: + # If there is a trained peft adapter in the hub, load its config. + remote_adapter_config = hf_hub_download(pretrained_model_name_or_path, "adapter_config.json") + except: + remote_adapter_config = None + + + local_adapter_present = os.path.exists( + os.path.join(pretrained_model_name_or_path, "adapter_config.json") ) + + # # Load the trained peft adapter config + if local_adapter_present: + trained_adapter_config = PeftConfig.from_pretrained(pretrained_model_name_or_path) + else: + trained_adapter_config = PeftConfig.from_pretrained(remote_adapter_config) + + # # Load the pretrained base model + pretrained_model = AutoModelForCausalLM.from_pretrained( + trained_adapter_config.base_model_name_or_path, + **pretrained_kwargs + ) + + # Load the Peft model by combing the base model with the trained adapter + is_trainable = True # TODO: If following merge+train new adapter flow. Below should not be trainable! + pretrained_model = PeftModel.from_pretrained(pretrained_model, pretrained_model_name_or_path, is_trainable=is_trainable) + + #pretrained_model.print_trainable_parameters() + print("\nTrained peft adapter loaded for policy model\n") + + # Alternatively, load a peft model from a local path. See https://huggingface.co/docs/peft/quicktour. # TODO: DELETE. doesn't work + # peft_model = AutoPeftModelForCausalLM.from_pretrained(pretrained_model_name_or_path) + + + # Add value head to the pretrained peft model to create a policy network. + if isinstance(pretrained_model, PeftModel): + is_peft_model = True + trl_model_args = {} # args for the value head + # TODO: weights of v_head initialized using v_head_init_strategy="random" by default. trl also suports initialization using "norm". + model = AutoModelForCausalLMWithValueHead(pretrained_model, **trl_model_args) + # TODO: 1 VALUE HEAD REQURIES GRAD = FALSE AND NOT IN CUDA. CHECK IF BELOW CODE FIX THIS. 2. PEFTMODEL PRINT TRAINABLE PARAMETERS REUTRNS ... AND NONE + + + # For back compatibility for class AutoModelForCausalLMWithValueHead. is_peft_model needs to be specified or calling model.state_dict() will fail. + model.is_peft_model = is_peft_model + # For back compatibility + model.is_sequential_parallel = True + model.current_device = Accelerator().local_process_index + reward_adapter = None # TODO: Consider adding reward adapter here? + if is_peft_model and reward_adapter is not None: + model.add_and_load_reward_modeling_adapter(reward_adapter) + model.supports_rm_adapter = True + else: + model.supports_rm_adapter = False + + + # Adding v_head to device and register hook. See AutoModelForCausalLMWithValueHead.post_init(). + # TODO: is register_forward_hook necessary? outputs should be already on cuda + first_device = list(set(model.pretrained_model.hf_device_map.values()))[0] + model.v_head = model.v_head.to(first_device) + def set_device_hook(module, input, outputs): + new_output = () + for output in outputs: + if isinstance(output, torch.Tensor): + new_output += (output.to(first_device),) + else: + new_output += (output,) + return new_output + model.register_forward_hook(set_device_hook) + self.base_model = model + #breakpoint() + # self.base_model = AutoModelForCausalLMWithValueHead.from_pretrained( + # rlhf_config.base_model_path, + # load_in_8bit=rlhf_config.load_in_8bit, + # device_map={"": Accelerator().local_process_index}, + # peft_config=rlhf_config.lora_config_rl, + # ) self.ppo_trainer = PPOTrainer( config=self.ppo_config, model=self.base_model, @@ -346,5 +478,16 @@ def train_and_save(self, output_path=None): Args: output_path (str, optional): The path to save the model to. Default to None. """ + start_event = RLStartEvent( + start_time=time.time(), date_time=datetime.utcfromtimestamp(time.time()) + ) + self._telemetry.capture(start_event) self.train(save_checkpoints_path=output_path) self.save(output_path) + self._telemetry.capture( + RLStopEvent( + end_time=time.time(), + date_time=datetime.utcfromtimestamp(time.time()), + duration=time.time() - start_event.start_time, + ) + ) diff --git a/pykoi/rlhf/rw_finetuning.py b/pykoi/rlhf/rw_finetuning.py index 4b35bf5..000a52b 100644 --- a/pykoi/rlhf/rw_finetuning.py +++ b/pykoi/rlhf/rw_finetuning.py @@ -1,6 +1,8 @@ """reward model finetuning.""" import os +import time +from datetime import datetime from dataclasses import dataclass from typing import Any, Dict, List, Optional @@ -25,6 +27,11 @@ RANKING_CSV_HEADER_UP_RANKING_ANSWER, ) from pykoi.chat.db.ranking_database import RankingDatabase +from pykoi.telemetry.telemetry import Telemetry +from pykoi.telemetry.events import ( + RWStartEvent, + RWStopEvent, +) @dataclass @@ -63,7 +70,10 @@ def extract_and_pad(key_ids, key_mask): class RewardFinetuning(Trainer): - def __init__(self, rlhf_config: RLHFConfig): + def __init__(self, + rlhf_config: RLHFConfig, + enable_telemetry: bool = True) -> None: + self._telemetry = Telemetry(enable_telemetry) self._rlhf_config = rlhf_config self.args = TrainingArguments( output_dir=rlhf_config.output_dir, @@ -304,5 +314,16 @@ def train_and_save(self, output_path=None): output_path (:obj:`str`, `optional`): The output path to save the model. If not provided, the model will be saved to the default output path. """ + start_event = RWStartEvent( + start_time=time.time(), date_time=datetime.utcfromtimestamp(time.time()) + ) + self._telemetry.capture(start_event) self.train() self.save(output_path) + self._telemetry.capture( + RWStopEvent( + end_time=time.time(), + date_time=datetime.utcfromtimestamp(time.time()), + duration=time.time() - start_event.start_time, + ) + ) diff --git a/pykoi/rlhf/supervised_finetuning.py b/pykoi/rlhf/supervised_finetuning.py index ef72e5c..ba6016c 100644 --- a/pykoi/rlhf/supervised_finetuning.py +++ b/pykoi/rlhf/supervised_finetuning.py @@ -2,7 +2,9 @@ import os from typing import Optional import torch +import time +from datetime import datetime from datasets import Dataset, load_dataset from peft import PeftConfig, PeftModel from transformers import ( @@ -22,6 +24,11 @@ ) from pykoi.chat.db.qa_database import QuestionAnswerDatabase from pykoi.rlhf.config import RLHFConfig +from pykoi.telemetry.telemetry import Telemetry +from pykoi.telemetry.events import ( + SFTStartEvent, + SFTStopEvent, +) class SupervisedFinetuning: @@ -39,13 +46,17 @@ class SupervisedFinetuning: trainer (SFTTrainer): The trainer object used for training the model. """ - def __init__(self, rlhf_config: RLHFConfig): + def __init__(self, + rlhf_config: RLHFConfig, + enable_telemetry: bool = True) -> None: """ Initializes the SFTTrainer object. Args: rlhf_config (RLHFConfig): The RLHF configuration object. + enbale_telemetry (bool): Whether to enable telemetry or not. """ + self._telemetry = Telemetry(enable_telemetry) self._rlhf_config = rlhf_config self.tokenizer = AutoTokenizer.from_pretrained( rlhf_config.base_model_path @@ -145,8 +156,19 @@ def save(self, output_path=None): self.trainer.save_model(output_path) def train_and_save(self, output_path=None): + start_event = SFTStartEvent( + start_time=time.time(), date_time=datetime.utcfromtimestamp(time.time()) + ) + self._telemetry.capture(start_event) self.trainer.train() self.save(output_path) + self._telemetry.capture( + SFTStopEvent( + end_time=time.time(), + date_time=datetime.utcfromtimestamp(time.time()), + duration=time.time() - start_event.start_time, + ) + ) def prepare_sample_text(self, example): """Prepare the text from a sample of the dataset.""" diff --git a/pykoi/telemetry/events.py b/pykoi/telemetry/events.py index 7a09783..53b9bde 100644 --- a/pykoi/telemetry/events.py +++ b/pykoi/telemetry/events.py @@ -4,8 +4,15 @@ from dataclasses import asdict, dataclass from typing import ClassVar, Dict, Any import platform -import torch import requests +import pynvml + + +try: + pynvml.nvmlInit() + HAS_GPU = True +except pynvml.NVMLError: + HAS_GPU = False def identify_cloud_provider(): @@ -89,11 +96,10 @@ class AppStartEvent(TelemetryEvent): system (str): The name of the operating system. release (str): The release version of the operating system. """ - name: ClassVar[str] = "app_start" start_time: float date_time: str - gpu: bool = torch.cuda.is_available() + gpu: bool = HAS_GPU cloud_provider: str = identify_cloud_provider() system: str = platform.system() release: str = platform.release() @@ -110,8 +116,127 @@ class AppStopEvent(TelemetryEvent): date_time (str): The date and time when the application stopped. duration (str): The duration of the application. """ - name: ClassVar[str] = "app_end" end_time: float date_time: str duration: str + + +@dataclass +class SFTStartEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the supervised finetuning starts. + + Attributes: + name (str): The name of the event. + start_time (float): The time when the supervised finetuning started. + date_time (str): The date and time when the supervised finetuning started. + gpu (bool): Whether or not a GPU is available. + cloud_provider (str): The name of the cloud provider, if running on a cloud platform. + system (str): The name of the operating system. + release (str): The release version of the operating system. + """ + name: ClassVar[str] = "sft_start" + start_time: float + date_time: str + gpu: bool = HAS_GPU + cloud_provider: str = identify_cloud_provider() + system: str = platform.system() + release: str = platform.release() + + +@dataclass +class SFTStopEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the supervised finetuning stops. + + Attributes: + name (str): The name of the event. + end_time (float): The time when the supervised finetuning stopped. + date_time (str): The date and time when the supervised finetuning stopped. + duration (str): The duration of the supervised finetuning. + """ + name: ClassVar[str] = "sft_end" + end_time: float + date_time: str + duration: str + + +@dataclass +class RWStartEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the reward learning starts. + + Attributes: + name (str): The name of the event. + start_time (float): The time when the reward learning started. + date_time (str): The date and time when the reward learning started. + gpu (bool): Whether or not a GPU is available. + cloud_provider (str): The name of the cloud provider, if running on a cloud platform. + system (str): The name of the operating system. + release (str): The release version of the operating system. + """ + name: ClassVar[str] = "rw_start" + start_time: float + date_time: str + gpu: bool = HAS_GPU + cloud_provider: str = identify_cloud_provider() + system: str = platform.system() + release: str = platform.release() + + +@dataclass +class RWStopEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the reward finetuning stops. + + Attributes: + name (str): The name of the event. + end_time (float): The time when the reward model finetuning stopped. + date_time (str): The date and time when the reward model finetuning stopped. + duration (str): The duration of the reward model finetuning. + """ + name: ClassVar[str] = "rw_end" + end_time: float + date_time: str + duration: str + + +@dataclass +class RLStartEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the reinforcement learning starts. + + Attributes: + name (str): The name of the event. + start_time (float): The time when the reinforcement learning started. + date_time (str): The date and time when the reinforcement learning started. + gpu (bool): Whether or not a GPU is available. + cloud_provider (str): The name of the cloud provider, if running on a cloud platform. + system (str): The name of the operating system. + release (str): The release version of the operating system. + """ + name: ClassVar[str] = "rl_start" + start_time: float + date_time: str + gpu: bool = HAS_GPU + cloud_provider: str = identify_cloud_provider() + system: str = platform.system() + release: str = platform.release() + + +@dataclass +class RLStopEvent(TelemetryEvent): + """ + A telemetry event that is triggered when the reinforcement finetuning stops. + + Attributes: + name (str): The name of the event. + end_time (float): The time when the reinforcement learning finetuning stopped. + date_time (str): The date and time when the reinforcement learning finetuning stopped. + duration (str): The duration of the reinforcement learning finetuning. + """ + name: ClassVar[str] = "rl_end" + end_time: float + date_time: str + duration: str diff --git a/pyproject.toml b/pyproject.toml index ce20af4..b731fe0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,38 +8,69 @@ readme = "README.md" [tool.poetry.dependencies] python = ">=3.9,<3.13" +packaging = "23.1" fastapi = "0.100.0" pydantic = "1.10.11" starlette = "0.27.0" uvicorn = "0.23.1" -transformers = "4.31.0" -einops = "0.6.1" -accelerate = "0.21.0" -bitsandbytes = "0.40.2" scipy = "1.11.1" pyngrok = "6.0.0" -pandas = "2.0.3" -datasets = "2.13.1" -evaluate = "0.4.0" -peft = "0.4.0" -trl = "0.4.7" openai = "0.27.8" passlib = "1.7.4" bcrypt = "4.0.1" -langchain = "0.0.220" -scikit-learn = "1.3.0" -chromadb = "0.3.26" -pdfminer-six = "20221105" -docx2txt = "0.8" -python-multipart = "0.0.6" -tiktoken = "0.4.0" -pyepsilla = ">=0.1.1" -torch = "2.0.1" posthog = "3.0.1" pynvml = "11.5.0" -sentence-transformers = "2.2.2" -python-dotenv = "^1.0.0" +pandas = "2.0.3" +python-dotenv = "1.0.0" + +transformers = { version = "4.31.0", optional = true } +einops = { version = "0.6.1", optional = true } +accelerate = { version = "0.21.0", optional = true } +bitsandbytes = { version = "0.40.2", optional = true } + +langchain = { version = "0.0.220", optional = true } +scikit-learn = { version = "1.3.0", optional = true } +chromadb = { version = "0.3.26", optional = true } +pyepsilla = { version = ">=0.1.1", optional = true } +pdfminer-six = { version = "20221105", optional = true } +docx2txt = { version = "0.8", optional = true } +python-multipart = { version = "0.0.6", optional = true } +tiktoken = { version = "0.4.0", optional = true } +sentence-transformers = { version = "2.2.2", optional = true } + +datasets = { version = "2.13.1", optional = true } +evaluate = { version = "0.4.0", optional = true } +peft = { version = "0.4.0", optional = true } +trl = { version = "0.4.7", optional = true } +[tool.poetry.extras] +huggingface = [ + "transformers", + "einops", + "accelerate", + "bitsandbytes", + "sentence-transformers" +] +rag = [ + "langchain", + "scikit-learn", + "chromadb", + "pyepsilla", + "pdfminer-six", + "docx2txt", + "python-multipart", + "tiktoken" +] +rlhf = [ + "transformers", + "einops", + "accelerate", + "bitsandbytes", + "datasets", + "evaluate", + "peft", + "trl" +] [tool.poetry.group.dev.dependencies] pre-commit = "^3.3.3" diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chat/__init__.py b/tests/chat/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chat/db/__init__.py b/tests/chat/db/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chat/db/test_qa_database.py b/tests/chat/db/test_qa_database.py new file mode 100644 index 0000000..d40368b --- /dev/null +++ b/tests/chat/db/test_qa_database.py @@ -0,0 +1,129 @@ +"""Test the QuestionAnswerDatabase class""" +import datetime +import os +import sqlite3 +import unittest + +from pykoi.chat.db.qa_database import QuestionAnswerDatabase + +# Define a temporary database file for testing +TEST_DB_FILE = "test_qd.db" + + +class TestQuestionAnswerDatabase(unittest.TestCase): + """ + Test the QuestionAnswerDatabase class. + """ + + def setUp(self): + # Create a temporary database for testing + self.qadb = QuestionAnswerDatabase(db_file=TEST_DB_FILE, debug=False) + + def tearDown(self): + # Remove the temporary database and close connections after each test + self.qadb.close_connection() + os.remove(TEST_DB_FILE) + + def test_create_table(self): + """ + Test whether the table is created correctly. + """ + # Test whether the table is created correctly + conn = sqlite3.connect(TEST_DB_FILE) + cursor = conn.cursor() + + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name='question_answer'" + ) + table_exists = cursor.fetchone() + + self.assertTrue(table_exists) + + # Clean up + cursor.close() + conn.close() + + def test_insert_and_retrieve_question_answer(self): + """ + Test inserting and retrieving a question-answer pair + """ + question = "What is the meaning of life?" + answer = "42" + + # Insert data and get the ID + qa_id = self.qadb.insert_question_answer(question, answer) + + # Retrieve the data + rows = self.qadb.retrieve_all_question_answers() + + # Check if the data was inserted correctly + self.assertEqual(len(rows), 1) + self.assertEqual(rows[0][0], qa_id) + self.assertEqual(rows[0][1], question) + self.assertEqual(rows[0][2], answer) + self.assertEqual(rows[0][3], "n/a") # Default vote status + + def test_update_vote_status(self): + """ + Test updating the vote status of a question-answer pair. + """ + question = "What is the meaning of life?" + answer = "42" + + # Insert data and get the ID + qa_id = self.qadb.insert_question_answer(question, answer) + + # Update the vote status + new_vote_status = "up" + self.qadb.update_vote_status(qa_id, new_vote_status) + + # Retrieve the data + rows = self.qadb.retrieve_all_question_answers() + + # Check if the vote status was updated correctly + self.assertEqual(len(rows), 1) + self.assertEqual(rows[0][0], qa_id) + self.assertEqual(rows[0][3], new_vote_status) + + def test_save_to_csv(self): + """ + Test saving data to a CSV file + """ + question1 = "What is the meaning of life?" + answer1 = "42" + question2 = "What is the best programming language?" + answer2 = "Python" + + # Insert data + timestamp = datetime.datetime.now() + self.qadb.insert_question_answer(question1, answer1) + self.qadb.insert_question_answer(question2, answer2) + + # Save to CSV + self.qadb.save_to_csv("test_csv_file.csv") + + # Check if the CSV file was created and contains the correct data + self.assertTrue(os.path.exists("test_csv_file.csv")) + + with open("test_csv_file.csv", "r") as file: + lines = file.readlines() + + # Verify the CSV file content + timestamp_trim = 10 # Trim 10 characters from the timestamp + self.assertEqual(len(lines), 3) # Header + 2 rows + self.assertEqual(lines[0].strip(), "ID,Question,Answer,Vote Status,Timestamp") + self.assertEqual( + lines[1].strip()[:-timestamp_trim], + f"1,{question1},{answer1},n/a,{timestamp}"[:-timestamp_trim], + ) # Default vote status + self.assertEqual( + lines[2].strip()[:-timestamp_trim], + f"2,{question2},{answer2},n/a,{timestamp}"[:-timestamp_trim], + ) # Default vote status + + # Clean up + os.remove("test_csv_file.csv") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/chat/db/test_ranking_database.py b/tests/chat/db/test_ranking_database.py new file mode 100644 index 0000000..f813e6f --- /dev/null +++ b/tests/chat/db/test_ranking_database.py @@ -0,0 +1,116 @@ +""" +Test the RankingDatabase class +""" +import os +import sqlite3 +import unittest + +from pykoi.chat.db.ranking_database import RankingDatabase + +# Define a temporary database file for testing +TEST_DB_FILE = "test_ranking.db" + + +class TestRankingDatabase(unittest.TestCase): + """ + Test the RankingDatabase class + """ + + def setUp(self): + # Create a temporary database for testing + self.ranking_db = RankingDatabase(db_file=TEST_DB_FILE, debug=False) + + def tearDown(self): + # Remove the temporary database and close connections after each test + self.ranking_db.close_connection() + os.remove(TEST_DB_FILE) + + def test_create_table(self): + """ + Test whether the table is created correctly. + """ + conn = sqlite3.connect(TEST_DB_FILE) + cursor = conn.cursor() + + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name='ranking'" + ) + table_exists = cursor.fetchone() + + self.assertTrue(table_exists) + + # Clean up + cursor.close() + conn.close() + + def test_insert_and_retrieve_ranking(self): + """ + Test inserting and retrieving a ranking entry + """ + question = "Which fruit is your favorite?" + up_ranking_answer = "Apple" + low_ranking_answer = "Banana" + + # Insert data and get the ID + ranking_id = self.ranking_db.insert_ranking( + question, up_ranking_answer, low_ranking_answer + ) + + # Retrieve the data + rows = self.ranking_db.retrieve_all_question_answers() + + # Check if the data was inserted correctly + self.assertEqual(len(rows), 1) + self.assertEqual(rows[0][0], ranking_id) + self.assertEqual(rows[0][1], question) + self.assertEqual(rows[0][2], up_ranking_answer) + self.assertEqual(rows[0][3], low_ranking_answer) + + def test_save_to_csv(self): + """ + Test saving data to a CSV file + """ + question1 = "Which fruit is your favorite?" + up_ranking_answer1 = "Apple" + low_ranking_answer1 = "Banana" + question2 = "Which country would you like to visit?" + up_ranking_answer2 = "Japan" + low_ranking_answer2 = "Italy" + + # Insert data + self.ranking_db.insert_ranking( + question1, up_ranking_answer1, low_ranking_answer1 + ) + self.ranking_db.insert_ranking( + question2, up_ranking_answer2, low_ranking_answer2 + ) + + # Save to CSV + self.ranking_db.save_to_csv("test_csv_file.csv") + + # Check if the CSV file was created and contains the correct data + self.assertTrue(os.path.exists("test_csv_file.csv")) + + with open("test_csv_file.csv", "r") as file: + lines = file.readlines() + + # Verify the CSV file content + self.assertEqual(len(lines), 3) # Header + 2 rows + self.assertEqual( + lines[0].strip(), "ID,Question,Up Ranking Answer,Low Ranking Answer" + ) + self.assertEqual( + lines[1].strip(), + f"1,{question1},{up_ranking_answer1},{low_ranking_answer1}", + ) + self.assertEqual( + lines[2].strip(), + f"2,{question2},{up_ranking_answer2},{low_ranking_answer2}", + ) + + # Clean up + os.remove("test_csv_file.csv") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/chat/llm/__init__.py b/tests/chat/llm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chat/llm/test_abs_llm.py b/tests/chat/llm/test_abs_llm.py new file mode 100644 index 0000000..3a8ce02 --- /dev/null +++ b/tests/chat/llm/test_abs_llm.py @@ -0,0 +1,40 @@ +""" +Test the AbsLlm class. +""" +import unittest + +from pykoi.chat.llm.abs_llm import AbsLlm + + +class DummyLlm(AbsLlm): + """ + Dummy class for testing the abstract base class AbsLlm. + """ + + def predict(self, message: str): + return f"Q: {message}, A: N/A." + + +class TestAbsLlm(unittest.TestCase): + """ + Test the AbsLlm class. + """ + + def test_predict_abstract_method(self): + """ + Test whether the abstract method `predict` raises NotImplementedError + """ + + test_message = "test" + llm = DummyLlm() + self.assertEqual(llm.predict(test_message), f"Q: {test_message}, A: N/A.") + + def test_docstring(self): + """ + Test whether the class has a docstring + """ + self.assertIsNotNone(AbsLlm.__doc__) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/chat/llm/test_constants.py b/tests/chat/llm/test_constants.py new file mode 100644 index 0000000..2e3e71f --- /dev/null +++ b/tests/chat/llm/test_constants.py @@ -0,0 +1,30 @@ +""" +Test the constants of the LLM module. +""" +import unittest + +from pykoi.chat.llm.constants import ModelSource + + +class TestLlmName(unittest.TestCase): + """ + Test the ModelSource enum. + """ + + def test_enum_values(self): + """ + Test whether the enum values are defined correctly + """ + self.assertEqual(ModelSource.OPENAI.value, "openai") + self.assertEqual(ModelSource.HUGGINGFACE.value, "huggingface") + + def test_enum_attributes(self): + """ + Test whether the enum attributes are defined correctly + """ + self.assertEqual(ModelSource.OPENAI.name, "OPENAI") + self.assertEqual(ModelSource.HUGGINGFACE.name, "HUGGINGFACE") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/chat/llm/test_huggingface.py b/tests/chat/llm/test_huggingface.py new file mode 100644 index 0000000..77e2d74 --- /dev/null +++ b/tests/chat/llm/test_huggingface.py @@ -0,0 +1,40 @@ +import unittest +from unittest.mock import patch, Mock +from transformers import AutoModelForCausalLM, AutoTokenizer + +from pykoi.chat.llm.abs_llm import AbsLlm +from pykoi.chat.llm.huggingface import HuggingfaceModel + + +class TestHuggingfaceModel(unittest.TestCase): + @patch.object(AutoModelForCausalLM, "from_pretrained") + @patch.object(AutoTokenizer, "from_pretrained") + def setUp(self, mock_model, mock_tokenizer): + self.model_name = "gpt2" + self.mock_model = mock_model + self.mock_tokenizer = mock_tokenizer + + # Mocking the pretrained model and tokenizer + self.mock_model.return_value = Mock() + self.mock_tokenizer.return_value = Mock() + + self.huggingface_model = HuggingfaceModel( + pretrained_model_name_or_path=self.model_name + ) + + def test_name(self): + expected_name = f"{HuggingfaceModel.model_source}_{self.model_name}_100" + self.assertEqual(self.huggingface_model.name, expected_name) + + @patch.object(HuggingfaceModel, "predict") + def test_predict(self, mock_predict): + mock_predict.return_value = ["Hello, how can I assist you today?"] + message = "Hello, chatbot!" + num_of_response = 1 + response = self.huggingface_model.predict(message, num_of_response) + mock_predict.assert_called_once_with(message, num_of_response) + self.assertEqual(response, ["Hello, how can I assist you today?"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/chat/llm/test_openai.py b/tests/chat/llm/test_openai.py new file mode 100644 index 0000000..5cf01d8 --- /dev/null +++ b/tests/chat/llm/test_openai.py @@ -0,0 +1,54 @@ +""" +Test the OpenAIModel class +""" +import unittest +from unittest.mock import MagicMock, patch + +from pykoi.chat.llm.openai import OpenAIModel + + +class TestOpenAIModel(unittest.TestCase): + """ + Test the OpenAIModel class + """ + + def test_predict(self): + """ + Test the predict method of the OpenAIModel class + """ + # Test predicting the next word based on a given message + message = "What is the meaning of life?" + predicted_word = "42" + + # Mock the OpenAI.Completion.create behavior + mock_response = MagicMock() + mock_response.choices = [MagicMock()] + mock_response.choices[0].text = f"Answer: {predicted_word}" + openai_completion_create_mock = MagicMock(return_value=mock_response) + + # Patch the OpenAI.Completion.create method to use the mocked version + with patch( + "pykoi.chat.llm.openai.openai.Completion.create", openai_completion_create_mock + ): + openai_model = OpenAIModel( + api_key="fake_api_key", + engine="davinci", + max_tokens=100, + temperature=0.5, + ) + result = openai_model.predict(message, 1) + + # Check if the OpenAI.Completion.create method was called with the correct arguments + openai_completion_create_mock.assert_called_once_with( + engine="davinci", + prompt=f"Question: {message}\nAnswer:", + max_tokens=100, + n=1, + stop="\n", + temperature=0.5, + ) + self.assertEqual(result[0], f"Answer: {predicted_word}") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/component/__init__.py b/tests/component/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/component/test_base.py b/tests/component/test_base.py new file mode 100644 index 0000000..84df2f9 --- /dev/null +++ b/tests/component/test_base.py @@ -0,0 +1,77 @@ +"""Tests for the base module.""" +import unittest + +from pykoi.component.base import Component, DataSource + + +class TestDataSource(unittest.TestCase): + """ + Unit test class for the DataSource class. + + This class tests the initialization of the DataSource class by creating a DataSource instance + with a specific id and a fetch function. It then asserts that the id and the data fetched by + the fetch function are as expected. + + Attributes: + fetch_func (function): A function that returns the data to be fetched. + ds (DataSource): An instance of the DataSource class. + + Methods: + test_init: Tests the initialization of the DataSource class. + """ + + def test_init(self): + """ + Tests the initialization of the DataSource class. + + This method creates a DataSource instance with a specific id and a fetch function. + It then asserts that the id and the data fetched by the fetch function are as expected. + """ + + def fetch_func(): + return "data" + + data_source = DataSource("test_id", fetch_func) + + self.assertEqual(data_source.id, "test_id") + self.assertEqual(data_source.fetch_func(), "data") + + +class TestComponent(unittest.TestCase): + """ + Unit test class for the Component class. + + This class tests the initialization of the Component class by creating a Component instance + with a specific fetch function, a svelte component, and properties. It then asserts that the id, + the data fetched by the fetch function, the svelte component, and the properties are as expected. + + Attributes: + fetch_func (function): A function that returns the data to be fetched. + comp (Component): An instance of the Component class. + + Methods: + test_init: Tests the initialization of the Component class. + """ + + def test_init(self): + """ + Tests the initialization of the Component class. + + This method creates a Component instance with a specific fetch function, a svelte component, + and properties. It then asserts that the id, the data fetched by the fetch function, the svelte + component, and the properties are as expected. + """ + + def fetch_func(): + return "data" + + comp = Component(fetch_func, "TestComponent", prop1="value1", prop2="value2") + + self.assertIsNotNone(comp.id) + self.assertEqual(comp.data_source.fetch_func(), "data") + self.assertEqual(comp.svelte_component, "TestComponent") + self.assertEqual(comp.props, {"prop1": "value1", "prop2": "value2"}) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_application.py b/tests/test_application.py new file mode 100644 index 0000000..4f5f3b9 --- /dev/null +++ b/tests/test_application.py @@ -0,0 +1,36 @@ +""" +Test the Application class. +""" + +import os +import unittest +from unittest.mock import MagicMock, patch + +from pykoi.application import Application + + +class TestApplication(unittest.TestCase): + """ + Unit test class for the Application class. + """ + + def setUp(self): + self.app = Application(share=False, debug=False) + + def test_add_component(self): + """ + Tests adding a component to the application. + """ + component = MagicMock() + component.data_source = MagicMock() + component.id = "test_component" + + self.app.add_component(component) + + # Check if the component and its data source are added to the application + self.assertIn("test_component", self.app.data_sources) + self.assertIn(component, [c["component"] for c in self.app.components]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_state.py b/tests/test_state.py new file mode 100644 index 0000000..3854817 --- /dev/null +++ b/tests/test_state.py @@ -0,0 +1,86 @@ +""" +Tests for the State class. +""" +import unittest + +from pykoi.state import State, Store + + +class TestState(unittest.TestCase): + """ + Unit test class for the State class. + """ + + def test_get_attribute(self): + """ + Tests getting an attribute from the state. + """ + state = State() + state.state = {"test_attr": 42} + + self.assertEqual(state.test_attr, 42) + + def test_get_non_existing_attribute(self): + """ + Tests getting a non-existing attribute from the state. + """ + state = State() + + with self.assertRaises(AttributeError): + attr = state.non_existing_attr + + def test_set_attribute(self): + """ + Tests setting an attribute in the state. + """ + state = State() + state.test_attr = 42 + + self.assertEqual(state.state["test_attr"], 42) + + def test_call_method(self): + """ + Tests calling a method from the state. + """ + state = State() + state.state = {"test_method": lambda x: x * 2} + + result = state.test_method(3) + self.assertEqual(result, 6) + + +class TestStore(unittest.TestCase): + """ + Unit test class for the Store class. + """ + + def test_increment(self): + """ + Tests incrementing the count. + """ + store = Store() + store.increment() + + self.assertEqual(store.count, 5) + + def test_decrement(self): + """ + Tests decrementing the count. + """ + store = Store() + store.decrement() + + self.assertEqual(store.count, 3) + + def test_hello(self): + """ + Tests the hello method. + """ + store = Store() + hello_msg = store.hello() + + self.assertEqual(hello_msg, "hello jared") + + +if __name__ == "__main__": + unittest.main()