README.md

GPT Deploy: One line to generate them all 🧙🚀

Turn your natural language descriptions into fully functional, deployed AI-powered microservices with a single command! Your imagination is the limit!

gptdeploy-tiny.mp4

This project streamlines the creation and deployment of AI-powered microservices. Simply describe your task using natural language, and the system will automatically build and deploy your microservice. To ensure the microservice accurately aligns with your intended task a test scenario is required.

Quickstart

Requirements

• OpenAI key with access to GPT-3.5 or GPT-4

Installation

pip install gptdeploy
gptdeploy configure --key <your openai api key>

If you set the environment variable OPENAI_API_KEY, the configuration step can be skipped. Your api key must have access to gpt-4 to use this tool. We are working on a way to use gpt-3.5-turbo as well.

Generate Microservice

gptdeploy generate \
--description "<description of the microservice>" \
--test "<specification of a test scenario>" \
--model <gpt-3.5 or gpt-4> \
--path </path/to/local/folder>

To generate your personal microservice two things are required:

• A description of the task you want to accomplish.
• A test scenario that ensures the microservice works as expected.
• The model you want to use - either gpt-3.5 or gpt-4. gpt-3.5 is ~10x cheaper, but will not be able to generate as complex microservices.
• A path on the local drive where the microservice will be generated.

The creation process should take between 5 and 15 minutes. During this time, GPT iteratively builds your microservice until it finds a strategy that make your test scenario pass.

Be aware that the costs you have to pay for openai vary between $0.50 and $3.00 per microservice (using GPT-4).

Run Microservice

Run the microservice locally in docker. In case docker is not running on your machine, it will try to run it without docker. With this command a playground opens in your browser where you can test the microservice.

gptdeploy run --path <path to microservice>

Deploy Microservice

If you want to deploy your microservice to the cloud a Jina account is required. When creating a Jina account, you get some free credits, which you can use to deploy your microservice ($0.025/hour). If you run out of credits, you can purchase more.

gptdeploy deploy --microservice_path <path to microservice>

Delete Microservice

To save credits you can delete your microservice via the following commands:

jc list # get the microservice id
jc delete <microservice id>

Examples

In this section you can get a feeling for the kind of microservices that can be generated with GPT Deploy.

Compliment Generator

gptdeploy generate \
--description "The user writes something and gets a related deep compliment." \
--test "Given the word test a deep compliment is generated" \
--model gpt-4 \
--path microservice

Extract and summarize news articles given a URL

gptdeploy generate \
--description "Extract text from a news article URL using Newspaper3k library and generate a summary using gpt." \
--test "input: 'http://fox13now.com/2013/12/30/new-year-new-laws-obamacare-pot-guns-and-drones/' output: assert a summarized version of the article exists" \
--model gpt-4 \
--path microservice

Chemical Formula Visualization

gptdeploy generate \
--description "Convert a chemical formula into a 2D chemical structure diagram" \
--test "C=C, CN=C=O, CCC(=O)O" \
--model gpt-4 \
--path microservice

2d rendering of 3d model

gptdeploy generate \
--description "create a 2d rendering of a whole 3d object and x,y,z object rotation using trimesh and pyrender.OffscreenRenderer with os.environ['PYOPENGL_PLATFORM'] = 'egl' and freeglut3-dev library" \
--test "input: https://graphics.stanford.edu/courses/cs148-10-summer/as3/code/as3/teapot.obj output: assert the image is not completely white or black" \
--model gpt-4 \
--path microservice

Product Recommendation

gptdeploy generate \
--description "Generate personalized product recommendations based on user product browsing history and the product categories fashion, electronics and sport" \
--test "Test that a user how visited p1(electronics),p2(fashion),p3(fashion) is more likely to buy p4(fashion) than p5(sports)" \
--model gpt-4 \
--path microservice

Hacker News Search

gptdeploy generate \
--description "Given a search query, find articles on hacker news using the hacker news api and return a list of (title, author, website_link, first_image_on_the_website)" \
--test "searching for GPT gives results" \
--model gpt-4 \
--path microservice

Animal Detector

gptdeploy generate \
--description "Given an image, return the image with bounding boxes of all animals (https://pjreddie.com/media/files/yolov3.weights, https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg)" \
--test "https://images.unsplash.com/photo-1444212477490-ca407925329e contains animals" \
--model gpt-4 \
--path microservice

Meme Generator

gptdeploy generate \
--description "Generate a meme from an image and a caption" \
--test "Surprised Pikachu: https://media.wired.com/photos/5f87340d114b38fa1f8339f9/master/w_1600%2Cc_limit/Ideas_Surprised_Pikachu_HD.jpg, TOP:When you discovered GPTDeploy" \
--model gpt-4 \
--path microservice

Rhyme Generator

gptdeploy generate \
--description "Given a word, return a list of rhyming words using the datamuse api" \
--test "hello" \
--model gpt-4 \
--path microservice

Word Cloud Generator

gptdeploy generate \
--description "Generate a word cloud from a given text" \
--test "Lorem ipsum dolor sit amet, consectetur adipiscing elit." \
--model gpt-4 \
--path microservice

3d model info

gptdeploy generate \
--description "Given a 3d object, return vertex count and face count" \
--test "https://raw.githubusercontent.com/polygonjs/polygonjs-assets/master/models/wolf.obj" \
--model gpt-4 \
--path microservice

Table extraction

gptdeploy generate \
--description "Given a URL, extract all tables as csv" \
--test "http://www.ins.tn/statistiques/90" \
--model gpt-4 \
--path microservice

Audio to mel spectrogram

gptdeploy generate \
--description "Create mel spectrograms from audio file" \
--test "https://cdn.pixabay.com/download/audio/2023/02/28/audio_550d815fa5.mp3" \
--model gpt-4 \
--path microservice

Text to speech

gptdeploy generate \
--description "Convert text to speech" \
--test "Hello, welcome to GPT Deploy!" \
--model gpt-4 \
--path microservice

Your browser does not support the audio element.

Heatmap Generator

gptdeploy generate \
--description "Create a heatmap from an image and a list of relative coordinates" \
--test "https://images.unsplash.com/photo-1574786198875-49f5d09fe2d2, [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.2, 0.1], [0.7, 0.2], [0.4, 0.2]]" \
--model gpt-4 \
--path microservice

QR Code Generator

gptdeploy generate \
--description "Generate QR code from URL" \
--test "https://www.example.com" \
--model gpt-4 \
--path microservice

Mandelbrot Set Visualizer

gptdeploy generate \
--description "Visualize the Mandelbrot set with custom parameters" \
--test "center=-0+1i, zoom=1.0, size=800x800, iterations=1000" \
--model gpt-4 \
--path microservice

Markdown to HTML Converter

gptdeploy generate --description "Convert markdown to HTML" --test "# Hello, welcome to GPT Deploy!"

Technical Insights

The graphic below illustrates the process of creating a microservice and deploying it to the cloud elaboration two different implementation strategies.

graph TB

    description[description: generate QR code from URL] --> make_strat{think a}

    test[test: https://www.example.com] --> make_strat[generate strategies]

    make_strat --> implement1[implement strategy 1]

    implement1 --> build1{build image}

    build1 -->|error message| implement1

    build1 -->|failed 10 times| implement2[implement strategy 2]

    build1 -->|success| registry[push docker image to registry]

    implement2 --> build2{build image}

    build2 -->|error message| implement2

    build2 -->|failed 10 times| all_failed[all strategies failed]

    build2 -->|success| registry[push docker image to registry]

    registry --> deploy[deploy microservice]

    deploy --> streamlit[generate streamlit playground]

    streamlit --> user_run[user tests microservice]

Loading

1. GPT Deploy identifies several strategies to implement your task.
2. It tests each strategy until it finds one that works.
3. For each strategy, it generates the following files:

• microservice.py: This is the main implementation of the microservice.
• test_microservice.py: These are test cases to ensure the microservice works as expected.
• requirements.txt: This file lists the packages needed by the microservice and its tests.
• Dockerfile: This file is used to run the microservice in a container and also runs the tests when building the image.

4. GPT Deploy attempts to build the image. If the build fails, it uses the error message to apply a fix and tries again to build the image.
5. Once it finds a successful strategy, it:

• Pushes the Docker image to the registry.
• Deploys the microservice.
• Generates a Streamlit playground where you can test the microservice.

6. If it fails 10 times in a row, it moves on to the next approach.

🔮 vision

Use natural language interface to generate, deploy and update your microservice infrastructure.

✨ Contributors

If you want to contribute to this project, feel free to open a PR or an issue. In the following, you can find a list of things that need to be done.

next steps:

• check if windows and linux support works
• add video to README.md
• bug: it can happen that the code generation is hanging forever - in this case aboard and redo the generation
• new user has free credits but should be told to verify account

Nice to have:

• smooth rendering animation of the responses
• if the user runs gptdeploy without any arguments, show the help message
• don't show this message: 🔐 You are logged in to Jina AI as florian.hoenicke (username:auth0-unified-448f11965ce142b6). To log out, use jina auth logout.
• put the playground into the custom gateway (without rebuilding the custom gateway)
• hide prompts in normal mode and show them in verbose mode
• tests
• clean up duplicate code
• support popular cloud providers - lambda, cloud run, cloud functions, ...
• support local docker builds
• autoscaling enabled for cost saving
• add more examples to README.md
• support multiple endpoints - example: todolist microservice with endpoints for adding, deleting, and listing todos
• support stateful microservices
• The playground is currently printed twice even if it did not change. Make sure it is only printed twice in case it changed.
• allow to update your microservice by providing feedback
• support for other large language models like Open Assistent
• for cost savings, it should be possible to insert less context during the code generation of the main functionality - no jina knowledge is required
• use gptdeploy list to show all deployments
• gptdeploy delete to delete a deployment
• gptdeploy update to update a deployment
• test param optional - in case the test param is not there first ask gpt if more information is required to write a test - like access to pdf data
• section for microservices built by the community
• test feedback for playground generation (could be part of the debugging)
• should we send everything via json in the text attribute for simplicity?
• fix release workflow
• after the user specified the task, ask them questions back if the task is not clear enough or something is missing

Proposal:

• just generate the non-jina related code and insert it into an executor template
• think about strategies after the first approach failed?