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fixed typos and improved quality of document
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Akash Kundu
2023-10-09 02:35:46 +05:30
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@@ -65,7 +65,7 @@ https://github.com/Pythagora-io/gpt-pilot/assets/10895136/0495631b-511e-451b-93d
- **Python 3**
- **PostgreSQL** (optional, projects default is SQLite)
- DB is needed for multiple reasons like continuing app development if you had to stop at any point or app crashed, going back to specific step so that you can change some later steps in development, easier debugging, for future we will add functionality to update project (change some things in existing project or add new features to the project and so on)...
- DB is needed for multiple reasons like continuing app development. If you have to stop at any point or the app crashes, go back to a specific step so that you can change some later steps in development, and easier debugging, in future we will add functionality to update project (change some things in existing project or add new features to the project and so on)...
# 🚦How to start using gpt-pilot?
@@ -126,7 +126,7 @@ python main.py workspace=<PATH_TO_PROJECT_WORKSPACE>
Each user can have their own workspace path for each App.
## `user_id`, `email` and `password`
## `user_id`, `email`, and `password`
These values will be saved to the User table in the DB.
```bash
@@ -211,9 +211,9 @@ Here are a couple of example apps GPT Pilot created by itself:
<br>
# 🏛 Main pillars of GPT Pilot:
1. For AI to create a fully working app, **a developer needs to be involved** in the process of app creation. They need to be able to change the code at any moment and GPT Pilot needs to continue working with those changes (e.g. add an API key or fix an issue if an AI gets stuck). <br><br>
2. **The app needs to be written step by step as a developer would write it** - Let's say you want to create a simple app, and you know everything you need to code and have the entire architecture in your head. Even then, you won't code it out entirely, then run it for the first time and debug all the issues at once. Rather, you will implement something simple, like add routes, run it, see how it works, and then move on to the next task. This way, you can debug issues as they arise. The same should be in the case when AI codes. It will make mistakes for sure so in order for it to have an easier time debugging issues and for the developer to understand what is happening, the AI shouldn't just spit out the entire codebase at once. Rather, the app should be developed step by step just like a developer would code it - e.g. setup routes, add database connection, etc. <br><br>
3. **The approach needs to be scalable** so that AI can create a production ready app:
1. For AI to create a fully working app, **a developer needs to be involved** in the app creation process. They need to be able to change the code at any moment and GPT Pilot needs to continue working with those changes (e.g. add an API key or fix an issue if an AI gets stuck). <br><br>
2. **The app needs to be written step by step as a developer would write it** - Let's say you want to create a simple app, and you know everything you need to code and have the entire architecture in your head. Even then, you won't code it out entirely, then run it for the first time and debug all the issues at once. Rather, you will implement something simple, like add routes, run it, see how it works, and then move on to the next task. This way, you can debug issues as they arise. The same should be the case when AI codes. It will make mistakes for sure so in order for it to have an easier time debugging issues and for the developer to understand what is happening, the AI shouldn't just spit out the entire codebase at once. Rather, the app should be developed step by step just like a developer would code it - e.g. setup routes, add database connection, etc. <br><br>
3. **The approach needs to be scalable** so that AI can create a production-ready app:
1. **Context rewinding** - for solving each development task, the context size of the first message to the LLM has to be relatively the same. For example, the context size of the first LLM message while implementing development task #5 has to be more or less the same as the first message while developing task #50. Because of this, the conversation needs to be rewound to the first message upon each task. [See the diagram here](https://blogpythagora.files.wordpress.com/2023/08/pythagora-product-development-frame-3-1.jpg?w=1714).
2. **Recursive conversations** are LLM conversations that are set up in a way that they can be used “recursively”. For example, if GPT Pilot detects an error, it needs to debug it but let’s say that, during the debugging process, another error happens. Then, GPT Pilot needs to stop debugging the first issue, fix the second one, and then get back to fixing the first issue. This is a very important concept that, I believe, needs to work to make AI build large and scalable apps by itself. It works by rewinding the context and explaining each error in the recursion separately. Once the deepest level error is fixed, we move up in the recursion and continue fixing that error. We do this until the entire recursion is completed.
3. **TDD (Test Driven Development)** - for GPT Pilot to be able to scale the codebase, it will need to be able to create new code without breaking previously written code. There is no better way to do this than working with TDD methodology. For each code that GPT Pilot writes, it needs to write tests that check if the code works as intended so that whenever new changes are made, all previous tests can be run.
@@ -232,9 +232,9 @@ Here are the steps GPT Pilot takes to create an app:
3. **Product Owner agent** writes user stories and asks you if they are all correct (this helps it create code later on).
4. **Architect agent** writes up technologies that will be used for the app.
5. **DevOps agent** checks if all technologies are installed on the machine and installs them if they are not.
6. **Tech Lead agent** writes up development tasks that Developer will need to implement. This is an important part because, for each step, Tech Lead needs to specify how the user (real world developer) can review if the task is done (e.g. open localhost:3000 and do something).
6. **Tech Lead agent** writes up development tasks that the Developer will need to implement. This is an important part because, for each step, Tech Lead needs to specify how the user (real world developer) can review if the task is done (e.g. open localhost:3000 and do something).
7. **Developer agent** takes each task and writes up what needs to be done to implement it. The description is in human-readable form.
8. Finally, **Code Monkey agent** takes the Developer's description and the existing file and implements the changes into it. We realized this works much better than giving it to Developer right away to implement changes.
8. Finally, **Code Monkey agent** takes the Developer's description and the existing file and implements the changes into it. We realized this works much better than giving it to the Developer right away to implement changes.
For more details on the roles of agents employed by GPT Pilot refer to [AGENTS.md](https://github.com/Pythagora-io/gpt-pilot/blob/main/pilot/helpers/agents/AGENTS.md)
@@ -244,15 +244,15 @@ For more details on the roles of agents employed by GPT Pilot refer to [AGENTS.m
<br>
# 🕴How's GPT Pilot different from _Smol developer_ and _GPT engineer_?
- **GPT Pilot works with the developer to create fully working production-ready app** - I don't think that AI can (at least in the near future) create apps without a developer being involved. So, **GPT Pilot codes the app step by step** just like a developer would in real life. This way, it can debug issues as they arise throughout the development process. If it gets stuck, you, the developer in charge, can review the code and fix the issue. Other similar tools give you the entire codebase at once - this way, bugs are much harder to fix both for AI and for you as a developer.
- **GPT Pilot works with the developer to create a fully working production-ready app** - I don't think that AI can (at least in the near future) create apps without a developer being involved. So, **GPT Pilot codes the app step by step** just like a developer would in real life. This way, it can debug issues as they arise throughout the development process. If it gets stuck, you, the developer in charge, can review the code and fix the issue. Other similar tools give you the entire codebase at once - this way, bugs are much harder to fix both for AI and for you as a developer.
<br><br>
- **Works at scale** - GPT Pilot isn't meant to create simple apps but rather so it can work at any scale. It has mechanisms that filter out the code so in each LLM conversation, it doesn't need to store the entire codebase in context, but it shows the LLM only the code that is relevant for the current task it's working on. Once an app is finished, you can always continue working on it by writing instructions on what feature you want to add.
# 🍻 Contributing
If you are interested in contributing to GPT Pilot, I would be more than happy to have you on board but also help you get started. Feel free to ping [zvonimir@pythagora.ai](mailto:zvonimir@pythagora.ai) and I'll help you get started.
If you are interested in contributing to GPT Pilot, I would be more than happy to have you on board and also help you get started. Feel free to ping [zvonimir@pythagora.ai](mailto:zvonimir@pythagora.ai) and I'll help you get started.
## 🔬️ Research
Since this is a research project, there are many areas that need to be researched on both practical and theoretical levels. We're happy to hear how the entire GPT Pilot concept can be improved. For example, maybe it would work better if we structured functional requirements differently or maybe technical requirements need to be specified in a different way.
Since this is a research project, there are many areas that need to be researched on both practical and theoretical levels. We're happy to hear how the entire GPT Pilot concept can be improved. For example, maybe it would work better if we structured functional requirements differently, or maybe technical requirements need to be specified in a different way.
## 🖥 Development
Other than the research, GPT Pilot needs to be debugged to work in different scenarios. For example, we realized that the quality of the code generated is very sensitive to the size of the development task. When the task is too broad, the code has too many bugs that are hard to fix but when the development task is too narrow, GPT also seems to struggle in getting the task implemented into the existing code.