Top LLM frameworks may not be as reliable as you think
Nearly a month ago, I decided to add Gemini support to Feeds Fun and did some research on top LLM frameworks — I didn't want to write my own bicycle.
As a result, I found an embarrassing bug (in my opinion, of course) in the integration with Gemini in LLamaIndex. Judging by the code, it is also present in Haystack and in the plugin for LangChain. And the root of the problem is in the Google SDK for Python.
When initializing a new client for Gemini, the framework code overwrites/replaces API keys in all clients created before. Because the API key, by default, is stored in a singleton.
It is death-like, if you have a multi-tenant application, and unnoticeable in all other cases. Multi-tenant means that your application works with multiple users.
For example, in my case, in Feeds Fun, a user can enter their API key to improve the quality of the service. Imagine what a funny situation could happen: a user entered an API key to process their news but spent tokens (paid for) for all service users.
I reported this bug only in LLamaIndex as a security issue, and there has been no reaction for 3 weeks. I'm too lazy to reproduce and report for Haystack and LangChain. So this is your chance to report a bug to a top repository. All the info will be below, reproducing is not difficult.
This error is notable for many reasons:
- The assessment of the criticality of the error depends a lot on taste, experience, and context. For me, in the projects I worked on, this is a critical security issue. However, it seems that this is not critical at all for most current projects that use LLMs. Which leads to some thoughts about mainstream near-LLM development.
- This is a good indicator of a low level of code quality control: code reviews, tests, all processes. After all, this is an integration with one of the major API providers. The problem could have been found in many different ways, but none worked.
- This is a good illustration of the vicious approach to development: "copy-paste from a tutorial and push to prod". To make such a mistake, you had to ignore both the basic architecture of your project and the logic of calling the code you are copying.
Ultimately, I gave up on these frameworks and implemented my own client over HTTP API.
My conclusion from this mess is: you can't trust the code under the hood of modern LLM frameworks. You need to double-check and proofread it. Just because they state that they are "production-ready" doesn't mean they are really production-ready.
Let me tell you more about the bug.
Places to discuss Feeds Fun
I continue developing my news reader: feeds.fun. To gather information and people together, I created several resources where you can discuss the project and find useful information:
- Blog blog.feeds.fun
- Discord server
- Reddit r/feedsfun
So far, there is no one and nothing there, but over time, there will definitely be news and people.
If you are interested in this project, join! I'll be glad to see you and will try to respond quickly to all questions.
Preparing a business plan for a game on Steam
Earning millions is easier than ever. I'll tell you how :-D
When I posted my final presentation [ru] (slides) for World Builders 2023 (my posts, site), I promised to tell how I made a roadmap and a financial model for the game. So, here they are.
At the end of this post, we will have:
- A brief strategy of our company: what we do, how, and why.
- A table with our beacons — successful games roughly similar to what we want to make. Similar in gameplay, team size, budget, etc.
- A composition of the team we need to assemble.
- A roadmap — a development plan for our game.
- An outline of our marketing strategy.
- A financial model — how much we will spend, how much we will earn.
- A large number of my caveats throughout the post.
- Jokes and
I hopewitty remarks.
All the final documents can be found here.
Grainau: hiking and beer at 3000 meters
How it all looks from the ground.
For her vacation, Yuliya decided to show me the beautiful German mountains and took me for a couple of days to Grainau — it's a piece of Bavaria that's almost like Switzerland. At least, it is similar to the pictures of Switzerland that I've seen :-D
In short, it's a lovely place with a measured pace of life. If you need to catch your breath, calm your nerves, and enjoy nature, then this is the place for you. But if you can't live without parties, you'll get bored quickly.
What's there:
- The highest mountain in Germany plus a couple of glaciers.
- There's skiing in winter. If you really need it, you can find a place to ski in summer, but the descent is short, and the lifts are turned off.
- A large clean lake and a couple of smaller ones.
- A huge number of trails for hiking.
- A huge number of waterfalls, streams, and a couple of mountain rivers.
- Restaurants with beer.
- Beautiful fallen trees in the forests, private property, fences, cows with bells, and "racing tractors" (I don't know how to name this phenomenon better, but tractors are moving fast there :-D).
This is briefly, and now in detail.
Migrating from GPT-3.5-turbo to GPT-4o-mini
Guess when I switched models.
Recently OpenAI released GPT-4o-mini — a new flagship model for the cheap segment, as it were.
- They say it works "almost like" GPT-4o, sometimes even better than GPT-4.
- It is almost three times cheaper than GPT-3.5-turbo.
- Context size 128k tokens, against 16k for GPT-3.5-turbo.
Of course, I immediately started migrating my news reader to this model.
In short, it's a cool replacement for GPT-3.5-turbo. I immediately replaced two LLM agents with one without changing prompts, reducing costs by a factor of 5 without losing quality.
However, then I started tuning the prompt to make it even cooler and began to encounter nuances. Let me tell you about them.