Powering your Copilot for Data – with Artem Keydunov of Cube.dev

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Text-to-SQL was one of the first applications of NLP. Thoughtspot offered “Ask your data questions” as their core differentiation compared to traditional dashboarding tools. In a way, they provide a much friendlier interface with your own structured (aka “tabular”, as in “SQL tables”) data, the same way that RLHF and Instruction Tuning helped turn the GPT-3 of 2020 into the ChatGPT of 2022.

Today, natural language queries on your databases are a commodity. There are 4 different ChatGPT plugins that offer this, as well as a bunch of startups like one of our previous guests, Seek.ai. Perplexity originally started with a similar product in 2022:

In March 2023 LangChain wrote a blog post on LLMs and SQL highlighting why they don’t consistently work:

* “LLMs can write SQL, but they are often prone to making up tables, making up field”

* “LLMs have some context window which limits the amount of text they can operate over”

* “The SQL it writes may be incorrect for whatever reason, or it could be correct but just return an unexpected result.”

For example, if you ask a model to “return all active users in the last 7 days” it might hallucinate a `is_active` column, join to an `activity` table that doesn’t exist, or potentially get the wrong date (especially in leap years!).

We previously talked to Shreya Rajpal at Guardrails AI, which also supports Text2SQL enforcement. Their approach was to run the actual SQL against your database and then use the error messages to improve the query:

Semantic Layers to the rescue

Cube is an open source semantic layer which recently integrated with LangChain to solve these issues in a different way. You can use YAML, Javascript, or Python to create definitions of different metrics, measures and dimensions for your data:

Creating these metrics and passing them in the model context limits the possibility for errors as the model just needs to query the `active_users` view, and Cube will then expand that into the full SQL in a reliable way. The downside of this approach compared to the Guardrails one for example is that it requires more upfront work to define metrics, but on the other hand it leads to more reliable and predictable outputs.

The promise of adding a great semantic layer to your LLM app is irresistible - you greatly minimize hallucinations, make much more token efficient prompts, and your data stays up to date without any retraining or re-indexing. However, there are also difficulties with implementing semantic layers well, so we were glad to go deep on the topic with Artem as one of the leading players in this space!

Timestamps

* [00:00:00] Introductions

* [00:01:28] Statsbot and limitations of natural language processing in 2017

* [00:04:27] Building Cube as the infrastructure for Statsbot

* [00:08:01] Open sourcing Cube in 2019

* [00:09:09] Explaining the concept of a semantic layer/Cube

* [00:11:01] Using semantic layers to provide context for AI models working with tabular data

* [00:14:47] Workflow of generating queries from natural language via semantic layer

* [00:21:07] Using Cube to power customer-facing analytics and natural language interfaces

* [00:22:38] Building data-driven AI applications and agents

* [00:25:59] The future of the modern data stack

* [00:29:43] Example use cases of Slack bots powered by Cube

* [00:30:59] Using GPT models and limitations around math

* [00:32:44] Tips for building data-driven AI apps

* [00:35:20] Challenges around monetizing embedded analytics

* [00:36:27] Lightning Round

Transcript

Swyx: Hey everyone, welcome to the Latent Space podcast. This is Swyx, writer, editor of Latent Space and founder of Smol.ai and Alessio, partner and CTO in residence at Decibel Partners. [00:00:15]

Alessio: Hey everyone, and today we have Artem Keydunov on the podcast, co-founder of Cube. Hey Artem. [00:00:21]

Artem: Hey Alessio, hi Swyx. Good to be here today, thank you for inviting me. [00:00:25]

Alessio: Yeah, thanks for joining. For people that don't know, I've known Artem for a long time, ever since he started Cube. And Cube is actually a spin-out of his previous company, which is Statsbot. And this kind of feels like going both backward and forward in time. So the premise of Statsbot was having a Slack bot that you can ask, basically like text to SQL in Slack, and this was six, seven years ago, something like that. A lot ahead of its time, and you see startups trying to do that today. And then Cube came out of that as a part of the infrastructure that was powering Statsbot. And Cube then evolved from an embedded analytics product to the semantic layer and just an awesome open source evolution. I think you have over 16,000 stars on GitHub today, you have a very active open source community. But maybe for people at home, just give a quick like lay of the land of the original Statsbot product. You know, what got you interested in like text to SQL and what were some of the limitations that you saw then, the limitations that you're also seeing today in the new landscape? [00:01:28]

Artem: I started Statsbot in 2016. The original idea was to just make sort of a side project based off my initial project that I did at a company that I was working for back then. And I was working for a company that was building software for schools, and we were using Slack a lot. And Slack was growing really fast, a lot of people were talking about Slack, you know, like Slack apps, chatbots in general. So I think it was, you know, like another wave of, you know, bots and all that. We have one more wave right now, but it always comes in waves. So we were like living through one of those waves. And I wanted to build a bot that would give me information from different places where like a data lives to Slack. So it was like developer data, like New Relic, maybe some marketing data, Google Analytics, and then some just regular data, like a production database, so it sells for sometimes. And I wanted to bring it all into Slack, because we were always chatting, you know, like in Slack, and I wanted to see some stats in Slack. So that was the idea of Statsbot, right, like bring stats to Slack. I built that as a, you know, like a first sort of a side project, and I published it on Reddit. And people started to use it even before Slack came up with that Slack application directory. So it was a little, you know, like a hackish way to install it, but people are still installing it. So it was a lot of fun. And then Slack kind of came up with that application directory, and they reached out to me and they wanted to feature Statsbot, because it was one of the already being kind of widely used bots on Slack. So they featured me on this application directory front page, and I just got a lot of, you know, like new users signing up for that. It was a lot of fun, I think, you know, like, but it was sort of a big limitation in terms of how you can process natural language, because the original idea was to let people ask questions directly in Slack, right, hey, show me my, you know, like opportunities closed last week or something like that. My co founder, who kind of started helping me with this Slack application, him and I were trying to build a system to recognize that natural language. But it was, you know, we didn't have LLMs right back then and all of that technology. So it was really hard to build the system, especially the systems that can kind of, you know, like keep talking to you, like maintain some sort of a dialogue. It was a lot of like one off requests, and like, it was a lot of hit and miss, right? If you know how to construct a query in natural language, you will get a result back. But you know, like, it was not a system that was capable of, you know, like asking follow up questions to try to understand what you actually want. And then kind of finally, you know, like, bring this all context and go to generate a SQL query, get the result back and all of that. So that was a really missing part. And I think right now, that's, you know, like, what is the difference? So right now, I kind of bullish that if I would start Statsbot again, probably would have a much better shot at it. But back then, that was a big limitation. We kind of build a queue, right, as we were working on Statsbot, because we needed it. [00:04:27]

Alessio: What was the ML stack at the time? Were you building, trying to build your own natural language understanding models, like were there open source models that were good that you were trying to leverage? [00:04:38]

Artem: I think it wa