Continuing my research on RAG (part 1 here)

The week before last I worked on a simple example of RAG using Spring. This involved a lot of yak shaving, in part because spring had updated their package structures since I last worked on my RAG demo. I also wanted to set up a local vector DB without using docker, finally settling on MariaDB. In the end I had a simple example that took a CSV, inserted the rows as embeddings into a vector database and could run simple queries against it. I still need to tidy this up and upload it to github.

Interesting Links

• I need to look into using vector databases for recommendation engines.
• “If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves.” – link
• Simon Willison gave a great talk about embeddings. This introduced the concept, showed how to do ‘vibes-based search’ against sqllite using llm, and talked about 2D visualisations.
  • “Being able to spin up this kind of ultra-specific search engine in a few hours is exactly the kind of trick that excites me about having embeddings as a tool in my toolbox.”
  • “A fascinating thing about RAG is that it has so many different knobs that you can tweak. You can try different distance functions, different embedding models, different prompting strategies and different LLMs. There’s a lot of scope for experimentation here.”
• Adding semantic search to datasette
• Lovely visualisation of 40 million Hacker news posts. This uses Uniform Manifold Approximation and Projection (UMAP) to reduce the vectors to a 2D geographic map (among other things). Some interesting applications of a massive dataset.
• The llm tool can be used for image search using CLIP
• This leads to someone searching images of faucets by image and phrase (what taps best represent the idea of ‘Bond villain?’)
• According to ChatGPT, people have played with using vector databases to analyse recipes. There is a paper on this, ‘Learning Cross-modal Embeddings for Cooking Recipes and Food Images‘, but I can’t find any details on applications with it, experimental or otherwise.
• Interesting discussion of search in RAG (look for the RAG section)
• Text Embedding Models Contain Bias. Here’s Why That Matters – interesting Google paper from 2018
• Using a vector database, SkyCLIP, and Leaflet to create a searchable aerial photograph
• “This is why Retrieval-Augmented Generation (RAG) is not going anywhere. RAG is basically the practice of telling the LLM what it needs to know and then immediately asking it for that information back in condensed form. LLMs are great at it, which is why RAG is so popular.” link
• Spring AI documentation on RAG

I’ve been thinking recently about Resource Augmented Generation (RAG) and vector databases, and I wanted to gather some notes towards a talk.

The question

Most of the applications I’m seeing for Generative AI seem to involve RAG. But I feel that the vector database is doing most of interesting work here – and, in a lot of cases, an LLM-generated response is not always the best ‘view’ for the data that is returned. I want to dig a little more into vector databases, how they work, and what can be done with them.

Panda Smith wrote, “If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves”; I want to learn more about this search part of RAG

Previous posts

• Playing with embeddings was a post I wrote in September ’24 looking at using vector databases for ‘vibes-based search’
• GenAI is already useful for historians discussed an article about a historian using GenAI to find diary entries relevant to their research
• Retrieval-augmented generation using SpringAI was a Spring AI RAG demo I built for a previous talk

Notes from wikipedia

Pulling out some notes from the wikipedia article:

• “[RAG] modifies interactions with a large language model so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information.”
• There are two phases – information retreival and response generation
• RAG was first proposed in the paper ‘Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks’ in 2020
• The process involves the following stages:
  • Indexing – the documents to be searched are stored – usually by converting the data into vector representations
  • Retrieval – given a user query, a document retriever finds the most relevant documents for the query
  • Augmentation – the documents are put in a prompt for an LLM
  • Generation – the LLM generates a response based upon the prompt
• The ‘chunking’ of the documents (how they are divided up into pieces to be stored) affects how good the responses are.
• Risks of RAG
  • While RAG reduces hallucinations in the responses, it cannot eliminate them.
  • There is a danger of losing important context in the chunking phase.

Interesting links

• Twitter thread by Jo Kristian Bergum on ‘The rise and fall of the vector database infrastructure category’
• The Best Way to Use Text Embeddings Portably is With Parquet and Polars – fascinating discussion of vector databases, using Magic: The Gathering cards as a dataset
• Embeddings: What they are and why they matter Text of a talk by Simon Willison: “Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon”

I woke up on Sunday morning with the following question: what do I believe about GenAI – and what should I be doing in response? Based on what I’ve been reading, here is what I currently think:

• GenAI is a revolution – cynics have dismissed GenAI as ‘fancy autocomplete’, but that ignores the magic of LLMs – both their ability to produce plausible text and their performance with previously difficult and imprecise tasks.
• GenAI is also overhyped – a lot of the problem with GenAI is that some companies are over-promising. LLMs are not going to lead to AGI and are not going to replace skilled people in most situations.
• The main benefit of LLMs is efficiency – LLMs are very good at some previously complicated tasks, and this will make those tasks much cheaper. I’m expecting this to produce a boom in programming as previously-expensive projects become feasible – similar to how Excel has produced a boom in accountancy.
• There is a correction coming – there’s a huge amount of money invested in GenAI and I think it will be some time before this pays off. I’m expecting to see a crash come before long term growth. But that’s the same thing as happened with the 2000 dotcom crash.
• RAG is boring – using RAG to find relevant data and interpret it rarely feels like a good user experience. In most cases, a decent search engine is faster and more practical.
• There are exciting surprises coming – I suspect that the large-scale models from people like OpenAI have peaked in their effectiveness, but smaller-scale models promise some interesting applications.

I am going to spend some time over Christmas coding with GenAI tools. I’m already sold on ChatGPT as a tool for teaching new technology and thinking through debugging, but there are many more tools out there.

I’m also going to do some personal research on how people are using Llama and other small open-source models. There must be more to GenAI than coding assistants and RAG.

I was inspired by Simon Willison‘s recent post on embeddings and decided to use them to explore some documents. I’d blocked out some time to do this, but ended up with decent results in just under an hour.

Introduction

Embeddings are functions that turn pieces of text into fixed length multi-dimensional vectors of floating point numbers. These can be considered as representing locations a within multi-dimensional space, where the position relates to a text’s semantic content “according to the embedding model’s weird, mostly incomprehensible understanding of the world”. While nobody understands the meaning of the individual numbers, the locations of points representing different documents can be used to learn about these documents.

The process

I decided to go with the grain of Willison’s tutorials by setting up Datasette, an open-source tool for exploring and publishing data. Since this is based on SQLLite, I was hoping this would be less hassle than using a full RDBMS. I did a quick install and got Datasette running against my Firefox history file.

OpenAI have a range of embedding models. What I needed to do was to find the embeddings for my input text and send that to OpenAI’s APIs. I’m something of a hack with python, so I searched for an example, finding a detailed one from Willison, which pointed me towards an OpenAI to SQLLite tool he’d written.

(Willison’s documentation of his work is exemplary, and makes it very easy to follow in his footsteps)

There was a page describing how to add the embeddings to SQLLite which seemed to have everything I needed – which means the main problem became wrangling the real-world data into Datasette. This sounds like the sort of specific problem that ChatGPT is very good at solving. I made a few prompts to specify a script that created an SQLLite DB whose posts table had two columns – title and body, with all of the HTML gubbins stripped out of the body text.

Once I’d set up my OPENAI_API_KEY enviroment variable, it was just a matter of following the tutorial. I then had a new table containing the embeddings – the big issue being I was accidentally using the post title as a key. But I could work with this for an experiment, and could quickly find similar documents. The power of this is in what Willison refers to as ‘vibes-based search’. I can now expand this to produce a small piece of arbitrary text, and find anything in my archive related to that text.

Conclusion

Playing with embeddings produced some interesting results. I understood the theory, but seeing it applied to a specific dataset I knew well was useful.

The most important thing here was how quickly I got the example set up. Part of this, as I’ve said, it due to Willison’s work in paving some of the paths to using these tools. But I also leaned heavily on ChatGPT to write the bespoke python code I needed. I’m not a python dev, but genAI allows me to produce useful code very quickly. (I chose python as it has better libraries for data work than Java, as well as more examples for the LLM to draw upon).

Referring yet again to Willison’s work, he’s wrote a blog post entitled AI-enhanced development makes me more ambitious with my projects. The above is an example of just this. I’m feeling more confident and ambitious about future genAI experiments.