Items tagged embeddings in 2023
Filters: Year: 2023 × embeddings × Sorted by date
Fleet Context. This project took the source code and documentation for 1221 popular Python libraries and ran them through the OpenAI text-embedding-ada-002 embedding model, then made those pre-calculated embedding vectors available as Parquet files for download from S3 or via a custom Python CLI tool.
I haven’t seen many projects release pre-calculated embeddings like this, it’s an interesting initiative. # 15th November 2023, 10:20 pm
Execute Jina embeddings with a CLI using llm-embed-jina
Berlin-based Jina AI just released a new family of embedding models, boasting that they are the “world’s first open-source 8K text embedding model” and that they rival OpenAI’s text-embedding-ada-002 in quality.
Embeddings: What they are and why they matter
Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon.
[... 5835 words]Bottleneck T5 Text Autoencoder (via) Colab notebook by Linus Lee demonstrating his Contra Bottleneck T5 embedding model, which can take up to 512 tokens of text, convert that into a 1024 floating point number embedding vector... and then then reconstruct the original text (or a close imitation) from the embedding again.
This allows for some fascinating tricks, where you can do things like generate embeddings for two completely different sentences and then reconstruct a new sentence that combines the weights from both. # 10th October 2023, 2:12 am
Finding Bathroom Faucets with Embeddings. Absolutely the coolest thing I’ve seen someone build on top of my LLM tool so far: Drew Breunig is renovating a bathroom and needed a way to filter through literally thousands of options for facet taps. He scraped 20,000 images of fixtures from a plumbing supply site and used LLM to embed every one of them via CLIP... and now he can ask for “faucets that look like this one”, or even run searches for faucets that match “Gawdy” or “Bond Villain” or “Nintendo 64”. Live demo included! # 27th September 2023, 6:18 pm
Weeknotes: Embeddings, more embeddings and Datasette Cloud
Since my last weeknotes, a flurry of activity. LLM has embeddings support now, and Datasette Cloud has driven some major improvements to the wider Datasette ecosystem.
[... 2427 words]Build an image search engine with llm-clip, chat with models with llm chat
LLM is my combination CLI tool and Python library for working with Large Language Models. I just released LLM 0.10 with two significant new features: embedding support for binary files and the llm chat command.
Symbex 1.4. New release of my Symbex tool for finding symbols (functions, methods and classes) in a Python codebase. Symbex can now output matching symbols in JSON, CSV or TSV in addition to plain text.
I designed this feature for compatibility with the new “llm embed-multi” command—so you can now use Symbex to find every Python function in a nested directory and then pipe them to LLM to calculate embeddings for every one of them.
I tried it on my projects directory and embedded over 13,000 functions in just a few minutes! Next step is to figure out what kind of interesting things I can do with all of those embeddings. # 5th September 2023, 5:29 pm
LLM now provides tools for working with embeddings
LLM is my Python library and command-line tool for working with language models. I just released LLM 0.9 with a new set of features that extend LLM to provide tools for working with embeddings.
[... 3466 words]Getting creative with embeddings (via) Amelia Wattenberger describes a neat application of embeddings I haven’t seen before: she wanted to build a system that could classify individual sentences in terms of how “concrete” or “abstract” they are. So she generated several example sentences for each of those categories, embedded then and calculated the average of those embeddings.
And now she can get a score for how abstract vs concrete a new sentence is by calculating its embedding and seeing where it falls in the 1500 dimension space between those two other points. # 10th August 2023, 7:05 pm
Vector Search. Amjith Ramanujam provides a very thorough tutorial on implementing vector similarity search using SentenceTransformers embeddings (all-MiniLM-L6-v2) executed using sqlite-utils, then served via datasette-sqlite-vss and deployed using Fly. # 2nd June 2023, 5:02 am
ImageBind. New model release from Facebook/Meta AI research: “An approach to learn a joint embedding across six different modalities—images, text, audio, depth, thermal, and IMU (inertial measurement units) data”. The non-interactive demo shows searching audio starting with an image, searching images starting with audio, using text to retrieve images and audio, using image and audio to retrieve images (e.g. a barking sound and a photo of a beach to get dogs on a beach) and using audio as input to an image generator. # 9th May 2023, 7:04 pm
Language models can explain neurons in language models (via) Fascinating interactive paper by OpenAI, describing how they used GPT-4 to analyze the concepts tracked by individual neurons in their much older GPT-2 model. “We generated cluster labels by embedding each neuron explanation using the OpenAI Embeddings API, then clustering them and asking GPT-4 to label each cluster.” # 9th May 2023, 5:35 pm
Browse the BBC In Our Time archive by Dewey decimal code. Matt Webb built Braggoscope, an alternative interface for browsing the 1,000 episodes of the BBC’s In Our Time dating back to 1998, organized by Dewey decimal system and with related episodes calculated using OpenAI embeddings and guests and reading lists extracted using GPT-3. “Using GitHub Copilot to write code and calling out to GPT-3 programmatically to dodge days of graft actually brought tears to my eyes.” # 13th February 2023, 4:03 pm
How to implement Q&A against your documentation with GPT3, embeddings and Datasette
If you’ve spent any time with GPT-3 or ChatGPT, you’ve likely thought about how useful it would be if you could point them at a specific, current collection of text or documentation and have it use that as part of its input for answering questions.
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