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lance
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
getting bunch of 404s on the docs. for example https://eto-ai.github.io/lance/format.html (But this works: https://lancedb.github.io/lance/*)
Did you guys just pivot from eto-ai to lancedb?
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I initially built this same "chat with PDFs" prototype with LangChain and qdrant. I then rebuilt it from scratch for the sake of learning and comparison.
Some context: I've been a jack-of-all-trades data scientist / machine learning engineer for the past 15 years (officially titled as an MLE the last four years).
I share that only because I think it plays a role in how I'm typically accustomed to working.
1. I found LangChain to be overkill for this use-case. While it might allow some to move more quickly when building, I found it to be cumbersome. My suspicion is this is largely because of my background - I understand how to build much of what's "under the hood" in LangChain. Because of this, I think it felt overly abstracted and I found the docs difficult to navigate and sometimes incomplete.
2. I used Qdrant via their docker image and it was simple to setup and start using. I didn't try to push the limits with it, so I can't say anything about performance. Because Qdrant runs as an http service, I found that it didn't fit well into my workflow - I'm accustomed to being able to visually inspect my data inside the interpreter, debugging, trying out commands, interacting and experimenting with my results, etc. Again, my suspicion is this is my own bias in how I typically work. Qdrant otherwise seemed very nice.
3. LanceDB felt powerful yet lightweight, and fit well into my workflow. It was far more intuitive for me. It was as if sqlite, the python data ecosystem, and a vector database had a child and named it LanceDB. Under the hood, it's built on Apache Arrow and integrates nicely with pandas, allowing me to seamlessly go from LanceDB table on disk, to pandas dataframe, and into some analysis or investigation of my LanceDB query results. This line [1] is a great example of why I liked it. This feels nicer to me than the world of API params and HTTP requests.
1. https://github.com/gjreda/scratch-pdf-bot/blob/main/gpt_pdf_...
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
