meta VS marqo

Remote GMT -8 to +2 | (i) Technical Writer (ii) Senior Developer | Full Time | Salaries all visible at posthog.com/handbook/compensation (location-dependent)

https://posthog.com - see handbook at posthog.com/handbook

Open source, 6x rev growth last year, heading for profitability soon, well paid experienced team of 37 today, 25k customers across free + paid products, 65k developers in community, 0 outbound sales - all inbound growth. Didn't raise a 2021 overpriced round - real (potential!) upside.

* Technical writer *

Looking to hire a developer who loves to write. We aim for each piece we produce to be the best on the internet for that topic.

Content marketing is huge for us - writing deep technical content is what we want! You'd be on our (small) marketing team, making a huge impact.

Previous blogging (especially personal/interesting!) and a few years of development experience needed.

If interested, james at posthog.com and charles at posthog.com for a chat.

* Full stack developer *

We are looking for someone to build the next generation of our analytics backbone. You'll be working on ClickHouse and Python to make sure all our queries are web scale and users are happy.

You will (probably) help us build HogQL https://github.com/PostHog/meta/issues/86 (our own wrapper around ClickHouse SQL), or whatever else you think makes sense to prioritize. We work in small teams of up to 6, and you'll have a lot of trust to pick what to work on.

You need experience building high performance data systems, must be an expert at Django with deep SQL/database skills. Ideally, but optionally, you'd have worked in a high growth SaaS company / analytics product and have extensive experience with TypeScript-based React. We need someone ideally that has both worked in a very scrappy / early stage way _and_ ideally has seen a company scale.

This work will potentially extend into providing a full warehouse to our customers. It's a chance to work on the next Snowflake, but open source and product led, working with new companies from day 1 and staying with them as they scale - so get here in the first 40 employees before we are 100x the size.

If interested, james at posthog.com + careers at posthog.com and please mention you saw us on HN.

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo

Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo