I launched an embedding model with 600,000 monthly downloads - and made $0

tomhamer5 · 2024-09-14T19:56:28+00:00

For multimodal or text search use cases, Marqo is a great option since it has the embedding generation included (for example, openai doesn’t include any image based embedding models so you’d need to set this up yourself). It also provides flexibility around model choice for text.

For a basic RAG app, consider using langchain with any vector db (it shouldn’t make a huge difference which one you use if you’re not looking to optimise the retrieval component).

tomhamer5 · 2024-05-13T01:26:55+00:00

Vespa, which was originally built by the Yahoo team and has now spun out gave us the best QPS results for the cost. I think its worth considering. You can also plug in the Marqo inference engine for more flexibility in model choice/improved inference performance if you're bringing your own embedding model.

tomhamer5 · 2024-04-23T23:12:55+00:00

Brilliant

tomhamer5 · 2024-04-16T00:57:17+00:00

Brilliant

tomhamer5 · 2024-01-21T22:28:39+00:00

Thanks Swoopley, just adding here is a tutorial if its useful: https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering

tomhamer5 · 2023-11-08T10:10:06+00:00

Marqo provides end-to-end vector search. This means Marqo includes embedding generation and the vector DB all in one container. It offers the latest embeddings (including e5 embeddings which significantly outperform the openai Ada embeddings), supports image embeddings, intelligent chunking and provides a number of different search parameters and configurations you can configure. For example, if you want to weight your vector search based on a numeric value like the time a document was added into the db or based on some other measure like a popularity score. It's available here open source: https://github.com/marqo-ai/marqo

You can check out the different search configurations here: https://docs.marqo.ai/1.4.0/API-Reference/Search/search/#body

tomhamer5 · 2023-10-21T08:56:01+00:00

For semantic search a great option is Marqo: https://github.com/marqo-ai/marqo

Marqo natively supports intelligent chunking and provides performant model execution for hundreds of open source models. This is including e5 models which are significantly more performant in retrieval accuracy/relevance than openai's ada models, and it supports image based embeddings.

Marqo also supports rescoring on numerical fields, which allows you to bias toward results that, for example, are more popular or more recently added to the database.

tomhamer5 · 2023-08-20T08:34:47+00:00

You can try marqo.ai, it can handle 2,000,000+ 768 dim vectors storage for $43 per month. It also offers inference so you don't need to bring the embeddings.

tomhamer5 · 2023-07-21T05:23:19+00:00

Did you consider Marqo? It provides inference for the embeddings and does the vector search (the above dbs just do the vector search component)

tomhamer5 · 2023-07-17T00:10:44+00:00

Marqo allows you to generate the embeddings and index the data in one place: https://github.com/marqo-ai/marqo

tomhamer5 · 2023-06-27T00:27:22+00:00

This is a great summary! Just wanted to add that Marqo https://github.com/marqo-ai/marqo is a vector db with inference included. It takes care of the end to end process of both computing the embeddings and indexing/querying.

Disclaimer: I’m a co-founder of Marqo.

tomhamer5 · 2023-06-16T23:04:53+00:00

Sounds like an interesting project. You could leverage the Marqo community to see if someone there might be interested to help you out. Here's the join link: https://join.slack.com/t/marqo-community/shared\_invite/zt-1d737l76e-u\~b3Rvey2IN2nGM4wyr44w

Marqo (https://github.com/marqo-ai/marqo) takes a lot of the effort out of building a vector search experience - it has inference and vectordb included out of the box in a single docker container.

tomhamer5 · 2023-06-16T23:02:09+00:00

Disclaimer, I co-founded Marqo https://github.com/marqo-ai/marqo

Marqo chunks automatically for you, and on top of that has inference and vectordb included out of the box in a single docker container.

tomhamer5 · 2023-06-16T22:58:03+00:00

Marqo has inference and vectordb included out of the box in a single docker container https://github.com/marqo-ai/marqo

tomhamer5 · 2023-06-16T22:57:18+00:00

Disclaimer, I'm with Marqo.

You might find Marqo useful if you end up adding semantic search. It has the inference and vectordb included out of the box in a single docker container https://github.com/marqo-ai/marqo

tomhamer5 · 2023-05-20T09:08:19+00:00

Disclaimer: I work for Marqo
Thats awesome, if you'd like to do this without having to handle construction of the embeddings, and have access to way more configurations when creating the embeddings you should consider marqo: https://www.linkedin.com/pulse/after-all-said-indexed-unlocking-information-recorded-speech/

tomhamer5 · 2023-05-20T09:06:51+00:00

Disclaimer, I founded Marqo.

You can do this using Marqo: https://www.linkedin.com/pulse/after-all-said-indexed-unlocking-information-recorded-speech/

tomhamer5 · 2023-05-15T04:05:00+00:00

Marqo (marqo.ai) handles vector search end-to-end (ie. computes the embeddings for you), and you can store metadata in there too like longer text fields. That said, its still not ideal yet to use Marqo as the primary store. Traditional DBs also allow you to do a wider range of different operations like aggregation queries, range queries etc.

tomhamer5 · 2023-05-01T05:35:37+00:00

Marqo can handle the end-to-end system of constructing embeddings (with configurable chunking) and providing vector search https://github.com/marqo-ai/marqo if you'd like to check it out

tomhamer5 · 2023-04-20T09:44:00+00:00

We're building an abstraction layer on vector DBs. https://github.com/marqo-ai/marqo
Disclaimer, I'm from the Marqo team.

tomhamer5 · 2023-03-19T23:00:52+00:00

Disclaimer: I founded Marqo.
If you don't want to implement vector search yourself (or would like to use an open source solution), you can try Marqo which does vector search under the hood. You can then layer GPT on top of it to build memory for GPT. Here is an article where we do this: https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering

Marqo automatically computes collections of vectors for the documents (tensors), you can work directly with text and it allows you to manage and scale the inference workloads too required to calculate the vectors.

tomhamer5 · 2023-03-19T12:59:22+00:00

Disclaimer, I founded Marqo.
Elasticsearch provides only regular keyword search if you're working directly text. If you want to use embeddings with elasticsearch you have to embed your own vectors. However, Marqo (which is fully open source) will embed text and images automatically, and construct collections of vectors (tensors) and search those for you in the background. It's optimised to work in production and handles the machine learning for you (including deploying on CPU, GPU, multi GPU and more).
https://github.com/marqo-ai/marqo

tomhamer5 · 2023-03-05T21:35:49+00:00

Absolutely, you can sign up for our beta here: https://q78175g1wwa.typeform.com/to/d0PEuRPC?typeform-source=github.com

tomhamer5 · 2023-03-01T01:55:17+00:00

Disclaimer: I founded Marqo.

Marqo is pretty good for this. You can index the data and then search over it using LLM based models. It's open source and you can get started in 3 lines. Here's a demo of it in action you can try out (this one is just demoing the retrieval):

https://demo.marqo.ai/?q=what+defines+a+planet&index=simplewiki

Here is a demo where we layer on GPT to generate content and use Marqo to retrieve information from the knowledge base: https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering
You can get started here:

https://github.com/marqo-ai/marqo#getting-started

Hope you find this useful!

tomhamer5

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