marqo-ai

marqo

Python2022–2024

marqo-ai / marqo

marqo

Ecommerce Search and Discovery - marqo.ai

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Ecommerce Search and Discovery - marqo.ai

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

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HN mentions by month51 dated mentions

Observed mentions: 2022-09-29 08:24:40 UTC to 2024-01-24 00:23:02 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
Sep 2022	2022-09-01 00:00:00 UTC	2022-10-01 00:00:00 UTC	1	1	0
Oct 2022	2022-10-01 00:00:00 UTC	2022-11-01 00:00:00 UTC	1	1	0
Nov 2022	2022-11-01 00:00:00 UTC	2022-12-01 00:00:00 UTC	2	1	1
Dec 2022	2022-12-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	2	2	0
Jan 2023	2023-01-01 00:00:00 UTC	2023-02-01 00:00:00 UTC	0	0	0
Feb 2023	2023-02-01 00:00:00 UTC	2023-03-01 00:00:00 UTC	7	0	7
Mar 2023	2023-03-01 00:00:00 UTC	2023-04-01 00:00:00 UTC	8	0	8
Apr 2023	2023-04-01 00:00:00 UTC	2023-05-01 00:00:00 UTC	9	2	7
May 2023	2023-05-01 00:00:00 UTC	2023-06-01 00:00:00 UTC	6	0	6
Jun 2023	2023-06-01 00:00:00 UTC	2023-07-01 00:00:00 UTC	2	0	2
Jul 2023	2023-07-01 00:00:00 UTC	2023-08-01 00:00:00 UTC	4	0	4
Aug 2023	2023-08-01 00:00:00 UTC	2023-09-01 00:00:00 UTC	3	1	2
Sep 2023	2023-09-01 00:00:00 UTC	2023-10-01 00:00:00 UTC	1	0	1
Oct 2023	2023-10-01 00:00:00 UTC	2023-11-01 00:00:00 UTC	2	0	2
Nov 2023	2023-11-01 00:00:00 UTC	2023-12-01 00:00:00 UTC	0	0	0
Dec 2023	2023-12-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	0	0	0
Jan 2024	2024-01-01 00:00:00 UTC	2024-02-01 00:00:00 UTC	3	0	3

What Hacker News says

51 sources

bumberg·Jan 24, 2024

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)

In Qdrant, the Vector Search Database, raised $28M in a Series A round

jn2clark·Jan 17, 2024

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).

In Ask HN: Is there any good semantic search GUI for images or documents?

jn2clark·Jan 2, 2024

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.

In 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

jn2clark·Oct 25, 2023

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.

In Embeddings: What they are and why they matter

jn2clark·Oct 4, 2023

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 mon

Read the rest

itoring 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.

In Choosing vector database: a side-by-side comparison

tomhamer·Sep 4, 2023

Marqo lets you use state of the art e5 embeddings (which are significantly more performant in retrieval than the openai embeddings), and will handle the embedding generation and retrieval on lucene indexes: https://www.marqo.ai/ It's also available opensource: https://github.com/marqo-ai/marqo

In Vector Search with OpenAI Embeddings: Lucene Is All You Need

tomhamer·Aug 21, 2023

Marqo is an end-to-end vector search engine that handles both embedding creation and retrieval: https://github.com/marqo-ai/marqo

In Vector databases: analyzing the trade-offs

jn2clark·Aug 17, 2023

At the moment you would need to do this yourself. It would be possible to have additional preprocessing to accommodate this though. Feel free to add a feature request here https://github.com/marqo-ai/marqo/issues. The other consideration is that you would want the distribution of the content and queries to match what the selected model was trained on.

In Show HN: Marqo – Vectorless Vector Search

jn2clark·Aug 16, 2023Submission

Show HN: Marqo – Vectorless Vector Search

Marqo is an end-to-end vector search engine. It contains everything required to integrate vector search into an application in a single API. Here is a code snippet for a minimal example of vector search with Marqo: mq = marqo.Client() mq.create_index("my-first-index") mq.index("my-first-index").add_documents([{"title": "The Travels of Marco Polo"}]) results = mq.index("my-first-index").search(q="Marqo Polo") Why Marqo? Vector similarity alone is not enough for vector search. Vector search requires more than a vector database - it also requires machine learning (ML) deployment and management, preprocessing and transformations of inputs as well as the ability to modify search behavior without retraining a model. Marqo contains all these pieces, enabling developers to build vector search into their application with minimal effort. Why not

Read the rest

X, Y, Z vector database? Vector databases are specialized components for vector similarity. They are “vectors in - vectors out”. They still require the production of vectors, management of the ML models, associated orchestration and processing of the inputs. Marqo makes this easy by being “documents in, documents out”. Preprocessing of text and images, embedding the content, storing meta-data and deployment of inference and storage is all taken care of by Marqo. We have been running Marqo for production workloads with both low-latency and large index requirements. Marqo features: - Low-latency (10’s ms - configuration dependent), large scale (10’s - 100’s M vectors). - Easily integrates with LLM’s and other generative AI - augmented generation using a knowledge base. - Pre-configured open source embedding models - SBERT, Huggingface, CLIP/OpenCLIP. - Pre-filtering and lexical search. - Multimodal model support - search text and/or images. - Custom models - load models fine tuned from your own data. - Ranking with document meta data - bias the similarity with properties like popularity. - Multi-term multi-modal queries - allows per query personalization and topic avoidance. - Multi-modal representations - search over documents that have both text and images. - GPU/CPU/ONNX/PyTorch inference support. See some examples here: Multimodal search: [1] https://www.marqo.ai/blog/context-is-all-you-need-multimodal... Refining image quality and identifying unwanted content: [2] https://www.marqo.ai/blog/refining-image-quality-and-elimina... Question answering over transcripts of speech: [3] https://www.marqo.ai/blog/speech-processing Question and answering over technical documents and augmenting NPC's with a backstory: [4] https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmen...

62 points · Open discussion

tomhamer·Jul 21, 2023

Just adding here that if you want a vector DB that handles inference for you too, you can try Marqo https://github.com/marqo-ai/marqo

In The fully bootstrapped and vector search engine

tomhamer·Jul 17, 2023

Marqo will generate embeddings for you as well as run on a mac/in the cloud. https://github.com/marqo-ai/marqo

In Ask HN: Seeking advice on open source end user distributed vector database

owen-elliott·Jul 12, 2023

You could use Marqo, it is a vector search engine that includes the text chunking, inference for calculating embeddings, vector storage, and vector search. You can pick from a heap of open-source models or bring your own fine-tuned ones. It all runs locally in docker https://github.com/marqo-ai/marqo

In Introduction to vector similarity search (2022)

tomhamer·Jul 7, 2023

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

In AI for AWS Documentation

Chachadef·Jun 29, 2023

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

In Ask HN: Which Vector Database do you recommend for LLM applications?

jeadie·Jun 9, 2023

Have you seen github.com/marqo-ai/marqo? It does all this wrapping, and you don't even need to pay for OpenAI or pinecone

In Show HN: Yes, another vector embeddings API

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