roadmap
marqo
roadmap | marqo | |
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11 | 114 | |
187 | 4,152 | |
1.1% | 2.3% | |
1.7 | 9.3 | |
10 months ago | about 18 hours ago | |
Python | ||
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
roadmap
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Embeddings: What they are and why they matter
In case anyone is interested, Heroku finally released pgvector support for Postgres yesterday: https://github.com/heroku/roadmap/issues/156
Pgvector is an extremely excellent way to experiment with embeddings in a lightweight way, without adding a bunch of extra infrastructure dependencies.
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11 Years of Hosting a SaaS
(I work at Heroku) Do you have more details on what sucks? Anything we're not already tracking to fix in our public roadmap? https://github.com/heroku/roadmap/issues
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Is there any updates as to when Heroku will support IPv6?
"GitHub - heroku/roadmap: This is the public roadmap for Salesforce Heroku services." https://github.com/heroku/roadmap
- Introducing Our New Low-Cost Plans [Heroku]
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Heroku - If I have a paid dyno can I keep my free ones? (using paid for production, using free for dev and staging)
You can upvote here to try get a version of free back - https://github.com/heroku/roadmap/issues/51
- Let’s try and make free come back
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Heroku Free Tier
I proposed this -> https://github.com/heroku/roadmap/issues/51 on the Heroku roadmap - hopefully to bring back some sort of free trial environment. Please take a look an upvote if you agree:
- Heroku make their development roadmap public on GitHub
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Heroku's Next Chapter
The public roadmap is a good idea but highlights how stale the product has become. https://github.com/heroku/roadmap/issues Only now researching adding Cloud Native Build Packs and HTTP2.
This will reaffirm for many the sense that Heroku is being dismantled from within. Feature sunsetting and removal of a free on-ramp doesn't help.
If you're looking for a production alternative to Heroku checkout Northflank.
https://northflank.com
marqo
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Are we at peak vector database?
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
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Qdrant, the Vector Search Database, raised $28M in a Series A round
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)
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Ask HN: Is there any good semantic search GUI for images or documents?
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).
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90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
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.
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Embeddings: What they are and why they matter
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.
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Choosing vector database: a side-by-side comparison
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.
- Show HN: Marqo – Vectorless Vector Search
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AI for AWS Documentation
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
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[N] Open-source search engine Meilisearch launches vector search
Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo
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Ask HN: Which Vector Database do you recommend for LLM applications?
Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo
What are some alternatives?
cgm-remote-monitor - nightscout web monitor
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
flyctl - Command line tools for fly.io services
Milvus - A cloud-native vector database, storage for next generation AI applications
Dokku - A docker-powered PaaS that helps you build and manage the lifecycle of applications
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
create-t3-app - The best way to start a full-stack, typesafe Next.js app
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
superfly-flyctl
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]