cadence
marqo
cadence | marqo | |
---|---|---|
19 | 114 | |
7,814 | 4,124 | |
1.0% | 1.6% | |
9.7 | 9.3 | |
5 days ago | 4 days ago | |
Go | Python | |
MIT License | Apache License 2.0 |
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cadence
- Show HN: Hatchet – Open-source distributed task queue
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Ask HN: Who is hiring? (December 2023)
Uber | Software Engineers | Hybrid (Denmark) | https://www.uber.com/dk/en/careers/locations/aarhus/
Work with an amazing team responsible for the infrastructure software that makes Uber’s data centers around the world reliable and scalable. If you want to solve the toughest engineering challenges alongside some of the smartest people in the industry, Uber Aarhus is the right place for you.
The team in Aarhus build and operate the stateless and stateful compute platforms used by nearly every other engineer in the company (Up - https://www.uber.com/en-GB/blog/up-portable-microservices-re... and Odin - https://www.uber.com/en-GB/blog/how-uber-optimized-cassandra...) as well as other related infrastructure projects such as Cadence - https://github.com/uber/cadence.
- Cadence – Fault-Tolerant Stateful Code Platform by Uber
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Best way to schedule events and handle them in the future?
May be this..https://cadenceworkflow.io/
- Mandala: experiment data management as a built-in (Python) language feature
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are you interested in an end to end queue/pubsub & worker platform
a managed esb orchestration for example is exactly same as step functions and workflow engines like cadence - https://github.com/uber/cadence
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Why messaging is much better than REST for inter-microservice communications
Having done a reasonable amount of messaging code in my time, I would say the final form of this sort of thing might look more like Cadence[0] than anything like this.
[0] https://github.com/uber/cadence
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cadence VS javactrl-kafka - a user suggested alternative
2 projects | 2 Feb 2023
- Fault-Tolerant Stateful Code Platform
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Emit events from your primary DB (postgres, etc.) to something like kafka or rabbitmq and then catch that in your search engine. There's also some end-to-end solutions like temporal (temporal.io) or cadence (https://cadenceworkflow.io/)
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?
temporal - Temporal service
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.
Flowable (V6) - A compact and highly efficient workflow and Business Process Management (BPM) platform for developers, system admins and business users.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
gocelery - Celery Distributed Task Queue in Go
Milvus - A cloud-native vector database, storage for next generation AI applications
Asynq - Simple, reliable, and efficient distributed task queue in Go
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
docker-compose - Temporal docker-compose files
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.
Faktory - Language-agnostic persistent background job server
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]