marqo
vault-ai
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marqo | vault-ai | |
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114 | 80 | |
4,111 | 3,222 | |
3.5% | - | |
9.3 | 5.7 | |
6 days ago | 9 months ago | |
Python | JavaScript | |
Apache License 2.0 | MIT License |
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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
vault-ai
- I built an open source website that lets you upload large files, such as in-depth novels/ebooks or academic papers, and ask GPT4 questions based on your specific knowledge base. So far, I've tested it with long books like the Odyssey and random research PDFs, and I'm shocked at how incisive it is.
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Any better alternatives to fine-tuning GPT-3 yet to create a custom chatbot persona based on provided knowledge for others to use?
There's this GitHub repo for Pinecone Vector with custom knowledge base: VaultAI. But I'm sure the costs would be exorbitant at scale. Basically trains it on specific files, but the API is expensive as expected. Edit: I didn't read and thought you were talking about training your own, sorry. But I'll leave the second paragraph up anyways lol. Someone mentioned LLaMA and another Falcon, the latter of which I hadn't heard of but which looks good too.
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I built an open source website that lets you upload large files such as academic PDFs or books and ask ChatGPT questions based on your custom knowledge base. So far, I've tried it with long ebooks like Plato's Republic, old letters, and random academic PDFs, and it works shockingly well.
Check out the instructions readme here! You may need a little bit of command line know-how but chatgpt can help guide you if you provide it the contents of the readme
- Are there any good free GPT-powered AI summarizer for very long text?
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I built an open source website that lets you upload large files, such as long ebooks or academic papers, and ask ChatGPT questions about your specific knowledge base. So far, I've tested it with long e-books like the Odyssey and random research PDFs, and I'm shocked at how incisive it is
Yes, this use-case is a perfect fit actually – This deals very well with any type of manual with lots of human readable text (as opposed to charts or code). It is also better at answering more specific questions, so the example you gave regarding diagnosing engine issues is a really good match for what this is capable of. If you want to try it out you can check out the deployed version of the code here: https://vault.pash.city
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Any help condensing academic journal articles using ChatGPT?
Have you tried Vault AI? Saw it pop up on a couple of other Reddits!
- 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.
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April 2023
OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (https://github.com/pashpashpash/vault-ai)
- Using ChatGPT to read multiple PDFs and create writing using them as sources
What are some alternatives?
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.
paper-qa - LLM Chain for answering questions from documents with citations
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
Milvus - A cloud-native vector database, storage for next generation AI applications
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
AGiXT - AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]
ChatGPT-Pokemon-StyleGame - Using ChatGPT to make pokemon style game
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
unstructured - Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.