marqo
knowledge_gpt
marqo | knowledge_gpt | |
---|---|---|
114 | 10 | |
4,124 | 1,516 | |
1.6% | - | |
9.3 | 8.2 | |
5 days ago | 4 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
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
knowledge_gpt
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Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
From "Artificial intelligence is ineffective and potentially harmful for fact checking" (2023) https://news.ycombinator.com/item?id=37226233 : pdfgpt, knowledge_gpt, elasticsearch
pdfGPT: https://github.com/bhaskatripathi/pdfGPT :
> PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities.
GH "pdfgpt" topic: https://github.com/topics/pdfgpt
knowledge_gpt: https://github.com/mmz-001/knowledge_gpt
From https://news.ycombinator.com/item?id=39112014 : paperai
neuml/paperai:
- Are there any good free GPT-powered AI summarizer for very long text?
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Tell me what AI product you wish existed or that you want to build, and I'll reply with resources, guides and tools you can use to build it
You could also do a basic version by writing the topics and info in a PDF, and then uploading that PDF to this site https://knowledgegpt.streamlit.app and then asking your questions using that
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I've built a few tools on top of GPT-3.5 (text generation, q&a with embeddings). AMA about resources and AI dev stacks for building with OpenAI's APIs
Yep, that'd work too -- then you can use something like https://knowledgegpt.streamlit.app
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Support KB Chatbot - how to train best?
I saw KnowledgeGPT praised earlier today for Q&A, that might be worth trying.
- KnowledgeGPT – Accurate answers and instant citations for your documents
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Show HN: DocsGPT, open-source documentation assistant, fully aware of libraries
Yesterday, an undergraduate from Sri Lanka released KnowledgeGPT[1] which allows you to upload your docs and get answers from ChatGPT. It also uses FAISS so I'm wondering if DocsGPT is somehow related or inspired by the former.
It also appears the Github library for DocsGPT was created shortly after the release of KnowledgeGPT.
1: https://github.com/mmz-001/knowledge_gpt
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.
openai-cookbook - Examples and guides for using the OpenAI API
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
awesome-text-summarization - The guide to tackle with the Text Summarization
Milvus - A cloud-native vector database, storage for next generation AI applications
devdocs - API Documentation Browser
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
chatgpt_telegram_bot - 💬 Telegram bot with ChatGPT, Python-based, using OpenAI's API.
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.
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.