paper-qa
marqo
paper-qa | marqo | |
---|---|---|
10 | 114 | |
3,664 | 4,177 | |
- | 2.9% | |
8.7 | 9.3 | |
15 days ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | 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.
paper-qa
-
Oracle of Zotero: LLM QA of Your Research Library
Why does this post link to a renamed fork of Paper-QA (https://github.com/whitead/paper-qa) which has made zero changes and is 19 commits behind the original?
-
[P] A Large Language Model for Healthcare | NHS-LLM and OpenGPT
To be honest, I'm not too sure about this part, and think that it is probably not the best approach to have the model itself generate references. I prefer the approach used in e.g. paperqa, but wanted to explore both options.
-
Looking for a paper summarizer
I’ve come across Paper QA (github page) and as a graduate student I loved the idea that when I do literature review and find tons of papers I can just ask the AI to find the info I’m looking for in the paper. However, this service requires OpenAI API key, which I’ve acquired but turns out it’s a paid service. Free key doesn’t get me anything. Is there a service/software like this that is free? Or something that I can host on my PC instead of using people’s servers so it’s cheaper/free?
-
ChatPDF – Chat with Any PDF
I tried it [1] a lot, but I must say it confuses me most of the time and I need to read the original text to check if it makes sense. Lots of times it doesn't.
[1] https://github.com/whitead/paper-qa
- Alternatives to Pinecone? (Vector databases) [D]
-
DIY natural language processing - How to start, techniques guidance
Have a look at this: https://github.com/whitead/paper-qa
-
Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
1: We are finding out. Someone else mentioned: https://github.com/whitead/paper-qa We're hoping to keep our service be accessible and easy to use, and add features. Such as from your other questions...
2: We are thinking of the website integration. Do you think OpenAI may release this too? Questions received by email is a new idea that sounds interesting!
3: Thanks for the suggestion – we will look into it.
- GitHub - whitead/paper-qa: LLM Chain for answering questions from documents with citations
- Paper QA: LLM Chain for answering questions from documents with citations
marqo
-
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
-
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)
-
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).
-
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.
-
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.
-
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
-
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
-
[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
-
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?
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.
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.
simple-llm-finetuner - Simple UI for LLM Model Finetuning
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
Milvus - A cloud-native vector database, storage for next generation AI applications
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
google-research - Google Research
OpenGPT - A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs).
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]