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e/ectric Curtis
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Hey everyone,
If you're a developer, building a basic RAG solution is pretty straightforward. There are tons of tutorials and how-tos, as well as Python code to reuse. But, if you're deploying your RAG solution within a company, or on end-user PCs, you will also have to figure out some potentially tricky deployment and maintenance issues. That also means deploying Python, a vector database,...
Dabarqus
Add PDF chat to your LLM app in less than 9 lines of code
Dabarqus gives you a practical way to add retrieval-augmented generation (RAG) to your app in less than 9 lines of code. Chat with your PDFs, summarize emails and messaging, and digest a vast range of facts, figures, and reports. A dash of genius for your LLM.
Dabarqus
Add PDF chat to your LLM app in less than 9 lines of code
e/ectric Curtis
left a comment
Hey Product Hunt!
There are so many models coming out these days, but it’s a pain to download them just to find out if it might work on my machine. I got tired of guessing if my MacBook could run Llama 2, or if my desktop could handle Mixtral 8x7B. Also, most of the other options for running Large Language Models locally are difficult to use. You have to code or use terminals, or they give you...
Wingman
Run Large language models locally for free in minutes.
Wingman is a chatbot that lets you run Large Language Models locally on PC and Mac (Intel or Apple Silicon). It has an easy-to-use chatbot interface so you can use local models without coding and or using a terminal. First beta release, Rooster, now available!
Wingman
Run Large language models locally for free in minutes.