paperai
text-generation-webui
paperai | text-generation-webui | |
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19 | 876 | |
1,198 | 36,552 | |
1.3% | - | |
5.9 | 9.9 | |
5 months ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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paperai
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Oracle of Zotero: LLM QA of Your Research Library
Nice project!
I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.
paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.
paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).
While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.
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Build Personal ChatGPT Using Your Data
https://github.com/neuml/paperai
Disclaimer: I am the author of both
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[P] Parse research papers into structured data
paperai | paperetl
- Show HN: Semantic search and workflows for medical/scientific papers
- Semantic search and workflows for medical/scientific papers
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# Run txtai in native code
action: translate input: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. output: txtai exécute des workflows d'apprentissage automatique pour transformer les données et construire des applications de recherche sémantique alimentées par l'IA. action: translate input: Traditional search systems use keywords to find data output: Les systèmes de recherche traditionnels utilisent des mots-clés pour trouver des données action: summary input: https://github.com/neuml/txtai output: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. API bindings for JavaScript, Java, Rust and Go. Cloud-native architecture scales out with container orchestration systems (e. g. Kubernetes) action: summary input: https://github.com/neuml/paperai output: paperai is an AI-powered literature discovery and review engine for medical/scientific papers. Paperai was used to analyze the COVID-19 Open Research Dataset (CORD-19) paperai and NeuML have been recognized in the following articles: Cord-19 Kaggle Challenge Awards Machine-Learning Experts Delve Into 47,000 Papers on Coronavirus Family. real 0m22.478s user 0m13.776s sys 0m3.218s
text-generation-webui
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Ask HN: What is the current (Apr. 2024) gold standard of running an LLM locally?
Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.
Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.
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Ask HN: How to get started with local language models?
You can use webui https://github.com/oobabooga/text-generation-webui
Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.
a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...
a news ai website:
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text-generation-webui VS LibreChat - a user suggested alternative
2 projects | 29 Feb 2024
- Show HN: I made an app to use local AI as daily driver
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Ask HN: People who switched from GPT to their own models. How was it?
The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.
If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui
All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.
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AI Girlfriend Is a Data-Harvesting Horror Show
The example waifu in text-generation-webui is good enough for me.
https://github.com/oobabooga/text-generation-webui/blob/main...
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Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
> Downloading text-generation-webui takes a minute, let's you use any model and get going.
What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:
1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...
2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...
3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...
Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.
This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".
That's the difference and it's very significant.
[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...
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Ask HN: What are your top 3 coolest software engineering tools?
Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.
[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...
[1] https://github.com/oobabooga/text-generation-webui
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Meta AI releases Code Llama 70B
You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
What are some alternatives?
txtai - đź’ˇ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
KoboldAI - KoboldAI is generative AI software optimized for fictional use, but capable of much more!
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
llama.cpp - LLM inference in C/C++
SciencePlots - Matplotlib styles for scientific plotting
gpt4all - gpt4all: run open-source LLMs anywhere
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
faiss - A library for efficient similarity search and clustering of dense vectors.
KoboldAI-Client
scibert - A BERT model for scientific text.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.