Fast_Sentence_Embeddings VS gpt4-pdf-chatbot-langchain

This is a great guide.

Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.

With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.

When you should use language model embeddings:

- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.

For LM embedding models, many are multilingual aligned right away.

- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.

This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.

1. sbert.net

2. https://collaborate.princeton.edu/en/publications/a-simple-b...

3. https://github.com/oborchers/Fast_Sentence_Embeddings

4. https://github.com/facebookresearch/MUSE

You can find some comparisons and evaluation datasets/tasks here: https://www.sbert.net/docs/pretrained_models.html

Generally MiniLM is a good baseline. For faster models you want this library:

https://github.com/oborchers/Fast_Sentence_Embeddings

For higher quality ones, just take the bigger/slower models in the SentenceTransformers library

Links: fse: https://github.com/oborchers/Fast_Sentence_Embeddings Sentence-transformers: https://github.com/oborchers/sentence-transformers

I am playing around with this github project, which takes a user question as input and immediately runs a vector search on it to find relevant storied information before delivering an answer.

I am using this https://github.com/mayooear/gpt4-pdf-chatbot-langchain as a reference to ingest PDFs into pinecone and chat with a document, but my results aren’t good. Since it’s looking for related documents, there’s no good relation to the meta question: “What questions were asked in this interview?”

Anyone can already do this locally with their own API keys for free, with no technical knowledge by cloning a github repo (e.g. https://github.com/mayooear/gpt4-pdf-chatbot-langchain - this one can also chat with multiple pdfs which is much better). Even with gpt-4, I just don't find the responses useful usually. I find the model doesn't do great with scientific stuff aside from asking very basic things. Might have to wait for gpt-5.

https://github.com/mayooear/gpt4-pdf-chatbot-langchain this repo uses gpt-3.5/4 which uses OpenAI API. Is there any work donw with free/open-source LLMs

Follow this guide https://github.com/mayooear/gpt4-pdf-chatbot-langchain

I am using this one on windows 10. Took 2 evenings to set up: https://github.com/mayooear/gpt4-pdf-chatbot-langchain

Thanks for your take on the subject. I agree that starting from scratch would be too much. I think my post above might be misleading in regard to training. I wouldn't suggest to start from scratch but to provide additional data to a pretrained AI. But you can use GPT-4 (through API) in combination with pinecone to provide data. Here is a project, where someone implemented this to work with large PDF files. I don't think it would be too hard, to start from there and adapt the project to the requirements of OP. Obviously this would require paid for API keys. LLama could be also a good starting point, with a lot of resources available.

I'm currently developing a chatbot application that interacts with PDF documents using GPT API, Langchain, and a Pinecone vector database. The project is built on this repository: mayooear/gpt4-pdf-chatbot-langchain.