gpt4-pdf-chatbot-langchain VS evals

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.

Fair question.

Evaluate refers to the phase after training to check if the training is good.

Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!

So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.

OpenAI open sourced their evals framework. You can use it to evaluate different models but also your entire prompt chain setup. https://github.com/openai/evals

They also have a registry of evals built in.

"What if" is all these "existential risk" conversations ever are.

Where is your evidence that we're approaching human level AGI, let alone SuperIntelligence? Because ChatGPT can (sometimes) approximate sophisticated conversation and deep knowledge?

How about some evidence that ChatGPT isn't even close? Just clone and run OpenAI's own evals repo https://github.com/openai/evals on the GPT-4 API.

It performs terribly on novel logic puzzles and exercises that a clever child could learn to do in an afternoon (there are some good chess evals, and I submitted one asking it to simulate a Forth machine).

Ah well, I think you just got lucky then, I did the same with the survey. I'll be compulsively checking mine all day today lol. People on Reddit like to say that if you did an Eval which is basically a performance test natively run using code on GPT models, then OpenAI is more likely to favor you when they’re releasing new features. If ydk, then I guess that answers that.

You can get GPT 4 access by submitting an eval if gets merged (https://github.com/openai/evals). Here's the one that got me access[1]

Although from the blog post it looks like they're planning to open up to everyone soon, so that may happen before you get through the evals backlog.

1: https://github.com/openai/evals/pull/778

来源：GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. 8