gpt-repository-loader VS langchain

A good approach is to use a tool like this that turns an entire repo into a single file you can upload to GPT: https://github.com/mpoon/gpt-repository-loader (not my own repo). I find that much cleaner than doing a bunch of copy-and-pasting or uploading files one-by-one.

I have made an AI-bot that knows ALL of NextJS set up on my localhost. I can ask anything and it outputs the answer, context, examples and references. I used https://github.com/mpoon/gpt-repository-loader to create the files and loaded them into https://github.com/dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt , then added OpenAI API to shape the { result } to my wishes. Works decently

You might start with this: https://github.com/mpoon/gpt-repository-loader

You may be interested in this: https://github.com/mpoon/gpt-repository-loader

I've had access to the 32k model for a bit and I've been using this to collect and stuff codebases into the context: https://github.com/mpoon/gpt-repository-loader

It works really well, you can tell it to implement new features or mutate parts of the code and it having the entire (or a lot of) the code in its context really improves the output.

The biggest caveat: shit is expensive! A full 32k token request will run you like $2, if you do dialog back and forth you can rack up quite the bill quickly.

https://github.com/mpoon/gpt-repository-loader here is open source take on part of this concept

Langchain and the implementation of Custom Tools also is a great (and very efficient) way to setup a dedicated Q&A (for example for chat purpose) agent.

We're using it via langchain talking to Amazon Bedrock which is hosting Claude 1.x. It's comparable to GPT3.x, not bad. The integration doesn't seem to be fully there though, I think langchain is expecting "Human:" and "AI:", but Claude uses "Assistant:".

https://github.com/hwchase17/langchain/issues/2638

Depending on how much work you want to put into it, you can get started at HuggingFace with their models and datasets, but you'd need compute power, multiple MLOps, etc. I was introduced to the concept in this video, since Google has their Vertex AI tools on Google Cloud, and there's always LangChain but I'm not sure about anything recent.

a documentation chatbot proof of concept using GPT3.5 and LangChain

I encourage everyone to pay attention to the Langchain open-source project and leverage it to achieve tasks that ChatGPT cannot handle.

Yeah I never know where memory goes exactly in langchain, it's not exactly clear all the time. But sure, the main insight I remember is this, take a look at their MULTI_PROMPT_ROUTER_TEMPLATE: https://github.com/hwchase17/langchain/blob/560c4dfc98287da1...

It's a lot of instructions for an LLM, they seem to forget an LLM is an auto-completion machine, and which data it is trained on. Using <<>> for sections is not a normal thing, it's not markdown, which probably the thing read way more often on the internet, instead of open json comments, why not type signatures, instead of so many rules, why not give it examples? It is an autocomplete machine!

They are relying too much on the LLM being smart because they probably only test stuff in GPT-4 and 3.5, but with GPT4All models this prompt was not working at all, so I had to rewrite it, for simple routing, we don't even need json, carying the `next_inputs` here is weird if you don't need it.

So this is my version of it: https://gist.github.com/rogeriochaves/b67676977eebb1936b9b5c...

It's so basic it's dumb, yet it is more powerful, as it does not rely on GPT-4 level intelligence, it's just what I needed