3 ms·

I'm more interested in Open Source/Local LLM's ability to consume private data as an internal knowledgeable. For example, feed in your entire source repo(s) and

I'm more interested in Open Source/Local LLM's ability to consume private data as an internal knowledgeable. For example, feed in your entire source repo(s) and then ask it questions about where something is or how it works.

zurtri 3y ago
I am constantly searching for this. To point a locally hosting LLM to a locally hosted code repo - and then start improving it from there would be awesome.
- pbmonster 3y ago
  Depending on how serious you are about this (and how well you can slap together a few different python packages), it is very doable today. Get one of the better llama versions fine-tuned on code (e.g. WizardCoder), take your entire code base and create embeddings from it, put those into a vector database. Now, every time you ask your LLM a question about your code base, you first turn that prompt into an embedding and perform a search on your vector database. The results of that search are appended as context to the actual prompt before passing it to the LLM itself. There's tons of packages that help with all of that, Langchain and Faiss are probably the most popular right now.
  - ek750 3y ago
    interesting, i'd love to do this too. but it sounds like there aren't any full-featured, opensource packages/projects that do this all-together? I'd love to hack the parts together, but i don't have the time/energy these days to do it. thanks for the helpful keywords though, it helps point me in the right direction.
nicce 3y ago
You would still need a huge training and data set for model before it can do something useful from your private data.
- selfhoster11 3y ago
  Retrieval Augmented Generation (RAG) is a good stopgap measure. It avoids the need for a lot of work on customising the LLM by supplying the relevant data via vector DB queries and letting LLM remain as a "calculator for words" that will just rephrase/extract the answer from the DB query result.

zurtri 3y ago

I am constantly searching for this. To point a locally hosting LLM to a locally hosted code repo - and then start improving it from there would be awesome.

pbmonster 3y ago
Depending on how serious you are about this (and how well you can slap together a few different python packages), it is very doable today. Get one of the better llama versions fine-tuned on code (e.g. WizardCoder), take your entire code base and create embeddings from it, put those into a vector database. Now, every time you ask your LLM a question about your code base, you first turn that prompt into an embedding and perform a search on your vector database. The results of that search are appended as context to the actual prompt before passing it to the LLM itself. There's tons of packages that help with all of that, Langchain and Faiss are probably the most popular right now.
- ek750 3y ago
  interesting, i'd love to do this too. but it sounds like there aren't any full-featured, opensource packages/projects that do this all-together? I'd love to hack the parts together, but i don't have the time/energy these days to do it. thanks for the helpful keywords though, it helps point me in the right direction.

nicce 3y ago

You would still need a huge training and data set for model before it can do something useful from your private data.

selfhoster11 3y ago
Retrieval Augmented Generation (RAG) is a good stopgap measure. It avoids the need for a lot of work on customising the LLM by supplying the relevant data via vector DB queries and letting LLM remain as a "calculator for words" that will just rephrase/extract the answer from the DB query result.