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Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

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Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#1
Hey hacker news,

We’re the cofounders at Psychic.dev (http://psychic.dev) where we help companies connect LLMs to private data. With the launch of Llama 2, we think it’s finally viable to self-host an internal application that’s on-par with ChatGPT, so we did exactly that and made it an open source project.

We also included a vector DB and API server so you can upload files and connect Llama 2 to your own data.

The RAG in RAGstack stands for Retrieval Augmented Generation, a technique where the capabilities of a large language model (LLM) are augmented by retrieving information from other systems and inserting them into the LLM’s context window via a prompt. This gives LLMs information beyond what was provided in their training data, which is necessary for almost every enterprise application. Examples include data from current web pages, data from SaaS apps like Confluence or Salesforce, and data from documents like sales contracts and PDFs.

RAG works better than fine-tuning the model because it’s cheaper, it’s faster, and it’s more reliable since the provenance of information is attached to each response.

While there are quite quite a few “chat with your data” apps at this point, most have external dependencies to APIs like OpenAI or Pinecone. RAGstack, on the other hand, only has open-source dependencies and lets you run the entire stack locally or on your cloud provider. This includes:

- Containerizing LLMs like Falcon, Llama2, and GPT4all with Truss - Vector search with Qdrant. - File parsing and ingestion with Langchain, PyMuPDF, and Unstructured.io - Cloud deployment with Terraform

If you want to dive into it yourself, we also published a couple of tutorials on how to deploy open source LLMs for your organization, and optionally give it access to internal documents without any data ever leaving your VPC.

- How to deploy Llama 2 to Google Cloud (GCP): https://www.psychic.dev/post/how-to-deploy-llama-2-to-google... - How to connect Llama 2 to your own data using RAGstack: https://www.psychic.dev/post/how-to-self-host-llama-2-and-co...

Let a thousand private corporate oracles bloom!

Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2
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Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#2
- Do you have plans to support other connectors, specifically OneDrive? - Do you have a demo somewhere? From the website and screenshots, it's not clear the functionalities you offer. A few min long screencast would help. - How do you differ youself from Quivr? Seems like another open source alternative and has some nice feature. Thanks for this. I will try to use this and see how well it works for my use case.

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#4
post #3

> only has open-source dependencies and lets you run the entire stack locally or Open source and on-prem are two different things. Llama 2 doesn't seem to be open source.

I don't think we've collectively figured out how to describe what "weights openly available" means, so open-source is probably a reasonable descriptor.

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#5
post #4
post #3

> only has open-source dependencies and lets you run the entire stack locally or Open source and on-prem are two different things. Llama 2 doesn't seem to be open source.

I don't think we've collectively figured out how to describe what "weights openly available" means, so open-source is probably a reasonable descriptor.

I disagree. Open source involves the "source" being available, not just the "compiled".

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#6
Approximately, what would the hourly cost of running this be on Google Cloud?

>In the default-pool > Nodes tab, set:

>Machine Configuration from General Purpose to GPU

>GPU type: Nvidia TF

>Number of GPUs: 1

>Enable GPU time sharing

>Max shared clients per GPU: 8

>Machine type: n1-standard-4

>Boot disk size: 50 GB

>Enable nodes on spot VMs

Not familiar with GCP, but I see n1-standard-4's are in an instance type that is $.19/hr. Are there any other significant costs to take into account?

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#7

- Do you have plans to support other connectors, specifically OneDrive? - Do you have a demo somewhere? From the website and screenshots, it's not clear the functionalities you offer. A few min long screencast would help. - How do you differ youself from Quivr? Seems like another open source alternative and has some nice feature. Thanks for this. I will try to use this and see how well it works for my use case.

We have about 10 other connectors in a separate project at https://github.com/psychic-api/psychic

Thanks for the feedback! We’ll include a demo soon.

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#8
post #5
post #4

Earlier quoted context omitted.

I don't think we've collectively figured out how to describe what "weights openly available" means, so open-source is probably a reasonable descriptor.

I disagree. Open source involves the "source" being available, not just the "compiled".

The concept of “source” is nebulous for ML models. If you have the weights you can recreate a model without access to the source code originally used to train it, and similarly just having the source code without the training data won’t allow you to recreate the model.

While it would be nice to have the data set Meta used I think open sourcing the weights is good enough.

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#9
post #5

Earlier quoted context omitted.

I disagree. Open source involves the "source" being available, not just the "compiled".

The concept of “source” is nebulous for ML models. If you have the weights you can recreate a model without access to the source code originally used to train it, and similarly just having the source code without the training data won’t allow you to recreate the model. While it would be nice to have the data set Meta used I think open sourcing the weights is good enough.

No. The weights encode recorded parameters they don’t encode essential components like hyperparameters or modules without recorded parameters.

Re: Show HN: RAGstack – private ChatGPT for enterprise VPCs, built with Llama 2

#10
post #4
post #3

> only has open-source dependencies and lets you run the entire stack locally or Open source and on-prem are two different things. Llama 2 doesn't seem to be open source.

I don't think we've collectively figured out how to describe what "weights openly available" means, so open-source is probably a reasonable descriptor.

Maybe we could call it... Open Weights™.
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