grucloud VS private-gpt

I wrote an open source tool called grucloud that can also list the asset inventory, check it out at www.grucloud.com

Check it out at https://www.grucloud.com/

I wrote a birectional infrastructure as code, where the code is generated automatically from a live infrastructure (aws, azure, gcp). Let me know your feedback on this open source tool, available at www.grucloud.com

Yes indeed, yaml and json are not suited for describing infrastructure. Off the self programming languages provide conditionals, loops and so on. I wrote an alternative to CDK/terraform called www.grucloud.com, check it out.

Dig into www.grucloud.com, an alternative to terraform/cdk/pulumi. However, the code is generated automatically forme your current live infrastructure, no need to manually write infrastructure as code.

Have a look at www.grucloud.com, an alternative to Terraform/CDK/pulumi. Instead of manually writing the code, grucloud can generate code from live infrastructures: AWS, Azure and GCP.

The local module defining the app on the k8s side is located at base.

The command gc graph generates this diagram from the code iac.js.

PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.

https://github.com/zylon-ai/private-gpt

Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion

Run https://github.com/imartinez/privateGPT

Then

make ingest /path/to/folder/with/files

Then chat to the LLM.

Done.

Docs: https://docs.privategpt.dev/overview/welcome/quickstart

PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.

I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].

The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.

[0] https://gpt4all.io/index.html

[1] https://www.danswer.ai/

[2] https://github.com/imartinez/privateGPT

https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...

https://github.com/imartinez/privateGPT

I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.

This resulted in Python package I call OnPrem.LLM.

In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/

Enjoy!