guidance VS private-gpt

This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.

https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.

... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.

https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...

Here's one thing I don't get.

Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...

...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.

This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance

But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!

Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?

I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].

[0]https://github.com/microsoft/guidance

LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.

So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.

I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201

Let's have a subreddit about Guidance!

You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)

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!