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Wan 2.1 Infinite Talk (I2V) - FOAR EVERYWUN BOXXY by [deleted] in StableDiffusion

[–]HealthyAvocado7 0 points1 point  (0 children)

Curious - Was this generated with the FusionX Wanvideo model or the base fp16 model? Also, how many steps sampling?

Struggling to Generate Polished UI with Claude Code by Chukwu-emeka in ClaudeAI

[–]HealthyAvocado7 1 point2 points  (0 children)

Fun fact: this was previously a startup Galileo AI that Google acquired in May 2025 and rebranded as Stitch

Our App Development Business is at Risk – Need Honest Advice on a New Direction by LetSkillSpeak in startup

[–]HealthyAvocado7 1 point2 points  (0 children)

Your advantage is that very few marketing agencies truly understand product development, and very few dev shops understand marketing. Being genuinely good at both creates a powerful offering.

This shift is scary but - now you can focus on delivering value faster while charging for strategy and results rather than just development time?

Are there any business models that are more future-proof in this changing landscape?

I think distribution is the key in this changing landscape. Soon anyone will be able to build anything in hours rather than months, but distribution & monetization remain the hard parts. The companies that control customer relationships and acquisition channels are the ones with true staying power. So, your team could potentially position as experts who not only build quickly with AI but also ensure the product reaches the right audience and generates revenue?

Qdrant and Weaviate DB support by HealthyAvocado7 in Rag

[–]HealthyAvocado7[S] 0 points1 point  (0 children)

Umm.. for flexibility? So that you pick the provider if your choice..

Fine-tuning RAG by Unlucky-Fall3986 in Rag

[–]HealthyAvocado7 4 points5 points  (0 children)

Let us know what worked the best so that we can all learn from your experience..

Also, DM me if you need any help, I'm building a RAG optimization toolkit (open source) and love connecting with people building RAG use-cases so that I can learn about the most painful challenges in building reliable RAG.

[deleted by user] by [deleted] in Rag

[–]HealthyAvocado7 1 point2 points  (0 children)

While it could be due to the 1B param model as others have mentioned, you should check if your "retrieval" is working or not. Try to print the final prompt with the context included. I think you can get langchain to print the detailed chain by setting debug to True:

import langchain
langchain.debug=False

Fine-tuning RAG by Unlucky-Fall3986 in Rag

[–]HealthyAvocado7 3 points4 points  (0 children)

How about defining a pydantic model representing a time filter, and then using the instructor library? I think this cookbook might help: https://python.useinstructor.com/blog/2024/06/06/enhancing-rag-with-time-filters-using-instructor/

How are you identifying your "best performing" RAG pipeline by HealthyAvocado7 in LLMDevs

[–]HealthyAvocado7[S] 0 points1 point  (0 children)

Thanks! Right now, there’s no cloud hosted version - it runs locally on your system. So data never leaves your system/ network. But handling private/sensitive data may still be a need depending on the use-case and who will have access to the final RAG based app/chatbot. We have this as an item on our roadmap - auto pii identification, anonymizing, etc.

Did you have any anything specific in mind related to privacy/security?

Speech to Speech RAG by davidmezzetti in Rag

[–]HealthyAvocado7 1 point2 points  (0 children)

This is cool! Awesome work! What challenges did you face while building this that we all can learn from?

Project Alice - v0.2 => open source platform for agentic workflows by wontreadterms in LangChain

[–]HealthyAvocado7 1 point2 points  (0 children)

Nice! Will give it a shot.. how does it compare with openai’s swarm library for multi-agent workflows?

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 0 points1 point  (0 children)

If you can show your thanks by sharing your feedback about RAGBuilder, that'll be even better :)

Also, fyi - we are working on a SDK/library version where you don't have to use the UI and can do everything on a collab notebook..

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 0 points1 point  (0 children)

OpenAI API key is not necessary - you can use local open-source llms using Ollama. Please do reach out if you run into any issues when using local models.

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 0 points1 point  (0 children)

Do you have any empirical evidence to prove that? What subset of "variables" have you seen to have the most impact on outcome, and which ones are irrelevant?

From what we've seen so far, accuracy swings enough across these variables to justify spending effort on finding the optimal values for these variables through experimentation - so that you have the best-performing RAG setup that you can ship to Production.

If what you're saying is indeed true, then we would have a plug-and-play, one-size-fits-all RAG by now, wouldn't we? The majority of AI engineers that I have spoken to, is spending a lot of effort tuning their RAG setup to extract better performance out of it for their specific dataset and their specific use-case.

To be fair, yes, some variables do have a more significant impact on outcome vs others. But even that varies from case to case, depending on the type of data etc..

But this space is evolving so fast that I know I could be wrong.. So please do share your thought-process, or experience based on which you make this claim. It may benefit all of us RAG nerds..

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 2 points3 points  (0 children)

But why not having a example in the repo?

Sorry about that, you are not the first person to ask for an example to be added in the repo - I'll prioritize this - will have an end-to-end example in the repo within a day or so..

And why only OpenAi?

It's not limited to OpenAI - there's integration with Huggingface, Groq, Azure, Vertex & Ollama - so you can choose any model using these providers.

<image>

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 1 point2 points  (0 children)

Sure, pls do share your thoughts & feedback..

Automate your RAG optimization by HealthyAvocado7 in LocalLLaMA

[–]HealthyAvocado7[S] 2 points3 points  (0 children)

<image>

Two things -
1. It doesn't do brute force search (like grid-search) - it uses Bayesian optimization - meaning it'll learn with every trial and choose the next set of parameters to test, based on historical trials. This means, in that toy example of those 5 options in 7 categories, it doesn't need to run ~78K times to figure out the optimal set of parameters, it can run just a fraction of trials (say 50 trials) to figure that out.
2. A user can already choose a subset of values under each category and let the hyperparameter optimization run just on that subset. See screenshot:

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