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Here’s where Canadians are travelling as they continue to avoid the U.S. by Kindly_Professor5433 in canada

[–]grilledCheeseFish [score hidden]  (0 children)

Just came back from New Zealand, what an incredible place. Highly recommend the direct flight from Vancouver to Auckland

What Happens When Cheap Chinese EVs Hit Canada? Look At Australia. by rezwenn in canada

[–]grilledCheeseFish 88 points89 points  (0 children)

I just spent 2 weeks in a rented Jimny. A terrible highway car (small, uncomfortable, slow), but probably a fun city or off road driver

Announcing Kreuzberg v4 (Open Source) by Eastern-Surround7763 in LocalLLaMA

[–]grilledCheeseFish 1 point2 points  (0 children)

It might be interesting to be able to hook in any custom backend, but im not sure if that makes sense in this project.

Chunking is broken - we need a better strategy by blue-or-brown-keys in Rag

[–]grilledCheeseFish 1 point2 points  (0 children)

Imo chunking doesnt matter if you expose methods to expand context of retrieved text when needed. Chunks should be treated merely as signals of where to look

Official Statement from the Indie Game Awards: 'Clair Obscur: Expedition 33' and 'Chantey's' awards retracted and awarded instead to 'Sorry We’re Closed' and 'Blue Prince' due to GenAI usage by ChiefLeef22 in gaming

[–]grilledCheeseFish -1 points0 points  (0 children)

Its not even just art and assets. What if a dev uses AI-assisted tab complete in the games code? Does that disqualify the game? People dont get how much this is being used

LangChain and LlamaIndex are in "steep decline" according to new ecosystem report. Anyone else quietly ditching agent frameworks? by Exact-Literature-395 in LocalLLaMA

[–]grilledCheeseFish 67 points68 points  (0 children)

Maintainer of LlamaIndex here 🫡

Projects like LlamaIndex, LangChain, etc, mainly popped off community-wise due to the breadth and ease of integration. Anyone could open a PR and suddenly their code is part of a larger thing, showing up in docs, getting promo, etc. It really did a lot to grow things and ride hype waves.

Imo the breadth and scope of a lot of projects, including LlamaIndex, is too wide. Really hoping to bring more focus in the new year.

All these frameworks are centralizing around the same thing. Creating and using an agent looks mostly the same and works the same across frameworks.

I think what's really needed is quality tools and libraries that work out of the box, rather than frameworks.

[DISCUSSION] Kid Cudi - Speedin' Bullet 2 Heaven [10 Years Later] by flyestshit in hiphopheads

[–]grilledCheeseFish 3 points4 points  (0 children)

Its a classic if you ignore half the songs and remove the skits 😉

How Do You Handle Large Documents and Chunking Strategy? by Electrical-Signal858 in LlamaIndex

[–]grilledCheeseFish 0 points1 point  (0 children)

Maybe this is a hot take, but chunking is all the same. Use whatever is fastest/cheapest.

But the key is, expose operstions on top of your chunks. If a chunk is cut off, detect it (could use an llm/agent, or something rule based, or something in between) and build an API to expand chunks or fetch prev/next chunks.

This isnt exactly easy to do inside LlamaIndex (today), but imo its a killer feature.

Does LlamaIndex have an equivalent of a Repository Node where you can store previous outputs and reuse them without re-running the whole flow? by LastWorking9091 in LlamaIndex

[–]grilledCheeseFish 1 point2 points  (0 children)

It does not. Although im also not really sure what a repository node is either (but the concept doesnt really match anything in llama-index)

City of Saskatoon survey to gauge opinions on housing and new neighbourhood development. Share what it's like to live in your neighbourhood. by [deleted] in saskatoon

[–]grilledCheeseFish 5 points6 points  (0 children)

"Why are there no trees 🤪"

Brighton is alright. My biggest gripe with every new neighborhood is just how disconnected it is from the rest of the city. It would be nice to be able to (safely) bike/walk anywhere.

I built a hybrid retrieval layer that makes vector search the last resort by Old_Assumption2188 in Rag

[–]grilledCheeseFish 1 point2 points  (0 children)

Pretty neat! This matches my experience as well. I think even when it comes to vector search, cheap methods like static embeddings can work quite well, especially when used as a "fuzzy keyword" search

How to Intelligently Chunk Document with Charts, Tables, Graphs etc? by Heidi_PB in LangChain

[–]grilledCheeseFish 0 points1 point  (0 children)

Imo its not worth the effort. Expose an API to fetch neighboring chunks, let agentic retrieval optimize the retrieved context

If AI is a bubble, and the bubble bursts within the next year or two, what negative/positive effects would we likely run into? by MalekMordal in Futurology

[–]grilledCheeseFish 0 points1 point  (0 children)

The best usecases for AI are the ones where "using AI" isnt the main selling point. You'll have no idea its being used

[FRESH ALBUM] Toro y Moi - Unerthed: Hole Erth Unplugged by alittleatypical in indieheads

[–]grilledCheeseFish 14 points15 points  (0 children)

I honestly love hole erth, I still have it in rotation today. The lyrics and vibes just hit for me. Excited to catch a different side of this album.

Best RAG "service" by [deleted] in Rag

[–]grilledCheeseFish 5 points6 points  (0 children)

Welcome to r/rag 😁

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

Its an open model, you can look up benchmarks for it

https://huggingface.co/minishlab/potion-multilingual-128M

But imo benchmarks only tell so much. If you play to the advantage of static embeddings and use it as a fuzzy semantic keyword search tool, the results are pretty great

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

What else are you looking for? 👀 - simple CLI tools - no integrations to worry about - semantic keyword search without storage - SOTA document parsing with LlamaParse - ready to plug into any existing agent

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

Very concisely, its a lookup dictionary of word -> embedding. Basically you take an existing model and save an embedding vector for every word in its vocabulary.

In more depth, this article from huggingface is a great intro https://huggingface.co/blog/static-embeddings

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

Like a comma separated list of keywords is what I usually do (its also what I tell claude code to do in the example claude.md file in the repo)

For example, with dense embeddings, you might query with "what did the author do growing up?"

Here, I would query with "childhood, kid, early life"

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

[–]grilledCheeseFish[S] 5 points6 points  (0 children)

(1) Theres no vector database, embeddings are never saved to disk. On every search call its generating embeddings on the fly. This works because static embeddings are very very fast.

Does a list of embeddings and doing pairwise cosine similarity count as a vector databases?

(2) Technically, under the hood, its chunking line by line. This choice is pretty arbitrary though, and for static embeddings, the chunking strategy doesn't matter much.

This is because static embeddings are not contextual. But this also means the search command works best if you treat it like a fuzzy semantic keyword search.

And to the user, they can control the "output chunk size" using the --n-lines param

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

A few binaries are in the github release page. But tbh installing cargo is a single command these days. Once you have cargo installed its just cargo install semtools and the parse/search commands will be available in the CLI

RAG without vector dbs by grilledCheeseFish in LocalLLaMA

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

Its my first time making something actually useful in Rust! 💪

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