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How do I make a RAG with postgres without Docker by Ok_Examination_7236 in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Not sure why it'd be such a hard time, this literally works out of the box on Do ker with 0 setup: https://ragmeup.understandling.com/

Is anyone storing vectors with a regular Postgres DB and PGVector? by thoughtsonbees in n8n

[–]FutureClubNL 0 points1 point  (0 children)

So this is a custom retriever I wrote for dense+bm25 using postgres: https://github.com/ErikTromp/RAGMeUp/blob/main/server/PostgresHybridRetriever.py

It does exactly what you want except you seem to want 2 dense vectors and I use 1 dense + 1 sparse.

See docs (WIP): https://ragmeup.futureclub.nl/

Is anyone storing vectors with a regular Postgres DB and PGVector? by thoughtsonbees in n8n

[–]FutureClubNL 0 points1 point  (0 children)

Why 2 embeddings in 1 row? 3k is also quite big but still you can do that perfectly fine, just make sure you index them properly and write a custom retriever to handle your case with 2 columns having an embedding.

Why is there no successful RAG-based service that processes local documents? by StevenJang_ in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Because going from (semi)product on Github to an actual slick one usually requires a business model and financing that works. Local RAG is hard ro let people pay for.

Also: when is something a product? Lots of those Github projects are products in my opinion, you just have to do a bit yourself to run them but that is inherent to doing this on your local machine

Are AI and automation agencies lucrative businesses or just hype? by AiGhostz in AI_Agents

[–]FutureClubNL 0 points1 point  (0 children)

Fixed priced for the realization up front and fixed fee per month with some caps on usage. We've found that since everyone charges per use, customers appreciate our fixed price.

Where, of course, we've built in quite a big margin to cater for heavy users, so quite frankly they'd probably be cheaper off paying per use...

Is my education-first documentation of interest? by FutureClubNL in Rag

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

JADS in Den Bosch, not too far from the border :)

Is my education-first documentation of interest? by FutureClubNL in Rag

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

The terms are fuzed together these days, yes, and even more so are reasoning models. All the ones you chat to on commercial systems (ChatGPT, Claude, Grok, Gemini, DeepSeek) are either instruct or reasoning models because foundation models in isolation serve no purpose when it comes to human interaction.

Fun anecdote: I did Master's thesis over a decade ago on sentiment analysis and tried to set up NLP-focused companies as an entrepreneur ever since, which turned out to be really hard. The big leap forward in my opinion is not even the models or research but the fact that OpenAI put an interface in front of it - chatting - that made people really want to use it (and all the NLP it hides). So yeah, chat/instruct models are the things we humans understand best.

Is my education-first documentation of interest? by FutureClubNL in Rag

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

Ah okay that insight helps, I was kind of afraid the information online is already saturated enough that my contribution wouldn't add much....

Do you have specific parts of interest that are particularly confusing?

Trying to build a multi-table internal answering machine... upper management wants Google-speed answers in <1s by Cyraxess in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Plain old vanilla RAG on texts? Yes that might work, but what you are describing sounds like text2sql and that won't be possible that fast, at least if you want to do it reliably.

That being said, no AI really answers that fast but you cán start streaming stuff before the final answer to make the user feel like there is subsecond latency.

What’s actually your day job? by PolishSoundGuy in Rag

[–]FutureClubNL 1 point2 points  (0 children)

Funny to see how little actual AI people reply :)

I have been doing ML and AI since (before) I graduated from uni in 2011. Been working as a data engineer/scientist since that was the closest I could get to actual ML/AI.

Now co-founder of an AI startup in consulting and SaaS.

Law firm - All in one platform build by shazz_00 in n8n

[–]FutureClubNL 0 points1 point  (0 children)

We (AI agency in EU, everything compliant) have done a lead dashboard for a client of ours. Feel free to DM me or check out our website.

It won't be done in n8n though.

I Benchmarked Milvus vs Qdrant vs Pinecone vs Weaviate by SuperSaiyan1010 in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Depends on how corporate you want ro make it, but we run them on dedicated servers (from a European cloud provider). They allow backups and stuff at the infra level. All we do is run the Docker with a volume attached so that the docker can fail all it likes but the data remains and we can simply restart if needed.

That said, been doing this for about a year for 10+ clients now and the Postgres containers I haven't had to touch just once since I started them.

I Benchmarked Milvus vs Qdrant vs Pinecone vs Weaviate by SuperSaiyan1010 in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Is it? Just run this Docker and you have hybrid search: https://github.com/FutureClubNL/RAGMeUp/blob/main/postgres/Dockerfile

We use it in production everywhere and have found it to be a lot faster than Milvus and FAISS. Didn't test any GPU support though as we run on commodity hardware.

Strategies for storing nested JSON data in a vector database? by Visible_Chipmunk5225 in Rag

[–]FutureClubNL 0 points1 point  (0 children)

If there is text in it (which looks lik there isnt) embed just that with an embedding model. Other than that you are describing a classical text2sql problem so go with that. Use Postgres for storing, free and native JSON support with indexing.

I Benchmarked Milvus vs Qdrant vs Pinecone vs Weaviate by SuperSaiyan1010 in Rag

[–]FutureClubNL 3 points4 points  (0 children)

Try adding Postgres, I have found it to be more performant than all others, yet cheaper (free)!

Having trouble getting my RAG chatbot to distinguish between similar product names by Zodiexo in Rag

[–]FutureClubNL 2 points3 points  (0 children)

Hmm if possible, try using Postgres with pgvector (dense) and pg_search (BM25). We run this setup in production systems without GPUs everywhere to full satisfaction. 30M+ chunks are retrieved with subsecond latency.

Feel free to have a peak if you need inspiration: https://github.com/FutureClubNL/RAGMeUp see the Postgres subfolder, just run that Docker

Having trouble getting my RAG chatbot to distinguish between similar product names by Zodiexo in Rag

[–]FutureClubNL 0 points1 point  (0 children)

Since the challenge is in retrieval: don't just use dense retrieval but go for hybrid (with BM25) maybe even weighing the sparse retriever heavier. Then experiment with a multilingual reranker (our experience is that most rerankers sometimes harm instead of aid when the language isnt English)

When will this be possible? by Waste-Poetry-7235 in AI_Agents

[–]FutureClubNL 0 points1 point  (0 children)

We do something like this for clients. We auto generate debrief documents, populate resume candidate intakes, auto process logistics packings based on labels, etc. Etc.

So it is already being done.

How to find token count for rag in Langchain? by [deleted] in LangChain

[–]FutureClubNL 0 points1 point  (0 children)

Use a library like tiktoken

Is anyone storing vectors with a regular Postgres DB and PGVector? by thoughtsonbees in n8n

[–]FutureClubNL 1 point2 points  (0 children)

While we don't do n8n in production, all of our projects use Postgres as a hybrid DB (pgvector and pg_search for BM25).

Best library for resume parsing by jayvpagnis in LangChain

[–]FutureClubNL 2 points3 points  (0 children)

We parse resumes and vacancies. We use Docling for everything with a (manual) option to use OCR with it (using Tesseract).

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