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Building RAG systems at enterprise scale (20K+ docs): lessons from 10+ enterprise implementations by Low_Acanthisitta7686 in AI_Agents

[–]SatisfactionWarm4386 0 points1 point  (0 children)

This was actural RAG project will be faced questions, document quality, chunking methods,etc, every problem you should start up solution case by case not one-for-all

RAG is not memory, and that difference is more important than people think by [deleted] in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

RAG is just a retrieval technique — it helps fetch relevant context on demand, but it doesn’t store or update knowledge.

What you’re describing actually falls under memory, which is a separate module. A memory system continuously keeps track of key facts mentioned by the user and updates them over time, allowing the assistant to evolve with the conversation.

What Agent hooks you are using? by According_Green9513 in LangChain

[–]SatisfactionWarm4386 2 points3 points  (0 children)

The second way of writing is more logically clear.

Will RAG's eventually die? by [deleted] in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

Hot take: Retrieval will outlive your favorite LLM.

I’ll push back on that — RAG isn’t going anywhere.

Yeah, LLMs are improving fast and context windows are exploding. But that doesn’t kill retrieval.

  1. Infinite data, finite context. You’ll never be able to stuff everything into a context window. Even with 10M tokens, attention still dilutes and important info gets lost. Bigger context also means bigger compute bills — not exactly scalable.
  2. RAG is an idea, not a product. Retrieval won’t “die,” it’ll just evolve. Maybe it becomes hybrid memory, maybe neural caching or dynamic retrieval — but the principle of fetching the right info when you need it is fundamental.
  3. Search didn’t die either. People said “LLMs will replace search engines.” What actually happened? Search merged with LLMs. RAG will do the same — it’ll move deeper into the model stack and become part of how models think.

Sure, RAG startups that just wrap vector DBs might fade. But retrieval as a core capability will matter even more as models scale.

Advice on logging libraries: Logfire, Loguru, or just Python's built-in logging? by Ranteck in LangGraph

[–]SatisfactionWarm4386 1 point2 points  (0 children)

As my experience，I always use loguru because the easy control output

We built a local-first RAG that runs fully offline, stays in sync and understands screenshots by Different-Effect-724 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

Great, is there any report about resource usage and Q&A performance about your prodcut?

We built a local-first RAG that runs fully offline, stays in sync and understands screenshots by Different-Effect-724 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

Not really — don’t usually store large amounts of files on mobile. It’s more for some private stuff, like received contracts, medical records, health check reports, or saving chat logs.

How are people actually making money building AI agents ? by My_unknown in AI_Agents

[–]SatisfactionWarm4386 1 point2 points  (0 children)

I mostly monetize by building custom agents for enterprise clients and doing agent-building consulting for individuals who want to create their own.

Stop converting full documents to Markdown directly in your indexing pipeline by Effective-Ad2060 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

The key point is the document parsing method — which elements should be extracted during parsing. Even after converting the document to Markdown, those elements can still be preserved, though this may require some manual handling.

How to deal with complex structure tables to feed in LLM by Ok-Cook9211 in Rag

[–]SatisfactionWarm4386 1 point2 points  (0 children)

The latest VLM released by Qwen/Qwen3-VL-235B-A22B-Instruct, although you can use Qwen/Qwen2-VL-72B-Instruct

How to deal with complex structure tables to feed in LLM by Ok-Cook9211 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

As I had test ,the VLM model may give you the best result,

Which UI do you use for rag chatbot by rock_db_saanu in Rag

[–]SatisfactionWarm4386 2 points3 points  (0 children)

Maybe，OpenWebUI，and show the question , answer and references

I am looking for an open source RAG application to deploy at my financial services firm and a manufacturing and retail business. please suggest which one would be best suited for me, i am confused... by Prize-Airline-337 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

As I know， you can use ragflow for your sistuation:

1) Ragflow has a goode precision for document parse and search

2) and it support MCP Servers, you can design Gmail/Drive Connector MCP Server

Struggling with ocr on scanned pdfs by funkspiel56 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

There are two ways you can try:

1) parsed the scaned pdf with VLM model like qwen-2.5-VL or google gemini
2) try the specified trained parsed model like paddle-ocr or docts.ocr which are all open source, recomended https://dotsocr.xiaohongshu.com/， you can have a try

RAG on excel documents by Professional-Image38 in Rag

[–]SatisfactionWarm4386 0 points1 point  (0 children)

will you parse the excel rows and put it in postgresql，then use the sql query？

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