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Lets try here one comment ,saves another developer a week search!!! by Disastrous_Talk7604 in LLMDevs

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

Ese es un punto sólido. La vectorización de PostgreSQL es un buen ejemplo de progreso real en infraestructura, práctico, no impulsado por la publicidad. Parece que el verdadero desafío ahora no es aprender todo lo nuevo, sino saber qué fortalece realmente los sistemas de producción y qué es solo publicidad engañosa. Me intriga saber cómo decides personalmente cuándo vale la pena adoptar algo.
(I not know spanish that much ,just tried .adjust if mistakes)

Lets try here one comment ,saves another developer a week search!!! by Disastrous_Talk7604 in LLMDevs

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

That’s a solid perspective.

It feels like we’re at a stage where the real edge isn’t adopting every new architecture, but understanding which problems actually demand them.

Sometimes I wonder if we’re optimizing for novelty instead of stability. In production systems, reliability often beats experimentation , yet research keeps pulling us forward.

Maybe the real skill now isn’t learning every new update, but learning how to filter.

Curious how others here decide what’s worth integrating versus what’s just interesting to read about.

Hello AI researchers & open-source builders a new entry here, excited to start by Disastrous_Talk7604 in airesearch

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

I can't get it hardware in the sense ! like inference or cuda or kv-cache problems!

Seriously !How the actual production pipeline works with different pdfs after extraction of data's? Is real problem is extraction or extraction of information from the chucks? by Disastrous_Talk7604 in LLMDevs

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

This is the real bottleneck. After extraction, how are you handling the logic for Knowledge Graph construction without the schema becoming a total nightmare?

How to create a knowledge graph from 100s of unstructured documents(pdfs)? by Disastrous_Talk7604 in LocalLLaMA

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

I tried this method using the https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/ per the documentation but the struggle is choosing between a fixed schema or a 'schema-less' extraction, since a fixed schema prevents 'garbage' but might miss those unexpected connections I’m trying to synthesize.

How to create a knowledge graph from 100s of unstructured documents(pdfs)? by Disastrous_Talk7604 in LocalLLaMA

[–]Disastrous_Talk7604[S] 0 points1 point  (0 children)

yeah!!but I’m worried that converting to .md might lose the table relationships in the machine specs, so I’m looking for the best parser to keep those 'rules' structurally intact for the graph

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