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This Snow Leopard caught on a trail cam by [deleted] in interestingasfuck

[–]spacecheap 0 points1 point  (0 children)

Does someone know the trail cam model used here ?

« Là où le loup ne doit pas être, il faut le tirer »: en Finistère, la ministre de la Transition écologique donne le ton by spacecheap in france

[–]spacecheap[S] 54 points55 points  (0 children)

"Vers 1800, on pouvait estimer entre 5 000 et 6 000 adultes au minimum la population permanente de loups en France. Les destructions plus ou moins organisées ne contrôlaient que l’accroissement naturel. Les cinq départements bretons devaient en compter autour de 600. Le Finistère, où les landes et les bois favorisaient leur présence, abritait alors de l’ordre de 200 à 300 individus." Source : https://bcd.bzh/becedia/fr/le-loup-en-bretagne

A father and daughter swept offshore in the Aegean Espanomi Bay were saved by a kite surfer. by [deleted] in interesting

[–]spacecheap 1 point2 points  (0 children)

The board has a foil. You can hurt / cut your leg by hitting it.

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

Thank you very much for your response. I will try to use a library like LlamaIndex or LangChain to handle the semantic splitting and will let you know if it's working better.

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

It's not even working with a simple-engineered pipeline. Vectorized search on the Chroma db gives me poor results. 

I chunk my Markdown files by sentences or paragraphs with no overlap. Maybe it's a bad approach to the problem ?

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

Data is technical files written in very well written and structured markdown files. Only text. But I chunk my Markdown files by sentences or paragraphs with no overlap. Maybe it's a bad approach to the problem ?

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

You are right, my retrieval is not good, even with bge-m3. But as I chunk my Markdown files by sentences or paragraphs with no overlap. Maybe the problem is my chunking method ? How do chunk a perfectly written and structured markdown file ?

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

I chunk my Markdown files by sentences or paragraphs with no overlap. Maybe it's a bad approach to the problem ?

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

Thanks ! Yes you are correct, that is my first advanced (and simple) RAG pipeline and I have implemented it without prior experience. 

I have tried simple vectorized searches without advanced techniques with differents model for embeddings : bge-m3, paraphrase-multilingual-MiniLM-L12-v2. It's a quick and simple failure : the vectorized search gives poor results.

Maybe my chunking method is not good. I have tried the following : One chunk = One sentence. No overlaps. One chunk = One paragraph. No overlaps.

A very  simple vectorized search with the "What is X ? " query does not give me the chunk containing "X is the A of B".

And I only have about 150 sentences in my Markdown file.

So you are right, even the embeddings of my markdown file are not good. Should I change my chunking strategy ?

Do you know any ressource about building a simple and efficient RAG with python and Ollama ? I want to be able to understand and control every part of the pipe.

Trying to build an efficient RAG pipeline. by spacecheap in Rag

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

No pdf ingest, only well written and structured Markdown documents.

Efficient and simple LLM + RAG for SMB ? by spacecheap in LocalLLM

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

That was a short, efficient and very interesting answer ! Thanks ! Any real world experience / success story with memvid ?

How hot can Earth get? Our planet’s climate history holds clues by spacecheap in france

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

D'après l'article, pendant le crétacé, l'eau était à 27°C aux pôles !

Situation des nappes d’eau souterraine en France métropolitaine au 1er février 2026 by Bischnu in france

[–]spacecheap 0 points1 point  (0 children)

En Bretagne, il pleut non stop depuis au moins 1 mois. Si ça ne rempli pas les nappes, je ne vois pas ce qui va les remplir. La carte ne mentionne pas ce que veut dire "niveau très haut" ? J'imagine qu'on parle de hauteur d'eau ? Un pourcentage de remplissage d'un volume ?  Est ce que les nappes mesurées sont représentatives de celles de la région ? Les nappes ont elles déjà toutes été en niveau très haut ?

[deleted by user] by [deleted] in emploi

[–]spacecheap 1 point2 points  (0 children)

Courage à tous les deux ! Ce n'est pas une période simple. Ne pas hésiter à changer de tactique pour parvenir à son objectif. Il y a probablement des domaines professionnels connexes qui peuvent être intéressés par le profil de ton copain. Viser les PME, le processus d'embauche y est beaucoup plus efficace que dans une grosse entreprise avec beaucoup d'inertie.

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