It would work the same way, you would just need to connect with your local model. For example, change the code to find the embeddings with your local model, and store that in Milvus. After that, do the inference calling your local model.
I've not used inference with local API, can't help with that, but for embeddings, I used this model and it worked quite fast, plus was a top2 model in Hugging Face. Leaderboard. Model.
I didn't do any training, just simple embed+interference.
Milvus documentation has a nice example: link. After this, you just need to use a persistent Milvus DB, instead of the ephimeral one in the documentation.
OP can also use an embedding model and work with vectorial databases for the RAG.
I use Milvus (vector DB engine; open source, can be self hosted) and OpenAI's text-embedding-small-3 for the embedding (extreeeemely cheap). There's also some very good open weights embed modelsln HuggingFace.
Oh, you can interpretate anti-matter as either matter that has negative energy and travels forward in time, or matter with positive energy that travels backwards in time, and both interpretation are valid under Dirac's equation.
Such a drastic change to 2026 regulations made no sense in almost mid 2025. Teams have to prepare new year's cars on a limited budget, and doing so with a new engine of different weight and dimensions is almost senseless just 9 months short of preseason tests.
Take for example the apartheid in South Africa. The rugby team was not able to participate in some of the world cups. While not the key action that ended apartheid, it did pressure the Government in some way. Not player's fault, not the spectators fault, not the world cup organization team fault, yet they all got punished in some way.
There are also anti neutrons that have a neutral charge
Expanding onto this, it raises the question: how is a neutron different to an anti-neutron?
A neutron can be though of a particle composed of 2 down and 1 up quarks and lot of gluon's that keep everything together. The gluon is its own antiparticle, so the antineutron has 2 anti-down quarks, 1 anti-up quarks and gluons. This way it becomes a different particle despite also being of neutral charge.
Back in university, I studied basically all day long, which was tiresome after long sessions of study, even if with friends. My great superpower is that it used to just take me ~10 seconds of resting with my eyes closed to feel a huuuuge boost of energy that lasted for 1-2 hours. After that boost expired, I just did it again.
It would work the same way, you would just need to connect with your local model. For example, change the code to find the embeddings with your local model, and store that in Milvus. After that, do the inference calling your local model.
I've not used inference with local API, can't help with that, but for embeddings, I used this model and it worked quite fast, plus was a top2 model in Hugging Face. Leaderboard. Model.
I didn't do any training, just simple embed+interference.