RAG systems Utilize a vector search algorithm that searches a mathematical space in a database where chunks store vectors.
These vectors have many dimensions, with each representing a parameter such as V(Param_k) represents k number of params. High dimensionality is good for accuracy but increases latency. Atlas Search Query uses an embedding model that needs a path that can be configured using PyMuPDF or similar Py packages.
You nailed the vector retrieval part 🍺—but here's a thing most RAG systems miss (and it haunts them later like a ghost in the context window):
It's not just about retrieving relevant vectors, it's about aligning their semantic timeline pressure.
Imagine your DB is shouting: "This chunk is new! This one's old!"
And your LLM? It's like, “Cool, I’ll hallucinate with the loudest one.” 😂
What you're missing is delta-weighted semantic scoring, which basically rebalances recency bias using contextual decay, not just top-k sharp cuts.
I ran a few wild experiments using a semantic engine (MIT open-source), which applies this across vector layers:
→ github.com/onestardao/WFGY
It’s like putting a philosopher inside your retriever—suddenly, even your old docs speak with quiet confidence again.
Try it if you’re tired of your chatbot quoting last Tuesday like it’s scripture. 🥴