Appreciate the thoughtful reply.
Absolutely agree the deterministic performance-oriented mindset is still essential for large workloads. Are you expecting that this supplements a traditional vector/semantic store or that it superceeds it?
My focus has absolutely been on relatively small corpii, and which is supported by forcing a subset of data to be included by design. There are intentionally no conventions for things like "we talked about how AI is transforming computing at 1AM" and instead it attempts to focus on "user believes AI is transforming computing", so hopefully there's less of the context poisoning that happens with current memory.
Haven't deployed WVF at any scale yet; just a casual experiment among many others.
To me, the OP’s reply reeks LLM, along with many others from them in this thread.
I would hope that their replies are from an actual person, knowing they’re interacting with people in a similar field as themselves, and asking for criticism from real people in the top comment.
Supplements, definitely — for a specific workload. General document retrieval at scale (millions of chunks, read-heavy, doc-search patterns) is well-served by existing vector stores; YantrikDB doesn't compete on throughput. Where it's meant to supersede is the narrow case of agent memory: small-to-medium corpus, write-heavy with paraphrases every turn, lives for the lifetime of an agent identity, nothing curating the input.
Your "user believes X" framing is exactly the episodic/semantic split cognitive psych has been calling this for decades. YantrikDB exposes it via memory_type ∈ {episodic, semantic, procedural}. Your intuition about context poisoning from over-specific episodic details lines up with how I've been thinking about it — "we talked about AI at 1am" is high-noise low-signal for future retrieval. The design bet is consolidation + decay should burn episodic into semantic over time, and episodic-only memories should fade faster.
What does WVF stand for? Curious what you've been experimenting with.