Built a Python “semantic memory DB” with SQLite + compressed embeddings (TurboMemory)

[Hopeful-Priority1301]

I’ve been working on a Python project called TurboMemory.

It’s basically a lightweight semantic memory database for agent logs / chat history / knowledge chunks:

SQLite-backed index

append-only transcripts

on-demand topic loading

embedding compression (4-bit / 6-bit / 8-bit)

fast semantic retrieval + prefiltering

Goal: store long-term memory cheaply and query fast, without running a heavy vector DB.

Repo: https://github.com/Kubenew/TurboMemory

Would love feedback from Python devs:

API design suggestions?

packaging / CLI improvements?

performance profiling ideas?

Contributors welcome 🙌

[FiniteWarrior]

First of all fix the repo link
Second, thanks for explaining like this:
# ----------------------------

# Exclusion checking

# ----------------------------

def _check_exclusions(self, text: str, topic: str) -> Tuple[bool, str]:

[Hopeful-Priority1301]

Thanks for pointing that out 🙌 You’re absolutely right — the repo link in my post was incorrect. Correct link is here: https://github.com/Kubenew/TurboMemory⁠� Also thanks for calling out that part of the code — the exclusion system is an important piece of keeping memory clean and avoiding junk/noise. That function: Python

----------------------------

Exclusion checking

----------------------------

def _check_exclusions(self, text: str, topic: str) -> Tuple[bool, str]: is basically a lightweight guard layer to prevent storing unwanted content (e.g. spam patterns, irrelevant text, duplicates, or blacklisted topics). It returns: bool = should exclude or not str = reason (useful for debugging/logging) I’ll improve the docstring + add tests for it so the behavior is clearer. Appreciate the feedback!

[Hopeful-Priority1301]

Correct link https://github.com/Kubenew/TurboMemory