Lab Notebook Search

Search the group’s notes, papers and run summaries.

Industry	Level	Difficulty	Recommended mode
Quantum computing	1 of 5	Beginner	flat

Coming soon

A complete, runnable Lab Notebook Search reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Run results are hard to find again.
• Notes are spread across tools.
• Unpublished work must stay private.

How SrvDB powers Lab Notebook Search

1. Notes and run summaries are embedded locally.
2. Questions retrieve relevant entries.
3. Private to the lab.
4. Added continuously.

What changes

Today	With Lab Notebook Search on SrvDB
Scattered notes	One search
Lost runs	Found by description
Privacy concerns	Local only

How it works

1. Collect notes.
2. Embed.
3. Search.
4. Add runs.
5. Back up.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• Fully offline: SrvDB runs in-process and never opens a network connection.

Use cases

• Past runs
• Calibration notes
• Paper notes
• Code snippets
• Meeting notes
• Onboarding

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./lab-notebook-search-db", dimension=384)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def find_notes(question, k=5):
    hits = db.search(embed(question), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• SrvDB is classical software running on regular computers.

Caution

SrvDB is classical software: it stores ordinary vectors describing experiments and runs on regular computers, not on quantum hardware.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Quantum computing projects

Level	Project	What it does
1 · Beginner	Lab Notebook Search (this page)	Search the group’s notes, papers and run summaries.
2 · Intermediate	Lab Q&A Assistant	Answers questions from the group’s own knowledge with citations.
3 · Advanced	Calibration Drift Detector	Compares calibration snapshots stored as vectors to catch drift.
4 · Expert	Experiment Memory Agent	An agent remembering runs, plots and decisions across experiments.
5 · Frontier	Circuit Similarity Research	Research platform for circuit embeddings and similarity search.

See also: Use cases · Live examples (coming soon)