Lab Q&A Assistant

Answers questions from the group’s own knowledge with citations.

Industry	Level	Difficulty	Recommended mode
Quantum computing	2 of 5	Intermediate	flat

Coming soon

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

The problem

• New members ask the same questions.
• Knowledge sits with senior researchers.
• Generic chatbots lack context.

How SrvDB powers Lab Q&A Assistant

1. Lab knowledge is embedded.
2. A local LLM answers with citations.
3. Updated as work progresses.
4. Private.

What changes

Today	With Lab Q&A Assistant on SrvDB
Interrupting seniors	Cited answers
Generic answers	Lab-specific answers
Lost knowledge	Captured knowledge

How it works

1. Ingest knowledge.
2. Ask.
3. Verify sources.
4. Update.
5. Review.

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.
• Thread-safe handles: one database object can be shared between threads; searches run in parallel.

Use cases

• Hardware procedures
• Calibration steps
• Software setup
• Algorithm notes
• Experiment design
• Write-ups

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-qa-assistant-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 lab_answer(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

• Answers cite sources; researchers verify.

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	Search the group’s notes, papers and run summaries.
2 · Intermediate	Lab Q&A Assistant (this page)	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)