Hardware Lab Agent

An agent that remembers bench sessions, scope captures and decisions.

Industry	Level	Difficulty	Recommended mode
Electrical & electronics	4 of 5	Expert	flat

Coming soon

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

The problem

• Bench knowledge is lost between sessions.
• Scope captures are not searchable.
• Teams repeat experiments.

How SrvDB powers Hardware Lab Agent

1. Notes and capture images are embedded together.
2. The agent recalls past sessions by meaning.
3. One database per project.
4. Crash-safe storage.

What changes

Today	With Hardware Lab Agent on SrvDB
Lost bench knowledge	Persistent memory
Unsearchable captures	Searchable images
Repeated experiments	Recalled results

How it works

1. Log sessions.
2. Embed notes and captures.
3. Recall.
4. Summarise.
5. Archive.

SrvDB features it uses

• Several databases: each database is a directory, so different data types or tenants stay separate.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Thread-safe handles: one database object can be shared between threads; searches run in parallel.

Use cases

• Debug sessions
• Characterisation
• Compliance pre-scans
• Design reviews
• Handover
• Root-cause analysis

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("./hardware-lab-agent-db", dimension=512)

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_recall(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

• Image embeddings need a suitable model.

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 Electrical & electronics projects

Level	Project	What it does
1 · Beginner	Datasheet Q&A	Ask questions of your component datasheets and get page citations.
2 · Intermediate	Second-source Finder	Suggests substitute components by specification similarity.
3 · Advanced	Test Log Analyzer	Finds similar past test failures and anomalous logs.
4 · Expert	Hardware Lab Agent (this page)	An agent that remembers bench sessions, scope captures and decisions.
5 · Frontier	RF Fingerprint Lab	Similarity search over RF spectrum fingerprints (research).

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