Datasheet Q&A

Ask questions of your component datasheets and get page citations.

Industry	Level	Difficulty	Recommended mode
Electrical & electronics	1 of 5	Beginner	flat

Coming soon

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

The problem

• Datasheets run to hundreds of pages.
• Errata are separate documents.
• Answers need exact sources.

How SrvDB powers Datasheet Q&A

1. Datasheets, errata and app notes are embedded per page section.
2. Questions retrieve passages; a local LLM answers with page citations.
3. New revisions replace old ones with upsert.
4. Local and private.

What changes

Today	With Datasheet Q&A on SrvDB
Scrolling PDFs	Cited answers
Missed errata	Errata retrieved alongside
Old revisions	Current only

How it works

1. Ingest documents.
2. Embed sections.
3. Ask.
4. Verify pages.
5. Update revisions.

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

• Pin functions
• Timing parameters
• Power sequencing
• Register settings
• Thermal limits
• Errata checks

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("./datasheet-qa-db", dimension=768)

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 datasheet_pages(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

• Always verify against the datasheet page.

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 (this page)	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	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)