Helpdesk Answer Bot

Drafts replies from similar solved tickets and articles.

Industry	Level	Difficulty	Recommended mode
Customer support	2 of 5	Intermediate	flat

Coming soon

A complete, runnable Helpdesk Answer Bot reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Agents rewrite the same answers daily.
• New agents lack context.
• Replies vary in quality.

How SrvDB powers Helpdesk Answer Bot

1. Solved tickets and articles are embedded.
2. Each new ticket retrieves the closest solutions.
3. A local LLM drafts a reply citing them.
4. Agent edits are fed back.

What changes

Today	With Helpdesk Answer Bot on SrvDB
Writing every reply	Reviewing a draft
Inconsistent answers	Consistent, sourced replies
Slow onboarding	Context for every agent

How it works

1. Embed solved tickets.
2. Retrieve per ticket.
3. Draft reply.
4. Agent edits and sends.
5. Upsert the final answer.

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

• Password and account issues
• Billing questions
• How-to questions
• Bug workarounds
• Shipping questions
• Multilingual replies

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("./helpdesk-answer-bot-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 suggested_answers(ticket_text, k=5):
    hits = db.search(embed(ticket_text), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Agents approve every reply.

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 Customer support projects

Level	Project	What it does
1 · Beginner	Knowledge Base Search	Customers and agents find the right article from their own words.
2 · Intermediate	Helpdesk Answer Bot (this page)	Drafts replies from similar solved tickets and articles.
3 · Advanced	Ticket Deduplicator	Finds duplicate tickets and emerging issue clusters.
4 · Expert	Customer Memory Agent	Remembers each customer’s history across conversations.
5 · Frontier	Offline Support Kiosk	A self-service assistant that works without internet.

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