agent's vector upsert loop re-embedding the same docs: dedupe guard

TL;DR

The agent's upsert loop re-embeds every document on every pass, including ones that never changed. Add a dedupe guard: hash each document's content before embedding, compare against the hash stored alongside the vector, and skip the embed call when the hash matches.

Symptom

An agent running a vector upsert loop re-embeds the same documents every cycle. Embedding spend climbs on every run, ingestion never converges, and unchanged documents get fresh vectors with new ids, which can disturb stable retrieval ordering.

When to use

• Ingestion costs climb on every run with no new documents
• Unchanged docs get re-embedded needlessly
• The upsert loop never converges to a no-op pass

When not to use

• Documents genuinely changed (those must be re-embedded)
• You migrated embedding models (all vectors need rebuilding, hashes do not help)
• The loop re-fetches but does not re-embed (thats a fetch-side problem)

Steps

1. Hash each document's content before any embedding call:

import hashlib
def content_hash(text):
    return hashlib.sha256(text.encode("utf-8")).hexdigest()

Expected output: identical documents always produce the identical hash. Hash the raw text, not the metadata, so metadata-only edits do not trigger re-embeds.

2. Store the hash alongside each vector at upsert time:

point = {"id": doc_id, "vector": embedding, "payload": {"text": text, "hash": content_hash(text)}}

Expected output: every stored point carries the hash of the text it was embedded from. Without this, the guard has nothing to compare against.

3. Skip the embed call when the stored hash matches:

stored = fetch_payload(doc_id)
if stored and stored.get("hash") == content_hash(text):
    continue  # unchanged, skip embedding
embedding = embed(text)

Expected output: steady-state runs embed zero documents. The loop converges to a cheap no-op pass instead of a full re-embed.

4. Backfill hashes for points written before the guard existed, or treat missing hashes as changed exactly once:

if stored is None or "hash" not in stored:
    embedding = embed(text)  # one-time backfill

Expected output: one full pass re-embeds everything and stores hashes; every pass after that is incremental.

Variant phrasings

vector db upsert re-embedding everything nightly

Same fix. Nightly jobs without a change check re-embed the world; the hash guard makes them incremental.

duplicate vectors accumulating in the collection

The loop upserts without stable ids, so each pass adds new points. Combine the hash guard with deterministic ids to stop the growth.

Why it happens

Agents write upsert loops as fetch-all then embed-all because it is the simplest correct shape. Nothing in that shape distinguishes changed from unchanged documents, so every pass pays the full embedding cost. The missing piece is a cheap change signal, and a content hash is the cheapest reliable one.

Edge cases

• Whitespace-only or encoding changes alter the hash. Normalize (strip, unify newlines) before hashing if those edits should not count as changes.
• Metadata-only updates (titles, tags) need a separate path: update the payload without re-embedding.
• Hash collisions are theoretically possible but sha256 makes them a non-issue in practice; do not downgrade to a short hash to save space.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_f0X6dCs-YUmlMnuGC0j5OA