Re-ingest-correctness SOP for production RAG. Activate when a calling agent
builds, reviews, or debugs an ingestion pipeline that runs more than once over
a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or
a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule —
**ingestion must be idempotent: a document's content hash decides
insert/update/skip, so re-running over unchanged docs is a no-op** — plus the
docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` +
`DocstoreStrategy`), the delete-propagation problem, and cross-framework
equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE
overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but
the re-ingest-correctness contract is not surfaced.
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Re-ingest-correctness SOP for production RAG. Activate when a calling agent
builds, reviews, or debugs an ingestion pipeline that runs more than once over
a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or
a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule —
**ingestion must be idempotent: a document's content hash decides
insert/update/skip, so re-running over unchanged docs is a no-op** — plus the
docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` +
`DocstoreStrategy`), the delete-propagation problem, and cross-framework
equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE
overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but
the re-ingest-correctness contract is not surfaced.
["any ingestion pipeline that will run more than once (cron, CI, webhook, manual re-run)","a corpus where source documents are added, edited, or deleted over time","debugging duplicate / stale chunks polluting retrieval after a re-run","reviewing a PR that calls VectorStoreIndex.from_documents / pipeline.run without a docstore","designing the ingestion side of a production RAG before first deploy"]
when_not_to_use
["a one-shot index built once and never refreshed (static corpus, throwaway notebook)","the corpus is small enough to fully rebuild from scratch in seconds and rebuild-on-every-run is genuinely acceptable (measure first)","pure retrieval / query-time work with no ingestion path"]
Idempotent Ingestion · Re-Ingest-Correctness SOP
Third-person operating model for a coder agent that owns ingestion
correctness across repeated runs. The audience is the LLM agent writing or
reviewing the pipeline code — not the end user.
One sentence: The interesting run is the second one. A correct pipeline
hashes each document, looks the hash up in a docstore, and inserts / updates
/ skips accordingly — so re-running over unchanged docs changes nothing.
This skill is an ENHANCE overlay over [[llamaindex]]. The base skill names
IngestionPipeline with docstore + UPSERTS_AND_DELETE as a hardening
must-have (OP-08, anti-pattern A10) but stops at "use it". This overlay
surfaces the correctness contract — what idempotency actually means, how the
hash decides the action, and how it generalises beyond LlamaIndex.
1. 何时激活 (Activation Rules)
Activate this skill whenever any of the following holds:
The codebase contains an ingestion call (VectorStoreIndex.from_documents,
pipeline.run(documents=...), vectorstore.add_documents, a custom embed
upsert loop) and that call will execute more than once over a corpus
that can change between runs.
The user mentions any of: re-ingest, re-index, scheduled / nightly index
refresh, incremental document updates, docstore, doc hash, upsert,
RecordManager, "keep the index in sync with the source".
A bug report says "I see the same answer chunk twice", "retrieval returns
duplicates", "deleted a file but it still shows up in answers", "the index
keeps growing every night even though nothing changed".
PR review: new ingestion code that builds the index with no docstore / no
hash-keyed dedup and the corpus is live (LlamaIndex base skill A10).
A production RAG is about to ship and the ingestion side has only ever been
tested as a cold first run.
Do not activate when:
The index is built once from a static corpus and never refreshed — there
is no second run to make idempotent.
The corpus is tiny and rebuilding from scratch each run is measurably
cheaper than maintaining a docstore (rare; verify, don't assume).
The task is purely query-time (retrieval, rerank, synthesis) with no
ingestion path in scope.
2. 核心心智模型 (Core Mental Model)
Three principles. Violating any one means a second run can duplicate or corrupt
the index — regardless of how good the retrieval stack downstream is.
Principle 1 — Ingestion must be idempotent; the hash is the decision
Re-running the pipeline over an unchanged document is a no-op. Run it
once, run it a thousand times — same index. The mechanism: each document is
reduced to a stable content hash (and a stable doc id); that hash is
looked up in a docstore (a key→hash record store separate from the vector
store); the lookup result decides the action.
doc_id seen? hash changed?
│ │
┌────────────┴───────────┐ ┌──────┴──────┐
no yes no yes
│ │ │ │
INSERT (embed + upsert) │ SKIP UPDATE
└──────────────►(delete old chunks,
re-embed, upsert)
The hash is the whole game. Without it, the pipeline cannot tell "I have seen
this exact content" from "this is new" — so it re-embeds and re-adds
everything, and the vector store accumulates duplicates. (LlamaIndex base skill
failure #3 / A10; developers.llamaindex.ai/.../loading/ingestion_pipeline/.)
Principle 2 — The docstore is a separate ledger, not the vector store
The vector store holds embeddings keyed by node id. The docstore holds, per
document, {doc_id → content_hash} (and the node ids derived from it). They are
two stores with two jobs:
Store
Holds
Answers
Docstore
doc_id → hash, doc→node mapping
"have I seen this content before?"
Vector store
node_id → embedding + metadata
"what is semantically near this query?"
The dedup decision happens against the docstore, before any embedding
call — so an unchanged corpus costs zero embedding tokens on re-run. If the
docstore is ephemeral (in-memory, lost on restart), every cold start looks like
a first run and re-embeds the world. The docstore must be persisted to the
same durability tier as the vector store (SimpleDocumentStore.persist(),
Redis, MongoDB, Postgres). A docstore that doesn't survive the process is not a
docstore.
Principle 3 — Deletes don't propagate for free
Insert and update are driven by documents that are present. A document that
disappeared from the source emits no event — so a naive upsert pipeline
never learns it should remove the orphaned chunks. Stale content keeps getting
retrieved long after the source file is gone. Propagating deletes requires the
pipeline to compare the full set of doc_ids seen this run against the set in
the docstore, and purge the difference. In LlamaIndex this is the _AND_DELETE
half of DocstoreStrategy.UPSERTS_AND_DELETE; in LangChain it is
cleanup="full" / cleanup="incremental". Choosing the upsert-only strategy
silently accepts stale ghosts — sometimes correct (append-only corpus), usually
not.
3. SOP 工作流 (Agentic Protocol)
Five stages. Each gates the next. Stop and remediate at the first failure. The
gate that matters most is Stage 4 — prove the second run is a no-op.
Stage 0 — Frame the re-run
Before code, answer:
Run cadence: how does the pipeline get re-triggered? (cron / webhook /
CI / manual). If the honest answer is "never, it's one-shot" → this skill is
over-engineering; stop.
Change shape: do source docs get added only, or also edited and
deleted? This decides upsert-only vs upsert-and-delete (Principle 3).
Doc identity: what is the stable doc_id? It must survive across runs —
a file path, a CMS id, a URL. Not a random uuid generated at load time
(that makes every run look new). LlamaIndex derives a hash from content +
doc_id; if doc_id is unstable, idempotency breaks even with a docstore.
Durability tier: where does the docstore live so it survives restarts?
Stage 1 — Attach a persisted docstore
from llama_index.core.ingestion import IngestionPipeline, DocstoreStrategy
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.openai import OpenAIEmbedding
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
OpenAIEmbedding(model="text-embedding-3-small"),
],
docstore=SimpleDocumentStore(), # the dedup ledger
vector_store=vector_store, # the embedding store
docstore_strategy=DocstoreStrategy.UPSERTS_AND_DELETE,
)
For production, swap SimpleDocumentStore for a server-backed one
(RedisDocumentStore, MongoDocumentStore, PostgresDocumentStore) so the
ledger is shared across workers and survives restarts. A SimpleDocumentStore
that is never .persist()-ed is the #1 cause of "it re-embeds everything every
night".
Stage 2 — Pin stable doc ids and let the pipeline hash
docs = SimpleDirectoryReader("./data", filename_as_id=True).load_data()
# filename_as_id=True → doc.id_ is the file path → stable across runs.# LlamaIndex then computes the content hash internally per node.
pipeline.run(documents=docs)
Hard rules:
doc_id comes from a stable external identity (path / CMS id / URL),
never a fresh uuid per load. Set filename_as_id=True or assign doc.id_
explicitly.
Do not mutate document text in a non-deterministic transformation before
the hash is taken (e.g. injecting datetime.now() into metadata) — that makes
the hash change every run and defeats the skip path. Keep volatile metadata
out of the hashed payload.
The embedding model is part of the index identity: changing it requires a
full re-embed (LlamaIndex base skill A2), not an incremental upsert.
Stage 3 — Choose the upsert strategy deliberately
Strategy
Re-run behaviour
Use when
UPSERTS
new+changed docs upserted; deletes not propagated
append-mostly corpus; deletes handled out-of-band
UPSERTS_AND_DELETE
new+changed upserted; vanished docs purged
the corpus is a mirror of a source that can shrink
DUPLICATES_ONLY
dedup identical docs within one run; no cross-run update
one-shot batch that may contain dupes; no live sync
Default for any system that mirrors a mutable source: UPSERTS_AND_DELETE.
DUPLICATES_ONLY is not a re-ingest strategy — it only de-dupes within a
single .run(); a second .run() with the same docs will not skip them.
Stage 4 — Prove the second run is a no-op (the gate)
Before merging, the PR must include — and CI must run — a test that runs the
pipeline twice and asserts the second run did nothing observable:
pipeline.run() returns the list of nodes it actually processed; on a
clean idempotent re-run over unchanged input that list is empty. If it
isn't, the docstore is missing, ephemeral, or the doc ids / hashes are
unstable — go back to Stage 1-2. Then add the edit case (change one doc → exactly
that doc's chunks replaced) and, for UPSERTS_AND_DELETE, the delete case
(remove one doc → its chunks gone, others untouched). See OP-06 / OP-07.
Stage 5 — Schedule, persist, observe
Persist the docstore after every run (storage_context.persist() /
server-backed store auto-persists) — otherwise the next cold start re-embeds
everything.
Run on a schedule, not by hand (LlamaIndex base skill OP-08): cron / Airflow /
serverless cron. The whole point of idempotency is that a missed-or-doubled
trigger is harmless.
Log per run: {docs_in, inserted, updated, skipped, deleted, embedding_calls}. On a steady-state corpus, inserted+updated+deleted should
trend to ~0 and skipped ≈ docs_in. A run that re-embeds everything every
night is the loud signal that idempotency is broken.
Trigger: any IngestionPipeline / ingestion path that will run more than
once with no docstore wired.
Action: pass docstore= (persisted: Redis/Mongo/Postgres in prod,
SimpleDocumentStore only for tests) plus docstore_strategy=. The docstore
is the dedup ledger; without it dedup is structurally impossible.
Output: pipeline that can answer "have I seen this content?" before
embedding.
Trigger: designing or reviewing the dedup decision point.
Action: ensure the content hash is computed and looked up in the docstore
before the embedding transformation runs, so unchanged docs cost zero
embedding tokens. In LlamaIndex this is automatic given a docstore; in a
manual pipeline, hash first, embed only on miss/change.
Output: re-run cost on a steady corpus ≈ 0 embedding calls.
Trigger: choosing pipeline behaviour for the corpus's change shape.
Action: UPSERTS (append-mostly), UPSERTS_AND_DELETE (mirror of a
shrinkable source — the production default), DUPLICATES_ONLY (in-run dedup
only, not cross-run). Decide from Stage 0 change shape, not by default.
Trigger: source docs can be deleted and stale chunks must not be
retrieved.
Action: use UPSERTS_AND_DELETE; the pipeline diffs the doc_ids seen this
run against the docstore and purges orphans. (LangChain: cleanup="full" for
a full snapshot run, cleanup="incremental" for streamed batches.)
Output: deleted source docs → their vectors removed; retrieval stays in
sync.
Action: run the pipeline twice over identical input; assert the second
run() returns 0 processed nodes and the vector count is unchanged (Stage 4).
Output: regression gate proving idempotency is wired through.
Evidence: pipeline.run() returns processed nodes; direct application of
Principle 1.
OP-07 IncrementalUpdateTest
Trigger: corpus supports edits and/or deletes.
Action: edit one doc → assert exactly that doc's old chunks are gone and
new ones present, others untouched. Delete one doc (under
UPSERTS_AND_DELETE) → assert its chunks gone, neighbours intact.
Output: proof that update/delete are surgical, not full-rebuild.
Evidence: hash-change → update path; delete path of
UPSERTS_AND_DELETE.
OP-08 PersistDocstore
Trigger: SimpleDocumentStore used, or docstore not persisted after run.
Action: storage_context.persist(persist_dir=...) after each run, or use
a server-backed docstore that auto-persists and is shared across workers.
Output: cold start reads the ledger → does not re-embed the world.
Trigger: no framework available (raw vector-store SDK, custom loader).
Action: maintain a {doc_id → sha256(content)} table next to the vector
store; on each doc compute the hash, compare, and INSERT / UPDATE
(delete-then-add chunks) / SKIP; track seen ids for delete propagation.
Output: framework-free idempotent ingestion with the same contract.
Evidence: this is what IngestionPipeline / RecordManager automate; see
R2 cross-framework table.
5. 困境决策案例 (Dilemma Cases)
Dilemma 1 — A document changed slightly: re-embed all, or diff?
困境: A 200-page handbook had one paragraph edited. The naive idempotent
pipeline hashes at the document level → the whole doc's hash changes → all
its chunks are deleted and re-embedded. Correct for safety, but on a large doc a
one-line edit triggers a full re-embed of that document. Should the pipeline
diff at the chunk level instead?
约束:
LlamaIndex's docstore dedup keys on the document hash by default — any
change re-processes the whole document's nodes.
Chunk-level diffing requires stable chunk identity across edits, but
SentenceSplitter re-segments the whole doc when text shifts, so chunk
boundaries (and ids) move even for unedited text downstream of the edit.
Re-embedding one document is usually cheap; re-embedding the whole corpus
every run (the failure this skill prevents) is the expensive thing.
决策步骤:
Default to document-level hashing — it is correct and simple. A
per-document re-embed on edit is acceptable for most corpora.
If single documents are huge and edited frequently, split the source into
smaller logical documents (per section / per page) so the hash granularity
matches the edit granularity — each becomes its own doc_id.
Only build true chunk-level content-addressed dedup (hash each chunk, stable
chunk id) if profiling proves per-document re-embed is the real bottleneck.
It is rarely worth the complexity.
结果: Move the granularity by splitting documents, not by hand-rolling
chunk-diffing. Document-level hashing stays the default; the fix is upstream in
how the source is segmented into docs.
可提取的操作: OP-02, OP-03, OP-04.
Dilemma 2 — Source documents were deleted: purge from the index, or keep?
困境: 50 files were removed from the source folder. The upsert-only pipeline
leaves their chunks in the vector store forever — they keep surfacing in
retrieval and the LLM cites documents that no longer exist. Switch to
UPSERTS_AND_DELETE? But what if a run sees a partial corpus (a flaky reader
returned only half the files) — would it then delete everything legitimately
missing-this-run?
约束:
UPSERTS_AND_DELETE purges any doc_id in the docstore that was not seen
in the current run. If the run's input is incomplete, that purge is
catastrophic — it deletes live content.
Therefore delete-propagation is only safe when the run input is a complete
snapshot of the source (LangChain calls this cleanup="full"); for streamed
/ partial batches you need cleanup="incremental" (per-batch, scoped by
source).
Stale chunks are a correctness bug (wrong citations); over-aggressive delete
is an availability bug (lost content). Both are bad.
决策步骤:
Confirm the run input is a full snapshot. If the reader can return a
partial set on failure, gate the delete: abort the run (don't purge) if the
seen-doc count drops more than a sane threshold vs the previous run.
For regulated corpora, prefer soft delete (deleted=true metadata +
query-time filter) over hard delete, so erasure is auditable and reversible.
结果: Purge — but only behind a "this is a complete snapshot" guard.
Idempotency for deletes is UPSERTS_AND_DELETEplus a partial-input circuit
breaker, never a blind diff-and-delete.
可提取的操作: OP-05, OP-07, OP-08.
6. 反模式与边界 (Anti-patterns & Boundaries)
Top anti-patterns (instant red flags in code review)
#
Anti-pattern
Why it's wrong
Correct move
A1
VectorStoreIndex.from_documents(docs) re-run every cron tick
No docstore → re-embeds + re-adds everything; vector store grows without bound; duplicates pollute top-k
Volatile metadata (timestamps, run id) in the hashed payload
Hash changes every run even for unchanged content → constant re-embed
Keep volatile metadata out of the hashed body
A6
DUPLICATES_ONLY treated as a re-ingest strategy
Only dedupes within one run; a second run does not skip
Use UPSERTS / UPSERTS_AND_DELETE for cross-run (OP-04)
A7
Upsert-only on a corpus whose source files get deleted
Stale chunks linger; LLM cites deleted docs
UPSERTS_AND_DELETE behind a snapshot guard (OP-05, Dilemma 2)
A8
Blind UPSERTS_AND_DELETE on a possibly-partial run input
A flaky reader returning half the files purges live content
Gate delete on "complete snapshot"; circuit-break on big drops
A9
No twice-run test in CI
First duplicate / ghost found by a user in production
OP-06 mandatory on every PR
A10
Swapping embedding model and expecting incremental upsert to "fix" old vectors
Old vectors were embedded by the old model; mixed space breaks retrieval
Full re-embed; tag index with model name+version ([[llamaindex]] A2)
Boundaries — when this skill is not the right move
B1 One-shot index, never refreshed → no second run; skip.
B2 Corpus tiny enough that full rebuild each run is measurably cheaper
than docstore maintenance → rebuild is fine; but profile before deciding.
B3 Append-only event log that never edits or deletes → UPSERTS (or even
plain add) is sufficient; delete propagation is dead weight.
B4 The "duplicate results" bug is actually a chunk-overlap or
retriever top_k issue, not a re-ingest issue → diagnose first; don't add a
docstore to a problem it doesn't solve.
PR review smells
from_documents(...) inside anything that runs on a schedule.
IngestionPipeline(...) with no docstore= keyword.
SimpleDocumentStore() and no persist( anywhere in the file.
doc.id_ = str(uuid4()) / loader without filename_as_id.
datetime.now() / time.time() written into document metadata before
ingestion.
docstore_strategy=DocstoreStrategy.DUPLICATES_ONLY on a live-sync pipeline.
A re-ingest function with no test that calls it twice.
7. 跨框架对照 (Cross-Framework Reference Table)
The same "hash → docstore → insert/update/skip" contract across the common
stacks. Compact below; full runnable code in references/R2-cross-framework.md.
Verified against current docs (May 2026).
LlamaIndex — IngestionPipeline(docstore=..., vector_store=..., docstore_strategy=DocstoreStrategy.UPSERTS_AND_DELETE). Hash is on the
document (content + doc_id); pipeline.run(documents=docs) returns only the
nodes it processed — empty list = clean no-op. Persist the docstore
(Redis/Mongo/Postgres in prod) to keep idempotency across restarts.
LangChain — index(docs, SQLRecordManager(...), vectorstore, cleanup="full", source_id_key="source"). The RecordManager is the
docstore-equivalent (a hash per source_id in a durable store). Returns
{num_added, num_updated, num_skipped, num_deleted}; on an unchanged re-run
num_skipped == len(docs) and the rest are 0. cleanup="full" propagates
deletes for a complete snapshot, "incremental" for source-scoped batches.
Manual — keep a persisted {doc_id: sha256(text)} table; per doc:
prev == h → SKIP, prev set → delete-then-add (UPDATE), else INSERT; after
the loop purge stored_keys − seen (DELETE); persist(). This is exactly
what the two frameworks automate.
7.x Side-by-side: "what happens on the second run?"
Stack
Mechanism
Unchanged re-run
Deletes propagate?
LlamaIndex IngestionPipeline + docstore
document hash in docstore
run() returns []
only with UPSERTS_AND_DELETE
LangChain index() + RecordManager
hash per source_id in SQL/record store
num_skipped == len(docs)
only with cleanup="full"/"incremental"
Manual hash table
{doc_id: sha256} table
inner loop all continue
only if you diff seen vs stored keys
from_documents / raw add (no ledger)
none
re-embeds + re-adds → duplicates
never
The bottom row is the anti-pattern (A1). Every correct row has the same shape:
a persisted hash ledger separate from the vector store, consulted before
embedding, with an explicit delete-propagation switch.
