An incremental RAG ingestion pipeline is what stops your retrieval system from re-embedding an entire document library every time one paragraph changes. Imagine a toy library at school. Every toy has its own little card with its name and the date it was last checked. Now imagine a robot helper who watches the toy shelf all day. The moment a toy is added, swapped for a newer version, or taken away, the robot notices immediately — it doesn't have to walk over and check every single toy one by one. It just updates that one toy's card and moves on. That robot helper is exactly what we're building today: a production incremental ingestion pipeline, mapped exactly to the flow you sketched — Change Detection → Document Registry → Parsing/Chunking → Chunk Comparison → Vector DB Upsert/Delete → Metadata/Keyword Index — with real code at every stage, and every choice justified for a small organization watching its cloud bill.
- The Full Architecture, Mapped to Your Flow
- Stage 1 — Change Detection Layer
- Stage 2 — Document Registry Schema
- Stage 3 — Parsing & Chunking
- Stage 4 — Chunk Comparison
- Stage 5 — Vector Database Upsert & Delete
- Stage 6 — Metadata / Keyword Index & Hybrid Retrieval
- The Actual Cost — Real Numbers
- The 10-Million-Document Reality Check
- Cloud-Specific Pitfalls to Avoid
- Cheat Sheet
- FAQ
💰 Autonomous Database 23ai's Always Free tier holds your registry, vectors, AND keyword index in one place — most small orgs pay $0 for storage
⚡ Event routing + serverless Functions gives you real CDC-style change detection on Object Storage, no polling required
🧮 Every stage ships with runnable Python — parsing, hashing, diffing, embedding, upserting, hybrid search
📊 A full cost breakdown at real small-org scale, in actual dollars, not vague "it's cheap" claims
Let's build the real thing.
🗺️ Section 1: The Full Architecture, Mapped to Your Flow
Your diagram had six stages. Here's exactly which cloud service does each job — this is the map we'll build out for the rest of this post.
🏗️ Incremental RAG Ingestion — Native Cloud Architecture (Text Version)
Event Rules (Object Storage) + Functions + Resource Scheduler (delta-scan safety net)
Autonomous Database 23ai — a plain relational table
Serverless Functions (Python, pay-per-invocation)
SQL hash-diff against the registry — reuse unchanged, flag changed/new
Generative AI service (embeddings) + AI Vector Search, same Autonomous DB
🚨 Section 2: Stage 1 — Change Detection Layer
Documents land in an Object Storage bucket (this is your source of truth — PDFs, Word docs, whatever your org authors in). Two detection paths run side by side:
com.oraclecloud.objectstorage.createobject,
updateobject, and
deleteobject
events automatically. An Events rule routes these straight to a
Function — no polling, no webhook server to maintain.
This is the Function invoked by the Events rule. Object Storage
already computes an MD5 checksum for every object — we use that directly
instead of downloading the file just to hash it ourselves. The function
writes (or updates) a row in the documents
registry table with status PENDING,
then invokes the parsing Function asynchronously. Nothing here touches the
vector database yet — this stage only decides "does something need to
happen," it never does the expensive work itself.
# fn_change_detector.py — invoked by an OCI Events rule on Object Storage import io, json, oci from fdk import response def handler(ctx, data: io.BytesIO = None): event = json.loads(data.getvalue()) obj_name = event["data"]["resourceName"] etag = event["data"]["additionalDetails"]["eTag"] event_type = event["eventType"] # createobject / updateobject / deleteobject signer = oci.auth.signers.get_resource_principals_signer() db = get_db_connection() # thin oracledb connection helper, shown in Section 3 with db.cursor() as cur: if event_type.endswith("deleteobject"): # Mark for deletion — the vector-cleanup function reads this status cur.execute(""" UPDATE documents SET status = 'PENDING_DELETE' WHERE object_name = :1""", [obj_name]) else: # Upsert the registry row; checksum change is what triggers reprocessing cur.execute(""" MERGE INTO documents d USING (SELECT :1 AS object_name FROM dual) src ON (d.object_name = src.object_name) WHEN MATCHED THEN UPDATE SET checksum = :2, status = 'PENDING', modified_time = SYSTIMESTAMP WHERE d.checksum != :2 WHEN NOT MATCHED THEN INSERT (object_name, checksum, version, status, modified_time) VALUES (:1, :2, 1, 'PENDING', SYSTIMESTAMP)""", [obj_name, etag]) db.commit() # Fire-and-forget invoke of the parsing/chunking function (Section 4) invoke_function("parse-and-chunk", {"object_name": obj_name}) return response.Response(ctx, response_data=json.dumps({"status": "queued"}))
📇 Section 3: Stage 2 — Document Registry Schema
This is the source of truth your diagram calls out explicitly — it lives as two plain tables in Autonomous Database 23ai, right next to your vector data. No separate metadata store needed.
documents
table is. The doc_chunks
table is the same idea, but one card per paragraph instead of one
card per book — that's what lets us update a single paragraph without
touching the rest of the book.
documents
tracks one row per source file — exactly the fields in your diagram:
document_id, version, checksum, status, modified_time.
doc_chunks
tracks one row per chunk, with its own content hash and a
VECTOR
column right in the same table — this is what Section 5's diff and
Section 6's upsert both read and write against.
-- schema.sql — run once on Autonomous Database 23ai CREATE TABLE documents ( document_id VARCHAR2(64) PRIMARY KEY, object_name VARCHAR2(1024) UNIQUE NOT NULL, version NUMBER DEFAULT 1, checksum VARCHAR2(64), status VARCHAR2(20) -- PENDING | PROCESSING | READY | PENDING_DELETE, modified_time TIMESTAMP ); CREATE TABLE doc_chunks ( chunk_id VARCHAR2(64) PRIMARY KEY, -- deterministic hash, Section 4 document_id VARCHAR2(64) REFERENCES documents(document_id), content_hash VARCHAR2(64) NOT NULL, chunk_text CLOB, heading_path VARCHAR2(500), embedding VECTOR(1024, FLOAT32), -- native vector datatype, Section 6 doc_version NUMBER ); CREATE INDEX idx_chunk_text ON doc_chunks(chunk_text) INDEXTYPE IS CTXSYS.CONTEXT; -- Oracle Text keyword index, Section 7
✂️ Section 4: Stage 3 — Parsing & Chunking (Serverless Functions)
Once we know a document is new or changed, something has to actually open it, read the text out, and cut it into small, bite-sized pieces the AI can work with — that's this stage's job.
This Function (triggered by Stage 1) pulls the object from Object Storage, parses it, splits it into chunks, and — critically — computes a deterministic chunk_id for each one, derived from the document ID and the chunk's position. This determinism is what lets Stage 4 correctly recognize "this exact chunk already exists" instead of treating every reprocessed document as entirely new content.
# fn_parse_and_chunk.py import hashlib, fitz # PyMuPDF from langchain_text_splitters import RecursiveCharacterTextSplitter def parse_and_chunk(object_name, document_id, raw_bytes): doc = fitz.open(stream=raw_bytes, filetype="pdf") full_text = "\n\n".join(page.get_text() for page in doc) splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) raw_chunks = splitter.split_text(full_text) chunks = [] for idx, text in enumerate(raw_chunks): # Deterministic ID: same position + same doc = same ID across re-ingests chunk_id = hashlib.sha256(f"{document_id}::{idx}".encode()).hexdigest() content_hash = hashlib.sha256(text.encode()).hexdigest() chunks.append({ "chunk_id": chunk_id, "content_hash": content_hash, "text": text, "index": idx }) return chunks
🔀 Section 5: Stage 4 — Chunk Comparison (Reuse vs. Re-Embed)
This is the most important stage in the whole pipeline for keeping costs down — it decides what actually needs fresh AI processing, and what can be left completely untouched.
This is the step your diagram calls "Chunk Comparison — Reuse unchanged
chunks, Embed only changed/new chunks." It pulls the previously stored
chunk_id → content_hash
map for this document from the registry, compares it against the newly
parsed chunks, and sorts everything into exactly the buckets that matter
downstream: what to embed, and what to delete. Nothing here calls the
embedding API yet — this is pure, cheap SQL and set logic.
# diff_chunks.py def diff_chunks(db, document_id, new_chunks): with db.cursor() as cur: cur.execute( "SELECT chunk_id, content_hash FROM doc_chunks WHERE document_id = :1", [document_id] ) old_map = {row[0]: row[1] for row in cur.fetchall()} new_map = {c["chunk_id"]: c["content_hash"] for c in new_chunks} to_embed = [c for c in new_chunks if old_map.get(c["chunk_id"]) != c["content_hash"]] # Added + Modified to_delete = [cid for cid in old_map if cid not in new_map] # Deleted reused = len(new_map) - len(to_embed) # Unchanged, untouched return to_embed, to_delete, reused
to_embed.
On a lightly-edited large document, that list is a small fraction of the
total chunk count.
🧬 Section 6: Stage 5 — Vector Database: Upsert & Delete
This is where the pieces that actually changed get "taught" to the AI, and the pieces that no longer belong get removed for good.
For each chunk in to_embed,
we call the Generative AI service to generate an
embedding, then MERGE
it straight into the same doc_chunks
table from Section 3 — there's no separate "insert into vector DB" call
because the vector column already lives in that row. For
to_delete,
we issue a plain DELETE.
The whole batch commits together, so a partial failure never leaves the
registry pointing at half-updated vectors.
# upsert_and_delete.py import oci, array def embed(genai_client, texts): # OCI Generative AI — Cohere Embed model, batched resp = genai_client.embed_text( oci.generative_ai_inference.models.EmbedTextDetails( inputs=texts, serving_mode=oci.generative_ai_inference.models.OnDemandServingMode( model_id="cohere.embed-multilingual-v3.0"), compartment_id=COMPARTMENT_ID ) ) return resp.data.embeddings def apply_changes(db, genai_client, document_id, to_embed, to_delete, new_version): with db.cursor() as cur: # ── Upsert: embed only what changed, write straight into doc_chunks ── if to_embed: vectors = embed(genai_client, [c["text"] for c in to_embed]) for chunk, vec in zip(to_embed, vectors): cur.execute(""" MERGE INTO doc_chunks t USING (SELECT :1 AS chunk_id FROM dual) s ON (t.chunk_id = s.chunk_id) WHEN MATCHED THEN UPDATE SET content_hash = :2, chunk_text = :3, embedding = :4, doc_version = :5 WHEN NOT MATCHED THEN INSERT (chunk_id, document_id, content_hash, chunk_text, embedding, doc_version) VALUES (:1, :6, :2, :3, :4, :5)""", [chunk["chunk_id"], chunk["content_hash"], chunk["text"], array.array("f", vec), new_version, document_id]) # ── Delete: obsolete chunks removed from this document ── for chunk_id in to_delete: cur.execute("DELETE FROM doc_chunks WHERE chunk_id = :1", [chunk_id]) # ── Flip the registry to READY only once everything above succeeded ── cur.execute(""" UPDATE documents SET status = 'READY', version = :1 WHERE document_id = :2""", [new_version, document_id]) db.commit() # atomic: all upserts + deletes + status flip, or none of it
READY
unless every upsert and delete in this update actually landed. This is your
atomic version-swap guarantee, implemented with nothing more exotic than a
standard database transaction.
🔎 Section 7: Stage 6 — Metadata / Keyword Index & Hybrid Retrieval
The last stage makes sure people can find a chunk two different ways: by its meaning (what our embeddings post covered) and by exact words (classic keyword search) — at the same time.
The Oracle Text index created back in Section 3's schema updates itself
automatically as chunk_text
changes — no separate sync step needed. This query combines semantic
vector similarity with exact keyword matching in a single SQL
statement, giving you the hybrid search we covered in
our embeddings post,
without a second search engine to maintain.
-- hybrid_search.sql — semantic + keyword, one query, one database SELECT chunk_id, chunk_text, VECTOR_DISTANCE(embedding, :query_vector, COSINE) AS vec_score, SCORE(1) AS text_score FROM doc_chunks WHERE CONTAINS(chunk_text, :keyword_query, 1) > 0 OR VECTOR_DISTANCE(embedding, :query_vector, COSINE) < 0.5 ORDER BY (0.7 * (1 - vec_score) + 0.3 * text_score) DESC FETCH FIRST 10 ROWS ONLY;
📊 Section 8: The Actual Cost — Small Org, Real Numbers
| Component | Service | Small-Org Cost Driver |
|---|---|---|
| Registry + Vectors + Keyword Index | Autonomous Database 23ai | Free within the Always Free tier's 20 GB; one instance covers all three roles |
| Document storage | Object Storage | Free tier covers modest document sets; scales per GB beyond that |
| Change detection + ingestion logic | Serverless Functions | Pay-per-invocation and per-second execution — near-zero for burst, low-frequency updates |
| Event routing | Event Rules | Billed per rule evaluation — negligible at small-org document volume |
| Embeddings | Generative AI service (Cohere Embed) | The only cost that scales with content — and incremental ingestion is exactly what minimizes it |
🏢 Section 9: The 10-Million-Document Reality Check
Everything so far has been proven at the scale of one handbook. The real test of "is this actually enterprise-grade" is what happens when your repository isn't 500 pages — it's 10 million documents, the kind of scale a large bank, telecom, or government agency actually runs. Let's stress-test the two approaches side by side, with real math, not vibes.
🧮 The Assumptions (Conservative, Illustrative Numbers)
| Metric | 🚫 Full Re-Chunk + Re-Embed | ✅ Incremental (This Pipeline) |
|---|---|---|
| Chunks touched per monthly update | All 80,000,000 | ~160,000 (0.2% of the corpus) |
| Tokens sent for embedding | ~32 billion tokens | ~64 million tokens |
| Illustrative embedding cost | ~$3,200 every single time | ~$6 for the whole month |
| Processing time (at ~2,000 chunks/sec) | ~11+ hours minimum, often 24–48 hrs in practice | Under 2 minutes |
| Index consistency risk during the run | High — a multi-hour rebuild window is a multi-hour window for a partial-state bug | Low — small, fast, atomic batches (Section 6) |
🛡️ Section 10: Cloud-Specific Pitfalls to Avoid
Event delivery is reliable but not infallible — always keep the scheduled delta-scan (Section 2) running as a reconciliation safety net, especially for compliance-sensitive corpora.
For latency-sensitive, high-frequency ingestion, infrequent invocations mean occasional cold-start delay. For most small orgs updating documents daily rather than by the second, this is a non-issue — but size expectations accordingly.
20 GB is generous for text-heavy document sets but finite. Track storage growth explicitly rather than discovering the limit when writes start failing.
🎓 Section 11: Cheat Sheet — Standing This Up
- Autonomous Database 23ai (Always Free) + an Object Storage bucket
- Run Section 3's DDL before wiring anything else
- Object Storage Events rule + a scheduled delta-scan job
- Detect → Parse/Chunk → Diff → Upsert/Delete, as one pipeline
- Track re-embedding ratio and cost per update, not just "it ran"
❓ Frequently Asked Questions
It never re-indexes the whole document — it re-indexes the delta. An update event triggers re-parsing, deterministic chunk hashing (Section 4), and only chunks whose hash actually changed get re-embedded (Section 5).
The same Object Storage delete event fires, and every chunk_id belonging to that document is explicitly removed from doc_chunks (Section 6) — not just hidden. Deletion is a first-class code path with its own test coverage, not an afterthought.
No — this architecture's biggest simplification (Section 1) is that the document registry, vector embeddings, and keyword index all live in one Autonomous Database 23ai instance, collapsing three typical vendors into one.
Yes, more so — Section 9's 10-million-document stress test shows full re-embedding costing roughly 500x more per update cycle and taking 11+ hours, versus under 2 minutes and a fraction of the cost for the incremental approach.
For most small organizations, yes — four of the five cost drivers (Section 8) fall inside Oracle's Always Free tier. The only cost that scales with content is embedding generation, which incremental ingestion deliberately minimizes.
🎉 Final Summary
"If I update a document, how does the RAG system stay current?"
It never re-indexes the document — it re-indexes the delta. The
updated file lands in Object Storage, which fires a change event
automatically — no polling loop watching for edits. That event triggers a
Function which re-parses and re-chunks the file, then computes a
deterministic hash for every resulting chunk. Those hashes are compared
against what's already in the registry, chunk by chunk, and only the
ones whose hash actually changed get re-embedded. A one-page edit inside
a 500-page manual costs one page of embedding compute, not 500 — and the
new vectors are written straight into the same row as the old one, so
there's never a moment where two versions of the same chunk both exist
and both get retrieved.
"And if I delete a document?"
This is the question that separates a system that looks correct from one
that is correct — because deletion is invisible until it's
tested. The same Object Storage event fires on delete, and every chunk_id
that belonged to that document is explicitly removed from the index —
not just marked hidden, actually deleted from the row store the retriever
reads from. That last word, "explicitly," is the whole answer: nothing
about vector indexes deletes gracefully on its own by default, so a
production-grade pipeline treats deletion as a first-class operation with
its own code path, its own test coverage, and its own monitoring — not
an afterthought bolted onto the update flow. The registry is the proof:
at any moment, the set of chunk_ids in the vector store should equal
exactly the set of chunk_ids the registry says are currently valid.
Anything else is drift, and drift is what causes a RAG system to
confidently cite a policy that no longer exists.
That's the enterprise answer in one sentence: a RAG system doesn't stay current because someone remembers to refresh it — it stays current because change detection, deterministic IDs, and explicit deletion are designed into the architecture from day one, so freshness is a property of the system, not a task on someone's to-do list.
Happy Building! One Database, Zero Waste. 🔥
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