RAG chunk overlap misconfiguration causing duplicate context windows after Kafka ingestion

Problem

The Retrieval‑Augmented Generation (RAG) service consumes Kafka topics during the CI/CD pipeline to build context windows for downstream LLM inference. Recent automated test runs reported:

• Duplicate or partially overlapping context windows in the retrieved chunks.
• Missing information in some windows because earlier chunks were overwritten.
• Increased latency and retrieval errors during model inference.

Example log excerpt from the RAG worker during a test build:

2026-10-27 14:03:12,874 INFO  rag_worker - Received 500 records from topic rag-input
2026-10-27 14:03:13,102 DEBUG rag_chunker - Chunk 0: ["The quick brown", "fox jumps over"]
2026-10-27 14:03:13,104 DEBUG rag_chunker - Chunk 1: ["fox jumps over", "the lazy dog"]
2026-10-27 14:03:13,106 DEBUG rag_chunker - Chunk 2: ["the lazy dog", "and runs away"]
2026-10-27 14:03:13,108 WARN  rag_indexer - Duplicate context window detected: ID 42
2026-10-27 14:03:13,110 ERROR rag_inference - Retrieval failed: missing required token at position 7

These symptoms appear intermittently, primarily after new Kafka partitions are added or during high‑throughput ingestion in the staging environment.

Root Cause

The RAG pipeline uses a sliding‑window chunker with two configurable parameters:

• chunk_size – number of tokens per chunk.
• chunk_overlap – number of tokens shared between consecutive chunks.

During a recent configuration change, the chunk_overlap value was set to chunk_size (i.e., 100 % overlap) instead of the intended 20 % overlap. Because the Kafka consumer processes records in batches, the chunker was invoked once per batch, and the overlapping logic unintentionally concatenated the tail of the previous batch with the head of the current batch. The resulting windows contained duplicated token sequences, causing the indexer to treat them as separate but identical entries, which manifested as duplicate context windows and retrieval errors.

Key interaction details:

Component	Interaction
Kafka Consumer	Fetches up to max.poll.records=500 per poll.
RAG Chunker	Applies chunk_overlap on the entire batch payload.
Index Builder	Creates a vector entry per chunk; duplicate vectors cause index collisions.

When chunk_overlap == chunk_size, each new chunk is identical to the previous one, leading to the duplicate‑window warnings shown above.

Debug

Step‑by‑step investigation that reproduced the issue:

1. Inspect Kafka consumer configuration:

cat config/kafka_consumer.yaml
consumer:
  bootstrap_servers: kafka-prod:9092
  group_id: rag-service
  max_poll_records: 500
  enable_auto_commit: true

2. Check the chunker settings used by the RAG service (environment variables and YAML):

cat config/rag_chunker.yaml
chunker:
  chunk_size: 512
  chunk_overlap: 512   # <-- misconfiguration
  tokenizer: bert-base-uncased

Note the overlap equals the size.

3. Reproduce with a minimal payload using kafka-console-producer:

printf "The quick brown fox jumps over the lazy dog and runs away." | \
kafka-console-producer --broker-list kafka-prod:9092 --topic rag-input

Then run the worker in debug mode and capture the generated chunks:

2026-10-27 14:12:01,210 DEBUG rag_chunker - Input tokens: 14
2026-10-27 14:12:01,211 DEBUG rag_chunker - Chunk 0: ["The quick brown", "fox jumps over"]
2026-10-27 14:12:01,212 DEBUG rag_chunker - Chunk 1: ["The quick brown", "fox jumps over"]   # duplicate

4. Validate the index for duplicate IDs:

curl -s http://rag-index:8080/v1/entries?term=fox | jq '.total'

2   # two identical vectors for the same term

The duplicate count confirmed the overlap bug.

Solution

Correct the chunk_overlap to a fraction of chunk_size (e.g., 20 %). Update the configuration file and redeploy the service.

Before

# config/rag_chunker.yaml
chunker:
  chunk_size: 512
  chunk_overlap: 512   # incorrect
  tokenizer: bert-base-uncased

After

# config/rag_chunker.yaml
chunker:
  chunk_size: 512
  chunk_overlap: 102   # 20 % of 512
  tokenizer: bert-base-uncased

Redeploy with the updated config (using Helm, Kubernetes, or your CI/CD tool of choice). Example for a Helm release:

helm upgrade rag-service ./helm/rag \
  --set chunker.chunkOverlap=102 \
  --namespace production

Why this works:

• The overlap now provides enough context for semantic continuity without recreating the same token sequence.
• Each batch processed by the Kafka consumer yields distinct, partially overlapping chunks, preventing duplicate vector entries.
• Downstream retrieval sees a single, correctly sized context window per logical segment.

Verify

Run the following validation steps after redeployment:

1. Consume a test message and inspect generated chunks:

printf "The quick brown fox jumps over the lazy dog and runs away." | \
kafka-console-producer --broker-list kafka-prod:9092 --topic rag-input

2026-10-27 14:45:03,112 DEBUG rag_chunker - Chunk 0: ["The quick brown", "fox jumps over"]
2026-10-27 14:45:03,113 DEBUG rag_chunker - Chunk 1: ["fox jumps over", "the lazy dog"]
2026-10-27 14:45:03,114 DEBUG rag_chunker - Chunk 2: ["the lazy dog", "and runs away"]

Query the index for duplicate IDs:

curl -s http://rag-index:8080/v1/entries?term=fox | jq '.total'

1   # duplicate eliminated

Run an end‑to‑end inference test in the CI pipeline:

./ci/run_inference_test.sh --prompt "What does the fox do?"

PASS: Retrieved context includes "fox jumps over the lazy dog"

If all checks pass, the issue is resolved.

Prevent

Implement safeguards to avoid recurrence:

• Configuration validation: Add a CI lint step that asserts chunk_overlap < chunk_size and optionally chunk_overlap ≤ 0.5 * chunk_size.
• Automated health check: Deploy a periodic job that ingests a known test payload and asserts that the number of generated chunks equals ceil((tokens - overlap) / (size - overlap)). Alert on mismatches.
• Metric monitoring: Export rag.chunker.duplicate_windows as a Prometheus counter. Create an alert when the rate exceeds 0.01 per minute.
• Version pinning: Keep the chunker library version locked and record the supported overlap range in the release notes.

Related Topic Hub: Data Infrastructure Troubleshooting Hub

FAQ

1. Why did the problem appear only after adding new Kafka partitions?
   New partitions changed the batch size distribution, causing the chunker to process larger payloads per poll. With 100 % overlap, each larger batch produced many duplicate windows, amplifying the symptom.
2. Can I set chunk_overlap to zero?
   Yes, but you lose contextual continuity between chunks, which can degrade retrieval relevance for long documents. A small overlap (10‑30 %) is generally recommended.
3. Is there a way to detect duplicate windows without re‑indexing?
   Enable the rag.chunker.duplicate_detection flag; the worker will log a warning and increment a Prometheus metric for each duplicate detected during ingestion.
4. How does this interact with the max.poll.records setting?
   A higher max.poll.records increases the number of tokens processed per chunking call. If chunk_overlap is mis‑scaled, the probability of full‑duplicate windows grows proportionally.
5. Should I adjust the overlap when changing chunk_size?
   Always recompute chunk_overlap after changing chunk_size. A rule of thumb is overlap = floor(0.2 * chunk_size), but validate against your document length distribution.