Case Study · Ingestion Pipeline

Knowledge Base Builder

A dedicated ingestion service that takes PDF/URL/markdown sources, normalizes and versions content, then triggers automatic re-indexing schedules.

ProblemKnowledge goes stale and indexing quality varies by source type.

SolutionUnified ingestion contract with versioned indexing pipeline.

ImpactReliable freshness and reproducible retrieval behavior.

Architecture

Pipeline Components

Designed as an ingestion control plane: source adapters, canonical normalization, version graphing, and policy-driven reindex scheduling.

source registry -> connector fetch -> normalize + redact -> chunk + checksum
      |                |                    |                 |
      v                v                    v                 v
change detector   parse contracts     metadata enrich      version graph
      |                                                        |
      v                                                        v
 reindex planner -> queue worker -> index upsert -> freshness monitor

Why this matters: ingestion runs become reproducible and rollback-safe while minimizing unnecessary re-embedding cost.

Connector + Validation Layer

Fetches PDF/URL/markdown sources with source-type contracts, parse checks, and failure taxonomy.

Versioned Content Pipeline

Normalized chunks are fingerprinted and mapped into immutable version snapshots for traceable updates.

Incremental Reindex Engine

Only changed documents enqueue indexing work, with retry/backoff, idempotency, and freshness windows.

Evidence

Operational Reliability

Freshness Tracking

Source update timestamps compared against active index version.

Failure Recovery

Failed jobs isolated per source and retried with backoff and error categorization.

Delivery Notes

Queue WorkerVersion TableScheduled JobsIndex Health Checks

Presentation path: projects/knowledge-base-builder/presentations/upcoming/

Technical Peek

Deterministic Ingestion Control Loop

def run_ingestion_cycle(env: IngestionEnv) -> IngestionRun:
    fetched = connector_hub.fetch_all(env.sources)
    normalized = [normalize_source(item, policy=env.policy) for item in fetched]
    planned = planner.compute_delta(normalized, latest_version=versions.latest(env.pipeline_id))

    if not planned.changed_sources:
        return build_noop_run(env, planned)

    chunks = chunk_builder.build(planned.changed_sources, chunk_policy=env.policy.chunking)
    run_version = versions.create_run_version(env.pipeline_id, chunks=chunks)

    queue.enqueue(IndexTask(version_id=run_version.version_id, chunks=chunks))
    worker.process_pending(indexer=indexer, retries=env.policy.retry_budget)

    freshness.record(run_version.version_id, changed=len(planned.changed_sources))
    scheduler.ensure_job(env.pipeline_id, cron=env.policy.reindex_cron)

    return finalize_run(env, run_version, planned)

Why this matters: version-bound indexing creates reproducible rollback points and prevents stale or partially ingested data from corrupting retrieval quality.

Advanced Breakdown

Most Important Engineering Decisions

1. Source Fingerprinting + Deduplication

All incoming sources are normalized and checksummed before processing so unchanged documents are skipped and duplicate payloads do not trigger redundant embedding cycles.

Why this matters: ingestion cost and latency stay predictable as content grows.

Efficiency

2. Immutable Version Graph

Each ingestion run creates version snapshots tied to source fingerprints, enabling exact rollback and side-by-side comparison between historical index states.

Why this matters: retrieval regressions can be traced to specific content changes.

Reproducibility

3. Source-Type Normalization Contracts

PDF, URL, and markdown adapters map to one canonical chunk schema with consistent metadata fields for source, section, and timestamp.

Why this matters: downstream retrieval quality remains stable across mixed inputs.

Data Quality

4. Incremental Reindex Scheduler

Scheduled runs target only changed or stale segments, avoiding full re-embedding when unnecessary while preserving freshness SLOs.

Why this matters: system scale improves without sacrificing update cadence.

Scalability

5. Failure Taxonomy + Replay Workflow

Parse, network, and index failures are categorized with replay-safe job IDs so operators can recover specific steps instead of rerunning full pipelines.

Why this matters: incident resolution becomes faster and less disruptive.

Operations