Ep4 Vector Databases and Retrieval Stores Keep Failing in Subtle Ways (FAISS, pgvector, Qdrant, Redis)

Vector Databases and Retrieval Stores Keep Failing in Subtle Ways (FAISS, pgvector, Qdrant, Redis)

This is part of the Global Fix Map series — a practical guide to debugging LLM pipelines at scale.

👉 Full index here: Global Fix Map README

Why this matters

If you’ve ever worked with vector databases like FAISS, pgvector, Qdrant, or Redis, you’ve probably seen it:

your data is in the store, ingestion looks successful, dashboards are green… but queries come back empty or off-target.

This is not just bad luck. These issues are systematic and repeatable, and they break real-world AI apps at scale.

Common failure modes in VectorDBs

Index drift — documents ingested but not searchable until a background index build finishes (FAISS, Milvus).
Metric mismatch — one side uses cosine similarity, the other defaults to L2 or dot product, silently tanking recall.
Chunk fracture — embeddings split inconsistently between ingestion and query, so alignment is lost.
Vector ghosts — deleted embeddings remain retrievable (seen in Qdrant / Redis setups).
Sharded blind spots — cross-shard queries miss slices of the data when routing isn’t aligned.

What’s really breaking under the hood

Most of these failures come down to broken contracts between ingestion and retrieval:

Index =/= query surface. Async builds leave gaps.
Metric defaults differ per library (cosine vs. L2 vs. IP).
Tokenization at ingestion != tokenization at query → chunk contracts drift.
Delete ops don’t enforce tombstones, so “ghost vectors” live on.
Sharding rules lack guardrails → partial coverage under load.

In other words: your vectorstore says “success” but the retrieval contract is silently broken.

Minimal fixes (works across FAISS, pgvector, Qdrant, Redis)

Post-ingest probes: immediately query new vectors back to confirm availability.
Metric alignment: explicitly set distance metric, don’t trust defaults.
Chunk contract enforcement: unify tokenization + window size at both ingest and query time.
Delete fences: add tombstones and verify zero recall after delete ops.
Shard probes: random test queries per shard, enforce recall coverage ratios.

Acceptance targets (for production reliability)

Ingest-to-query ΔS ≤ 0.25 across 10k+ documents.
Metric mismatch error rate ≤ 0.05.
Recall coverage ≥ 0.90 under shard load.
Ghost vector retrieval ≤ 0.5%.

How to apply this in practice

Open the Global Fix Map README.
Navigate to VectorDBs & Retrieval Stores section.
Run the minimal fix checklist above in your pipeline.
Validate against the acceptance targets with stress tests.

💡 This episode is about vector database stability.

Next episode (5): Embeddings pipeline — why normalization, casing, and chunk contracts drift more than you think.