SQLite + Embeddings: A Practical Guide to Compact RAG

Learn how to run embeddings and retrieval in SQLite for fast, low-cost RAG—practical steps, pitfalls, and a compact implementation checklist to get started today.

Using SQLite to store and serve vector embeddings is a pragmatic choice for small-to-medium retrieval-augmented generation (RAG) tasks. This guide walks through when to choose SQLite, how to design a minimal schema and data flow, selecting models and dimensions, indexing and search approaches, evaluation, and common mistakes to avoid.

• Why SQLite can be the right home for embeddings and when to choose it.
• Concrete schema and data-flow patterns for storing vectors and metadata.
• How to select embedding dimensionality, model, and storage format, plus indexing/search options and trade-offs.

Quick answer — one-paragraph summary

SQLite is a solid choice for embedding storage and lightweight vector search when datasets are modest (tens of thousands to low hundreds of thousands of vectors), latency requirements are moderate (ms–tens of ms on local SSDs), and operational simplicity matters; use compact schemas with an embeddings table, store vectors as binary blobs or arrays, use approximate search with SQLite extensions or brute-force on CPU depending on scale, and combine retrieval with concise prompts for effective RAG.

Decide when to use SQLite + embeddings

Choose SQLite when you prioritize simplicity, low operational overhead, and local-first deployment. It works well for single-user apps, prototypes, embedded devices, desktop tools, and small services where running a dedicated vector DB is overkill.

• Good fit: ≤200k vectors, mostly read-heavy workloads, single-region or local deployment, constrained infrastructure.
• Not ideal: multi-million vector corpora, high-concurrency distributed search, or strict SLAs requiring multi-replication and cross-node sharding.

Consider the dataset growth plan: SQLite is easy to start with, but design for migration (export/ingest scripts) if you expect rapid scale-up.

Design minimal schema and data flow

A compact schema keeps queries fast and maintenance simple. Focus on an embeddings table plus optional auxiliary tables for sources and chunks.

Data flow (ingest → query):

1. Chunk documents into manageable lengths (e.g., 200–500 tokens) with overlap.
2. Compute embeddings and store as binary blobs with IDs and norms.
3. When querying, compute query embedding, retrieve top-N candidate vectors, re-rank with dot-product or cosine, then fetch text chunks for RAG.

Select embeddings model, dimensionality, and storage format

Pick a model that balances semantic quality and speed. Open models (e.g., small/medium OpenAI alternatives, or local LLM provider embeddings) are often sufficient.

• Dimensionality: 256–1536 are common. 512–768 is a sweet spot for many tasks—lower dims reduce storage/cpu; higher dims often improve recall but cost more.
• Model considerations: pick based on domain alignment, licensing, and inference latency in your environment.
• Storage format: use fixed-size binary blobs (FLOAT32 little-endian) or quantized integers for space savings. Store precomputed norms for faster cosine similarity.

Example: 768-d float32 → 3,072 bytes per vector. For 100k vectors that’s ~300 MB plus overhead; quantization (e.g., 8-bit) can reduce this ~4x.

Store, index, and search vectors in SQLite

There are three practical search approaches with SQLite:

• Brute-force in Python: load vectors from SQLite and perform vectorized dot products in NumPy. Simple but memory-bound.
• SQLite extensions: use SQLite vector extensions (e.g., sqlite-icu/pgvector-like extensions) or FTS-style helpers for approximate search integrated with SQL.
• Approximate indexes stored alongside SQLite: keep an HNSW or IVF index on disk (FAISS, hnswlib) and use SQLite for metadata; store vector pointers (ids) in both.

Recommended patterns:

• Small scale (≤50k): store Float32 blobs and brute-force with vectorized NumPy on each query.
• Medium scale (50k–500k): use hnswlib or FAISS for nearest-neighbor search; store the ANN index on disk and keep metadata in SQLite.
• Use precomputed norms to convert dot products to cosine quickly: cosine(a,b) = dot(a,b) / (||a|| * ||b||).

Integrate retrieval and RAG into your app

Keep retrieval and generation responsibilities separated: retrieval returns candidate passages; the generator composes answers using those passages and the original user query.

1. Compute query embedding and retrieve top-K chunk IDs (K typically 5–20).
2. Optionally rerank using cross-encoder or similarity with the full prompt.
3. Assemble context with strict token budgeting (e.g., sort by score and sum tokens until budget reached).
4. Include provenance metadata (doc_id, url, chunk offsets) in the LLM prompt or UI for transparency.

Concrete prompt pattern: system instruction + user question + concatenated top chunks labeled with source IDs, then a final query instructing the model to answer and cite sources.

Evaluate accuracy, latency, and storage trade-offs

Measure three axes: retrieval accuracy (recall/precision on annotated queries), latency (p95, p99 in ms), and storage (DB size, index size).

• Accuracy: use a relevance-labeled dev set to compute recall@K and MRR. Compare embedding models and chunk sizes.
• Latency: profile end-to-end (embedding compute + retrieval + generation). For local CPU embeddings, embed time dominates; for remote API, network adds variability.
• Storage: track raw vector bytes and index files. Evaluate quantization and dimension reduction if storage is limiting.

Run A/B tests with different K values and reranking strategies to find the best balance of succinct context and answer quality.

Common pitfalls and how to avoid them

• Storing vectors as text floats: avoid CSV/text; use binary blobs (BLOB) to save space and speed reads.
• Neglecting norms: precompute norms to speed cosine calculations and avoid repeated sqrt calls at query time.
• Overly large chunks: chunk size >1,000 tokens can reduce retrieval precision; target 200–500 tokens with overlap.
• Not planning for backups/migration: export vector files or keep reproducible ingest scripts so you can move to a vector DB later.
• Relying solely on ANN without re-ranking: ANN top-N can include false positives—use a reranker for high-precision scenarios.
• Ignoring token budgets: failing to enforce context token limits leads to truncated prompts and unpredictable LLM responses.

Implementation checklist

• Choose embedding model and target dimensionality.
• Design tables: embeddings, chunks, documents.
• Decide vector storage: Float32 blobs or quantized format; store norms.
• Pick search approach: brute-force, ANN index, or SQLite extension.
• Implement chunking, embedding, and insert pipeline with idempotency.
• Build retrieval → rerank → prompt assembly flow with token budgeting.
• Create evaluation set and measure recall@K, latency, and storage.
• Add backups, export paths, and migration tools for future scaling.

FAQ

Q: How many vectors can SQLite realistically handle?

A: For brute-force setups, tens of thousands comfortably; with ANN indexes and careful tuning, hundreds of thousands are possible, but multimillion-scale workloads suit specialized vector DBs.

Q: Should I store vectors in separate files or inside the SQLite DB?

A: Store vectors inside SQLite as BLOBs for portability and transactional guarantees. For very large datasets and ANN tools, store vectors in the index files and keep metadata in SQLite.

Q: How do I handle updates or deletions?

A: Use soft-delete flags in the embeddings table for safe removal, and rebuild ANN indexes periodically to reclaim space and maintain index quality.

Q: Is quantization safe for semantic tasks?

A: 8-bit quantization often preserves enough semantic quality for many retrieval tasks, but validate on your dev set—some domains need higher precision.

Q: When should I migrate from SQLite to a vector DB?

A: Migrate when latency degrades under load, dataset growth exceeds operational comfort, or you need distributed replication, multi-region access, or advanced index management features.