tqdb 0.8.3

Embedded vector database using the TurboQuant algorithm (arXiv:2504.19874) — zero training, 2-4 bit compression, fast inner-product search

TurboQuantDB

An embedded vector database with a Python API. Built around the TurboQuant algorithm (arXiv:2504.19874) — two-stage quantization with zero training time and 5–10× compression at near-paper recall.

100k vectors at d=1536 fit in ~84 MB on disk (b=4) or ~47 MB (b=2) and run queries with ~200 MB RAM. No daemon, no train() step, no eval set required to start.

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Why TurboQuantDB?

• 🪶 Lightweight — pip install tqdb is a 10 MB install with no Python dependencies beyond numpy. Runs in your process; no server, no sidecar.
• 🧠 No training — codebooks are derived from a closed-form Beta(d/2) marginal at construction; vectors are quantized on the very first insert.
• 💾 5–10× disk compression with strong recall — benchmarked across d=65–3072 with recall / storage / latency trade-offs documented in docs/BENCHMARKS.md. At d=1536, TQDB reaches near-paper recall under the benchmark configuration while cutting disk ~5×.
• ⚡ Low query-time RAM — n=100k at d=200 needs ~17 MB for active search structures; d=1536 needs ~200 MB. Fits comfortably on a laptop.
• 🛡️ Crash-safe by default — writes go through a CRC-protected WAL with truncation guards; reopen replays automatically after crash or power loss. No manual flush() for normal use. (WAL writes are batched for throughput; an explicit db.checkpoint() forces durable persistence to a segment.)
• 🌍 Cross-platform pre-built wheels — Linux (x86_64 + aarch64), macOS Apple Silicon, Windows. One pip install everywhere.

Use TQDB if you're building RAG / search on a laptop, edge device, or single VM and want compression without a training pipeline.

Look elsewhere if you need managed cloud, multi-node replication, SQL joins, or a full enterprise search platform. If your corpus is tiny (<10k vectors), raw-vector stores may be simpler and the compression benefit may not matter yet.

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Install

pip install tqdb

Optional integration extras: tqdb[langchain], tqdb[llamaindex], tqdb[migrate] (Chroma + LanceDB import). Build from source: see DEVELOPMENT.md. Upgrading from v0.7: see docs/WHAT_S_NEW_0_8.md.

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Quick Start

import numpy as np
from sentence_transformers import SentenceTransformer
from tqdb import Database

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
dim = model.get_sentence_embedding_dimension()  # 384

db = Database.open("./my_db", dimension=dim, bits=4, metric="ip", rerank=True)

docs = [
    ("rust",   "Rust uses ownership and borrowing for memory safety."),
    ("python", "Python prioritizes readability and rapid prototyping."),
    ("vector", "A vector database stores embeddings for nearest-neighbour search."),
]
ids   = [d[0] for d in docs]
texts = [d[1] for d in docs]
db.insert_batch(ids, model.encode(texts, normalize_embeddings=True).astype("f4"), documents=texts)

q = model.encode("How do I avoid memory bugs?", normalize_embeddings=True).astype("f4")
for r in db.search(q, top_k=2):
    print(f"  [{r['score']:.3f}] {r['id']} — {r['document']}")

Output:

  [0.687] rust   — Rust uses ownership and borrowing for memory safety.
  [0.298] vector — A vector database stores embeddings for nearest-neighbour search.

➡️ Runnable end-to-end demo: examples/quickstart.py. RAG retriever loop: examples/rag.py. Migrate from Chroma: examples/migrate_from_chroma.py.

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What makes TurboQuantDB different?

TurboQuantDB is built around a few deliberate design choices:

• Compression-first storage — embeddings are quantized on insert, so large corpora can fit on laptops, edge devices, and small VMs.
• Zero-training quantization — no PQ/IVF training phase, no sample corpus, no eval set required to start.
• Embedded-first deployment — the default path is pip install tqdb and in-process Python usage, not operating a separate service.
• RAG-ready retrieval — document storage, MongoDB-style metadata filters, hybrid BM25+dense search, and LangChain/LlamaIndex integrations are built in.
• Durability without ceremony — writes go through a CRC-protected WAL, and crash recovery replays automatically on reopen.
• Server-capable when needed — an optional Axum HTTP server adds API keys, RBAC, quotas, async jobs, snapshots, restore, and Prometheus metrics.

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Where TurboQuantDB fits

TurboQuantDB is not a managed vector database and not a distributed search cluster. It is built for developers who want compressed local vector search inside a Python or Rust application.

Use it for

• local / private RAG
• laptop-scale document search
• edge deployments
• compressed embedding stores
• bring-your-own-embedding workflows
• migration experiments from existing local vector stores

Use something else when you need

• multi-node clustering or replication
• managed cloud operations
• SQL joins and relational transactions
• enterprise search pipelines
• hosted embedding / reranking services

---

Benchmarks

All numbers below come from runs on a single Windows laptop; absolute values will differ on your hardware. Reproduction commands are in docs/BENCHMARKS.md. The three sub-tables below are distinct runs with different configs — read the "Config" line under each header carefully.

A. Paper-validation (n=100k, brute-force, fast_mode=True)

Config: dbpedia-1536, b=4, rerank=True, brute-force, quantizer_type=None (dense). Matches arXiv:2504.19874 Figure 5b's bit allocation.

Metric	Value
Recall@1	99.7%
Recall@4	100.0%
Disk (incl. INT8 rerank vectors)	230.4 MB
Disk (codes only, rerank=False)	83.6 MB
p50 latency (3-iter median)	12.8 ms

B. Rerank unlocks recall at low bit-rate (n=10k, brute-force, fast_mode=True)

Config: quantizer_type=None, brute-force, fast_mode=True. bits=2 + rerank=True matches bits=4 + rerank=True recall at ~10% less disk.

Dataset	b=2, no rerank	b=4, no rerank	b=2 + rerank	b=4 + rerank
GloVe-200 (d=200)	0.528 (1.8 MB)	0.822 (2.3 MB)	0.992 (3.8 MB)	0.992 (4.2 MB)
arXiv-768 (d=768)	0.426 (7.4 MB)	0.696 (9.2 MB)	0.978 (14.7 MB)	0.978 (16.6 MB)
GIST-960 (d=960)	0.294 (10.4 MB)	0.566 (12.7 MB)	0.974 (19.6 MB)	0.974 (21.9 MB)

C. Coverage across dimensions (n=10k, b=4, rerank=True, brute-force, fast_mode=True)

R@1 ≥ 0.87 across 9 benchmark datasets spanning d=65 to d=3072.

Dataset	d	R@1	Disk	p50
lastfm-64	65	0.874	2.0 MB	1.1 ms
deep-96	96	0.980	2.5 MB	1.2 ms
glove-100	100	0.990	2.6 MB	1.4 ms
glove-200	200	0.992	4.2 MB	1.7 ms
nytimes-256	256	0.992	5.2 MB	2.0 ms
arXiv-768	768	0.978	16.6 MB	7.6 ms
GIST-960	960	0.974	21.9 MB	7.3 ms
DBpedia-1536	1536	0.998	41.1 MB	10.3 ms
DBpedia-3072	3072	1.000	117.0 MB	46.8 ms

Full tables (all 8 configs × 3 datasets) including ANN runs: docs/BENCHMARKS.md.

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Config Advisor

Not sure whether to use b=2 or b=4, rerank, ANN, or fast mode? The interactive Config Advisor recommends settings from benchmark data for your embedding dimension and retrieval priorities, with adjustable weights for recall, compression, and speed.

👉 jyunming.github.io/TurboQuantDB/advisor.html

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Migrate from Chroma or LanceDB

Already have a local vector store? TQDB can import an existing collection into a compressed TurboQuantDB database in one command — IDs, vectors, metadata, and document text are preserved.

pip install 'tqdb[migrate]'
python -m tqdb.migrate chroma   ./chroma_db ./tqdb_db
python -m tqdb.migrate lancedb  ./lancedb   ./tqdb_db --table docs

Programmatic API + verification example: examples/migrate_from_chroma.py. Full migration guide: docs/MIGRATION.md.

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Hybrid retrieval

Dense vectors are good at semantic similarity, but RAG queries often include exact terms: paper IDs, product names, function names, error messages, or code symbols. TQDB maintains a BM25 keyword index from the document field and can fuse sparse + dense results with Reciprocal Rank Fusion.

results = db.search(
    query_vec,
    top_k=10,
    hybrid={"text": "error message WAL replay", "weight": 0.3, "rrf_k": 60},
)

Omit hybrid= for pure dense search — behaviour is unchanged. The BM25 index builds incrementally as documents are inserted; no separate train() or build_text_index() call required.

---

Framework integrations

pip install 'tqdb[langchain]'
pip install 'tqdb[llamaindex]'

TQDB ships native vector-store classes for LangChain v2 and LlamaIndex; both expose the same TurboQuantVectorStore class name in their respective namespaces. Use these for new RAG applications.

# LangChain v2
from tqdb.vectorstore import TurboQuantVectorStore as LCStore
store = LCStore.from_texts(texts, embedding=my_embedder, path="./db", dimension=384)

# LlamaIndex
from tqdb.llama_index import TurboQuantVectorStore as LIStore
vstore = LIStore.open("./db", dimension=1536)

For simple scripts and backward compatibility, the older tqdb.rag.TurboQuantRetriever wrapper remains available.

Detailed setup, pagination, hybrid wiring, and async patterns: LangChain integration | LlamaIndex integration.

---

Async API

For FastAPI / Starlette / async RAG services, AsyncDatabase exposes awaitable versions of every long-running operation. The Rust extension releases the GIL inside, so concurrent await db.search(...) calls run in real parallel.

import asyncio
from tqdb.aio import AsyncDatabase

async def main():
    db = await AsyncDatabase.open("./db", dimension=1536, bits=4)
    await db.insert("doc-1", vec, document="...")
    hits = await db.search(query_vec, top_k=5)
    await db.close()

asyncio.run(main())

Pass executor= to share a thread pool across multiple databases or to control its size.

---

Configurations for common goals

rerank=True stores raw INT8 vectors alongside compressed codes for exact second-pass rescoring. fast_mode=True (default) uses MSE-only quantization — optimal for d < 1536.

from tqdb import Database

# Best recall, any dimension — brute-force, default INT8 rerank
db = Database.open("./db", dimension=384, bits=4, rerank=True)
# DBpedia-1536 benchmark: R@1 ≈ 0.997 | ~231 MB disk
# arXiv-768 benchmark:    R@1 ≈ 0.98  | ~116 MB disk
# GloVe-200 benchmark:    R@1 ≈ 1.00  |  ~30 MB disk

# Compression-first rerank — same recall ceiling at ~31% less disk (b=4 only)
db = Database.open("./db", dimension=1536, bits=4,
                   rerank=True, rerank_precision="residual_int4")
# DBpedia-1536 benchmark: R@1 ≈ 0.985 (vs 0.995 int8)  |  ~158 MB disk (vs 230 MB int8)
# Note: at b=2 the residual is larger; int8 still preferred for compression-first b=2 setups.

# Best recall, high-d (d ≥ 1536) — also enable QJL residuals
db = Database.open("./db", dimension=1536, bits=4, rerank=True, fast_mode=False)

# Minimum disk — MSE codes only (no rerank file at all)
db = Database.open("./db", dimension=384, bits=4)

# Low latency at N ≥ 100k — HNSW index
db = Database.open("./db", dimension=384, bits=4, rerank=True)
db.create_index()
results = db.search(query, top_k=10, _use_ann=True)       # benchmarked p50 < 10 ms at d≥1536

# Tune rerank oversampling at query time (default 10×)
results = db.search(query, top_k=10, rerank_factor=20)    # higher recall, higher latency

Full configuration guide: docs/CONFIGURATION.md.

Rerank precision picker (rerank_precision=)

Value	Disk per vector at d=1536	Recall vs int8 (b=4)	When to pick
"int8" (default)	1540 B	baseline (R@1 ≈ 0.995)	Best recall; pick when disk isn't the bottleneck
"residual_int4"	772 B	−0 to −1pp at b=4	Compression-first: same effective recall at half the disk
"f16"	3076 B	matches int8	Higher precision needed for non-normalized vectors
"f32"	6144 B	exact	Debugging or when storage is free
"int4"	772 B	strictly worse than rerank=False	Deprecated — kept for backward compat with existing dbs only

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Server Mode

For team deployments, the optional Axum server adds REST access, API-key auth, RBAC, quotas, async index/compaction/snapshot jobs, snapshot/restore, and Prometheus metrics. The binary is bundled in the tqdb wheel — no extra install on Linux x86-64, macOS, or Windows.

pip install tqdb
tqdb-server                            # listens on 127.0.0.1:8080

In the default local setup, the server can bootstrap an auth_store.json with a development API key (dev-key) under tenant dev. Replace it before production use. Three minimal curl examples — create a collection, insert vectors, query:

AUTH='Authorization: ApiKey dev-key'

# 1. Create a 3-dim collection (dimension is fixed at creation; production uses 384/768/1536)
curl -X POST http://127.0.0.1:8080/v1/tenants/dev/databases/main/collections \
  -H "$AUTH" -H 'Content-Type: application/json' \
  -d '{"name": "docs", "dimension": 3, "bits": 4}'

# 2. Insert two vectors (length must equal the collection dimension)
curl -X POST http://127.0.0.1:8080/v1/tenants/dev/databases/main/collections/docs/add \
  -H "$AUTH" -H 'Content-Type: application/json' \
  -d '{
    "ids": ["doc-1", "doc-2"],
    "embeddings": [[0.10, 0.20, 0.30], [0.40, 0.50, 0.60]],
    "metadatas": [{"source": "faq"}, {"source": "blog"}],
    "documents": ["FAQ entry", "Blog post"]
  }'

# 3. Query for the top 5 nearest neighbours
curl -X POST http://127.0.0.1:8080/v1/tenants/dev/databases/main/collections/docs/query \
  -H "$AUTH" -H 'Content-Type: application/json' \
  -d '{"query_embeddings": [[0.10, 0.20, 0.30]], "n_results": 5}'

Full endpoint reference, environment variables, and the Server Recovery Runbook: docs/SERVER_API.md.

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Advanced features

• Two quantizer modes — dense (default, best recall) and srht (faster ingest at high d). See docs/QUANTIZER_MODES.md.
• Optional ANN index — HNSW graph for low-latency search at n ≥ 100k; auto-fallback to brute-force when N is small.
• IVF coarse routing — db.create_coarse_index(n_clusters=256) + nprobe=N to score ~6% of the corpus at very large N.
• MongoDB-style metadata filters — $eq $ne $gt $gte $lt $lte $in $nin $exists $and $or $contains; $in / $nin / $or use O(1) indexed fast-paths.
• Per-query rerank tuning — rerank_factor= exchanges recall and latency at query time, no rebuild required.

Preview: Multi-vector / ColBERT-style retrieval

MultiVectorStore lets each document hold N token vectors and scores queries with MaxSim (Σ_i max_j <q_i, d_j>), useful for late-interaction retrieval experiments.

This is currently a Python-layer wrapper over the single-vector engine; native engine-level support is planned for a future v0.9 release. The public API is designed to stay stable across that move. See docs/MULTI_VECTOR.md.

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Python API at a glance

db = Database.open("./db", dimension=1536, bits=4, metric="ip", rerank=True)
db.insert("id", vector, metadata={"source": "docs"}, document="...")
hits = db.search(query, top_k=10, filter={"source": "docs"})

Supported operations:

• insert / insert_batch / upsert / update / update_metadata
• delete / delete_batch / get / get_many / list_all / list_ids / count / stats
• search (brute / _use_ann=True / nprobe=N / hybrid={...}) and batched query
• create_index (HNSW), create_coarse_index (IVF)
• checkpoint (WAL flush + segment compaction)
• container protocol: len(db) / "id" in db

Full reference with every parameter and shape: docs/PYTHON_API.md.

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Dataset Recovery (WAL)

TurboQuantDB replays wal.log automatically on reopen. For a local crash/power-loss recovery:

1. Stop all writers to the DB directory.
2. Make a copy of the DB folder (manifest.json, live_codes.bin, live_ids.bin, wal.log, etc.).
3. Reopen the DB normally:

   db = Database.open("./my_db")
   

4. Validate state:
   • db.stats()["vector_count"]
   • sample db.get(...) / db.search(...)
5. Persist a clean post-recovery state:

   db.checkpoint()   # flush WAL + compact
   db.close()
   

If files are corrupted beyond WAL replay, restore from a snapshot/backup copy (server mode also supports snapshot/restore jobs; see docs/SERVER_API.md).

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Troubleshooting

Database.open requires dimension — how do I find mine? Embed one document and read the array shape:

vec = model.encode("hello")            # sentence-transformers
print(vec.shape)                       # (384,) → dimension=384
# Or: model.get_sentence_embedding_dimension()

For OpenAI text-embedding-3-small it's 1536; text-embedding-3-large is 3072. The dimension is fixed for the lifetime of the DB — it's persisted in manifest.json.

ImportError: DLL load failed / symbol not found on macOS Apple Silicon You likely have an Intel-built wheel installed. Reinstall with the right architecture:

pip uninstall tqdb && pip install --no-cache-dir tqdb

If you still see the error, check python -c "import platform; print(platform.machine())" — should report arm64 on Apple Silicon.

WAL replay is slow on reopen for a large DB Replay is O(uncheckpointed-writes). Run db.checkpoint() before close to flush the WAL into a segment so subsequent reopens skip the replay. Schedule this after big batch ingests.

Search returns scores near 0 or in unexpected ranges Two common causes:

1. Forgot to L2-normalize embeddings before insert — for metric="ip" (default), most embedding models expect normalized inputs to make IP scores meaningful (<a, b> = cos(a, b) only for unit vectors). Use model.encode(..., normalize_embeddings=True) or normalize manually.
2. Mixed metric= between insert and query — the metric is fixed at Database.open time and cannot be changed without rebuilding.

Multi-query batch returned wrong scores under metric="cosine" (pre-v0.8.3) Fixed in v0.8.3 — score_batch_brute was applying doc_norm on the cosine path. Upgrade to tqdb >= 0.8.3 or pass single queries through db.search(...) instead of db.query(...).

For more, see the closed GitHub issues and docs/CONFIGURATION.md.

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Research Basis

This is an independent implementation of ideas from the TurboQuant paper. The algorithm itself was authored by the original researchers.

Zandieh, A., Daliri, M., Hadian, M., & Mirrokni, V. (2025). TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate. arXiv:2504.19874

@article{zandieh2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate},
  author={Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  journal={arXiv preprint arXiv:2504.19874},
  year={2025}
}

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License

Apache License 2.0 — see LICENSE.