tqdb 0.8.2

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 that achieves near-optimal vector compression with zero training time.

Goal: make massive embedding datasets practical on lightweight hardware. A 100k-vector, 1536-dim collection that would occupy 586 MB as raw float32 fits in 108 MB on disk with TQDB b=4, or just 59 MB with b=2 — enabling laptop-scale RAG over millions of documents without a dedicated server.

Two deployment modes:

• Embedded — tqdb Python package (pip install tqdb), runs in-process (no daemon)
• Server — Axum HTTP service in server/, with multi-tenancy, RBAC, quotas, and async jobs

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Key Properties

• Zero training — No train() step. Vectors are quantized and stored immediately on insert.
• 5–10× compression — b=4 reduces 1536-dim float32 embeddings from 586 MB to 108 MB (5.4×); b=2 reaches 59 MB (9.9×) at 100k vectors.
• Two quantizer modes — default (dense, best recall) and a faster ingest variant (srht) for streaming/high-d workloads. See docs/QUANTIZER_MODES.md for a full breakdown.
• Optional ANN index — Build an HNSW graph after loading data for fast approximate search.
• Hybrid retrieval — Built-in BM25 keyword index fuses with dense search via RRF (db.search(..., hybrid={"text": "..."})). Pure-dense behaviour is unchanged when the kwarg is omitted.
• Multi-vector / ColBERT — MultiVectorStore for late-interaction retrieval with N token vectors per document and MaxSim scoring (Σ_i max_j <q_i, d_j>). See docs/MULTI_VECTOR.md.
• Framework integrations — native VectorStore for LangChain v2 and LlamaIndex, plus an asyncio-friendly AsyncDatabase.
• Drop-in migration — python -m tqdb.migrate {chroma|lancedb} <src> <dst> imports an existing collection in one command. See docs/MIGRATION.md.
• Metadata filtering — MongoDB-style filter operators on any metadata field.
• Crash recovery — Write-ahead log (WAL) ensures durability without explicit flushing.
• Python native — pip install tqdb; no server or sidecar required.

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Installation

pip install tqdb                       # core engine + Python API

# Optional integrations (install only what you need):
pip install 'tqdb[langchain]'          # LangChain v2 VectorStore
pip install 'tqdb[llamaindex]'         # LlamaIndex BasePydanticVectorStore
pip install 'tqdb[migrate]'            # Chroma + LanceDB import toolkit
pip install 'tqdb[migrate-chroma]'     # Chroma migrator only
pip install 'tqdb[migrate-lancedb]'    # LanceDB migrator only

Building from source (Rust toolchain required): see DEVELOPMENT.md. Upgrading from v0.7? See docs/WHAT_S_NEW_0_8.md.

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Config Advisor

The interactive Config Advisor selects the best configuration for your embedding dimension and use case (RAG, search-at-scale, edge deployment, etc.), scored against real benchmark data with adjustable priority weights for recall, compression, and speed.

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Recommended Setup

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
db = Database.open(path, dimension=DIM, bits=4, rerank=True)   # INT8 rerank storage
results = db.search(query, top_k=10)
# GloVe-200 (d=200):     R@1 ≈ 1.00  |  ~30 MB disk
# arXiv-768 (d=768):     R@1 ≈ 0.98  |  ~116 MB disk
# DBpedia-1536 (d=1536): R@1 ≈ 0.95  |  ~231 MB disk

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

# Minimum disk — MSE codes only (library default, no extra vector storage)
db = Database.open(path, dimension=DIM, bits=4)

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

# 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 | Python API: docs/PYTHON_API.md

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Quick Start

import numpy as np
from tqdb import Database

db = Database.open("./my_db", dimension=1536, bits=4, metric="ip", rerank=True)

db.insert("doc-1", np.random.randn(1536).astype("f4"), metadata={"topic": "ml"}, document="Machine learning intro")
db.insert("doc-2", np.random.randn(1536).astype("f4"), metadata={"topic": "systems"}, document="Rust memory model")

results = db.search(np.random.randn(1536).astype("f4"), top_k=5)
for r in results:
    print(r["id"], r["score"], r["document"])

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Python API

Full reference: docs/PYTHON_API.md

# Open / create
db = Database.open(path, dimension, bits=4, seed=42, metric="ip",
                   rerank=True, fast_mode=False, rerank_precision=None,
                   collection=None, wal_flush_threshold=None,
                   quantizer_type=None)  # None/"dense" = default (Haar QR + Gaussian); "srht" = fast O(d log d) ingest
# NOTE: rerank=True with rerank_precision=None uses per-vector-scaled INT8 reranking (default),
#       which is approximate. Use rerank_precision="f16" or "f32" for higher-precision rescoring.
#       rerank_factor (default 10× brute / 20× ANN) controls oversampling.

# Write
db.insert(id, vector, metadata=None, document=None)
db.insert_batch(ids, vectors, metadatas=None, documents=None, mode="insert")  # "insert"|"upsert"|"update"
db.upsert(id, vector, metadata=None, document=None)
db.update(id, vector, metadata=None, document=None)        # RuntimeError if not found
db.update_metadata(id, metadata=None, document=None)       # RuntimeError if not found

# Delete & retrieve
db.delete(id)                        # → bool
db.delete_batch(ids)                 # → int (count deleted)
db.get(id)                           # → {id, metadata, document} | None
db.get_many(ids)                     # → list[dict | None]
db.list_all()                        # → list[str]
db.list_ids(where_filter=None, limit=None, offset=0)       # paginated
db.count(filter=None)                # → int
db.stats()                           # → dict
len(db) / "id" in db                 # container protocol

# Search — brute-force by default; pass _use_ann=True to use HNSW index
results = db.search(query, top_k=10, filter=None, _use_ann=False,
                    ann_search_list_size=None, rerank_factor=None, include=None,
                    nprobe=None,        # nprobe=N activates IVF routing (see create_coarse_index)
                    hybrid=None)        # hybrid={"text": "...", "weight": 0.5} = sparse+dense via RRF
# include: list of "id"|"score"|"metadata"|"document" (default all)
# ann_search_list_size: HNSW ef_search override (only used when _use_ann=True)
# rerank_factor: candidate oversampling multiplier (default 10 brute / 20 ANN)

# Hybrid (sparse BM25 + dense) — recovers keyword/exact-match queries dense alone misses
results = db.search(query, top_k=10,
                    hybrid={"text": "user query string", "weight": 0.3, "rrf_k": 60})

all_results = db.query(query_embeddings, n_results=10, where_filter=None,
                       rerank_factor=None, include=None,
                       hybrid=None)  # also accepts hybrid={"texts": [str], ...} for per-row text
# query_embeddings: np.ndarray (N, D) — returns list[list[dict]]

# Manual maintenance checkpoint (WAL flush + segment compaction)
db.checkpoint()

# Index
db.create_index(max_degree=32, ef_construction=200, n_refinements=5,
                search_list_size=128, alpha=1.2)

# IVF coarse routing (fast approximate search at large N)
db.create_coarse_index(n_clusters=256)          # build once after loading data
results = db.search(query, top_k=10, nprobe=16) # score ~6% of corpus

# Metadata filter operators — $in/$nin/$or use index fast paths (O(1) per field)
# $eq $ne $gt $gte $lt $lte $in $nin $exists $and $or $contains
db.search(query, top_k=5, filter={"year": {"$gte": 2023}})
db.search(query, top_k=5, filter={"$and": [{"topic": "ml"}, {"year": {"$gte": 2023}}]})
db.search(query, top_k=5, filter={"topic": {"$in": ["ml", "systems"]}})    # O(1) indexed

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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Benchmarks

Three datasets, 100k vectors each, matching arXiv:2504.19874 Figure 5. Benchmark config: quantizer_type=None (dense), fast_mode=True, rerank=True (MSE-only, matching paper Figure 5 bit allocation).

Key results at 100k × d=1536 (DBpedia), brute-force, b=4, rerank=True:

Metric	Value
Recall@1	92.2%
Recall@4	99.9%
Disk	108 MB (5.4× compression)
p50 latency	~51ms

Full tables (all 8 configs × 3 datasets), ANN guidance, and reproduction steps: docs/BENCHMARKS.md

Rerank unlocks recall at any bit depth

bits=2, rerank=True matches bits=4, rerank=True recall while using ~10% less disk, and outperforms bits=4, rerank=False at lower disk cost. (bit_sweep, n=10k, brute-force, fast_mode=True)

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)

Coverage across d=65–3072

R@1 ≥ 0.87 across all 9 benchmark datasets at b=4, rerank=True, brute-force, fast_mode=True, n=10k:

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

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RAG Integration

from tqdb.rag import TurboQuantRetriever

retriever = TurboQuantRetriever(db_path="./rag_db", dimension=1536, bits=4)
retriever.add_texts(texts=texts, embeddings=embeddings, metadatas=metadatas)

results = retriever.similarity_search(query_embedding=query_vec, k=5)
for r in results:
    print(r["score"], r["text"])

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Server Mode

An optional Axum HTTP server in server/ adds multi-tenancy, RBAC, and async jobs. See docs/SERVER_API.md for setup, launch, and the full API reference.

For disaster recovery beyond local WAL replay, see Server Recovery Runbook (Snapshot/Restore) in docs/SERVER_API.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.