sqlite-hybrid-search

Ultra-fast, in-process hybrid search & agent memory engine in C++17 with Python bindings.

License: MIT C++17 Python PyPI

Drop it inside an app to give a local LLM two things it lacks: knowledge of your private data (RAG) and memory that survives across sessions — with no vector database to run and no cloud. SQLite is the source of truth; the usearch HNSW graph is persisted to a .usearch sidecar and memory-loaded on open, so startup is instant regardless of corpus size.

Why this exists
Embedded / zero-service One process, one SQLite file plus a .usearch sidecar. No Docker, no daemon, no port. A query is a function call — sub-millisecond for dense search (see benchmarks).
Instant startup The vector index is loaded from disk, not rebuilt — ~40 ms to open a 100k-chunk store, versus ~15 s to reconstruct it from SQLite.
Hybrid retrieval Dense vector search (usearch, cosine HNSW) + sparse keyword search (SQLite FTS5 / BM25), fused by Reciprocal Rank Fusion (RRF, k=60). Rank-based fusion — no score normalisation, no per-query tuning.
Agent memory Exponential recency decay — score × e^(−λ·age_days) — so a fresher, slightly-less-similar memory can outrank a stale one. λ = 0 is an exact no-op.
Explainable search_explained() returns the full per-result score trail: dense distance & rank, BM25 score & rank, fused score, recency factor, decayed score.
Bring your own embeddings The core takes caller-supplied vectors and computes nothing. An optional built-in embedder (ONNX Runtime, e.g. all-MiniLM-L6-v2) is layered on top for a text-in path.

Install

pip install sqlite-hybrid-search

Prebuilt wheels cover Linux x86-64 and macOS (Apple Silicon), CPython 3.9–3.13. On any other platform pip builds from the sdist — that needs the toolchain in From source below.

From source (other platforms / development)

You need a C++17 toolchain, CMake ≥ 3.24, Python ≥ 3.9 with venv, and SQLite (with FTS5 — the default on mainstream builds). usearch and GoogleTest are fetched automatically.

git clone https://github.com/ashray-00/sqlite-hybrid-search
cd sqlite-hybrid-search
python3 -m venv .venv && .venv/bin/pip install -e .

Dependencies:

macOS (Homebrew) Debian / Ubuntu Fedora / RHEL Arch
Toolchain + CMake xcode-select --install
brew install cmake		 sudo apt install build-essential cmake python3-venv sudo dnf install gcc-c++ cmake python3-devel sudo pacman -S base-devel cmake
SQLite brew install sqlite sudo apt install libsqlite3-dev sudo dnf install sqlite-devel sudo pacman -S sqlite
  • macOS only: Homebrew's sqlite is keg-only, so configure the C++ build with -DCMAKE_PREFIX_PATH=/opt/homebrew (the CMake project also autodetects it via brew --prefix). On Linux the system SQLite is found with no extra flags.
  • Ubuntu 22.04 ships CMake 3.22; either .venv/bin/pip install "cmake>=3.24" or add the Kitware APT repo.

Optional built-in ONNX embedder. Without ONNX Runtime the engine still builds and every caller-supplied-vector path works unchanged; only load_embedding_model() / add_text() / search_text() are unavailable.

Install ONNX Runtime
macOS brew install onnxruntime
Linux Download a release from microsoft/onnxruntime (onnxruntime-linux-x64-*.tgz), then sudo cp -r onnxruntime-linux-x64-*/include/* /usr/local/include/ and sudo cp -rP onnxruntime-linux-x64-*/lib/* /usr/local/lib/ && sudo ldconfig. Or point CMake at it directly: -DONNXRUNTIME_INCLUDE_DIR=<dir> -DONNXRUNTIME_LIBRARY=<dir>/libonnxruntime.so.

Quickstart (Python)

import sqlite_hybrid_search

engine = sqlite_hybrid_search.Engine("memory.sqlite3", dim=3)

# Ingest documents with caller-supplied embeddings (one vector per document).
engine.add(
    documents=[
        {"id": "home", "text": "I live in Munich, Germany."},
        {"id": "pet",  "text": "My cat is named Pixel."},
    ],
    embeddings=[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
)

# Hybrid search: dense vector + BM25 keyword, RRF-fused.
hits = engine.search_hybrid("Where do I live?", [1.0, 0.0, 0.0], top_k=2)
print(hits[0]["document_id"], hits[0]["score"])          # -> home 0.0328

# Memory retrieval: same, discounted by recency (decay_lambda=0 disables it).
recall = engine.search_memory("Where do I live?", [1.0, 0.0, 0.0], top_k=2, decay_lambda=0.1)

# Full score breakdown for one result.
trail = engine.search_explained("Where do I live?", [1.0, 0.0, 0.0], top_k=1)[0]
print(trail["dense_rank"], trail["sparse_rank"], trail["fused_score"], trail["recency_factor"])

Per-chunk timestamps (so recency decay can actually reorder results) are set through the native sqlite_hybrid_search._sqlite_hybrid_search_ext types — see tests/test_memory_search.py.

Quickstart (CLI)

The hybrid-search console script chunks a folder of .txt files into an index in the current directory. It uses a deterministic hashing stand-in for embeddings unless you pass --model / --model-dim.

$ hybrid-search ingest ./docs
Ingested 2 document(s), 2 chunk(s), into /path/to/cwd/.hybrid_search.sqlite3

$ hybrid-search query "where do I live" --decay 0.1
1. [notes.txt] (score=0.0328) I live in Munich. My office is near the river.
2. [work.txt] (score=0.0161) The quarterly report is due on Friday.

$ hybrid-search query "where do I live" --decay 0.1 --explain    # full score trail

Empirical benchmarks (verified)

Reproduce with python benchmarks/run_eval.py; full method and raw data in BENCHMARKS.md and benchmarks/results.json. Machine: Apple Silicon (macOS arm64), single thread, embedding dim 64, 100 labelled queries.

Read the corpus honestly. It is synthetic (a Zipf-distributed pseudo-word vocabulary), and the dense column uses a 64-dim hashed bag-of-words, not a trained model — treat it as a floor. sparse gets a clean per-query exact-match cue, so read its perfect score as an upper bound. Latency and memory transfer directly. A real BEIR run with a trained embedder is tracked follow-up work.

Retrieval quality (Recall@10 / nDCG@10)

Approach 1k 10k 100k
Dense only 1.000 / 0.987 0.990 / 0.947 0.883 / 0.752
Sparse only (BM25) 1.000 / 1.000 1.000 / 1.000 1.000 / 1.000
Hybrid (RRF) 1.000 / 1.000 1.000 / 1.000 1.000 / 0.993
Hybrid + recency decay 1.000 / 1.000 1.000 / 1.000 1.000 / 1.000

Hybrid recovers the recall and ranking dense loses at scale (1.000 / 0.993 vs 0.883 / 0.752 at 100k) — RRF lets the BM25 side carry the query when the vector side weakens.

Latency (warm cache, single thread)

Approach 1k p50 / p99 100k p50 / p99 100k throughput
Dense 0.092 / 0.103 ms 0.185 / 0.334 ms 5,209 q/s
Hybrid 0.169 / 0.192 ms 6.7 / 8.0 ms 148 q/s

Dense stays sub-0.2 ms p50 at 100k. Hybrid stays single-digit ms and grows with corpus size (FTS5 posting-list merge). The agent-memory read (hybrid_decay) is ~5× slower — a per-candidate created_at lookup that's next on the optimisation list.

Startup: instant, disk-backed index

The usearch graph is serialised to a <db>.usearch sidecar and memory-loaded on the next open, instead of being rebuilt from SQLite:

1k 10k 100k
Index load on open 0.6 ms 3.0 ms ~40 ms
(previously: rebuild from SQLite) 40 ms 0.7 s 14.6 s

SQLite stays authoritative — a missing, truncated, or out-of-sync sidecar is rejected and the engine rebuilds transparently.

Memory & storage footprint

1k 10k 100k
SQLite on disk 0.59 MB 5.4 MB 55 MB
.usearch sidecar ~0.5 MB ~4 MB ~41 MB
Peak RSS (Python-driven) 36 MB 79 MB 431 MB

The native C++ cross-check (benchmarks/run_benchmarks.cpp) puts the engine-only peak RSS at ~37 MB for 20,000 documents — most of the Python-driven figure is the benchmark driver holding the corpus, not the engine.

Comparison

sqlite-hybrid-search sqlite-vec + glue ChromaDB Qdrant / Milvus / Weaviate
Deployment in-process library, 1 file in-process (SQLite ext) embedded lib or server separate server / cluster
Process to run none none none (embedded) / one (server) one+
Dense + sparse hybrid built in (RRF) DIY (wire up FTS5 + fusion) dense-first built in (server-side)
Recency / memory semantics built in (search_memory) DIY DIY DIY (metadata + custom scoring)
Score-trail / explainability search_explained() DIY limited varies
Text + metadata storage SQLite (authoritative) second table you design built in built in
Chunking token-window built in DIY some DIY / integrations
Vector-index persistence .usearch sidecar, auto-managed rows in SQLite persisted persisted
Ops surface none none small real (scaling, backups, upgrades)
Best fit desktop / CLI / edge agents, local-first, privacy you already live in SQLite Python RAG prototypes multi-tenant, large-scale, networked

Reach for a dedicated vector DB instead if you need horizontal scale, multi-writer concurrency, or sub-10 ms keyword search over millions of documents.

C++ integration

The public header exposes no usearch or SQLite types (Pimpl idiom); the only hard dependency is SQLite (usearch is fetched by CMake). The C++ symbols live in the retrieval_engine namespace (header path retrieval_engine/) — an internal name kept stable across the Python-package rename.

include(FetchContent)
FetchContent_Declare(sqlite_hybrid_search
    GIT_REPOSITORY https://github.com/ashray-00/sqlite-hybrid-search
    GIT_TAG main)
FetchContent_MakeAvailable(sqlite_hybrid_search)

target_link_libraries(your_target PRIVATE sqlite_hybrid_search::core)

#include "retrieval_engine/retrieval_engine.hpp"

retrieval_engine::RetrievalEngine engine("memory.sqlite3", /*dim=*/384);

retrieval_engine::DocumentInput doc;
doc.document_id = "home";
doc.chunks.push_back({ "I live in Munich.", embedding /*std::vector<float>*/, 0, 4 });
engine.add_documents({ doc });

auto hits = engine.search_hybrid("Where do I live?", query_vec, /*k=*/5);
auto memory = engine.search_memory("Where do I live?", query_vec, /*k=*/5, /*decay_lambda=*/0.1f);

One instance is safe to share across threads under a single-writer / concurrent-reader model, with no external locking: search_* / embed / chunk_count run in parallel (each on its own read-only SQLite connection against a WAL snapshot), while add_documents / add_text / load_embedding_model take an exclusive lock. Concurrent readers are capped at std::thread::hardware_concurrency() — an extra reader blocks until one returns. See ADR-11.

The database runs in WAL mode, so <db>-wal / <db>-shm files appear alongside it — back them up together, or checkpoint first.

Project layout

core/        C++17 engine (chunking, dense index, FTS5, RRF fusion, recency decay)
bindings/    nanobind extension module
python/      sqlite_hybrid_search package (friendly wrapper + `hybrid-search` CLI)
benchmarks/  reproducible recall / latency / memory harness
docs/        architecture decision records + development log

Building & testing

# Linux
cmake -B build && cmake --build build

# macOS (Homebrew SQLite / ONNX Runtime live under /opt/homebrew)
cmake -B build -DCMAKE_PREFIX_PATH=/opt/homebrew && cmake --build build

ctest --test-dir build --output-on-failure      # C++ suite
.venv/bin/pytest                                 # Python + CLI suite

Roadmap

  • Batched recency lookup. search_memory fetches each candidate's created_at with its own query; one batched lookup removes the hybrid_decay latency gap.
  • Real retrieval eval. A BEIR run (SciFact / NFCorpus) with a trained ONNX embedder, alongside the mechanism benchmark.
  • Wider wheels. Windows and Linux aarch64.

Contributing

Issues and pull requests welcome. The project follows a strict TDD workflow (failing test first, then implementation, then an independent review pass) and enforces formatting with .clang-format (C++) and ruff (Python). Run both test suites before opening a PR. Design rationale is recorded as ADRs in docs/DECISIONS.md.

License

MIT © 2026 Ashray Adhikari

Project links

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Credits

Author: Ashray Adhikari

GitHub Statistics

Data verified by PyPI on Sep 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Tags

retrieval rag vector-search hybrid-search bm25 reciprocal-rank-fusion agent-memory embeddings sqlite usearch

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0.2.0 This release

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