scrydb

scrydb's purpose is making lexical, dense, and hybrid search possible with SQLite. Hardware requirements are kept low and everything is self-contained in a single file, i.e., the raw documents, their embeddings, and the lexical index are stored in a single SQLite file.

• Lexical search is made possible by the FTS5 extension for SQLite.
• Semantic search over the entire index is made possible by sqlite-vec, a small, dependency-free vector search extension for SQLite. Every embedding can be searched at three precisions: binary (1 bit/dim, Hamming distance), int8 (1 byte/dim, cosine), and float (full precision, cosine) — trading index size and speed against ranking quality, and combinable as a two-stage rerank (e.g. binary-first >> float-precision rerank).
• Hybrid search relies on Reciprocal Rank Fusion to fuse lexical and semantic search results.

The library is compatible with Sentence Transformers. However, it is also possible to store precomputed embeddings for both queries and documents.

Usage examples

scrydb can be used interactively as follows:

from scrydb import Index, SentenceEmbedding

with Index.open("idx.db") as index:
    index.add_model(SentenceEmbedding())
    index.index_documents("corpus.jsonl", id_field="docid", text_field="text")
    results = index.search("some query", mode="hybrid", rerank=True)

Batch search for Information Retrieval benchmarks with precomputed embeddings can be run as follows:

import scrydb

idx = scrydb.Index.open("./path/to/index.db")

idx.index_documents(
    source="./path/to/corpus.jsonl",
    id_field="docid",
    text_field="text",
    embedding_field="embedding",
    store_int8_embeddings=True,  # opt in to int8 storage alongside binary/float
    )

idx.index_queries(
    source="./path/to/queries.jsonl",
    id_field="qid",
    text_field="text",
    embedding_field="embedding",
    store_int8_embeddings=True,
    )

idx.batch_search(mode="lexical").write_trec("./path/to/lexical/run")
idx.batch_search(mode="semantic", precision="binary").write_trec("./path/to/binary/run")
idx.batch_search(mode="semantic", precision="int8").write_trec("./path/to/int8/run")
idx.batch_search(mode="semantic", precision="float").write_trec("./path/to/float/run")
idx.batch_search(mode="semantic", precision="binary", rerank="float").write_trec("./path/to/binary-rerank-float/run")
idx.batch_search(mode="hybrid").write_trec("./path/to/hybrid/run")

Search modes, precision, and rerank

search()/batch_search() take three orthogonal knobs:

• mode — "lexical" (BM25 over FTS5), "semantic" (vector search), or "hybrid" (Reciprocal Rank Fusion of both).
• precision — which vector representation mode="semantic"/the semantic side of mode="hybrid" ranks with: "binary" (default), "int8", or "float".
• rerank — False (default), or a second-stage rerank over the top candidates from mode, at "binary", "int8", or "float" precision (True is a synonym for "float").

idx.search("some query", mode="lexical")                                         # BM25
idx.search("some query", mode="lexical", rerank="float")                         # BM25 >> Float/Cosine
idx.search("some query", mode="semantic", precision="binary")                    # Binary/Hamming
idx.search("some query", mode="semantic", precision="int8")                      # Int8/Cosine
idx.search("some query", mode="semantic", precision="binary", rerank="float")    # Binary >> Float/Cosine
idx.search("some query", mode="hybrid", rerank=True)                             # Hybrid/RRF

Installing

pip install scrydb

Or with uv (faster, and manages the virtualenv for you):

uv venv && uv pip install scrydb
# or, inside a uv-managed project:
uv add scrydb

No C compiler or SQLite development headers required: vector search is powered by sqlite-vec, a pure pip dependency that ships prebuilt binaries, so pip install scrydb is a plain, fast, wheel-only install.

Index.open() loads the sqlite-vec extension automatically. This requires a Python build whose sqlite3 module supports enable_load_extension() — true for Homebrew, pyenv, and uv-managed builds on macOS, virtually all Linux distro packages, and the official Windows builds, but not for macOS Pythons that link against Apple's SQLite, which disables extension loading. That includes macOS's system Python and the CPython that actions/setup-python installs on GitHub Actions runners. If you hit a RuntimeError mentioning enable_load_extension, switch to Python from Homebrew (brew install python), uv (uv python install <version>), or pyenv (PYTHON_CONFIGURE_OPTS='--enable-loadable-sqlite-extensions' pyenv install <version>).

If you'd rather not load the extension at all, you can still install and use scrydb for lexical (BM25) search only — just disable it explicitly:

Index.open("idx.db", vec_ext_path=None)

Try the CLI without installing (uvx)

uvx runs the scrydb command-line tool (index/search/batch-search/auto — see the Docker section below for the full reference) in a throwaway environment, no venv or persistent install needed:

uvx scrydb index --documents corpus.jsonl --queries queries.jsonl --db idx.db
uvx scrydb search "some query" --db idx.db --mode hybrid --rerank float
uvx scrydb batch-search --db idx.db --mode hybrid --output run.trec

Since scrydb has no compiled artifacts of its own, uvx just downloads the wheel and its dependencies (including sqlite-vec's prebuilt binary) into its cache — no build step at all.

Docker

No local Python install needed: the Dockerfile builds a Linux image with scrydb already installed, driven by a bundled scrydb CLI. Everything it reads and writes -- input JSONL, the SQLite index, TREC run files -- lives under /data, so bind-mount a host directory there.

docker build -t scrydb .

Drop documents.jsonl (and, optionally, queries.jsonl) into ./data and run the image with no arguments: it indexes whatever's present into ./data/index.db, then either batch-searches the stored queries into ./data/run.trec or -- if there's nothing to index and no stored queries -- tells you what it's waiting for.

docker run --rm -v "$PWD/data":/data scrydb

If ./data/index.db already exists (say, you built it locally, or a previous run produced it), the same command re-uses it: skips indexing whatever source files aren't present and searches straight away. To query an existing index ad hoc instead of running a full batch, override the default command:

docker run --rm -v "$PWD/data":/data scrydb search "some query" --mode hybrid --rerank float

Every option is also settable as an SCRYDB_* environment variable (handy for docker run -e), and the JSONL id-field names for documents/queries default to docid/qid-style overrides when they differ from id:

docker run --rm -v "$PWD/data":/data \
  -e SCRYDB_DOC_ID_FIELD=docid -e SCRYDB_QUERY_ID_FIELD=qid \
  -e SCRYDB_MODEL=mixedbread-ai/mxbai-embed-large-v1 \
  -e SCRYDB_MODE=hybrid -e SCRYDB_RERANK=float \
  scrydb

Run docker run --rm scrydb --help (or ... <subcommand> --help) for the full index/search/batch-search/auto reference, or see the module docstring in src/scrydb/cli.py.

Notes:

• Dense/hybrid search (sentence-transformers) isn't in the image by default -- build with --build-arg EXTRAS=all (or dense) to add it. This installs the CPU-only torch build so the image doesn't pull in multi-gigabyte CUDA packages it can't use.
• Multi-platform: build once for both Intel/AMD and Apple Silicon/ARM hosts (each running natively, no QEMU emulation) with docker buildx build --platform linux/amd64,linux/arm64 -t scrydb .
• Plain Python access: docker run --rm -it -v "$PWD/data":/data --entrypoint python3 scrydb drops into an interpreter with scrydb importable.

How extension discovery works at runtime

Index.open()/Index() default to vec_ext_path="auto", which loads sqlite_vec.loadable_path() — the copy of the extension bundled inside the installed sqlite-vec pip package, prebuilt for the current platform. Pass an explicit path to load a different build (e.g. a newer vec0 release), or None to skip loading it entirely.

Development / editable installs

git clone <repo>
cd scrydb
python -m venv .venv && source .venv/bin/activate
pip install -e ".[all]"
pytest

Or with uv:

git clone <repo>
cd scrydb
uv venv
uv pip install -e ".[all]"
uv run pytest

uv run picks up .venv automatically, so there's no source .venv/bin/activate step — any command after it (uv run pytest, uv run python -m scrydb.cli --help, uv run python your_script.py) runs inside the project's venv.

Optional extras

• pip install "scrydb[dense]" / uv pip install "scrydb[dense]" — dense/hybrid search via sentence-transformers
• pip install "scrydb[eval]" / uv pip install "scrydb[eval]" — Run.to_dataframe() via pandas
• pip install "scrydb[all]" / uv pip install "scrydb[all]" — both