heliosdb-nano (Python)
In-process Python binding for HeliosDB-Nano's
embedded database. Calls the Rust EmbeddedDatabase API directly — no subprocess,
no wire protocol, no serialization hop — so Python sees the same path behind the
"embedded beats SQLite" numbers.
import heliosdb_nano
db = heliosdb_nano.EmbeddedDatabase("/path/to/data") # or .in_memory()
db.execute("CREATE TABLE messages (id INT, session_id TEXT, body TEXT)")
db.execute_many(
"INSERT INTO messages (id, session_id, body) VALUES ($1, $2, $3)",
[(1, "s1", "hi"), (2, "s1", "yo"), (3, "s2", "hey")],
)
db.query("SELECT COUNT(DISTINCT session_id) AS n FROM messages") # [{'n': 2}]
db.query("SELECT * FROM messages WHERE session_id = $1", ("s1",)) # [{...}, {...}]
# Vector search (HNSW)
db.create_vector_store("emb", 3)
ids = db.insert_vectors("emb", [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
db.vector_search("emb", [1.0, 0.0, 0.0], k=1) # [(id, distance)]
API
| method | returns |
|---|---|
EmbeddedDatabase(path) / EmbeddedDatabase.in_memory() |
a handle |
query(sql, params=None) |
list[dict] |
execute(sql, params=None) |
affected row count (int) |
execute_many(sql, rows) |
total affected (int) |
vector_search(store, query, k) |
list[(id, distance)] |
create_vector_store(name, dimensions) / insert_vectors(store, vectors) |
— / list[id] |
flush() |
— |
params is a tuple/list binding positionally to $1..$n. The GIL is released
around every engine call, so multiple Python threads can query concurrently.
Build from source
pip install maturin
maturin develop -m bindings/python/Cargo.toml # editable install into the active venv
maturin build --release -m bindings/python/Cargo.toml # abi3 wheel in target/wheels/
1 maintainer
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