embedvec 0.5.1

Fast, lightweight, in-process vector database with HNSW indexing, metadata filtering, E8 quantization, and PyO3 bindings
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105

[dependencies.ordered-float]
version = "4.0"

[dependencies.parking_lot]
version = "0.12"

[dependencies.pyo3]
features = ["extension-module"]
optional = true
version = "0.22"

[dependencies.rand]
version = "0.8"

[dependencies.rand_chacha]
version = "0.3"

[dependencies.rayon]
version = "1.10"

[dependencies.rocksdb]
optional = true
version = "0.22"

[dependencies.serde]
features = ["derive"]
version = "1.0"

[dependencies.serde_json]
version = "1.0"

[dependencies.sled]
optional = true
version = "0.34"

[dependencies.thiserror]
version = "2.0"

[dependencies.tokio]
features = ["rt-multi-thread", "sync", "macros"]
optional = true
version = "1.0"

[dependencies.wee_alloc]
optional = true
version = "0.4"

[dev-dependencies.approx]
version = "0.5"

[dev-dependencies.criterion]
version = "0.5"

[dev-dependencies.tempfile]
version = "3.10"

[dev-dependencies.tokio]
features = ["rt-multi-thread", "macros"]
version = "1.0"

[features]
async = ["tokio"]
default = ["persistence-sled", "async"]
full = ["persistence-sled", "async", "simd"]
full-rocksdb = ["persistence-rocksdb", "async", "simd"]
persistence = ["persistence-sled"]
persistence-rocksdb = ["rocksdb"]
persistence-sled = ["sled"]
python = ["pyo3"]
simd = []
wasm = ["wee_alloc"]

[lib]
crate-type = ["cdylib", "rlib"]
name = "embedvec"
path = "src/lib.rs"

[package]
authors = ["EmbedVec Contributors"]
autobenches = false
autobins = false
autoexamples = false
autolib = false
autotests = false
build = false
categories = ["database", "science", "algorithms"]
description = "Fast, lightweight, in-process vector database with HNSW indexing, metadata filtering, E8 quantization, and PyO3 bindings"
documentation = "https://docs.rs/embedvec"
edition = "2021"
keywords = ["vector-database", "hnsw", "embedding", "ann", "rag"]
license = "MIT"
name = "embedvec"
readme = "README.md"
repository = "https://github.com/WeaveITMeta/embedvec"
version = "0.5.1"

[profile.bench]
codegen-units = 1
lto = true
opt-level = 3

[profile.release]
codegen-units = 1
lto = true
opt-level = 3