Matcher Rust Implement C FFI bindings

Overview

A high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust.

Installation

Build from source

git clone https://github.com/Lips7/Matcher.git
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- --default-toolchain nightly -y
cargo build --release

Then you should find the libmatcher_c.so/libmatcher_c.dylib/matcher_c.dll in the target/release directory.

Install pre-built binary

Visit the release page to download the pre-built binary.

Python usage example

import json

from cffi import FFI

from extension_types import MatchTableType, ProcessType, MatchTable

## define ffi
ffi = FFI()
ffi.cdef(open("./matcher_c.h", "r", encoding="utf-8").read())
lib = ffi.dlopen("./matcher_c.so")

# init matcher
matcher = lib.init_matcher(
    json.dumps({
        1: [
            MatchTable(
                table_id=1,
                match_table_type=MatchTableType.Simple(
                    process_type=ProcessType.MatchNone
                ),
                word_list=["hello,world", "hello", "world"],
                exemption_process_type=ProcessType.MatchNone,
                exemption_word_list=[],
            )
        ]
    }).encode()
)

# check is match
lib.matcher_is_match(matcher, "hello".encode("utf-8"))  # True

# match as list
res = lib.matcher_process_as_string(matcher, "hello,world".encode("utf-8"))
print(ffi.string(res).decode("utf-8"))
# [{"match_id":1,"table_id":1,"word_id":0,"word":"hello,world","similarity":1.0},{"match_id":1,"table_id":1,"word_id":1,"word":"hello","similarity":1.0},{"match_id":1,"table_id":1,"word_id":2,"word":"world","similarity":1.0}]
lib.drop_string(res)

# match as dict
res = lib.matcher_word_match_as_string(matcher, "hello,world".encode("utf-8"))
print(ffi.string(res).decode("utf-8"))
# {"1":[{"match_id":1,"table_id":1,"word_id":0,"word":"hello,world","similarity":1.0},{"match_id":1,"table_id":1,"word_id":1,"word":"hello","similarity":1.0},{"match_id":1,"table_id":1,"word_id":2,"word":"world","similarity":1.0}]}
lib.drop_string(res)

# drop matcher
lib.drop_matcher(matcher)

# init simple matcher
simple_matcher = lib.init_simple_matcher(
    json.dumps(({
        ProcessType.MatchFanjianDeleteNormalize | ProcessType.MatchPinYinChar: {
            1: "妳好&世界",
            2: "hello",
        }
    })).encode()
)

# check is match
lib.simple_matcher_is_match(simple_matcher, "你好世界".encode("utf-8"))  # True

# match as list
res = lib.simple_matcher_process_as_string(
    simple_matcher, "nihaoshijie!hello!world!".encode("utf-8")
)
print(ffi.string(res).decode("utf-8"))
# [{"word_id":1,"word":"妳好&世界"},{"word_id":2,"word":"hello"}]
lib.drop_string(res)

# drop simple matcher
lib.drop_simple_matcher(simple_matcher)

Important Notes

1. The extension_types.py is not required, you can use the dynamic library directly.
2. Always call drop_matcher, drop_simple_matcher, and drop_string after initializing and processing to avoid memory leaks.