Matcher Rust Implement C FFI bindings

Overview

Matcher is a high-performance matching library implemented in Rust, providing C FFI bindings for seamless integration with other programming languages. This library is designed for various matching tasks, including simple and complex match types with normalization and deletion capabilities.

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 msgspec

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(
    msgspec.msgpack.encode({
        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=[],
            )
        ]
    })
)

# 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(
    msgspec.msgpack.encode(({
        ProcessType.MatchFanjianDeleteNormalize | ProcessType.MatchPinYinChar: {
            1: "妳好&世界",
            2: "hello",
        }
    }))
)

# 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.