mcp-victorialogs

# go-faiss [](https://pkg.go.dev/github.com/DataIntelligenceCrew/go-faiss) Go bindings for [Faiss](https://github.com/facebookresearch/faiss), a library for vector similarity search. ## Install First you will need to build and install Faiss: ``` git clone https://github.com/blevesearch/faiss.git cd faiss cmake -B build -DFAISS_ENABLE_GPU=OFF -DFAISS_ENABLE_C_API=ON -DBUILD_SHARED_LIBS=ON . make -C build sudo make -C build install ``` On osX ARM64, the instructions needed to be slightly adjusted based on https://github.com/facebookresearch/faiss/issues/2111: ``` LDFLAGS="-L/opt/homebrew/opt/llvm/lib" CPPFLAGS="-I/opt/homebrew/opt/llvm/include" CXX=/opt/homebrew/opt/llvm/bin/clang++ CC=/opt/homebrew/opt/llvm/bin/clang cmake -B build -DFAISS_ENABLE_GPU=OFF -DFAISS_ENABLE_C_API=ON -DBUILD_SHARED_LIBS=ON . // set FAISS_ENABLE_PYTHON to OFF in CMakeLists.txt to ignore libpython dylib make -C build sudo make -C build install ``` Building will produce the dynamic library `faiss_c`. You will need to install it in a place where your system will find it (e.g. `/usr/local/lib` on mac or `/usr/lib` on Linux). You can do this with: sudo cp build/c_api/libfaiss_c.so /usr/local/lib Now you can install the Go module: go get github.com/blevesearch/go-faiss ## Usage API documentation is available at <https://pkg.go.dev/github.com/DataIntelligenceCrew/go-faiss>. See the [Faiss wiki](https://github.com/facebookresearch/faiss/wiki) for more information. Examples can be found in the [_example](_example) directory.