MSVBASE
MSVBASE is a system that efficiently supports complex queries of both approximate similarity search and relational operators. It integrates high-dimensional vector indices into PostgreSQL, a relational database to facilitate complex approximate similarity queries.
File Explorer
Download Latest Version (.zip)- codeql.yml
- docker-image.yml
- convert_bin.py
- cook_data.sh
- data_download.sh
- data_gen.py
- data_gen_spann.py
- data_prepare.sh
- load_data.sh
- spann_embedding_data.py
- spann_embedding_query.py
- spann_process.sh
- tag_data_gen.py
- tag_data_gen_spann.py
- create_vbase_docker.sh
- query_1_gt.out
- query_2_gt.out
- query_3_gt.out
- query_4_gt.out
- query_5_gt.out
- query_6_gt.out
- query_7_gt.out
- query_8_gt.out
- query_selectivity_0.03_gt.out
- query_selectivity_0.3_gt.out
- query_selectivity_0.9_gt.out
- latency.py
- latency_q8.py
- recall.py
- recall_q8.py
- run.sh
- run_table_4.sh
- run_table_5.sh
- run_table_6.sh
- load.sql
- query_1.py
- query_2.py
- query_3.py
- query_4.py
- query_5.py
- query_6.py
- query_7.py
- query_7_gt.py
- query_8.py
- query_8_gt.py
- analysis_table8.sh
- create_index.sh
- create_index_image.config
- load_data.sh
- query_1.sh
- query_2.sh
- query_3.sh
- query_4.sh
- query_5.sh
- query_6.sh
- query_7.sh
- query_8.sh
- query_generate.sh
- run_table8.sh
- load.sql
- query_1.py
- query_2.py
- query_3.py
- query_4.py
- query_5.py
- query_6.py
- query_7.py
- query_8.py
- query_selectivity_0.03.py
- query_selectivity_0.3.py
- query_selectivity_0.9.py
- analysis_table4.sh
- analysis_table5.sh
- analysis_table6.sh
- load_data.sh
- query_1.sh
- query_2.sh
- query_3.sh
- query_4.sh
- query_5.sh
- query_6.sh
- query_7.sh
- query_8.sh
- query_generate.sh
- query_selectivity_0.03.sh
- query_selectivity_0.3.sh
- query_selectivity_0.9.sh
- run.sh
- run_table4.sh
- run_table5.sh
- hnsw.patch
- new_pg.patch
- Postgres.patch
- spann.patch
- config_pgbuild_dev.sh
- docker-entrypoint.sh
- pg_start.sh
- pg_stop.sh
- dockerbuild.sh
- dockerrm.sh
- dockerrun.sh
- dockerstop.sh
- patch.sh
- vectordb.sql
- hnswindex.cpp
- hnswindex.hpp
- hnswindex_builder.cpp
- hnswindex_builder.hpp
- hnswindex_scan.cpp
- hnswindex_scan.hpp
- index.cpp
- index.hpp
- index_builder.cpp
- index_builder.hpp
- index_scan.cpp
- index_scan.hpp
- lib.cpp
- model_mng.cpp
- model_mng.hpp
- multicol_topk.cpp
- operator.cpp
- operator.hpp
- pase_hnswindex.cpp
- pase_hnswindex.hpp
- pase_hnswindex_builder.cpp
- pase_hnswindex_builder.hpp
- pase_hnswindex_scan.cpp
- pase_hnswindex_scan.hpp
- spannindex.cpp
- spannindex.hpp
- spannindex_builder.hpp
- spannindex_scan.cpp
- spannindex_scan.hpp
- topk.cpp
- util.cpp
- util.hpp
- CMakeLists.txt
- hnsw
- Postgres
- SPTAG
- .gitmodules
- azure-pipelines.yml
- CMakeLists.txt
- CODE_OF_CONDUCT.md
- Dockerfile
- LICENSE
- README.md
- SECURITY.md
- SUPPORT.md
- vectordb.control.in
# Installation Guide
1. Get the code
git clone https://github.com/microsoft/MSVBASE
Downloads the entire project code from GitHub to your computer.
cd MSVBASE
Moves into the project folder you just downloaded.
2. Docker
Easy RecommendedPrerequisites
- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker exec -it --privileged --user=root vbase_open_source bash
Type this command into your terminal and run it.
Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.
Pulled directly from this repo's README.
3. CMake
MediumPrerequisites
mkdir build && cd build
Creates a folder to hold the build output and moves into it.
cmake ..
Analyzes the source code and generates build configuration files (must be run inside the build folder).
make
Compiles the code based on the generated build configuration to produce an executable.
Check that an executable was created inside the build folder, then run it directly (e.g. ./build/app_name).
// repository documentation
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