{"library":"simsimd","title":"Portable Mixed-Precision BLAS-like Vector Math Library","description":"SimSIMD is a highly optimized, mixed-precision math library providing over 350 SIMD-accelerated kernels for common vector similarity functions and dot-products. It is extensively used in AI, Search, and Database Management Systems workloads to achieve significant performance and accuracy improvements over standard NumPy and SciPy operations. The library is actively developed with frequent releases, though its main development has transitioned to a new project name, NumKong, starting with version 7.x.x.","language":"python","status":"active","last_verified":"Sat May 16","install":{"commands":["pip install simsimd"],"cli":null},"imports":["import simsimd"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import simsimd\nimport numpy as np\n\n# Create two random 1536-dimensional vectors for demonstration\nvec1 = np.random.randn(1536).astype(np.float32)\nvec2 = np.random.randn(1536).astype(np.float32)\n\n# Calculate cosine similarity\ndistance = simsimd.cosine(vec1, vec2)\nprint(f\"Cosine distance: {distance}\")\n\n# Calculate squared Euclidean distance\ndistance_sqeuclidean = simsimd.sqeuclidean(vec1, vec2)\nprint(f\"Squared Euclidean distance: {distance_sqeuclidean}\")\n\n# Calculate inner product\ninner_product = simsimd.inner(vec1, vec2)\nprint(f\"Inner product: {inner_product}\")","lang":"python","description":"This example demonstrates how to calculate various vector similarity metrics (cosine, squared Euclidean, inner product) between two NumPy arrays using SimSIMD. Ensure NumPy is installed as it's commonly used for array creation, although not a direct dependency of SimSIMD.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-16","installed_version":"6.5.16","pypi_latest":"6.5.16","is_stale":false,"summary":{"python_range":"3.10–3.9","success_rate":100,"avg_install_s":2.1,"avg_import_s":0,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":0,"mem_mb":0,"disk_size":"19.4M"},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":2.3,"import_time_s":0,"mem_mb":0,"disk_size":"20M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":0,"mem_mb":0,"disk_size":"21.2M"},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":2,"import_time_s":0,"mem_mb":0,"disk_size":"22M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":0,"mem_mb":0,"disk_size":"13.1M"},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":1.7,"import_time_s":0,"mem_mb":0,"disk_size":"14M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":0,"mem_mb":0,"disk_size":"12.8M"},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":1.7,"import_time_s":0,"mem_mb":0,"disk_size":"14M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":null,"import_time_s":0,"mem_mb":0,"disk_size":"18.9M"},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"simsimd","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":2.8,"import_time_s":0,"mem_mb":0,"disk_size":"20M"}]}}