databases/weaviate: New port: Vector database that stores both objects and vectors

2025-08-24 03:21:07 -07:00
parent 46780d9665
commit 40527017c0
5 changed files with 45 additions and 0 deletions
@@ -1130,6 +1130,7 @@
    SUBDIR += valkey
    SUBDIR += vfront
    SUBDIR += virtualpg
+    SUBDIR += weaviate
    SUBDIR += webdis
    SUBDIR += wfb2sql
    SUBDIR += xapian-bindings
@@ -0,0 +1,23 @@
+PORTNAME=	weaviate
+DISTVERSIONPREFIX=	v
+DISTVERSION=	1.30.17
+CATEGORIES=	databases # artificial-intelligence
+
+MAINTAINER=	yuri@FreeBSD.org
+COMMENT=	Vector database that stores both objects and vectors
+WWW=		https://weaviate.io/ \
+		https://github.com/weaviate/weaviate
+
+LICENSE=	BSD3CLAUSE
+LICENSE_FILE=	${WRKSRC}/LICENSE
+
+USES=		go:modules
+
+GO_MODULE=	github.com/weaviate/weaviate
+GO_TARGET=	./cmd/weaviate-server
+
+PLIST_FILES=	bin/${PORTNAME}-server
+
+# how to start: https://docs.weaviate.io/deploy/installation-guides/docker-installation#run-weaviate-with-default-settings
+
+.include <bsd.port.mk>
@@ -0,0 +1,5 @@
+TIMESTAMP = 1755162557
+SHA256 (go/databases_weaviate/weaviate-v1.30.17/v1.30.17.mod) = 24d201cdcab7916a1e0fdc7e3790b1d8dd259ff714ab15c4560340c411f89f28
+SIZE (go/databases_weaviate/weaviate-v1.30.17/v1.30.17.mod) = 11838
+SHA256 (go/databases_weaviate/weaviate-v1.30.17/v1.30.17.zip) = 038b13fb36181c1a958e4f01ad8a7a105cead47b5ba06769e08a7679deaf1c94
+SIZE (go/databases_weaviate/weaviate-v1.30.17/v1.30.17.zip) = 7134666
@@ -0,0 +1,10 @@
+--- usecases/cluster/disk_use_unix.go.orig	2025-08-14 19:49:00 UTC
+++ usecases/cluster/disk_use_unix.go
+@@ -26,6 +26,6 @@ func diskSpace(path string) (DiskUsage, error) {
+ 	}
+ 	return DiskUsage{
+ 		Total:     fs.Blocks * uint64(fs.Bsize),
+-		Available: fs.Bavail * uint64(fs.Bsize),
+		Available: uint64(fs.Bavail) * fs.Bsize,
+ 	}, nil
+ }
@@ -0,0 +1,6 @@
+Weaviate is an open-source, cloud-native vector database that stores both
+objects and vectors, enabling semantic search at scale. It combines vector
+similarity search with keyword filtering, retrieval-augmented generation
+(RAG), and reranking in a single query interface. Common use cases include
+RAG systems, semantic and image search, recommendation engines, chatbots,
+and content classification.