v1.0.0-alpha.2

Author: JHZhang2736

README.md

@@ -1,6 +1,6 @@
 # magda-pdf-semantic-indexer
 
-![Version: 1.0.0-alpha.0](https://img.shields.io/badge/Version-1.0.0--alpha.0-informational?style=flat-square)
+![Version: 1.0.0-alpha.2](https://img.shields.io/badge/Version-1.0.0--alpha.2-informational?style=flat-square)
 
 A Helm chart for Magda PDF Semantic Indexer
 
@@ -36,14 +36,13 @@ Kubernetes: `>= 1.14.0-0`
 | defaultSemanticIndexerConfig.overlap | int | `50` |  |
 | defaultSemanticIndexerConfig.overlap | int | `50` |  |
 | embeddingApiURL | string | `"http://magda-embedding-api"` |  |
-| global | object | `{"image":{},"rollingUpdate":{},"searchEngine":{"defaultDatasetBucket":"magda-datasets","semanticIndexer":{"indexName":null,"indexVersion":null,"knnVectorFieldConfig":{"compressionLevel":null,"dimension":768,"efConstruction":100,"efSearch":100,"encoder":{"clip":false,"name":"sq","type":"fp16"},"m":16,"mode":"in_memory","spaceType":"l2"},"numberOfReplicas":0,"numberOfShards":1}}}` | only for providing appropriate default value for helm lint |
-| global.searchEngine.semanticIndexer.knnVectorFieldConfig.compressionLevel | string | `nil` | The compression_level mapping parameter selects a quantization encoder that reduces vector memory consumption by the given factor. |
+| global | object | `{"image":{},"rollingUpdate":{},"searchEngine":{"defaultDatasetBucket":"magda-datasets","semanticIndexer":{"indexName":null,"indexVersion":null,"knnVectorFieldConfig":{"compressionLevel":"32x","dimension":768,"efConstruction":100,"efSearch":100,"m":16,"mode":"on_disk","spaceType":"l2"},"numberOfReplicas":0,"numberOfShards":1}}}` | only for providing appropriate default value for helm lint |
+| global.searchEngine.semanticIndexer.knnVectorFieldConfig.compressionLevel | string | `"32x"` | The compression_level mapping parameter selects a quantization encoder that reduces vector memory consumption by the given factor. |
 | global.searchEngine.semanticIndexer.knnVectorFieldConfig.dimension | int | `768` | Dimension of the embedding vectors. |
 | global.searchEngine.semanticIndexer.knnVectorFieldConfig.efConstruction | int | `100` | Similar to efSearch but used during index construction. Higher values improve search quality but increase index build time. |
 | global.searchEngine.semanticIndexer.knnVectorFieldConfig.efSearch | int | `100` | The size of the candidate queue during search. Larger values may improve search quality but increase search latency. |
-| global.searchEngine.semanticIndexer.knnVectorFieldConfig.encoder | object | `{"clip":false,"name":"sq","type":"fp16"}` | FAISS Encoder configuration (If compressionLevel is set, encoder will be ignored). |
 | global.searchEngine.semanticIndexer.knnVectorFieldConfig.m | int | `16` | The maximum number of graph edges per vector. Higher values increase memory usage but may improve search quality. |
-| global.searchEngine.semanticIndexer.knnVectorFieldConfig.mode | string | `"in_memory"` | Vector workload mode: `on_disk` or `in_memory`. |
+| global.searchEngine.semanticIndexer.knnVectorFieldConfig.mode | string | `"on_disk"` | Vector workload mode: `on_disk` or `in_memory`. |
 | image.name | string | `"magda-pdf-semantic-indexer"` |  |
 | minioConfig.defaultDatasetBucket | string | `""` |  |
 | minioConfig.endPoint | string | `"magda-minio"` |  |

deploy/magda-pdf-semantic-indexer/Chart.yaml

@@ -1,6 +1,6 @@
 apiVersion: v2
 name: magda-pdf-semantic-indexer
-version: 1.0.0-alpha.0
+version: 1.0.0-alpha.2
 kubeVersion: ">= 1.14.0-0"
 description: A Helm chart for Magda PDF Semantic Indexer
 home: "https://github.com/magda-io/magda-pdf-semantic-indexer"

package.json

@@ -1,6 +1,6 @@
 {
     "name": "@magda/magda-pdf-semantic-indexer",
-    "version": "1.0.0-alpha.0",
+    "version": "1.0.0-alpha.2",
     "description": "Magda PDF Semantic Indexer",
     "type": "module",
     "files": [