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set to on_disk mode in default
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deploy/magda-csv-semantic-indexer/templates/deployment.yaml

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@@ -44,7 +44,7 @@ spec:
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{{ toYaml .Values.resources | indent 10 }}
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env:
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- name: NODE_PORT
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value: "80"
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value: {{ .Values.port | quote }}
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- name: REGISTRY_URL
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value: "http://registry-api/v0"
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- name: REGISTRY_READ_ONLY_URL

deploy/magda-csv-semantic-indexer/values.yaml

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@@ -10,11 +10,11 @@ global:
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numberOfReplicas: 0
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knnVectorFieldConfig:
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# -- Vector workload mode: `on_disk` or `in_memory`.
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mode: "in_memory"
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mode: "on_disk"
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# -- Dimension of the embedding vectors.
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dimension: 768
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# -- The compression_level mapping parameter selects a quantization encoder that reduces vector memory consumption by the given factor.
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compressionLevel: null
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compressionLevel: 32x
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# Supported values: l1, l2, innerProduct, cosine, linf
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spaceType: "l2"
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# -- Similar to efSearch but used during index construction. Higher values improve search quality but increase index build time.
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# -- The maximum number of graph edges per vector. Higher values increase memory usage but may improve search quality.
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m: 16
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# -- FAISS Encoder configuration (If compressionLevel is set, encoder will be ignored).
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encoder:
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name: "sq"
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type: "fp16"
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clip: false
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# encoder: null
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# name: "sq"
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# type: "fp16"
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# clip: false
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defaultDatasetBucket: "magda-datasets"
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opensearchURL: http://opensearch:9200

package.json

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"@langchain/core": "^0.3.61",
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"@langchain/textsplitters": "https://gitpkg.vercel.app/JHZhang2736/langchainjs/libs/langchain-textsplitters?textsplitters-build",
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"@magda/esm-utils": "^1.0.1",
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"@magda/semantic-indexer-sdk": "6.0.0-alpha.6",
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"@magda/semantic-indexer-sdk": "6.0.0-alpha.8",
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"csv-parse": "^6.1.0",
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"fs-extra": "^11.3.0",
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"yargs": "17.6.2"

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