Expose source_model parameter for vector-enabled collections #1606
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Hazel-Datastax
commented
Oct 25, 2024
Comment on lines
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| // convert a vector jsonNode from cql table comment to vectorConfig, used for collection | ||
| private static VectorConfig.ColumnVectorDefinition fromJson( | ||
| JsonNode jsonNode, ObjectMapper objectMapper) { | ||
| // dimension, similarityFunction, must exist | ||
| int dimension = jsonNode.get("dimension").asInt(); | ||
| SimilarityFunction similarityFunction = | ||
| SimilarityFunction.fromString(jsonNode.get("metric").asText()); | ||
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| return VectorConfig.ColumnVectorDefinition.fromJson( | ||
| DocumentConstants.Fields.VECTOR_EMBEDDING_TEXT_FIELD, | ||
| dimension, | ||
| similarityFunction, | ||
| jsonNode, | ||
| objectMapper); | ||
| } | ||
|
|
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Remove the unused method. It's been removed to VectorConfig class
Hazel-Datastax
commented
Oct 28, 2024
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15
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| SimilarityFunction.DOT_PRODUCT, | ||
| "openai_v3_small", | ||
| "openai-v3-small", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "openai_v3_large", | ||
| "openai-v3-large", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "bert", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "gecko", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "nv_qa_4", | ||
| "nv-qa-4", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "cohere_v3", | ||
| "cohere-v3", | ||
| SimilarityFunction.DOT_PRODUCT, | ||
| "other", | ||
| SimilarityFunction.COSINE); |
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According to the document, here should be hyphens not underscores. Since changing the value in here will affect the code in tables. I will create another PR to replace those hardcoded values to Emun.
Hazel-Datastax
commented
Oct 28, 2024
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+200
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| String sourceModel = vector.sourceModel(); | ||
| String metric = vector.metric(); | ||
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| // decide sourceModel and metric value | ||
| if (sourceModel != null) { | ||
| if (metric == null) { | ||
| // (1) sourceModel is provided but metric is not - set metric to cosine or dot_product based | ||
| // on the map | ||
| metric = SUPPORTED_SOURCES.get(sourceModel).getMetric(); | ||
| } | ||
| // (2) both sourceModel and metric are provided - do nothing | ||
| } else { | ||
| if (metric != null) { | ||
| // (3) sourceModel is not provided but metric is - set sourceModel to 'other' | ||
| sourceModel = SourceModel.OTHER.getSourceModel(); | ||
| } else { | ||
| // (4) both sourceModel and metric are not provided - set sourceModel to 'other' and metric | ||
| // to 'cosine' | ||
| sourceModel = SourceModel.OTHER.getSourceModel(); | ||
| metric = SimilarityFunction.COSINE.getMetric(); | ||
| } | ||
| } | ||
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| if (service != null) { | ||
| // Validate service configuration and auto populate vector dimension. | ||
| vectorDimension = validateVectorize.validateService(service, vectorDimension); | ||
| vector = | ||
| new CreateCollectionCommand.Options.VectorSearchConfig( | ||
| vectorDimension, vector.metric(), vector.vectorizeConfig()); | ||
| vectorDimension, metric, sourceModel, vector.vectorizeConfig()); | ||
| } else { |
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Do we want to optimize the sourceModel for service? For example, if the user only specifies openai and text-embedding-3-small in the service, do we want to optimize the sourceModel to openai-v3-small. Currently, we will use other as the default value.
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What this PR does:
Expose source_model parameter for vector-enabled collections
Which issue(s) this PR fixes:
Fixes JiraC2-3495
Checklist