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https://github.com/discourse/discourse-ai.git
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534b0df391
In a previous refactor, we moved the responsibility of querying and storing embeddings into the `Schema` class. Now, it's time for embedding generation. The motivation behind these changes is to isolate vector characteristics in simple objects to later replace them with a DB-backed version, similar to what we did with LLM configs.
57 lines
1.2 KiB
Ruby
57 lines
1.2 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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module VectorRepresentations
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class AllMpnetBaseV2 < Base
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class << self
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def name
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"all-mpnet-base-v2"
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end
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def correctly_configured?
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SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? ||
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SiteSetting.ai_embeddings_discourse_service_api_endpoint.present?
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end
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def dependant_setting_names
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%w[
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ai_embeddings_discourse_service_api_key
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ai_embeddings_discourse_service_api_endpoint_srv
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ai_embeddings_discourse_service_api_endpoint
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]
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end
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end
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def dimensions
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768
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end
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def max_sequence_length
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384
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end
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def id
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1
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end
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def version
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1
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end
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def pg_function
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"<#>"
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end
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def tokenizer
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DiscourseAi::Tokenizer::AllMpnetBaseV2Tokenizer
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end
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def inference_client
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DiscourseAi::Inference::DiscourseClassifier.instance(self.class.name)
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end
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end
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end
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end
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end
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