rogee
c72e359e48
- ArticleEmbeddingRepo (new): Upsert, GetByArticleID, DeleteByArticleID, SearchByEmbedding using pgvector cosine distance (vector_embedding column) - ArticleEmbedding model: add VectorEmbedding pgvector.Vector field alongside existing JSONB Embedding (backward compatible) - ArticleService: add SemanticSearch() — generates query embedding via LLM, searches articles by cosine similarity; add GenerateEmbedding() — creates and stores article embedding from title+description+content - ArticleHandler: add SemanticSearch endpoint GET /portals/:portal_id/articles/semantic_search?query=... - bootstrap.go: inject articleEmbeddingRepo + llmProvider into ArticleService - router.go: register /articles/semantic_search route - migration 000049: add vector(1536) column to article_embeddings table, create ivfflat index, migrate existing JSONB data to vector format Verified: go build + go vet + go test all pass
21 lines
816 B
Go
21 lines
816 B
Go
package model
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import (
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"encoding/json"
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"github.com/pgvector/pgvector-go"
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)
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// ArticleEmbedding stores vector embeddings for semantic article search.
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// Reference: Chatwoot ArticleEmbedding (enterprise) + P2B M9 spec
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type ArticleEmbedding struct {
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Base
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ArticleID uint `gorm:"not null;index" json:"article_id"`
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Embedding json.RawMessage `gorm:"type:jsonb" json:"embedding"` // original JSONB storage (backward compat)
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VectorEmbedding pgvector.Vector `gorm:"type:vector(1536)" json:"-"` // pgvector column for cosine similarity search
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Term string `gorm:"type:text;not null" json:"term"` // searchable text content
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Article Article `gorm:"foreignKey:ArticleID" json:"article,omitempty"`
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}
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func (ArticleEmbedding) TableName() string { return "article_embeddings" } |