mlogclub
853ae71b3d
- Implemented directory validation in Create and Update knowledge document services. - Added directory selection in document and FAQ edit dialogs with a new KnowledgeDirectoryPanel component. - Enhanced API to support fetching, creating, updating, and deleting knowledge directories. - Updated data models to include directory information for knowledge documents and FAQs. - Added translations for directory-related messages in English and Chinese.
62 lines
2.0 KiB
Go
62 lines
2.0 KiB
Go
package rag
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import (
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"context"
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"fmt"
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"time"
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"agent-desk/internal/ai"
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"agent-desk/internal/ai/rag/vectordb"
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"agent-desk/internal/models"
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"agent-desk/internal/pkg/enums"
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)
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func buildFAQChunkModel(knowledgeBase models.KnowledgeBase, faq models.KnowledgeFAQ, content string) (models.KnowledgeChunk, string) {
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chunkID := buildKnowledgeFAQChunkVectorID(knowledgeBase.ID, faq.ID, 0)
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now := time.Now()
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sectionPath := loadKnowledgeDirectoryPath(faq.DirectoryID)
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return models.KnowledgeChunk{
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KnowledgeBaseID: knowledgeBase.ID,
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FaqID: faq.ID,
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ChunkNo: 0,
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Title: faq.Question,
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Content: content,
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ContentHash: buildChunkContentHash(content),
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CharCount: len([]rune(content)),
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TokenCount: len([]rune(content)) / 2,
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ChunkType: string(enums.KnowledgeChunkTypeFAQ),
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SectionPath: sectionPath,
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Provider: string(enums.KnowledgeChunkProviderFAQ),
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VectorID: chunkID,
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Status: enums.StatusOk,
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CreatedAt: now,
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UpdatedAt: now,
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}, chunkID
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}
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func (s *index) prepareFAQVector(ctx context.Context, knowledgeBase models.KnowledgeBase, faq models.KnowledgeFAQ, content string) (vectordb.Vector, models.KnowledgeChunk, int, error) {
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embeddingResult, err := ai.Embedding.GenerateEmbedding(ctx, content)
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if err != nil {
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return vectordb.Vector{}, models.KnowledgeChunk{}, 0, fmt.Errorf("failed to generate embedding for faq %d: %w", faq.ID, err)
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}
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chunkModel, chunkID := buildFAQChunkModel(knowledgeBase, faq, content)
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sectionPath := chunkModel.SectionPath
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vector := vectordb.Vector{
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ID: chunkID,
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Vector: embeddingResult.Vector,
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Payload: vectordb.ChunkPayload{
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KnowledgeBaseID: knowledgeBase.ID,
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FaqID: faq.ID,
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FaqQuestion: faq.Question,
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ChunkNo: 0,
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ChunkType: string(enums.KnowledgeChunkTypeFAQ),
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SectionPath: sectionPath,
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Content: content,
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Title: faq.Question,
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Provider: string(enums.KnowledgeChunkProviderFAQ),
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},
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}
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return vector, chunkModel, embeddingResult.Dimension, nil
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}
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