mlogclub
5d7c10aeab
- Updated runtime configuration to use __CS_AI_AGENT_WIDGET_CONFIG__ instead of __CS_AGENT_WIDGET_CONFIG__. - Changed message types in support host bridge from "cs-agent" to "cs-ai-agent". - Minified SDK script updated to reflect new AI agent naming conventions. - Adjusted scrollbar styles in main.scss to use .cs-ai-agent-scrollbar instead of .cs-agent-scrollbar.
97 lines
3.2 KiB
Go
97 lines
3.2 KiB
Go
package rag
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import (
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"context"
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"fmt"
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"cs-ai-agent/internal/ai"
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"cs-ai-agent/internal/ai/rag/vectordb"
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"cs-ai-agent/internal/models"
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"cs-ai-agent/internal/repositories"
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"github.com/mlogclub/simple/common/strs"
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"github.com/mlogclub/simple/sqls"
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)
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func (s *index) runDocumentIndex(ctx context.Context, document models.KnowledgeDocument, knowledgeBase models.KnowledgeBase) ([]vectordb.Vector, int, error) {
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existingChunks := repositories.KnowledgeChunkRepository.FindByDocumentID(sqls.DB(), document.ID)
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chunks, err := s.buildDocumentChunks(ctx, document, knowledgeBase)
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if err != nil {
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return nil, 0, err
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}
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collectionName := s.getCollectionName()
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provider := vectordb.GetProvider()
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if provider == nil {
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return nil, 0, fmt.Errorf("vectordb provider not initialized")
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}
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if _, err := ai.Embedding.GetModel(ctx); err != nil {
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return nil, 0, fmt.Errorf("failed to get embedding model: %w", err)
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}
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existingVectorIDs := collectExistingVectorIDs(existingChunks)
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vectors, chunkModels, dimension, err := s.prepareDocumentVectors(ctx, knowledgeBase, document, chunks)
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if err != nil {
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return nil, 0, err
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}
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if err := s.ensureCollection(ctx, provider, collectionName, dimension); err != nil {
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return nil, 0, err
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}
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if len(existingVectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, existingVectorIDs); err != nil {
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return nil, 0, fmt.Errorf("failed to delete old vectors: %w", err)
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}
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}
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if err := provider.UpsertVectors(ctx, collectionName, vectors); err != nil {
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return nil, 0, fmt.Errorf("failed to upsert vectors: %w", err)
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}
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if err := s.replaceDocumentChunks(document.ID, chunkModels); err != nil {
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return nil, 0, fmt.Errorf("failed to save chunks: %w", err)
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}
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return vectors, len(chunks), nil
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}
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func (s *index) runFAQIndex(ctx context.Context, faq models.KnowledgeFAQ, knowledgeBase models.KnowledgeBase) error {
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existingChunks := repositories.KnowledgeChunkRepository.FindByFaqID(sqls.DB(), faq.ID)
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content := buildFAQChunkContent(faq)
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if content == "" {
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return fmt.Errorf("faq content is empty")
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}
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provider := vectordb.GetProvider()
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if provider == nil {
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return fmt.Errorf("vectordb provider not initialized")
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}
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if _, err := ai.Embedding.GetModel(ctx); err != nil {
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return fmt.Errorf("failed to get embedding model: %w", err)
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}
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vector, chunkModel, dimension, err := s.prepareFAQVector(ctx, knowledgeBase, faq, content)
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if err != nil {
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return err
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}
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collectionName := s.getCollectionName()
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if err := s.ensureCollection(ctx, provider, collectionName, dimension); err != nil {
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return err
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}
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existingVectorIDs := make([]string, 0, len(existingChunks))
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for _, chunk := range existingChunks {
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if strs.IsNotBlank(chunk.VectorID) {
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existingVectorIDs = append(existingVectorIDs, chunk.VectorID)
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}
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}
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if len(existingVectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, existingVectorIDs); err != nil {
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return fmt.Errorf("failed to delete old vectors: %w", err)
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}
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}
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if err := provider.UpsertVectors(ctx, collectionName, []vectordb.Vector{vector}); err != nil {
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return fmt.Errorf("failed to upsert vectors: %w", err)
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}
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if err := s.replaceFAQChunk(faq.ID, &chunkModel); err != nil {
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return fmt.Errorf("failed to save faq chunk: %w", err)
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}
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return nil
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}
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