hgaol
22c2a5c276
- Added a new vector search syncer to aggregate questions and answers with comments for vector embedding. - Introduced a new VectorSearch interface and related structures for managing vector storage and similarity search. - Refactored embedding service to delegate semantic search to the new vector search plugin. - Removed embedding-related fields from SiteAIProvider and UI forms as part of the transition to the new vector search architecture. - Updated plugin registration to include vector search capabilities. - Cleaned up embedding service methods and removed unused dependencies.
61 lines
1.9 KiB
Go
61 lines
1.9 KiB
Go
/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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package embedding
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import (
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"context"
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"fmt"
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"github.com/apache/answer/plugin"
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)
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// EmbeddingService is a thin facade that delegates semantic search to a VectorSearch plugin.
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// If no plugin is enabled, semantic search is unavailable.
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type EmbeddingService struct{}
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// NewEmbeddingService creates a new EmbeddingService.
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func NewEmbeddingService() *EmbeddingService {
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return &EmbeddingService{}
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}
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// SearchSimilar delegates to the VectorSearch plugin.
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// Returns an error if no plugin is enabled.
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func (s *EmbeddingService) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) {
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var results []plugin.VectorSearchResult
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var searchErr error
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found := false
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err := plugin.CallVectorSearch(func(vs plugin.VectorSearch) error {
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found = true
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results, searchErr = vs.SearchSimilar(ctx, query, topK)
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return nil
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})
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if err != nil {
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return nil, fmt.Errorf("call vector search plugin failed: %w", err)
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}
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if !found {
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return nil, fmt.Errorf("semantic search is not available: no vector search plugin is enabled")
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}
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if searchErr != nil {
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return nil, searchErr
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}
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return results, nil
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}
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