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commit 22c2a5c276c5dd53014450ecd99009d305ab7118 Author: hgaol <[email protected]> AuthorDate: Sun Apr 12 13:22:00 2026 +0800 feat: implement vector search plugin and syncer for question/answer embeddings - 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. --- cmd/wire_gen.go | 22 +- docs/docs.go | 23 - docs/swagger.json | 23 - docs/swagger.yaml | 17 - i18n/en_US.yaml | 18 - i18n/zh_CN.yaml | 18 - internal/controller/mcp_controller.go | 3 +- internal/entity/embedding.go | 59 --- internal/migrations/init_data.go | 1 - internal/migrations/migrations.go | 1 - internal/migrations/v32.go | 9 - internal/repo/embedding/embedding_repo.go | 197 -------- internal/repo/provider.go | 2 - internal/repo/vector_search_sync/syncer.go | 196 ++++++++ internal/schema/siteinfo_schema.go | 13 +- internal/service/embedding/embedding_service.go | 496 +-------------------- .../service/plugin_common/plugin_common_service.go | 17 + internal/service/siteinfo/siteinfo_service.go | 6 - plugin/plugin.go | 4 + plugin/vector_search.go | 174 ++++++++ ui/src/common/interface.ts | 5 - ui/src/pages/Admin/AiSettings/index.tsx | 175 -------- 22 files changed, 427 insertions(+), 1052 deletions(-) diff --git a/cmd/wire_gen.go b/cmd/wire_gen.go index 60b2f893..2c593169 100644 --- a/cmd/wire_gen.go +++ b/cmd/wire_gen.go @@ -34,7 +34,7 @@ import ( "github.com/apache/answer/internal/base/server" "github.com/apache/answer/internal/base/translator" "github.com/apache/answer/internal/controller" - "github.com/apache/answer/internal/controller/template_render" + templaterender "github.com/apache/answer/internal/controller/template_render" "github.com/apache/answer/internal/controller_admin" "github.com/apache/answer/internal/repo/activity" "github.com/apache/answer/internal/repo/activity_common" @@ -49,7 +49,6 @@ import ( "github.com/apache/answer/internal/repo/collection" "github.com/apache/answer/internal/repo/comment" "github.com/apache/answer/internal/repo/config" - "github.com/apache/answer/internal/repo/embedding" "github.com/apache/answer/internal/repo/export" "github.com/apache/answer/internal/repo/file_record" "github.com/apache/answer/internal/repo/limit" @@ -77,18 +76,18 @@ import ( activity_common2 "github.com/apache/answer/internal/service/activity_common" "github.com/apache/answer/internal/service/activityqueue" ai_conversation2 "github.com/apache/answer/internal/service/ai_conversation" - "github.com/apache/answer/internal/service/answer_common" + answercommon "github.com/apache/answer/internal/service/answer_common" "github.com/apache/answer/internal/service/apikey" auth2 "github.com/apache/answer/internal/service/auth" badge2 "github.com/apache/answer/internal/service/badge" collection2 "github.com/apache/answer/internal/service/collection" - "github.com/apache/answer/internal/service/collection_common" + collectioncommon "github.com/apache/answer/internal/service/collection_common" comment2 "github.com/apache/answer/internal/service/comment" "github.com/apache/answer/internal/service/comment_common" config2 "github.com/apache/answer/internal/service/config" "github.com/apache/answer/internal/service/content" "github.com/apache/answer/internal/service/dashboard" - embedding2 "github.com/apache/answer/internal/service/embedding" + "github.com/apache/answer/internal/service/embedding" "github.com/apache/answer/internal/service/eventqueue" export2 "github.com/apache/answer/internal/service/export" "github.com/apache/answer/internal/service/feature_toggle" @@ -96,13 +95,13 @@ import ( "github.com/apache/answer/internal/service/follow" "github.com/apache/answer/internal/service/importer" meta2 "github.com/apache/answer/internal/service/meta" - "github.com/apache/answer/internal/service/meta_common" + metacommon "github.com/apache/answer/internal/service/meta_common" "github.com/apache/answer/internal/service/noticequeue" "github.com/apache/answer/internal/service/notification" - "github.com/apache/answer/internal/service/notification_common" + notificationcommon "github.com/apache/answer/internal/service/notification_common" "github.com/apache/answer/internal/service/object_info" "github.com/apache/answer/internal/service/plugin_common" - "github.com/apache/answer/internal/service/question_common" + questioncommon "github.com/apache/answer/internal/service/question_common" rank2 "github.com/apache/answer/internal/service/rank" reason2 "github.com/apache/answer/internal/service/reason" report2 "github.com/apache/answer/internal/service/report" @@ -118,7 +117,7 @@ import ( tag_common2 "github.com/apache/answer/internal/service/tag_common" "github.com/apache/answer/internal/service/uploader" "github.com/apache/answer/internal/service/user_admin" - "github.com/apache/answer/internal/service/user_common" + usercommon "github.com/apache/answer/internal/service/user_common" user_external_login2 "github.com/apache/answer/internal/service/user_external_login" user_notification_config2 "github.com/apache/answer/internal/service/user_notification_config" "github.com/segmentfault/pacman" @@ -249,9 +248,8 @@ func initApplication(debug bool, serverConf *conf.Server, dbConf *data.Database, reasonService := reason2.NewReasonService(reasonRepo) reasonController := controller.NewReasonController(reasonService) themeController := controller_admin.NewThemeController() - embeddingRepo := embedding.NewEmbeddingRepo(dataData) - embeddingService := embedding2.NewEmbeddingService(embeddingRepo, searchService, answerService, questionCommon, commentRepo, siteInfoCommonService) - siteInfoService := siteinfo.NewSiteInfoService(siteInfoRepo, siteInfoCommonService, emailService, tagCommonService, configService, questionCommon, fileRecordService, embeddingService) + embeddingService := embedding.NewEmbeddingService() + siteInfoService := siteinfo.NewSiteInfoService(siteInfoRepo, siteInfoCommonService, emailService, tagCommonService, configService, questionCommon, fileRecordService) siteInfoController := controller_admin.NewSiteInfoController(siteInfoService) controllerSiteInfoController := controller.NewSiteInfoController(siteInfoCommonService) notificationCommon := notificationcommon.NewNotificationCommon(dataData, notificationRepo, userCommon, activityRepo, followRepo, objService, noticequeueService, userExternalLoginRepo, siteInfoCommonService) diff --git a/docs/docs.go b/docs/docs.go index 8f65e05d..b50a3cf7 100644 --- a/docs/docs.go +++ b/docs/docs.go @@ -11712,24 +11712,6 @@ const docTemplate = `{ "type": "string", "maxLength": 256 }, - "embedding_crontab": { - "type": "string", - "maxLength": 100 - }, - "embedding_dimensions": { - "type": "integer" - }, - "embedding_level": { - "type": "string", - "enum": [ - "question", - "answer" - ] - }, - "embedding_model": { - "type": "string", - "maxLength": 100 - }, "model": { "type": "string", "maxLength": 100 @@ -11738,11 +11720,6 @@ const docTemplate = `{ "type": "string", "maxLength": 50 }, - "similarity_threshold": { - "type": "number", - "maximum": 1, - "minimum": 0 - } } }, "schema.SiteAIReq": { diff --git a/docs/swagger.json b/docs/swagger.json index 71e802df..44186220 100644 --- a/docs/swagger.json +++ b/docs/swagger.json @@ -11685,24 +11685,6 @@ "type": "string", "maxLength": 256 }, - "embedding_crontab": { - "type": "string", - "maxLength": 100 - }, - "embedding_dimensions": { - "type": "integer" - }, - "embedding_level": { - "type": "string", - "enum": [ - "question", - "answer" - ] - }, - "embedding_model": { - "type": "string", - "maxLength": 100 - }, "model": { "type": "string", "maxLength": 100 @@ -11711,11 +11693,6 @@ "type": "string", "maxLength": 50 }, - "similarity_threshold": { - "type": "number", - "maximum": 1, - "minimum": 0 - } } }, "schema.SiteAIReq": { diff --git a/docs/swagger.yaml b/docs/swagger.yaml index 5dc68d6c..7a7adb68 100644 --- a/docs/swagger.yaml +++ b/docs/swagger.yaml @@ -2250,29 +2250,12 @@ definitions: api_key: maxLength: 256 type: string - embedding_crontab: - maxLength: 100 - type: string - embedding_dimensions: - type: integer - embedding_level: - enum: - - question - - answer - type: string - embedding_model: - maxLength: 100 - type: string model: maxLength: 100 type: string provider: maxLength: 50 type: string - similarity_threshold: - maximum: 1 - minimum: 0 - type: number type: object schema.SiteAIReq: properties: diff --git a/i18n/en_US.yaml b/i18n/en_US.yaml index ec385e76..9a0d198b 100644 --- a/i18n/en_US.yaml +++ b/i18n/en_US.yaml @@ -2355,24 +2355,6 @@ ui: label: Model msg: Model is required add_success: AI settings updated successfully. - embedding_settings: Embedding Settings - embedding_model: - label: Embedding model - text: "The model used to generate vector embeddings for semantic search (e.g. text-embedding-3-small)." - embedding_dimensions: - label: Embedding dimensions - text: "The number of dimensions for the embedding vectors (e.g. 1536 for text-embedding-3-small)." - embedding_level: - label: Embedding level - text: "Choose whether to create embeddings at the question level (question + all answers + comments) or answer level (each answer separately)." - question: Question level - answer: Answer level - embedding_crontab: - label: Embedding schedule - text: "Cron expression for periodic embedding calculation (e.g. '0 */6 * * *' for every 6 hours). Leave empty to disable automatic indexing." - similarity_threshold: - label: Similarity threshold - text: "Minimum cosine similarity score (0-1) for semantic search results. Only results with a score above this threshold will be returned. Default is 0 (no filtering)." conversations: topic: Topic helpful: Helpful diff --git a/i18n/zh_CN.yaml b/i18n/zh_CN.yaml index 191619dd..f16ed9fa 100644 --- a/i18n/zh_CN.yaml +++ b/i18n/zh_CN.yaml @@ -2319,24 +2319,6 @@ ui: label: 模型 msg: 模型是必需的 add_success: AI 设置更新成功。 - embedding_settings: Embedding 设置 - embedding_model: - label: Embedding 模型 - text: "用于生成语义搜索向量 Embedding 的模型（例如 text-embedding-3-small）。" - embedding_dimensions: - label: Embedding 维度 - text: "Embedding 向量的维度数（例如 text-embedding-3-small 为 1536）。" - embedding_level: - label: Embedding 级别 - text: "选择在问题级别（问题 + 所有回答 + 评论）还是回答级别（每个回答单独）创建 Embedding。" - question: 问题级别 - answer: 回答级别 - embedding_crontab: - label: Embedding 计划 - text: "定期计算 Embedding 的 Cron 表达式（例如 '0 */6 * * *' 表示每 6 小时）。留空则禁用自动索引。" - similarity_threshold: - label: 相似度阈值 - text: "语义搜索结果的最低余弦相似度分数（0-1）。只有分数高于此阈值的结果才会被返回。默认值为 0（不过滤）。" conversations: topic: 主题 helpful: 有帮助 diff --git a/internal/controller/mcp_controller.go b/internal/controller/mcp_controller.go index fecdbef6..b40c58cf 100644 --- a/internal/controller/mcp_controller.go +++ b/internal/controller/mcp_controller.go @@ -37,6 +37,7 @@ import ( "github.com/apache/answer/internal/service/siteinfo_common" tagcommonser "github.com/apache/answer/internal/service/tag_common" usercommon "github.com/apache/answer/internal/service/user_common" + "github.com/apache/answer/plugin" "github.com/mark3labs/mcp-go/mcp" "github.com/segmentfault/pacman/log" ) @@ -381,7 +382,7 @@ func (c *MCPController) MCPSemanticSearchHandler() func(ctx context.Context, req resp := make([]*schema.MCPSemanticSearchResp, 0, len(results)) for _, r := range results { - var meta entity.EmbeddingMetadata + var meta plugin.VectorSearchMetadata _ = json.Unmarshal([]byte(r.Metadata), &meta) item := &schema.MCPSemanticSearchResp{ diff --git a/internal/entity/embedding.go b/internal/entity/embedding.go deleted file mode 100644 index 3ea500d9..00000000 --- a/internal/entity/embedding.go +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. - */ - -package entity - -import "time" - -// Embedding stores vector embeddings for questions or answers. -type Embedding struct { - ID int `xorm:"not null pk autoincr INT(11) id"` - CreatedAt time.Time `xorm:"created not null default CURRENT_TIMESTAMP TIMESTAMP created_at"` - UpdatedAt time.Time `xorm:"updated not null default CURRENT_TIMESTAMP TIMESTAMP updated_at"` - ObjectID string `xorm:"not null BIGINT(20) INDEX object_id unique(object_embedding)"` - ObjectType string `xorm:"not null default '' VARCHAR(20) object_type unique(object_embedding)"` - ContentHash string `xorm:"not null default '' VARCHAR(64) content_hash"` - Metadata string `xorm:"not null MEDIUMTEXT metadata"` - Embedding string `xorm:"not null MEDIUMTEXT embedding"` - Dimensions int `xorm:"not null default 0 INT(11) dimensions"` -} - -// TableName returns the table name -func (Embedding) TableName() string { - return "embedding" -} - -// EmbeddingMetadata holds IDs for URI composition and content retrieval at query time. -type EmbeddingMetadata struct { - QuestionID string `json:"question_id"` - AnswerID string `json:"answer_id,omitempty"` - Answers []EmbeddingMetadataAnswer `json:"answers,omitempty"` - Comments []EmbeddingMetadataComment `json:"comments,omitempty"` -} - -// EmbeddingMetadataAnswer stores answer ID and comment IDs in metadata. -type EmbeddingMetadataAnswer struct { - AnswerID string `json:"answer_id"` - Comments []EmbeddingMetadataComment `json:"comments,omitempty"` -} - -// EmbeddingMetadataComment stores comment ID in metadata. -type EmbeddingMetadataComment struct { - CommentID string `json:"comment_id"` -} diff --git a/internal/migrations/init_data.go b/internal/migrations/init_data.go index e65c9f11..5af41bbf 100644 --- a/internal/migrations/init_data.go +++ b/internal/migrations/init_data.go @@ -79,7 +79,6 @@ var ( &entity.APIKey{}, &entity.AIConversation{}, &entity.AIConversationRecord{}, - &entity.Embedding{}, } roles = []*entity.Role{ diff --git a/internal/migrations/migrations.go b/internal/migrations/migrations.go index e6722cc0..682a7b20 100644 --- a/internal/migrations/migrations.go +++ b/internal/migrations/migrations.go @@ -108,7 +108,6 @@ var migrations = []Migration{ NewMigration("v1.8.0", "change admin menu", updateAdminMenuSettings, true), NewMigration("v1.8.1", "ai feat", aiFeat, true), NewMigration("v2.0.1", "change avatar type to text", updateAvatarType, false), - NewMigration("v2.0.2", "add embedding table", addEmbeddingTable, false), } func GetMigrations() []Migration { diff --git a/internal/migrations/v32.go b/internal/migrations/v32.go index 43809734..fc6614b1 100644 --- a/internal/migrations/v32.go +++ b/internal/migrations/v32.go @@ -24,7 +24,6 @@ import ( "fmt" "github.com/apache/answer/internal/entity" - "github.com/segmentfault/pacman/log" "xorm.io/xorm" ) @@ -36,11 +35,3 @@ func updateAvatarType(ctx context.Context, x *xorm.Engine) error { } return nil } - -func addEmbeddingTable(ctx context.Context, x *xorm.Engine) error { - if err := x.Context(ctx).Sync(new(entity.Embedding)); err != nil { - return fmt.Errorf("sync embedding table failed: %w", err) - } - log.Info("Embedding table migration completed successfully") - return nil -} diff --git a/internal/repo/embedding/embedding_repo.go b/internal/repo/embedding/embedding_repo.go deleted file mode 100644 index 67bde1d5..00000000 --- a/internal/repo/embedding/embedding_repo.go +++ /dev/null @@ -1,197 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. - */ - -package embedding - -import ( - "context" - "encoding/json" - "math" - "sort" - - "github.com/apache/answer/internal/base/data" - "github.com/apache/answer/internal/entity" - "github.com/segmentfault/pacman/log" - "xorm.io/builder" -) - -// EmbeddingRepo defines the interface for embedding data access. -type EmbeddingRepo interface { - Upsert(ctx context.Context, emb *entity.Embedding) error - GetByObjectID(ctx context.Context, objectID, objectType string) (*entity.Embedding, bool, error) - GetAll(ctx context.Context) ([]*entity.Embedding, error) - SearchSimilar(ctx context.Context, queryVector []float32, topK int) ([]SimilarResult, error) - DeleteByObjectID(ctx context.Context, objectID, objectType string) error - Count(ctx context.Context) (int64, error) -} - -// SimilarResult holds a similarity search result. -type SimilarResult struct { - ObjectID string `json:"object_id"` - ObjectType string `json:"object_type"` - Metadata string `json:"metadata"` - Score float64 `json:"score"` -} - -type embeddingRepo struct { - data *data.Data -} - -// NewEmbeddingRepo creates a new EmbeddingRepo. -func NewEmbeddingRepo(data *data.Data) EmbeddingRepo { - return &embeddingRepo{data: data} -} - -// Upsert inserts or updates an embedding by (object_id, object_type). -func (r *embeddingRepo) Upsert(ctx context.Context, emb *entity.Embedding) error { - existing := &entity.Embedding{} - exist, err := r.data.DB.Context(ctx). - Where(builder.Eq{"object_id": emb.ObjectID, "object_type": emb.ObjectType}). - Get(existing) - if err != nil { - log.Errorf("check embedding existence failed: %v", err) - return err - } - - if exist { - emb.ID = existing.ID - _, err = r.data.DB.Context(ctx).ID(existing.ID). - Cols("content_hash", "metadata", "embedding", "dimensions", "updated_at"). - Update(emb) - if err != nil { - log.Errorf("update embedding failed: %v", err) - return err - } - return nil - } - - _, err = r.data.DB.Context(ctx).Insert(emb) - if err != nil { - log.Errorf("insert embedding failed: %v", err) - return err - } - return nil -} - -// GetByObjectID returns an embedding by object ID and type. -func (r *embeddingRepo) GetByObjectID(ctx context.Context, objectID, objectType string) (*entity.Embedding, bool, error) { - emb := &entity.Embedding{} - exist, err := r.data.DB.Context(ctx). - Where(builder.Eq{"object_id": objectID, "object_type": objectType}). - Get(emb) - if err != nil { - log.Errorf("get embedding failed: %v", err) - return nil, false, err - } - return emb, exist, nil -} - -// GetAll returns all embeddings. -func (r *embeddingRepo) GetAll(ctx context.Context) ([]*entity.Embedding, error) { - var list []*entity.Embedding - err := r.data.DB.Context(ctx).Find(&list) - if err != nil { - log.Errorf("get all embeddings failed: %v", err) - return nil, err - } - return list, nil -} - -// SearchSimilar performs brute-force cosine similarity search in Go. -func (r *embeddingRepo) SearchSimilar(ctx context.Context, queryVector []float32, topK int) ([]SimilarResult, error) { - allEmbeddings, err := r.GetAll(ctx) - if err != nil { - return nil, err - } - - type scored struct { - emb *entity.Embedding - score float64 - } - results := make([]scored, 0, len(allEmbeddings)) - - for _, emb := range allEmbeddings { - var vec []float32 - if err := json.Unmarshal([]byte(emb.Embedding), &vec); err != nil { - log.Warnf("skip embedding id=%d, unmarshal failed: %v", emb.ID, err) - continue - } - score := cosineSimilarity(queryVector, vec) - results = append(results, scored{emb: emb, score: score}) - } - - sort.Slice(results, func(i, j int) bool { - return results[i].score > results[j].score - }) - - if topK > len(results) { - topK = len(results) - } - - out := make([]SimilarResult, 0, topK) - for i := 0; i < topK; i++ { - out = append(out, SimilarResult{ - ObjectID: results[i].emb.ObjectID, - ObjectType: results[i].emb.ObjectType, - Metadata: results[i].emb.Metadata, - Score: results[i].score, - }) - } - return out, nil -} - -// DeleteByObjectID deletes an embedding by object ID and type. -func (r *embeddingRepo) DeleteByObjectID(ctx context.Context, objectID, objectType string) error { - _, err := r.data.DB.Context(ctx). - Where(builder.Eq{"object_id": objectID, "object_type": objectType}). - Delete(&entity.Embedding{}) - if err != nil { - log.Errorf("delete embedding failed: %v", err) - return err - } - return nil -} - -// Count returns the total number of embeddings. -func (r *embeddingRepo) Count(ctx context.Context) (int64, error) { - count, err := r.data.DB.Context(ctx).Count(&entity.Embedding{}) - if err != nil { - log.Errorf("count embeddings failed: %v", err) - return 0, err - } - return count, nil -} - -// cosineSimilarity computes cosine similarity between two vectors. -func cosineSimilarity(a, b []float32) float64 { - if len(a) != len(b) || len(a) == 0 { - return 0 - } - var dotProduct, normA, normB float64 - for i := range a { - dotProduct += float64(a[i]) * float64(b[i]) - normA += float64(a[i]) * float64(a[i]) - normB += float64(b[i]) * float64(b[i]) - } - denom := math.Sqrt(normA) * math.Sqrt(normB) - if denom == 0 { - return 0 - } - return dotProduct / denom -} diff --git a/internal/repo/provider.go b/internal/repo/provider.go index 2a9717d0..510a94aa 100644 --- a/internal/repo/provider.go +++ b/internal/repo/provider.go @@ -34,7 +34,6 @@ import ( "github.com/apache/answer/internal/repo/collection" "github.com/apache/answer/internal/repo/comment" "github.com/apache/answer/internal/repo/config" - "github.com/apache/answer/internal/repo/embedding" "github.com/apache/answer/internal/repo/export" "github.com/apache/answer/internal/repo/file_record" "github.com/apache/answer/internal/repo/limit" @@ -114,5 +113,4 @@ var ProviderSetRepo = wire.NewSet( file_record.NewFileRecordRepo, api_key.NewAPIKeyRepo, ai_conversation.NewAIConversationRepo, - embedding.NewEmbeddingRepo, ) diff --git a/internal/repo/vector_search_sync/syncer.go b/internal/repo/vector_search_sync/syncer.go new file mode 100644 index 00000000..f27b1fd2 --- /dev/null +++ b/internal/repo/vector_search_sync/syncer.go @@ -0,0 +1,196 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package vector_search_sync + +import ( + "context" + "encoding/json" + "fmt" + "strings" + + "github.com/apache/answer/internal/base/data" + "github.com/apache/answer/internal/entity" + "github.com/apache/answer/pkg/uid" + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +// NewPluginSyncer creates a new VectorSearchSyncer that reads from the database. +func NewPluginSyncer(data *data.Data) plugin.VectorSearchSyncer { + return &PluginSyncer{data: data} +} + +// PluginSyncer implements plugin.VectorSearchSyncer. +// It aggregates question/answer text with comments for vector embedding. +type PluginSyncer struct { + data *data.Data +} + +// GetQuestionsPage returns a page of questions with aggregated text +// (question title + body + all answers + all comments). +func (p *PluginSyncer) GetQuestionsPage(ctx context.Context, page, pageSize int) ( + []*plugin.VectorSearchContent, error) { + questions := make([]*entity.Question, 0) + startNum := (page - 1) * pageSize + err := p.data.DB.Context(ctx).Limit(pageSize, startNum).Find(&questions) + if err != nil { + return nil, err + } + return p.buildQuestionContents(ctx, questions) +} + +// GetAnswersPage returns a page of answers with aggregated text +// (parent question title + answer body + answer comments). +func (p *PluginSyncer) GetAnswersPage(ctx context.Context, page, pageSize int) ( + []*plugin.VectorSearchContent, error) { + answers := make([]*entity.Answer, 0) + startNum := (page - 1) * pageSize + err := p.data.DB.Context(ctx).Limit(pageSize, startNum).Find(&answers) + if err != nil { + return nil, err + } + return p.buildAnswerContents(ctx, answers) +} + +// buildQuestionContents aggregates each question with its answers and comments. +func (p *PluginSyncer) buildQuestionContents(ctx context.Context, questions []*entity.Question) ( + []*plugin.VectorSearchContent, error) { + result := make([]*plugin.VectorSearchContent, 0, len(questions)) + for _, q := range questions { + meta := plugin.VectorSearchMetadata{ + QuestionID: uid.DeShortID(q.ID), + } + + var parts []string + parts = append(parts, fmt.Sprintf("Question: %s\n%s", q.Title, q.OriginalText)) + + // Get answers for this question + answers := make([]*entity.Answer, 0) + err := p.data.DB.Context(ctx).Where("question_id = ?", q.ID).Find(&answers) + if err != nil { + log.Warnf("get answers for question %s failed: %v", q.ID, err) + } else { + for _, a := range answers { + parts = append(parts, fmt.Sprintf("Answer: %s", a.OriginalText)) + answerMeta := plugin.VectorSearchMetadataAnswer{ + AnswerID: uid.DeShortID(a.ID), + } + + // Get comments on this answer + answerComments := make([]*entity.Comment, 0) + err := p.data.DB.Context(ctx).Where("object_id = ?", a.ID). + OrderBy("created_at ASC").Limit(50).Find(&answerComments) + if err != nil { + log.Warnf("get comments for answer %s failed: %v", a.ID, err) + } else { + for _, c := range answerComments { + parts = append(parts, fmt.Sprintf("Comment on answer: %s", c.OriginalText)) + answerMeta.Comments = append(answerMeta.Comments, plugin.VectorSearchMetadataComment{ + CommentID: uid.DeShortID(c.ID), + }) + } + } + meta.Answers = append(meta.Answers, answerMeta) + } + } + + // Get comments on the question + questionComments := make([]*entity.Comment, 0) + err = p.data.DB.Context(ctx).Where("object_id = ?", q.ID). + OrderBy("created_at ASC").Limit(50).Find(&questionComments) + if err != nil { + log.Warnf("get comments for question %s failed: %v", q.ID, err) + } else { + for _, c := range questionComments { + parts = append(parts, fmt.Sprintf("Comment: %s", c.OriginalText)) + meta.Comments = append(meta.Comments, plugin.VectorSearchMetadataComment{ + CommentID: uid.DeShortID(c.ID), + }) + } + } + + metaJSON, _ := json.Marshal(meta) + result = append(result, &plugin.VectorSearchContent{ + ObjectID: uid.DeShortID(q.ID), + ObjectType: "question", + Title: q.Title, + Content: strings.Join(parts, "\n\n"), + Metadata: string(metaJSON), + }) + } + return result, nil +} + +// buildAnswerContents aggregates each answer with its parent question title and comments. +func (p *PluginSyncer) buildAnswerContents(ctx context.Context, answers []*entity.Answer) ( + []*plugin.VectorSearchContent, error) { + result := make([]*plugin.VectorSearchContent, 0, len(answers)) + for _, a := range answers { + // Get parent question for title + question := &entity.Question{} + exist, err := p.data.DB.Context(ctx).Where("id = ?", a.QuestionID).Get(question) + if err != nil { + log.Errorf("get question %s failed: %v", a.QuestionID, err) + continue + } + if !exist { + continue + } + + meta := plugin.VectorSearchMetadata{ + QuestionID: uid.DeShortID(a.QuestionID), + AnswerID: uid.DeShortID(a.ID), + } + + var parts []string + parts = append(parts, fmt.Sprintf("Question: %s", question.Title)) + parts = append(parts, fmt.Sprintf("Answer: %s", a.OriginalText)) + + answerMeta := plugin.VectorSearchMetadataAnswer{ + AnswerID: uid.DeShortID(a.ID), + } + + // Get comments on this answer + answerComments := make([]*entity.Comment, 0) + err = p.data.DB.Context(ctx).Where("object_id = ?", a.ID). + OrderBy("created_at ASC").Limit(50).Find(&answerComments) + if err != nil { + log.Warnf("get comments for answer %s failed: %v", a.ID, err) + } else { + for _, c := range answerComments { + parts = append(parts, fmt.Sprintf("Comment: %s", c.OriginalText)) + answerMeta.Comments = append(answerMeta.Comments, plugin.VectorSearchMetadataComment{ + CommentID: uid.DeShortID(c.ID), + }) + } + } + meta.Answers = append(meta.Answers, answerMeta) + + metaJSON, _ := json.Marshal(meta) + result = append(result, &plugin.VectorSearchContent{ + ObjectID: uid.DeShortID(a.ID), + ObjectType: "answer", + Title: question.Title, + Content: strings.Join(parts, "\n\n"), + Metadata: string(metaJSON), + }) + } + return result, nil +} diff --git a/internal/schema/siteinfo_schema.go b/internal/schema/siteinfo_schema.go index 84bc54e3..bdf2308d 100644 --- a/internal/schema/siteinfo_schema.go +++ b/internal/schema/siteinfo_schema.go @@ -281,15 +281,10 @@ func (s *SiteAIResp) GetProvider() *SiteAIProvider { } type SiteAIProvider struct { - Provider string `validate:"omitempty,lte=50" form:"provider" json:"provider"` - APIHost string `validate:"omitempty,lte=512" form:"api_host" json:"api_host"` - APIKey string `validate:"omitempty,lte=256" form:"api_key" json:"api_key"` - Model string `validate:"omitempty,lte=100" form:"model" json:"model"` - EmbeddingModel string `validate:"omitempty,lte=100" form:"embedding_model" json:"embedding_model"` - EmbeddingDimensions int `validate:"omitempty" form:"embedding_dimensions" json:"embedding_dimensions"` - EmbeddingLevel string `validate:"omitempty,oneof=question answer" form:"embedding_level" json:"embedding_level"` - EmbeddingCrontab string `validate:"omitempty,lte=100" form:"embedding_crontab" json:"embedding_crontab"` - SimilarityThreshold float64 `validate:"omitempty,gte=0,lte=1" form:"similarity_threshold" json:"similarity_threshold"` + Provider string `validate:"omitempty,lte=50" form:"provider" json:"provider"` + APIHost string `validate:"omitempty,lte=512" form:"api_host" json:"api_host"` + APIKey string `validate:"omitempty,lte=256" form:"api_key" json:"api_key"` + Model string `validate:"omitempty,lte=100" form:"model" json:"model"` } // SiteAIResp AI configuration response diff --git a/internal/service/embedding/embedding_service.go b/internal/service/embedding/embedding_service.go index 72e008ab..c69d60d8 100644 --- a/internal/service/embedding/embedding_service.go +++ b/internal/service/embedding/embedding_service.go @@ -21,496 +21,40 @@ package embedding import ( "context" - "crypto/sha256" - "encoding/json" "fmt" - "strings" - "sync" - "github.com/apache/answer/internal/base/pager" - "github.com/apache/answer/internal/entity" - embeddingRepo "github.com/apache/answer/internal/repo/embedding" - "github.com/apache/answer/internal/schema" - "github.com/apache/answer/internal/service/comment" - "github.com/apache/answer/internal/service/content" - questioncommon "github.com/apache/answer/internal/service/question_common" - "github.com/apache/answer/internal/service/siteinfo_common" - "github.com/robfig/cron/v3" - "github.com/sashabaranov/go-openai" - "github.com/segmentfault/pacman/log" + "github.com/apache/answer/plugin" ) -const ( - EmbeddingLevelQuestion = "question" - EmbeddingLevelAnswer = "answer" -) - -// EmbeddingService handles embedding generation, text aggregation, and indexing. -type EmbeddingService struct { - embeddingRepo embeddingRepo.EmbeddingRepo - searchService *content.SearchService - answerService *content.AnswerService - questionCommon *questioncommon.QuestionCommon - commentRepo comment.CommentRepo - siteInfoService siteinfo_common.SiteInfoCommonService - - mu sync.Mutex - cronJob *cron.Cron - cronSpec string -} +// EmbeddingService is a thin facade that delegates semantic search to a VectorSearch plugin. +// If no plugin is enabled, semantic search is unavailable. +type EmbeddingService struct{} // NewEmbeddingService creates a new EmbeddingService. -func NewEmbeddingService( - embeddingRepo embeddingRepo.EmbeddingRepo, - searchService *content.SearchService, - answerService *content.AnswerService, - questionCommon *questioncommon.QuestionCommon, - commentRepo comment.CommentRepo, - siteInfoService siteinfo_common.SiteInfoCommonService, -) *EmbeddingService { - return &EmbeddingService{ - embeddingRepo: embeddingRepo, - searchService: searchService, - answerService: answerService, - questionCommon: questionCommon, - commentRepo: commentRepo, - siteInfoService: siteInfoService, - } -} - -// getAIConfig returns the current AI configuration. -func (s *EmbeddingService) getAIConfig(ctx context.Context) (*schema.SiteAIResp, *schema.SiteAIProvider, error) { - aiConfig, err := s.siteInfoService.GetSiteAI(ctx) - if err != nil { - return nil, nil, fmt.Errorf("get AI config failed: %w", err) - } - if !aiConfig.Enabled { - return nil, nil, fmt.Errorf("AI feature is disabled") - } - provider := aiConfig.GetProvider() - if provider.EmbeddingModel == "" { - return nil, nil, fmt.Errorf("embedding model not configured") - } - return aiConfig, provider, nil -} - -// createEmbeddingClient creates an OpenAI-compatible client for embedding requests. -func (s *EmbeddingService) createEmbeddingClient(provider *schema.SiteAIProvider) *openai.Client { - config := openai.DefaultConfig(provider.APIKey) - config.BaseURL = provider.APIHost - if !strings.HasSuffix(config.BaseURL, "/v1") { - config.BaseURL += "/v1" - } - return openai.NewClientWithConfig(config) -} - -// GenerateEmbedding generates an embedding vector for the given text. -func (s *EmbeddingService) GenerateEmbedding(ctx context.Context, text string) ([]float32, error) { - _, provider, err := s.getAIConfig(ctx) - if err != nil { - return nil, err - } - - client := s.createEmbeddingClient(provider) - resp, err := client.CreateEmbeddings(ctx, openai.EmbeddingRequestStrings{ - Input: []string{text}, - Model: openai.EmbeddingModel(provider.EmbeddingModel), - }) - if err != nil { - return nil, fmt.Errorf("create embeddings failed: %w", err) - } - if len(resp.Data) == 0 { - return nil, fmt.Errorf("no embedding returned") - } - return resp.Data[0].Embedding, nil -} - -// ComputeContentHash computes SHA256 of the text. -func ComputeContentHash(text string) string { - h := sha256.Sum256([]byte(text)) - return fmt.Sprintf("%x", h) -} - -// BuildTextForQuestion aggregates question title + body + all answers + comments into one text. -// Uses SearchService and QuestionCommon to respect the plugin architecture. -func (s *EmbeddingService) BuildTextForQuestion(ctx context.Context, questionID string) (text string, meta *entity.EmbeddingMetadata, err error) { - // Get question detail via service layer - question, err := s.questionCommon.Info(ctx, questionID, "") - if err != nil { - return "", nil, fmt.Errorf("get question info failed: %w", err) - } - - meta = &entity.EmbeddingMetadata{ - QuestionID: questionID, - } - - var parts []string - parts = append(parts, fmt.Sprintf("Question: %s\n%s", question.Title, question.Content)) - - // Get answers via AnswerService - answerInfoList, _, err := s.answerService.SearchList(ctx, &schema.AnswerListReq{ - QuestionID: questionID, - Page: 1, - PageSize: 50, - }) - if err != nil { - log.Warnf("get answers for question %s failed: %v", questionID, err) - } else { - for _, a := range answerInfoList { - parts = append(parts, fmt.Sprintf("Answer: %s", a.Content)) - answerMeta := entity.EmbeddingMetadataAnswer{ - AnswerID: a.ID, - } - - // Get comments on this answer - answerComments, _, err := s.commentRepo.GetCommentPage(ctx, &comment.CommentQuery{ - PageCond: pager.PageCond{Page: 1, PageSize: 50}, - ObjectID: a.ID, - QueryCond: "newest", - }) - if err != nil { - log.Warnf("get comments for answer %s failed: %v", a.ID, err) - } else { - for _, c := range answerComments { - parts = append(parts, fmt.Sprintf("Comment on answer: %s", c.OriginalText)) - answerMeta.Comments = append(answerMeta.Comments, entity.EmbeddingMetadataComment{ - CommentID: c.ID, - }) - } - } - meta.Answers = append(meta.Answers, answerMeta) - } - } - - // Get comments on the question - commentList, _, err := s.commentRepo.GetCommentPage(ctx, &comment.CommentQuery{ - PageCond: pager.PageCond{Page: 1, PageSize: 50}, - ObjectID: questionID, - QueryCond: "newest", - }) - if err != nil { - log.Warnf("get comments for question %s failed: %v", questionID, err) - } else { - for _, c := range commentList { - parts = append(parts, fmt.Sprintf("Comment: %s", c.OriginalText)) - meta.Comments = append(meta.Comments, entity.EmbeddingMetadataComment{ - CommentID: c.ID, - }) - } - } - - return strings.Join(parts, "\n\n"), meta, nil -} - -// BuildTextForAnswer aggregates answer body + parent question title + answer comments into one text. -func (s *EmbeddingService) BuildTextForAnswer(ctx context.Context, answerID, questionID string) (text string, meta *entity.EmbeddingMetadata, err error) { - // Get parent question title - question, err := s.questionCommon.Info(ctx, questionID, "") - if err != nil { - return "", nil, fmt.Errorf("get question info for answer failed: %w", err) - } - - meta = &entity.EmbeddingMetadata{ - QuestionID: questionID, - AnswerID: answerID, - } - - // Get the specific answer's content via AnswerService - answerInfo, err := s.answerService.GetDetail(ctx, answerID) - if err != nil { - return "", nil, fmt.Errorf("get answer failed: %w", err) - } - - var answerText string - if answerInfo != nil { - answerText = answerInfo.Content - } - - var parts []string - parts = append(parts, fmt.Sprintf("Question: %s", question.Title)) - if answerText != "" { - parts = append(parts, fmt.Sprintf("Answer: %s", answerText)) - meta.Answers = append(meta.Answers, entity.EmbeddingMetadataAnswer{ - AnswerID: answerID, - }) - } - - // Get comments on the answer - commentList, _, err := s.commentRepo.GetCommentPage(ctx, &comment.CommentQuery{ - PageCond: pager.PageCond{Page: 1, PageSize: 50}, - ObjectID: answerID, - QueryCond: "newest", - }) - if err != nil { - log.Warnf("get comments for answer %s failed: %v", answerID, err) - } else { - for _, c := range commentList { - parts = append(parts, fmt.Sprintf("Comment: %s", c.OriginalText)) - if len(meta.Answers) > 0 { - meta.Answers[0].Comments = append(meta.Answers[0].Comments, entity.EmbeddingMetadataComment{ - CommentID: c.ID, - }) - } else { - meta.Comments = append(meta.Comments, entity.EmbeddingMetadataComment{ - CommentID: c.ID, - }) - } - } - } - - return strings.Join(parts, "\n\n"), meta, nil -} - -// IndexQuestion indexes a single question embedding. -func (s *EmbeddingService) IndexQuestion(ctx context.Context, questionID string) error { - text, meta, err := s.BuildTextForQuestion(ctx, questionID) - if err != nil { - return err - } - - contentHash := ComputeContentHash(text) - - // Check staleness - existing, exist, _ := s.embeddingRepo.GetByObjectID(ctx, questionID, EmbeddingLevelQuestion) - if exist && existing.ContentHash == contentHash { - return nil // already up to date - } - - vec, err := s.GenerateEmbedding(ctx, text) - if err != nil { - return fmt.Errorf("generate embedding for question %s failed: %w", questionID, err) - } - - metaJSON, _ := json.Marshal(meta) - vecJSON, _ := json.Marshal(vec) - - return s.embeddingRepo.Upsert(ctx, &entity.Embedding{ - ObjectID: questionID, - ObjectType: EmbeddingLevelQuestion, - ContentHash: contentHash, - Metadata: string(metaJSON), - Embedding: string(vecJSON), - Dimensions: len(vec), - }) +func NewEmbeddingService() *EmbeddingService { + return &EmbeddingService{} } -// IndexAnswer indexes a single answer embedding. -func (s *EmbeddingService) IndexAnswer(ctx context.Context, answerID, questionID string) error { - text, meta, err := s.BuildTextForAnswer(ctx, answerID, questionID) - if err != nil { - return err - } - - contentHash := ComputeContentHash(text) +// SearchSimilar delegates to the VectorSearch plugin. +// Returns an error if no plugin is enabled. +func (s *EmbeddingService) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + var results []plugin.VectorSearchResult + var searchErr error + found := false - existing, exist, _ := s.embeddingRepo.GetByObjectID(ctx, answerID, EmbeddingLevelAnswer) - if exist && existing.ContentHash == contentHash { + err := plugin.CallVectorSearch(func(vs plugin.VectorSearch) error { + found = true + results, searchErr = vs.SearchSimilar(ctx, query, topK) return nil - } - - vec, err := s.GenerateEmbedding(ctx, text) - if err != nil { - return fmt.Errorf("generate embedding for answer %s failed: %w", answerID, err) - } - - metaJSON, _ := json.Marshal(meta) - vecJSON, _ := json.Marshal(vec) - - return s.embeddingRepo.Upsert(ctx, &entity.Embedding{ - ObjectID: answerID, - ObjectType: EmbeddingLevelAnswer, - ContentHash: contentHash, - Metadata: string(metaJSON), - Embedding: string(vecJSON), - Dimensions: len(vec), }) -} - -// SearchSimilar performs semantic search and returns top-K similar results. -// Results below the configured similarity threshold are filtered out. -func (s *EmbeddingService) SearchSimilar(ctx context.Context, query string, topK int) ([]embeddingRepo.SimilarResult, error) { - vec, err := s.GenerateEmbedding(ctx, query) - if err != nil { - return nil, fmt.Errorf("generate query embedding failed: %w", err) - } - results, err := s.embeddingRepo.SearchSimilar(ctx, vec, topK) if err != nil { - return nil, err + return nil, fmt.Errorf("call vector search plugin failed: %w", err) } - - for _, r := range results { - log.Debugf("semantic search result: object_id=%s object_type=%s score=%.6f", r.ObjectID, r.ObjectType, r.Score) + if !found { + return nil, fmt.Errorf("semantic search is not available: no vector search plugin is enabled") } - - // Apply similarity threshold from config (default 0 means no filtering) - _, provider, cfgErr := s.getAIConfig(ctx) - if cfgErr == nil && provider.SimilarityThreshold > 0 { - filtered := make([]embeddingRepo.SimilarResult, 0, len(results)) - for _, r := range results { - if r.Score >= provider.SimilarityThreshold { - filtered = append(filtered, r) - } - } - log.Debugf("semantic search: %d/%d results passed threshold %.4f", len(filtered), len(results), provider.SimilarityThreshold) - return filtered, nil + if searchErr != nil { + return nil, searchErr } - return results, nil } - -// GetEmbeddingCount returns the total number of stored embeddings. -func (s *EmbeddingService) GetEmbeddingCount(ctx context.Context) (int64, error) { - return s.embeddingRepo.Count(ctx) -} - -// RemoveEmbedding removes an embedding by object ID and type. -func (s *EmbeddingService) RemoveEmbedding(ctx context.Context, objectID, objectType string) error { - return s.embeddingRepo.DeleteByObjectID(ctx, objectID, objectType) -} - -// IndexAll indexes all questions (and optionally answers) based on the configured embedding level. -func (s *EmbeddingService) IndexAll(ctx context.Context) error { - _, provider, err := s.getAIConfig(ctx) - if err != nil { - log.Warnf("embedding indexer: %v", err) - return err - } - - level := provider.EmbeddingLevel - if level == "" { - level = EmbeddingLevelQuestion - } - - log.Debugf("Starting embedding indexer at level: %s", level) - - page := 1 - totalIndexed := 0 - for { - searchResp, err := s.searchService.Search(ctx, &schema.SearchDTO{ - Query: "is:question", - Page: page, - Size: 50, - Order: "newest", - }) - if err != nil { - return fmt.Errorf("search questions for indexing failed: %w", err) - } - if searchResp == nil || len(searchResp.SearchResults) == 0 { - break - } - - for _, result := range searchResp.SearchResults { - if result.Object == nil { - continue - } - qID := result.Object.QuestionID - if level == EmbeddingLevelQuestion { - if err := s.IndexQuestion(ctx, qID); err != nil { - log.Warnf("index question %s failed: %v", qID, err) - continue - } - totalIndexed++ - } else if level == EmbeddingLevelAnswer { - // Index each answer for this question via AnswerService - answerInfoList, _, err := s.answerService.SearchList(ctx, &schema.AnswerListReq{ - QuestionID: qID, - Page: 1, - PageSize: 50, - }) - if err != nil { - log.Warnf("get answers for question %s failed: %v", qID, err) - continue - } - for _, a := range answerInfoList { - if err := s.IndexAnswer(ctx, a.ID, qID); err != nil { - log.Warnf("index answer %s failed: %v", a.ID, err) - continue - } - totalIndexed++ - } - } - } - - if int64((page)*50) >= searchResp.Total { - break - } - page++ - } - - log.Infof("Embedding indexer completed: %d items indexed", totalIndexed) - return nil -} - -// StartScheduler starts a cron job to periodically run IndexAll. -func (s *EmbeddingService) StartScheduler(spec string) error { - s.mu.Lock() - defer s.mu.Unlock() - - // Stop existing cron if running - if s.cronJob != nil { - s.cronJob.Stop() - s.cronJob = nil - s.cronSpec = "" - } - - if spec == "" { - return nil - } - - c := cron.New() - _, err := c.AddFunc(spec, func() { - ctx := context.Background() - log.Infof("embedding cron triggered (spec=%s)", spec) - if err := s.IndexAll(ctx); err != nil { - log.Errorf("embedding cron IndexAll failed: %v", err) - } - }) - if err != nil { - return fmt.Errorf("invalid cron expression %q: %w", spec, err) - } - - c.Start() - s.cronJob = c - s.cronSpec = spec - log.Infof("embedding scheduler started with cron: %s", spec) - return nil -} - -// StopScheduler stops the embedding cron scheduler. -func (s *EmbeddingService) StopScheduler() { - s.mu.Lock() - defer s.mu.Unlock() - if s.cronJob != nil { - s.cronJob.Stop() - s.cronJob = nil - s.cronSpec = "" - log.Infof("embedding scheduler stopped") - } -} - -// ApplyConfig reads the current AI config and starts or stops the scheduler accordingly. -func (s *EmbeddingService) ApplyConfig(ctx context.Context) { - aiConfig, provider, err := s.getAIConfig(ctx) - if err != nil || aiConfig == nil || provider == nil { - s.StopScheduler() - return - } - - if provider.EmbeddingModel == "" || provider.EmbeddingCrontab == "" { - s.StopScheduler() - return - } - - // Only restart if the cron spec changed - s.mu.Lock() - currentSpec := s.cronSpec - s.mu.Unlock() - - if currentSpec == provider.EmbeddingCrontab { - return - } - - if err := s.StartScheduler(provider.EmbeddingCrontab); err != nil { - log.Errorf("failed to start embedding scheduler: %v", err) - } -} diff --git a/internal/service/plugin_common/plugin_common_service.go b/internal/service/plugin_common/plugin_common_service.go index eb46b5ac..b3674a23 100644 --- a/internal/service/plugin_common/plugin_common_service.go +++ b/internal/service/plugin_common/plugin_common_service.go @@ -25,6 +25,7 @@ import ( "github.com/apache/answer/internal/base/data" "github.com/apache/answer/internal/repo/search_sync" + "github.com/apache/answer/internal/repo/vector_search_sync" "github.com/segmentfault/pacman/errors" "github.com/segmentfault/pacman/log" @@ -103,6 +104,12 @@ func (ps *PluginCommonService) UpdatePluginConfig(ctx context.Context, req *sche } return nil }) + _ = plugin.CallVectorSearch(func(vs plugin.VectorSearch) error { + if vs.Info().SlugName == req.PluginSlugName { + vs.RegisterSyncer(ctx, vector_search_sync.NewPluginSyncer(ps.data)) + } + return nil + }) _ = plugin.CallImporter(func(importer plugin.Importer) error { importer.RegisterImporterFunc(ctx, ps.importerService.NewImporterFunc()) return nil @@ -176,6 +183,16 @@ func (ps *PluginCommonService) initPluginData() { }) } + // register syncers for search and vector search plugins on startup + _ = plugin.CallSearch(func(search plugin.Search) error { + search.RegisterSyncer(context.Background(), search_sync.NewPluginSyncer(ps.data)) + return nil + }) + _ = plugin.CallVectorSearch(func(vs plugin.VectorSearch) error { + vs.RegisterSyncer(context.Background(), vector_search_sync.NewPluginSyncer(ps.data)) + return nil + }) + // init plugin user config plugin.RegisterGetPluginUserConfigFunc(func(userID, pluginSlugName string) []byte { pluginUserConfig, exist, err := ps.pluginUserConfigRepo.GetPluginUserConfig(context.Background(), userID, pluginSlugName) diff --git a/internal/service/siteinfo/siteinfo_service.go b/internal/service/siteinfo/siteinfo_service.go index 70003984..c29051b4 100644 --- a/internal/service/siteinfo/siteinfo_service.go +++ b/internal/service/siteinfo/siteinfo_service.go @@ -33,7 +33,6 @@ import ( "github.com/apache/answer/internal/entity" "github.com/apache/answer/internal/schema" "github.com/apache/answer/internal/service/config" - embeddingService "github.com/apache/answer/internal/service/embedding" "github.com/apache/answer/internal/service/export" "github.com/apache/answer/internal/service/file_record" questioncommon "github.com/apache/answer/internal/service/question_common" @@ -54,7 +53,6 @@ type SiteInfoService struct { configService *config.ConfigService questioncommon *questioncommon.QuestionCommon fileRecordService *file_record.FileRecordService - embeddingService *embeddingService.EmbeddingService } func NewSiteInfoService( @@ -65,7 +63,6 @@ func NewSiteInfoService( configService *config.ConfigService, questioncommon *questioncommon.QuestionCommon, fileRecordService *file_record.FileRecordService, - embeddingSvc *embeddingService.EmbeddingService, ) *SiteInfoService { plugin.RegisterGetSiteURLFunc(func() string { generalSiteInfo, err := siteInfoCommonService.GetSiteGeneral(context.Background()) @@ -84,7 +81,6 @@ func NewSiteInfoService( configService: configService, questioncommon: questioncommon, fileRecordService: fileRecordService, - embeddingService: embeddingSvc, } } @@ -416,8 +412,6 @@ func (s *SiteInfoService) SaveSiteAI(ctx context.Context, req *schema.SiteAIReq) return err } - // Apply embedding scheduler config (start/stop cron based on settings) - go s.embeddingService.ApplyConfig(ctx) return nil } diff --git a/plugin/plugin.go b/plugin/plugin.go index 8778b162..3a657fc4 100644 --- a/plugin/plugin.go +++ b/plugin/plugin.go @@ -130,6 +130,10 @@ func Register(p Base) { if _, ok := p.(Sidebar); ok { registerSidebar(p.(Sidebar)) } + + if _, ok := p.(VectorSearch); ok { + registerVectorSearch(p.(VectorSearch)) + } } type Stack[T Base] struct { diff --git a/plugin/vector_search.go b/plugin/vector_search.go new file mode 100644 index 00000000..a0170152 --- /dev/null +++ b/plugin/vector_search.go @@ -0,0 +1,174 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package plugin + +import ( + "context" + "fmt" + "strings" + + "github.com/sashabaranov/go-openai" + "github.com/segmentfault/pacman/log" +) + +// VectorSearchResult holds a single similarity search result returned by a VectorSearch plugin. +type VectorSearchResult struct { + // ObjectID is the unique identifier of the matched object (question ID or answer ID). + ObjectID string `json:"object_id"` + // ObjectType is "question" or "answer". + ObjectType string `json:"object_type"` + // Metadata is a JSON string containing VectorSearchMetadata for link composition and content retrieval. + Metadata string `json:"metadata"` + // Score is the cosine similarity score (0-1). + Score float64 `json:"score"` +} + +// VectorSearchContent is the document structure passed to plugins for indexing. +type VectorSearchContent struct { + // ObjectID is the unique identifier (question ID or answer ID). + ObjectID string `json:"objectID"` + // ObjectType is "question" or "answer". + ObjectType string `json:"objectType"` + // Title is the question title. + Title string `json:"title"` + // Content is the aggregated text to be embedded (question body + answers + comments). + Content string `json:"content"` + // Metadata is a JSON string containing VectorSearchMetadata. + Metadata string `json:"metadata"` +} + +// VectorSearchDesc describes the vector search engine for display purposes. +type VectorSearchDesc struct { + // Icon is an SVG icon for display. Optional. + Icon string `json:"icon"` + // Link is the URL of the vector search engine. Optional. + Link string `json:"link"` +} + +// VectorSearchMetadata holds IDs for URI composition and content retrieval at query time. +// Shared between plugins and the core MCP controller. +type VectorSearchMetadata struct { + QuestionID string `json:"question_id"` + AnswerID string `json:"answer_id,omitempty"` + Answers []VectorSearchMetadataAnswer `json:"answers,omitempty"` + Comments []VectorSearchMetadataComment `json:"comments,omitempty"` +} + +// VectorSearchMetadataAnswer stores answer ID and its comment IDs in metadata. +type VectorSearchMetadataAnswer struct { + AnswerID string `json:"answer_id"` + Comments []VectorSearchMetadataComment `json:"comments,omitempty"` +} + +// VectorSearchMetadataComment stores a comment ID in metadata. +type VectorSearchMetadataComment struct { + CommentID string `json:"comment_id"` +} + +// VectorSearch is the plugin interface for vector/semantic search engines. +// Plugins implementing this interface manage their own vector storage, embedding computation, +// data synchronization schedule, and similarity search. +type VectorSearch interface { + Base + + // Description returns metadata about the vector search engine. + Description() VectorSearchDesc + + // RegisterSyncer is called by the core to provide a data syncer. + // The plugin should store the syncer and use it to bulk-sync content + // (typically in a background goroutine). + RegisterSyncer(ctx context.Context, syncer VectorSearchSyncer) + + // SearchSimilar performs a semantic similarity search. + // The plugin is responsible for embedding the query text and searching its vector store. + // Returns up to topK results sorted by similarity score (descending). + SearchSimilar(ctx context.Context, query string, topK int) ([]VectorSearchResult, error) + + // UpdateContent upserts a single document in the vector store. + // Called by the core on incremental content changes. + UpdateContent(ctx context.Context, content *VectorSearchContent) error + + // DeleteContent removes a document from the vector store by object ID. + DeleteContent(ctx context.Context, objectID string) error +} + +// VectorSearchSyncer is implemented by the core and provided to plugins via RegisterSyncer. +// Plugins call these methods to pull all content for bulk indexing. +type VectorSearchSyncer interface { + // GetQuestionsPage returns a page of questions with aggregated text (title + body + answers + comments). + GetQuestionsPage(ctx context.Context, page, pageSize int) ([]*VectorSearchContent, error) + // GetAnswersPage returns a page of answers with aggregated text (answer body + parent question title + comments). + GetAnswersPage(ctx context.Context, page, pageSize int) ([]*VectorSearchContent, error) +} + +var ( + // CallVectorSearch is a function that calls all registered VectorSearch plugins. + CallVectorSearch, + registerVectorSearch = MakePlugin[VectorSearch](false) +) + +// GenerateEmbedding is a base utility function that generates an embedding vector +// using an OpenAI-compatible API. Plugins that don't have a built-in vectorizer +// (most vector databases) can call this function with their own credentials. +// Plugins with built-in vectorizers (e.g., Weaviate) can skip this and use their own. +// +// Parameters: +// - ctx: context for cancellation +// - apiHost: the API base URL (e.g. "https://api.openai.com";); "/v1" is appended if missing +// - apiKey: the API key for authentication +// - model: the embedding model name (e.g. "text-embedding-3-small") +// - text: the text to embed +// +// Returns the embedding vector as []float32, or an error. +func GenerateEmbedding(ctx context.Context, apiHost, apiKey, model, text string) ([]float32, error) { + if model == "" { + return nil, fmt.Errorf("embedding model is not configured") + } + if text == "" { + return nil, fmt.Errorf("text is empty") + } + + config := openai.DefaultConfig(apiKey) + config.BaseURL = apiHost + if !strings.HasSuffix(config.BaseURL, "/v1") { + config.BaseURL += "/v1" + } + + log.Debugf("embedding: requesting model=%s baseURL=%s textLen=%d", model, config.BaseURL, len(text)) + + client := openai.NewClientWithConfig(config) + + resp, err := client.CreateEmbeddings(ctx, openai.EmbeddingRequestStrings{ + Input: []string{text}, + Model: openai.EmbeddingModel(model), + }) + if err != nil { + log.Errorf("embedding: request failed model=%s baseURL=%s err=%v", model, config.BaseURL, err) + return nil, fmt.Errorf("create embeddings failed: %w", err) + } + if len(resp.Data) == 0 { + log.Errorf("embedding: no data returned model=%s baseURL=%s", model, config.BaseURL) + return nil, fmt.Errorf("no embedding returned") + } + + log.Debugf("embedding: success model=%s dimensions=%d usage={prompt=%d,total=%d}", + model, len(resp.Data[0].Embedding), resp.Usage.PromptTokens, resp.Usage.TotalTokens) + return resp.Data[0].Embedding, nil +} diff --git a/ui/src/common/interface.ts b/ui/src/common/interface.ts index b01b621f..308726e8 100644 --- a/ui/src/common/interface.ts +++ b/ui/src/common/interface.ts @@ -833,11 +833,6 @@ export interface AiConfig { api_host: string; api_key: string; model: string; - embedding_model: string; - embedding_dimensions: number; - embedding_level: string; - embedding_crontab: string; - similarity_threshold: number; }>; } diff --git a/ui/src/pages/Admin/AiSettings/index.tsx b/ui/src/pages/Admin/AiSettings/index.tsx index de284ff0..2270aa5c 100644 --- a/ui/src/pages/Admin/AiSettings/index.tsx +++ b/ui/src/pages/Admin/AiSettings/index.tsx @@ -68,31 +68,6 @@ const Index = () => { isInvalid: false, errorMsg: '', }, - embedding_model: { - value: '', - isInvalid: false, - errorMsg: '', - }, - embedding_dimensions: { - value: '', - isInvalid: false, - errorMsg: '', - }, - embedding_level: { - value: 'question', - isInvalid: false, - errorMsg: '', - }, - embedding_crontab: { - value: '', - isInvalid: false, - errorMsg: '', - }, - similarity_threshold: { - value: '0', - isInvalid: false, - errorMsg: '', - }, }); const [apiHostPlaceholder, setApiHostPlaceholder] = useState(''); const [modelsData, setModels] = useState<{ id: string }[]>([]); @@ -171,31 +146,6 @@ const Index = () => { isInvalid: false, errorMsg: '', }, - embedding_model: { - value: findHistoryProvider?.embedding_model || '', - isInvalid: false, - errorMsg: '', - }, - embedding_dimensions: { - value: String(findHistoryProvider?.embedding_dimensions || ''), - isInvalid: false, - errorMsg: '', - }, - embedding_level: { - value: findHistoryProvider?.embedding_level || 'question', - isInvalid: false, - errorMsg: '', - }, - embedding_crontab: { - value: findHistoryProvider?.embedding_crontab || '', - isInvalid: false, - errorMsg: '', - }, - similarity_threshold: { - value: String(findHistoryProvider?.similarity_threshold || '0'), - isInvalid: false, - errorMsg: '', - }, }); const provider = aiProviders?.find((item) => item.name === value); const host = findHistoryProvider?.api_host || provider?.default_api_host; @@ -268,13 +218,6 @@ const Index = () => { api_host: formData.api_host.value, api_key: formData.api_key.value, model: formData.model.value, - embedding_model: formData.embedding_model.value, - embedding_dimensions: - Number(formData.embedding_dimensions.value) || 0, - embedding_level: formData.embedding_level.value, - embedding_crontab: formData.embedding_crontab.value, - similarity_threshold: - Number(formData.similarity_threshold.value) || 0, }; } return v; @@ -352,31 +295,6 @@ const Index = () => { isInvalid: false, errorMsg: '', }, - embedding_model: { - value: currentAiConfig?.embedding_model || '', - isInvalid: false, - errorMsg: '', - }, - embedding_dimensions: { - value: String(currentAiConfig?.embedding_dimensions || ''), - isInvalid: false, - errorMsg: '', - }, - embedding_level: { - value: currentAiConfig?.embedding_level || 'question', - isInvalid: false, - errorMsg: '', - }, - embedding_crontab: { - value: currentAiConfig?.embedding_crontab || '', - isInvalid: false, - errorMsg: '', - }, - similarity_threshold: { - value: String(currentAiConfig?.similarity_threshold || '0'), - isInvalid: false, - errorMsg: '', - }, }); }; @@ -559,99 +477,6 @@ const Index = () => { <div className="invalid-feedback">{formData.model.errorMsg}</div> </div> - <hr className="my-4" /> - <h5 className="mb-3">{t('embedding_settings')}</h5> - - <Form.Group className="mb-3" controlId="embedding_model"> - <Form.Label>{t('embedding_model.label')}</Form.Label> - <Form.Control - type="text" - autoComplete="off" - placeholder="text-embedding-3-small" - value={formData.embedding_model.value} - onChange={(e) => - handleValueChange({ - embedding_model: { - value: e.target.value, - errorMsg: '', - isInvalid: false, - }, - }) - } - /> - <Form.Text className="text-muted"> - {t('embedding_model.text')} - </Form.Text> - </Form.Group> - - <Form.Group className="mb-3" controlId="embedding_level"> - <Form.Label>{t('embedding_level.label')}</Form.Label> - <Form.Select - value={formData.embedding_level.value} - onChange={(e) => - handleValueChange({ - embedding_level: { - value: e.target.value, - errorMsg: '', - isInvalid: false, - }, - }) - }> - <option value="question">{t('embedding_level.question')}</option> - <option value="answer">{t('embedding_level.answer')}</option> - </Form.Select> - <Form.Text className="text-muted"> - {t('embedding_level.text')} - </Form.Text> - </Form.Group> - - <Form.Group className="mb-3" controlId="embedding_crontab"> - <Form.Label>{t('embedding_crontab.label')}</Form.Label> - <Form.Control - type="text" - autoComplete="off" - placeholder="0 */6 * * *" - value={formData.embedding_crontab.value} - onChange={(e) => - handleValueChange({ - embedding_crontab: { - value: e.target.value, - errorMsg: '', - isInvalid: false, - }, - }) - } - /> - <Form.Text className="text-muted"> - {t('embedding_crontab.text')} - </Form.Text> - </Form.Group> - - <Form.Group className="mb-3" controlId="similarity_threshold"> - <Form.Label>{t('similarity_threshold.label')}</Form.Label> - <Form.Control - type="number" - autoComplete="off" - placeholder="0" - min="0" - max="1" - step="0.01" - value={formData.similarity_threshold.value} - onChange={(e) => - handleValueChange({ - similarity_threshold: { - value: e.target.value, - errorMsg: '', - isInvalid: false, - }, - }) - } - /> - <Form.Text className="text-muted"> - {t('similarity_threshold.text')} - </Form.Text> - </Form.Group> - <Button type="submit">{t('save', { keyPrefix: 'btns' })}</Button> </Form> </div>
