From 01826cb6031b73710fb45964778b4667d5febfea Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 22:20:46 +0800 Subject: [PATCH 01/12] feat(pgvector): add PostgreSQL pgvector vector search plugin with configuration and sync functionality --- vector-search-pgvector/README.md | 49 +++ vector-search-pgvector/go.mod | 11 + vector-search-pgvector/i18n/en_US.yaml | 61 ++++ vector-search-pgvector/i18n/translation.go | 45 +++ vector-search-pgvector/i18n/zh_CN.yaml | 61 ++++ vector-search-pgvector/info.yaml | 22 ++ vector-search-pgvector/pgvector.go | 398 +++++++++++++++++++++ vector-search-pgvector/sync.go | 105 ++++++ 8 files changed, 752 insertions(+) create mode 100644 vector-search-pgvector/README.md create mode 100644 vector-search-pgvector/go.mod create mode 100644 vector-search-pgvector/i18n/en_US.yaml create mode 100644 vector-search-pgvector/i18n/translation.go create mode 100644 vector-search-pgvector/i18n/zh_CN.yaml create mode 100644 vector-search-pgvector/info.yaml create mode 100644 vector-search-pgvector/pgvector.go create mode 100644 vector-search-pgvector/sync.go diff --git a/vector-search-pgvector/README.md b/vector-search-pgvector/README.md new file mode 100644 index 00000000..ce074041 --- /dev/null +++ b/vector-search-pgvector/README.md @@ -0,0 +1,49 @@ +# Apache Answer Plugin: pgvector Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [PostgreSQL](https://www.postgresql.org/) with the [pgvector](https://github.com/pgvector/pgvector) extension. + +## Prerequisites + +- PostgreSQL 12+ with the [pgvector](https://github.com/pgvector/pgvector) extension installed +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-pgvector +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **DSN** | PostgreSQL connection string | `postgres://user:pass@localhost:5432/answer?sslmode=disable` | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +## How It Works + +- **Embedding dimensions** are auto-detected from the configured model. No manual dimension configuration is needed. +- On first configuration, the plugin creates a table `answer_vector_embeddings` with a `vector` column matching the detected dimensions. +- If the embedding model changes and produces different dimensions, the table is automatically dropped and recreated. +- Uses cosine similarity (`<=>` operator) for vector search. +- A full sync of all questions/answers is triggered when the plugin starts. + +## Database Setup + +Ensure the pgvector extension is installed in your PostgreSQL database: + +```sql +CREATE EXTENSION IF NOT EXISTS vector; +``` + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-pgvector/go.mod b/vector-search-pgvector/go.mod new file mode 100644 index 00000000..e0fbfe3f --- /dev/null +++ b/vector-search-pgvector/go.mod @@ -0,0 +1,11 @@ +module github.com/apache/answer-plugins/vector-search-pgvector + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/lib/pq v1.10.9 + github.com/pgvector/pgvector-go v0.2.2 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f +) diff --git a/vector-search-pgvector/i18n/en_US.yaml b/vector-search-pgvector/i18n/en_US.yaml new file mode 100644 index 00000000..6059eb35 --- /dev/null +++ b/vector-search-pgvector/i18n/en_US.yaml @@ -0,0 +1,61 @@ +# 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. + +plugin: + pgvector_search: + backend: + info: + name: + other: PostgreSQL pgvector Search + description: + other: Use PostgreSQL with pgvector extension for semantic/vector search + config: + dsn: + title: + other: PostgreSQL DSN + description: + other: "PostgreSQL connection string (e.g. postgres://user:pass@localhost:5432/dbname?sslmode=disable)" + api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-pgvector/i18n/translation.go b/vector-search-pgvector/i18n/translation.go new file mode 100644 index 00000000..254a19a6 --- /dev/null +++ b/vector-search-pgvector/i18n/translation.go @@ -0,0 +1,45 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.pgvector_search.backend.info.name" + InfoDescription = "plugin.pgvector_search.backend.info.description" + + ConfigDSNTitle = "plugin.pgvector_search.backend.config.dsn.title" + ConfigDSNDescription = "plugin.pgvector_search.backend.config.dsn.description" + + ConfigAPIHostTitle = "plugin.pgvector_search.backend.config.api_host.title" + ConfigAPIHostDescription = "plugin.pgvector_search.backend.config.api_host.description" + + ConfigAPIKeyTitle = "plugin.pgvector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.pgvector_search.backend.config.api_key.description" + + ConfigEmbeddingModelTitle = "plugin.pgvector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.pgvector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.pgvector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.pgvector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.pgvector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.pgvector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.pgvector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.pgvector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-pgvector/i18n/zh_CN.yaml b/vector-search-pgvector/i18n/zh_CN.yaml new file mode 100644 index 00000000..66887d8f --- /dev/null +++ b/vector-search-pgvector/i18n/zh_CN.yaml @@ -0,0 +1,61 @@ +# 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. + +plugin: + pgvector_search: + backend: + info: + name: + other: PostgreSQL pgvector 搜索 + description: + other: 使用 PostgreSQL pgvector 扩展进行语义/向量搜索 + config: + dsn: + title: + other: PostgreSQL 连接字符串 + description: + other: "PostgreSQL 连接字符串（如 postgres://user:pass@localhost:5432/dbname?sslmode=disable）" + api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-pgvector/info.yaml b/vector-search-pgvector/info.yaml new file mode 100644 index 00000000..b9b2f600 --- /dev/null +++ b/vector-search-pgvector/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: pgvector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-pgvector diff --git a/vector-search-pgvector/pgvector.go b/vector-search-pgvector/pgvector.go new file mode 100644 index 00000000..dc50601e --- /dev/null +++ b/vector-search-pgvector/pgvector.go @@ -0,0 +1,398 @@ +/* + * 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 pgvector_search + +import ( + "context" + "database/sql" + "embed" + "encoding/json" + "fmt" + "strconv" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-pgvector/i18n" + "github.com/apache/answer/plugin" + _ "github.com/lib/pq" + pgv "github.com/pgvector/pgvector-go" + "github.com/segmentfault/pacman/log" +) + +//go:embed info.yaml +var Info embed.FS + +const tableName = "answer_vector_embeddings" + +// VectorSearchEngine implements plugin.VectorSearch using PostgreSQL + pgvector. +type VectorSearchEngine struct { + Config *VectorSearchConfig + db *sql.DB + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex + embeddingDimensions int // auto-detected from embedding model +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + DSN string `json:"dsn"` + APIHost string `json:"api_host"` + APIKey string `json:"api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +// Description returns metadata about this vector search engine. +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://github.com/pgvector/pgvector", + } +} + +// RegisterSyncer stores the syncer and triggers a full sync. +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + log.Debugf("pgvector: RegisterSyncer called, db=%v, embeddingLevel=%s", e.db != nil, e.Config.EmbeddingLevel) + e.syncer = syncer + if e.db != nil { + e.sync() + } +} + +// SearchSimilar performs a cosine similarity search using pgvector. +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.db == nil { + return nil, fmt.Errorf("pgvector: database not initialized") + } + if topK <= 0 { + topK = 10 + } + + log.Debugf("pgvector: SearchSimilar query=%q topK=%d model=%s", query, topK, e.Config.EmbeddingModel) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, query) + if err != nil { + return nil, fmt.Errorf("generate query embedding: %w", err) + } + + log.Debugf("pgvector: search embedding generated, dimensions=%d", len(embedding)) + + vec := pgv.NewVector(embedding) + + // Use cosine distance operator (<=>). Score = 1 - distance. + sqlQuery := fmt.Sprintf( + `SELECT object_id, object_type, metadata, 1 - (embedding <=> $1) AS score + FROM %s + ORDER BY embedding <=> $1 + LIMIT $2`, tableName) + + rows, err := e.db.QueryContext(ctx, sqlQuery, vec, topK) + if err != nil { + return nil, fmt.Errorf("pgvector search failed: %w", err) + } + defer rows.Close() + + results := make([]plugin.VectorSearchResult, 0, topK) + for rows.Next() { + var r plugin.VectorSearchResult + if err := rows.Scan(&r.ObjectID, &r.ObjectType, &r.Metadata, &r.Score); err != nil { + log.Warnf("pgvector: scan row: %v", err) + continue + } + if e.Config.SimilarityThreshold > 0 && r.Score < e.Config.SimilarityThreshold { + log.Debugf("pgvector: skipping result %s score=%.4f below threshold=%.4f", r.ObjectID, r.Score, e.Config.SimilarityThreshold) + continue + } + results = append(results, r) + } + log.Debugf("pgvector: SearchSimilar returning %d results", len(results)) + return results, rows.Err() +} + +// UpdateContent upserts a single document into the pgvector table. +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.db == nil { + return fmt.Errorf("pgvector: database not initialized") + } + + log.Debugf("pgvector: UpdateContent objectID=%s objectType=%s titleLen=%d contentLen=%d", + content.ObjectID, content.ObjectType, len(content.Title), len(content.Content)) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + + log.Debugf("pgvector: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding)) + + vec := pgv.NewVector(embedding) + + query := fmt.Sprintf( + `INSERT INTO %s (object_id, object_type, title, content, metadata, embedding) + VALUES ($1, $2, $3, $4, $5, $6) + ON CONFLICT (object_id) DO UPDATE SET + object_type = EXCLUDED.object_type, + title = EXCLUDED.title, + content = EXCLUDED.content, + metadata = EXCLUDED.metadata, + embedding = EXCLUDED.embedding`, tableName) + + _, err = e.db.ExecContext(ctx, query, + content.ObjectID, content.ObjectType, content.Title, content.Content, content.Metadata, vec) + if err != nil { + return fmt.Errorf("upsert document %s: %w", content.ObjectID, err) + } + log.Debugf("pgvector: upserted document %s successfully", content.ObjectID) + return nil +} + +// DeleteContent removes a document by object ID. +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.db == nil { + return fmt.Errorf("pgvector: database not initialized") + } + log.Debugf("pgvector: DeleteContent objectID=%s", objectID) + _, err := e.db.ExecContext(ctx, fmt.Sprintf(`DELETE FROM %s WHERE object_id = $1`, tableName), objectID) + if err != nil { + return fmt.Errorf("delete document %s: %w", objectID, err) + } + log.Debugf("pgvector: deleted document %s", objectID) + return nil +} + +// ConfigFields returns the plugin configuration form fields. +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "dsn", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigDSNTitle), + Description: plugin.MakeTranslator(i18n.ConfigDSNDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.DSN, + }, + { + Name: "api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.APIHost, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +// ConfigReceiver applies configuration from the admin UI. +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + log.Debugf("pgvector: ConfigReceiver called, raw config=%s", string(config)) + + // Pre-process: convert string numbers to actual numbers before unmarshalling, + // because the admin UI sends all form values as strings. + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + log.Debugf("pgvector: config parsed: dsn=%s model=%s level=%s threshold=%.2f", + conf.DSN, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + + if !plugin.StatusManager.IsEnabled("pgvector_search") { + log.Debugf("pgvector: plugin not active, skipping initialization") + return nil + } + + // Auto-detect embedding dimensions by generating a probe embedding. + log.Debugf("pgvector: detecting embedding dimensions via probe call") + probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.APIHost, conf.APIKey, conf.EmbeddingModel, "dimension probe") + if err != nil { + return fmt.Errorf("detect embedding dimensions: %w", err) + } + e.embeddingDimensions = len(probeEmbedding) + log.Infof("pgvector: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel) + + log.Debugf("pgvector: connecting to PostgreSQL") + + db, err := sql.Open("postgres", conf.DSN) + if err != nil { + return fmt.Errorf("open postgres: %w", err) + } + if err := db.Ping(); err != nil { + db.Close() + return fmt.Errorf("ping postgres: %w", err) + } + + log.Debugf("pgvector: connected to PostgreSQL successfully") + + // Close previous connection if any. + if e.db != nil { + e.db.Close() + } + e.db = db + + if err := e.ensureTable(context.Background()); err != nil { + return fmt.Errorf("ensure table: %w", err) + } + + log.Debugf("pgvector: ConfigReceiver completed successfully") + return nil +} + +// ensureTable creates the pgvector extension and the embeddings table if they don't exist. +// If the table exists but the embedding dimensions differ, it drops and recreates the table. +func (e *VectorSearchEngine) ensureTable(ctx context.Context) error { + // Enable the pgvector extension. + if _, err := e.db.ExecContext(ctx, `CREATE EXTENSION IF NOT EXISTS vector`); err != nil { + return fmt.Errorf("create vector extension: %w", err) + } + + dims := e.embeddingDimensions + if dims <= 0 { + dims = 1536 + } + + log.Debugf("pgvector: ensureTable with dimensions=%d", dims) + + // Check if table exists and verify embedding dimensions match. + var existingDims int + err := e.db.QueryRowContext(ctx, fmt.Sprintf( + `SELECT atttypmod FROM pg_attribute + WHERE attrelid = '%s'::regclass AND attname = 'embedding'`, tableName)).Scan(&existingDims) + if err == nil && existingDims > 0 && existingDims != dims { + log.Warnf("pgvector: embedding dimensions changed from %d to %d, recreating table", existingDims, dims) + if _, err := e.db.ExecContext(ctx, fmt.Sprintf(`DROP TABLE IF EXISTS %s`, tableName)); err != nil { + return fmt.Errorf("drop table for dimension change: %w", err) + } + } else if err != nil { + log.Debugf("pgvector: table does not exist yet or dimension check skipped: %v", err) + } else { + log.Debugf("pgvector: existing table dimensions=%d, matches configured=%d", existingDims, dims) + } + + createSQL := fmt.Sprintf(`CREATE TABLE IF NOT EXISTS %s ( + object_id TEXT PRIMARY KEY, + object_type TEXT NOT NULL, + title TEXT NOT NULL DEFAULT '', + content TEXT NOT NULL DEFAULT '', + metadata TEXT NOT NULL DEFAULT '', + embedding vector(%d) NOT NULL + )`, tableName, dims) + + if _, err := e.db.ExecContext(ctx, createSQL); err != nil { + return fmt.Errorf("create table: %w", err) + } + + // Create an IVFFlat index for faster cosine similarity search if it doesn't exist. + indexSQL := fmt.Sprintf( + `CREATE INDEX IF NOT EXISTS idx_%s_embedding ON %s USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100)`, + tableName, tableName) + if _, err := e.db.ExecContext(ctx, indexSQL); err != nil { + // IVFFlat requires data in the table to build the index; log and continue if empty. + log.Warnf("pgvector: create ivfflat index (may need data first): %v", err) + } + + return nil +} diff --git a/vector-search-pgvector/sync.go b/vector-search-pgvector/sync.go new file mode 100644 index 00000000..e4a30631 --- /dev/null +++ b/vector-search-pgvector/sync.go @@ -0,0 +1,105 @@ +/* + * 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 pgvector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +// sync triggers a full bulk sync of all Q&A content into the pgvector table. +// It runs in a background goroutine and is guarded by the syncing flag. +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("pgvector: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("pgvector: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("pgvector: starting question sync...") + page = 1 + for { + log.Infof("pgvector: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("pgvector: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("pgvector: starting answer sync...") + page = 1 + for { + log.Infof("pgvector: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("pgvector: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("pgvector: sync complete") + }() +} + +// bulkIndex indexes a batch of documents, computing embeddings for each. +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + log.Debugf("pgvector: bulkIndex batch size=%d", len(contents)) + success, failed := 0, 0 + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("pgvector: index %s failed: %v", c.ObjectID, err) + failed++ + } else { + success++ + } + } + log.Debugf("pgvector: bulkIndex batch done, success=%d failed=%d", success, failed) +} From 9d2a63257f117edcf977842e04daa9034b0ed7ca Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 22:29:14 +0800 Subject: [PATCH 02/12] feat: add Weaviate vector search plugin with configuration and synchronization --- vector-search-weaviate/README.md | 43 + vector-search-weaviate/go.mod | 76 ++ vector-search-weaviate/go.sum | 886 +++++++++++++++++++++ vector-search-weaviate/i18n/en_US.yaml | 66 ++ vector-search-weaviate/i18n/translation.go | 48 ++ vector-search-weaviate/i18n/zh_CN.yaml | 66 ++ vector-search-weaviate/info.yaml | 22 + vector-search-weaviate/sync.go | 105 +++ vector-search-weaviate/weaviate.go | 551 +++++++++++++ 9 files changed, 1863 insertions(+) create mode 100644 vector-search-weaviate/README.md create mode 100644 vector-search-weaviate/go.mod create mode 100644 vector-search-weaviate/go.sum create mode 100644 vector-search-weaviate/i18n/en_US.yaml create mode 100644 vector-search-weaviate/i18n/translation.go create mode 100644 vector-search-weaviate/i18n/zh_CN.yaml create mode 100644 vector-search-weaviate/info.yaml create mode 100644 vector-search-weaviate/sync.go create mode 100644 vector-search-weaviate/weaviate.go diff --git a/vector-search-weaviate/README.md b/vector-search-weaviate/README.md new file mode 100644 index 00000000..1d338050 --- /dev/null +++ b/vector-search-weaviate/README.md @@ -0,0 +1,43 @@ +# Apache Answer Plugin: Weaviate Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [Weaviate](https://weaviate.io/). + +## Prerequisites + +- Weaviate instance (self-hosted or Weaviate Cloud) +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-weaviate +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **Weaviate Endpoint** | Weaviate server URL | `http://localhost:8080` | +| **Weaviate API Key** | API key for Weaviate authentication (optional for local instances) | | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +> **Note:** This plugin uses a dual API key pattern -- one for Weaviate authentication and a separate one for the embedding API. + +## How It Works + +- Weaviate handles vector dimensions automatically via its `vectorizer: none` configuration, so no manual dimension setup is needed. +- On first configuration, the plugin creates a class `AnswerVector` with cosine distance metric. +- Uses deterministic UUIDs derived from object IDs for consistent upsert behavior. +- A full sync of all questions/answers is triggered when the plugin starts. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-weaviate/go.mod b/vector-search-weaviate/go.mod new file mode 100644 index 00000000..aa66d26f --- /dev/null +++ b/vector-search-weaviate/go.mod @@ -0,0 +1,76 @@ +module github.com/apache/answer-plugins/vector-search-weaviate + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f + github.com/weaviate/weaviate v1.28.4 + github.com/weaviate/weaviate-go-client/v4 v4.16.1 +) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-openapi/analysis v0.21.2 // indirect + github.com/go-openapi/errors v0.22.0 // indirect + github.com/go-openapi/jsonpointer v0.21.0 // indirect + github.com/go-openapi/jsonreference v0.21.0 // indirect + github.com/go-openapi/loads v0.21.1 // indirect + github.com/go-openapi/spec v0.21.0 // indirect + github.com/go-openapi/strfmt v0.23.0 // indirect + github.com/go-openapi/swag v0.23.0 // indirect + github.com/go-openapi/validate v0.21.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/uuid v1.6.0 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/josharian/intern v1.0.0 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mailru/easyjson v0.7.7 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/mitchellh/mapstructure v1.5.0 // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/oklog/ulid v1.3.1 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/pkg/errors v0.9.1 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + go.mongodb.org/mongo-driver v1.14.0 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/oauth2 v0.23.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/genproto/googleapis/rpc v0.0.0-20240903143218-8af14fe29dc1 // indirect + google.golang.org/grpc v1.66.2 // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-weaviate/go.sum b/vector-search-weaviate/go.sum new file mode 100644 index 00000000..3fa2a601 --- /dev/null +++ b/vector-search-weaviate/go.sum @@ -0,0 +1,886 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA= +filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4= 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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. + +plugin: + weaviate_vector_search: + backend: + info: + name: + other: Weaviate Vector Search + description: + other: Use Weaviate for semantic/vector search with built-in vector indexing + config: + endpoint: + title: + other: Weaviate Endpoint + description: + other: "Weaviate server URL (e.g. http://localhost:8080)" + api_key: + title: + other: Weaviate API Key + description: + other: "API key for Weaviate authentication (leave empty if not required)" + embedding_api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + embedding_api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-weaviate/i18n/translation.go b/vector-search-weaviate/i18n/translation.go new file mode 100644 index 00000000..8a30e935 --- /dev/null +++ b/vector-search-weaviate/i18n/translation.go @@ -0,0 +1,48 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.weaviate_vector_search.backend.info.name" + InfoDescription = "plugin.weaviate_vector_search.backend.info.description" + + ConfigEndpointTitle = "plugin.weaviate_vector_search.backend.config.endpoint.title" + ConfigEndpointDescription = "plugin.weaviate_vector_search.backend.config.endpoint.description" + + ConfigAPIKeyTitle = "plugin.weaviate_vector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.weaviate_vector_search.backend.config.api_key.description" + + ConfigEmbeddingAPIHostTitle = "plugin.weaviate_vector_search.backend.config.embedding_api_host.title" + ConfigEmbeddingAPIHostDescription = "plugin.weaviate_vector_search.backend.config.embedding_api_host.description" + + ConfigEmbeddingAPIKeyTitle = "plugin.weaviate_vector_search.backend.config.embedding_api_key.title" + ConfigEmbeddingAPIKeyDescription = "plugin.weaviate_vector_search.backend.config.embedding_api_key.description" + + ConfigEmbeddingModelTitle = "plugin.weaviate_vector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.weaviate_vector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.weaviate_vector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.weaviate_vector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.weaviate_vector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.weaviate_vector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.weaviate_vector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.weaviate_vector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-weaviate/i18n/zh_CN.yaml b/vector-search-weaviate/i18n/zh_CN.yaml new file mode 100644 index 00000000..6e59db8e --- /dev/null +++ b/vector-search-weaviate/i18n/zh_CN.yaml @@ -0,0 +1,66 @@ +# 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. + +plugin: + weaviate_vector_search: + backend: + info: + name: + other: Weaviate 向量搜索 + description: + other: 使用 Weaviate 进行语义/向量搜索，支持内置向量索引 + config: + endpoint: + title: + other: Weaviate 地址 + description: + other: "Weaviate 服务器地址（如 http://localhost:8080）" + api_key: + title: + other: Weaviate API 密钥 + description: + other: "Weaviate 认证 API 密钥（如不需要可留空）" + embedding_api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + embedding_api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-weaviate/info.yaml b/vector-search-weaviate/info.yaml new file mode 100644 index 00000000..d15ef9e6 --- /dev/null +++ b/vector-search-weaviate/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: weaviate_vector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-weaviate diff --git a/vector-search-weaviate/sync.go b/vector-search-weaviate/sync.go new file mode 100644 index 00000000..4e3b8511 --- /dev/null +++ b/vector-search-weaviate/sync.go @@ -0,0 +1,105 @@ +/* + * 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 weaviate_vector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +// sync triggers a full bulk sync of all Q&A content into Weaviate. +// It runs in a background goroutine and is guarded by the syncing flag. +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("weaviate: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("weaviate: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("weaviate: starting question sync...") + page = 1 + for { + log.Infof("weaviate: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("weaviate: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("weaviate: starting answer sync...") + page = 1 + for { + log.Infof("weaviate: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("weaviate: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("weaviate: sync complete") + }() +} + +// bulkIndex indexes a batch of documents, computing embeddings for each. +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + log.Debugf("weaviate: bulkIndex batch size=%d", len(contents)) + success, failed := 0, 0 + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("weaviate: index %s failed: %v", c.ObjectID, err) + failed++ + } else { + success++ + } + } + log.Debugf("weaviate: bulkIndex batch done, success=%d failed=%d", success, failed) +} diff --git a/vector-search-weaviate/weaviate.go b/vector-search-weaviate/weaviate.go new file mode 100644 index 00000000..342a5ce8 --- /dev/null +++ b/vector-search-weaviate/weaviate.go @@ -0,0 +1,551 @@ +/* + * 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 weaviate_vector_search + +import ( + "context" + "crypto/sha1" + "embed" + "encoding/json" + "fmt" + "strconv" + "strings" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-weaviate/i18n" + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" + "github.com/weaviate/weaviate-go-client/v4/weaviate" + "github.com/weaviate/weaviate-go-client/v4/weaviate/auth" + "github.com/weaviate/weaviate-go-client/v4/weaviate/graphql" + "github.com/weaviate/weaviate/entities/models" +) + +//go:embed info.yaml +var Info embed.FS + +const ( + className = "AnswerVector" +) + +// VectorSearchEngine implements plugin.VectorSearch using Weaviate. +type VectorSearchEngine struct { + Config *VectorSearchConfig + client *weaviate.Client + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + Endpoint string `json:"endpoint"` + APIKey string `json:"api_key"` + EmbeddingAPIHost string `json:"embedding_api_host"` + EmbeddingAPIKey string `json:"embedding_api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +// Description returns metadata about this vector search engine. +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://weaviate.io/developers/weaviate", + } +} + +// RegisterSyncer stores the syncer and triggers a full sync. +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + e.syncer = syncer + if e.client != nil { + e.sync() + } +} + +// SearchSimilar performs a nearVector search in Weaviate. +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.client == nil { + return nil, fmt.Errorf("weaviate client not initialized") + } + if topK <= 0 { + topK = 10 + } + + log.Debugf("weaviate: SearchSimilar query=%q topK=%d model=%s", query, topK, e.Config.EmbeddingModel) + + // Generate embedding for the query text. + log.Debugf("weaviate: generating query embedding via %s model=%s", e.Config.EmbeddingAPIHost, e.Config.EmbeddingModel) + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.EmbeddingAPIHost, e.Config.EmbeddingAPIKey, e.Config.EmbeddingModel, query) + if err != nil { + log.Errorf("weaviate: generate query embedding failed: %v", err) + return nil, fmt.Errorf("generate query embedding: %w", err) + } + log.Debugf("weaviate: query embedding generated, dimensions=%d", len(embedding)) + + embeddingFloats := make([]float32, len(embedding)) + copy(embeddingFloats, embedding) + + nearVector := e.client.GraphQL().NearVectorArgBuilder().WithVector(embeddingFloats) + + fields := []graphql.Field{ + {Name: "objectID"}, + {Name: "objectType"}, + {Name: "title"}, + {Name: "metadata"}, + {Name: "_additional", Fields: []graphql.Field{ + {Name: "certainty"}, + {Name: "id"}, + }}, + } + + log.Debugf("weaviate: executing nearVector search on class=%s limit=%d", className, topK) + result, err := e.client.GraphQL().Get(). + WithClassName(className). + WithFields(fields...). + WithNearVector(nearVector). + WithLimit(topK). + Do(ctx) + if err != nil { + log.Errorf("weaviate: nearVector search failed: %v", err) + return nil, fmt.Errorf("weaviate nearVector search failed: %w", err) + } + + if result.Errors != nil { + msgs := make([]string, 0, len(result.Errors)) + for _, e := range result.Errors { + msgs = append(msgs, e.Message) + } + log.Errorf("weaviate: GraphQL errors: %s", strings.Join(msgs, "; ")) + return nil, fmt.Errorf("weaviate GraphQL errors: %s", strings.Join(msgs, "; ")) + } + + data, ok := result.Data["Get"].(map[string]interface{}) + if !ok { + log.Warnf("weaviate: unexpected response format from GraphQL") + return nil, fmt.Errorf("unexpected response format") + } + objects, ok := data[className].([]interface{}) + if !ok { + log.Debugf("weaviate: no objects returned from search") + return nil, nil + } + + log.Debugf("weaviate: search returned %d raw results", len(objects)) + + results := make([]plugin.VectorSearchResult, 0, len(objects)) + filtered := 0 + for _, obj := range objects { + m, ok := obj.(map[string]interface{}) + if !ok { + continue + } + + objectID, _ := m["objectID"].(string) + objectType, _ := m["objectType"].(string) + metadata, _ := m["metadata"].(string) + + score := 0.0 + if additional, ok := m["_additional"].(map[string]interface{}); ok { + if certainty, ok := additional["certainty"].(float64); ok { + // Weaviate certainty is in [0, 1] where 1 = identical. + score = certainty + } + } + + if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold { + log.Debugf("weaviate: filtered out objectID=%s score=%.4f < threshold=%.4f", objectID, score, e.Config.SimilarityThreshold) + filtered++ + continue + } + + log.Debugf("weaviate: result objectID=%s objectType=%s score=%.4f", objectID, objectType, score) + results = append(results, plugin.VectorSearchResult{ + ObjectID: objectID, + ObjectType: objectType, + Metadata: metadata, + Score: score, + }) + } + + log.Debugf("weaviate: SearchSimilar done, returning %d results (%d filtered by threshold)", len(results), filtered) + return results, nil +} + +// UpdateContent upserts a single document into Weaviate. +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.client == nil { + return fmt.Errorf("weaviate client not initialized") + } + + log.Debugf("weaviate: UpdateContent objectID=%s objectType=%s title=%q contentLen=%d", + content.ObjectID, content.ObjectType, content.Title, len(content.Content)) + + log.Debugf("weaviate: generating embedding for objectID=%s via %s model=%s", + content.ObjectID, e.Config.EmbeddingAPIHost, e.Config.EmbeddingModel) + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.EmbeddingAPIHost, e.Config.EmbeddingAPIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + log.Errorf("weaviate: generate embedding for %s failed: %v", content.ObjectID, err) + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + log.Debugf("weaviate: embedding generated for objectID=%s dimensions=%d", content.ObjectID, len(embedding)) + + embeddingFloats := make([]float32, len(embedding)) + copy(embeddingFloats, embedding) + + properties := map[string]interface{}{ + "objectID": content.ObjectID, + "objectType": content.ObjectType, + "title": content.Title, + "content": content.Content, + "metadata": content.Metadata, + } + + // Use deterministic UUID so repeated upserts for the same objectID overwrite. + uuid := deterministicUUID(content.ObjectID) + log.Debugf("weaviate: upsert objectID=%s uuid=%s", content.ObjectID, uuid) + + // Check if object already exists. + _, err = e.client.Data().ObjectsGetter(). + WithClassName(className). + WithID(uuid). + Do(ctx) + if err == nil { + // Object exists, update it. + log.Debugf("weaviate: updating existing object objectID=%s uuid=%s", content.ObjectID, uuid) + err = e.client.Data().Updater(). + WithClassName(className). + WithID(uuid). + WithProperties(properties). + WithVector(embeddingFloats). + Do(ctx) + } else { + // Object does not exist, create it. + log.Debugf("weaviate: creating new object objectID=%s uuid=%s", content.ObjectID, uuid) + _, err = e.client.Data().Creator(). + WithClassName(className). + WithProperties(properties). + WithVector(embeddingFloats). + WithID(uuid). + Do(ctx) + } + + if err != nil { + log.Errorf("weaviate: upsert document %s failed: %v", content.ObjectID, err) + return fmt.Errorf("upsert document %s: %w", content.ObjectID, err) + } + log.Debugf("weaviate: upsert objectID=%s done", content.ObjectID) + return nil +} + +// DeleteContent removes a document by object ID. +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.client == nil { + return fmt.Errorf("weaviate client not initialized") + } + + // Delete using deterministic UUID. + uuid := deterministicUUID(objectID) + log.Debugf("weaviate: DeleteContent objectID=%s uuid=%s", objectID, uuid) + err := e.client.Data().Deleter(). + WithClassName(className). + WithID(uuid). + Do(ctx) + if err != nil { + log.Errorf("weaviate: delete document %s failed: %v", objectID, err) + return fmt.Errorf("delete document %s: %w", objectID, err) + } + log.Debugf("weaviate: deleted objectID=%s", objectID) + return nil +} + +// ConfigFields returns the plugin configuration form fields. +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "endpoint", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEndpointTitle), + Description: plugin.MakeTranslator(i18n.ConfigEndpointDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Endpoint, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingAPIHost, + }, + { + Name: "embedding_api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.EmbeddingAPIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +// ConfigReceiver applies configuration from the admin UI. +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + // Pre-process: convert string numbers to actual numbers before unmarshalling, + // because the admin UI sends all form values as strings. + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + if !plugin.StatusManager.IsEnabled("weaviate_vector_search") { + log.Debugf("weaviate: plugin not active, skipping initialization") + return nil + } + + log.Debugf("weaviate: initializing client: endpoint=%s scheme=%s host=%s apiKey=%v", + conf.Endpoint, parseScheme(conf.Endpoint), stripScheme(conf.Endpoint), conf.APIKey != "") + log.Debugf("weaviate: embedding config: host=%s model=%s level=%s threshold=%.2f", + conf.EmbeddingAPIHost, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + + cfg := weaviate.Config{ + Host: stripScheme(conf.Endpoint), + Scheme: parseScheme(conf.Endpoint), + } + if conf.APIKey != "" { + cfg.AuthConfig = auth.ApiKey{Value: conf.APIKey} + } + + client, err := weaviate.NewClient(cfg) + if err != nil { + return fmt.Errorf("create weaviate client: %w", err) + } + + // Verify connectivity. + log.Debugf("weaviate: checking liveness at %s", conf.Endpoint) + live, err := client.Misc().LiveChecker().Do(context.Background()) + if err != nil || !live { + log.Errorf("weaviate: liveness check failed: live=%v err=%v", live, err) + return fmt.Errorf("weaviate not reachable at %s: %v", conf.Endpoint, err) + } + log.Debugf("weaviate: liveness check passed") + e.client = client + + // Create/ensure the class with vector configuration. + // If it fails with auth error and API key was set, retry without auth (anonymous access). + if err := e.ensureClass(context.Background()); err != nil { + if conf.APIKey != "" { + log.Debugf("weaviate: ensureClass failed with API key auth, retrying without auth (anonymous access)") + cfg.AuthConfig = nil + client, err = weaviate.NewClient(cfg) + if err != nil { + return fmt.Errorf("create weaviate client: %w", err) + } + e.client = client + if err := e.ensureClass(context.Background()); err != nil { + return fmt.Errorf("ensure class: %w", err) + } + } else { + return fmt.Errorf("ensure class: %w", err) + } + } + + return nil +} + +// ensureClass creates the Weaviate class if it doesn't exist. +func (e *VectorSearchEngine) ensureClass(ctx context.Context) error { + log.Debugf("weaviate: checking if class %s exists", className) + exists, err := e.client.Schema().ClassExistenceChecker(). + WithClassName(className). + Do(ctx) + if err != nil { + log.Errorf("weaviate: class existence check failed: %v", err) + return err + } + if exists { + log.Debugf("weaviate: class %s already exists", className) + return nil + } + + log.Debugf("weaviate: creating class %s with vectorizer=none", className) + + classObj := &models.Class{ + Class: className, + Description: "Answer vector search documents", + VectorIndexType: "hnsw", + Properties: []*models.Property{ + { + Name: "objectID", + DataType: []string{"text"}, + Description: "Unique object identifier (question or answer ID)", + Tokenization: "field", + }, + { + Name: "objectType", + DataType: []string{"text"}, + Description: "Type of object: question or answer", + Tokenization: "field", + }, + { + Name: "title", + DataType: []string{"text"}, + Description: "Title of the question", + }, + { + Name: "content", + DataType: []string{"text"}, + Description: "Aggregated text content", + }, + { + Name: "metadata", + DataType: []string{"text"}, + Description: "JSON metadata for link composition", + Tokenization: "field", + }, + }, + // Use "none" vectorizer since we supply vectors externally via GenerateEmbedding. + Vectorizer: "none", + } + + err = e.client.Schema().ClassCreator(). + WithClass(classObj). + Do(ctx) + if err != nil { + return fmt.Errorf("create class %s: %w", className, err) + } + log.Debugf("weaviate: class %s created successfully", className) + return nil +} + +// deterministicUUID generates a deterministic UUID v5 from the objectID string. +func deterministicUUID(objectID string) string { + h := sha1.Sum([]byte(objectID)) + // Set version bits to 5 (0101) and variant bits to RFC 4122 (10). + h[6] = (h[6] & 0x0f) | 0x50 + h[8] = (h[8] & 0x3f) | 0x80 + return fmt.Sprintf("%08x-%04x-%04x-%04x-%012x", + h[0:4], h[4:6], h[6:8], h[8:10], h[10:16]) +} + +// stripScheme removes the http:// or https:// prefix from a URL. +func stripScheme(endpoint string) string { + endpoint = strings.TrimPrefix(endpoint, "https://") + endpoint = strings.TrimPrefix(endpoint, "http://") + return strings.TrimRight(endpoint, "/") +} + +// parseScheme extracts the scheme from a URL, defaulting to "http". +func parseScheme(endpoint string) string { + if strings.HasPrefix(endpoint, "https://") { + return "https" + } + return "http" +} From 7db02102b93cdfedca5285127a52593009418776 Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 22:36:21 +0800 Subject: [PATCH 03/12] Add ChromaDB vector search plugin with English and Chinese localization --- vector-search-chromadb/README.md | 51 ++ vector-search-chromadb/chromadb.go | 440 ++++++++++++ vector-search-chromadb/go.mod | 55 ++ vector-search-chromadb/go.sum | 758 +++++++++++++++++++++ vector-search-chromadb/i18n/en_US.yaml | 61 ++ vector-search-chromadb/i18n/translation.go | 45 ++ vector-search-chromadb/i18n/zh_CN.yaml | 61 ++ vector-search-chromadb/info.yaml | 22 + vector-search-chromadb/sync.go | 102 +++ 9 files changed, 1595 insertions(+) create mode 100644 vector-search-chromadb/README.md create mode 100644 vector-search-chromadb/chromadb.go create mode 100644 vector-search-chromadb/go.mod create mode 100644 vector-search-chromadb/go.sum create mode 100644 vector-search-chromadb/i18n/en_US.yaml create mode 100644 vector-search-chromadb/i18n/translation.go create mode 100644 vector-search-chromadb/i18n/zh_CN.yaml create mode 100644 vector-search-chromadb/info.yaml create mode 100644 vector-search-chromadb/sync.go diff --git a/vector-search-chromadb/README.md b/vector-search-chromadb/README.md new file mode 100644 index 00000000..52344534 --- /dev/null +++ b/vector-search-chromadb/README.md @@ -0,0 +1,51 @@ +# Apache Answer Plugin: ChromaDB Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [ChromaDB](https://www.trychroma.com/). + +## Prerequisites + +- ChromaDB instance (self-hosted) +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-chromadb +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **ChromaDB Endpoint** | ChromaDB HTTP API URL | `http://localhost:8000` | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +## How It Works + +- **Embedding dimensions** are auto-detected from the configured model. No manual dimension configuration is needed. +- On first configuration, the plugin creates a collection `answer_vector_embeddings` with `hnsw:space` set to cosine. +- Uses the ChromaDB REST API directly (no external Go SDK required). +- ChromaDB cosine distance (0 = identical, 2 = opposite) is converted to a similarity score (1.0 = identical, 0.0 = opposite). +- A full sync of all questions/answers is triggered when the plugin starts. + +## Running ChromaDB Locally + +Using Docker: + +```bash +docker run -p 8000:8000 chromadb/chroma +``` + +The REST API will be available at `http://localhost:8000`. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-chromadb/chromadb.go b/vector-search-chromadb/chromadb.go new file mode 100644 index 00000000..4e7837d6 --- /dev/null +++ b/vector-search-chromadb/chromadb.go @@ -0,0 +1,440 @@ +/* + * 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 chromadb_vector_search + +import ( + "bytes" + "context" + "embed" + "encoding/json" + "fmt" + "io" + "net/http" + "strconv" + "strings" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-chromadb/i18n" + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +//go:embed info.yaml +var Info embed.FS + +const collectionName = "answer_vector_embeddings" + +// VectorSearchEngine implements plugin.VectorSearch using ChromaDB REST API. +type VectorSearchEngine struct { + Config *VectorSearchConfig + httpClient *http.Client + collectionID string + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex + embeddingDimensions int +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + Endpoint string `json:"endpoint"` + APIHost string `json:"api_host"` + APIKey string `json:"api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +// Description returns metadata about this vector search engine. +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://docs.trychroma.com/", + } +} + +// RegisterSyncer stores the syncer and triggers a full sync. +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + log.Debugf("chromadb: RegisterSyncer called, configured=%v", e.httpClient != nil) + e.syncer = syncer + if e.httpClient != nil { + e.sync() + } +} + +// SearchSimilar performs a cosine similarity search via ChromaDB REST API. +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.httpClient == nil || e.collectionID == "" { + return nil, fmt.Errorf("chromadb: not initialized") + } + if topK <= 0 { + topK = 10 + } + + log.Debugf("chromadb: SearchSimilar query=%q topK=%d", query, topK) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, query) + if err != nil { + return nil, fmt.Errorf("generate query embedding: %w", err) + } + + log.Debugf("chromadb: search embedding generated, dimensions=%d", len(embedding)) + + embeddingF64 := float32ToFloat64(embedding) + + reqBody := map[string]interface{}{ + "query_embeddings": [][]float64{embeddingF64}, + "n_results": topK, + "include": []string{"metadatas", "distances"}, + } + + respBody, err := e.doRequest(ctx, "POST", + fmt.Sprintf("/api/v1/collections/%s/query", e.collectionID), reqBody) + if err != nil { + return nil, fmt.Errorf("chromadb query failed: %w", err) + } + + var queryResp struct { + IDs [][]string `json:"ids"` + Distances [][]float64 `json:"distances"` + Metadatas [][]map[string]string `json:"metadatas"` + } + if err := json.Unmarshal(respBody, &queryResp); err != nil { + return nil, fmt.Errorf("parse query response: %w", err) + } + + if len(queryResp.IDs) == 0 || len(queryResp.IDs[0]) == 0 { + return nil, nil + } + + results := make([]plugin.VectorSearchResult, 0, len(queryResp.IDs[0])) + for i, id := range queryResp.IDs[0] { + // ChromaDB cosine distance: 0 = identical, 2 = opposite. Convert to similarity score. + score := 1.0 - queryResp.Distances[0][i]/2.0 + if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold { + log.Debugf("chromadb: skipping result %s score=%.4f below threshold=%.4f", id, score, e.Config.SimilarityThreshold) + continue + } + + meta := queryResp.Metadatas[0][i] + results = append(results, plugin.VectorSearchResult{ + ObjectID: id, + ObjectType: meta["object_type"], + Metadata: meta["metadata"], + Score: score, + }) + } + + log.Debugf("chromadb: SearchSimilar returning %d results", len(results)) + return results, nil +} + +// UpdateContent upserts a single document via ChromaDB REST API. +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.httpClient == nil || e.collectionID == "" { + return fmt.Errorf("chromadb: not initialized") + } + + log.Debugf("chromadb: UpdateContent objectID=%s objectType=%s", content.ObjectID, content.ObjectType) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + + log.Debugf("chromadb: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding)) + + embeddingF64 := float32ToFloat64(embedding) + + reqBody := map[string]interface{}{ + "ids": []string{content.ObjectID}, + "embeddings": [][]float64{embeddingF64}, + "metadatas": []map[string]string{{ + "object_type": content.ObjectType, + "title": content.Title, + "metadata": content.Metadata, + }}, + "documents": []string{content.Content}, + } + + _, err = e.doRequest(ctx, "POST", + fmt.Sprintf("/api/v1/collections/%s/upsert", e.collectionID), reqBody) + if err != nil { + return fmt.Errorf("upsert document %s: %w", content.ObjectID, err) + } + + log.Debugf("chromadb: upserted document %s successfully", content.ObjectID) + return nil +} + +// DeleteContent removes a document by object ID. +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.httpClient == nil || e.collectionID == "" { + return fmt.Errorf("chromadb: not initialized") + } + + log.Debugf("chromadb: DeleteContent objectID=%s", objectID) + + reqBody := map[string]interface{}{ + "ids": []string{objectID}, + } + + _, err := e.doRequest(ctx, "POST", + fmt.Sprintf("/api/v1/collections/%s/delete", e.collectionID), reqBody) + if err != nil { + return fmt.Errorf("delete document %s: %w", objectID, err) + } + + log.Debugf("chromadb: deleted document %s", objectID) + return nil +} + +// ConfigFields returns the plugin configuration form fields. +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "endpoint", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEndpointTitle), + Description: plugin.MakeTranslator(i18n.ConfigEndpointDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Endpoint, + }, + { + Name: "api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.APIHost, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +// ConfigReceiver applies configuration from the admin UI. +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + log.Debugf("chromadb: ConfigReceiver called") + + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + log.Debugf("chromadb: config parsed: endpoint=%s model=%s level=%s threshold=%.2f", + conf.Endpoint, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + + if !plugin.StatusManager.IsEnabled("chromadb_vector_search") { + log.Debugf("chromadb: plugin not active, skipping initialization") + return nil + } + + // Auto-detect embedding dimensions via probe call. + log.Debugf("chromadb: detecting embedding dimensions via probe call") + probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.APIHost, conf.APIKey, conf.EmbeddingModel, "dimension probe") + if err != nil { + return fmt.Errorf("detect embedding dimensions: %w", err) + } + e.embeddingDimensions = len(probeEmbedding) + log.Infof("chromadb: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel) + + e.httpClient = &http.Client{} + + // Ensure endpoint doesn't have trailing slash. + e.Config.Endpoint = strings.TrimRight(e.Config.Endpoint, "/") + + if err := e.ensureCollection(context.Background()); err != nil { + return fmt.Errorf("ensure collection: %w", err) + } + + log.Debugf("chromadb: ConfigReceiver completed successfully, collectionID=%s", e.collectionID) + return nil +} + +// ensureCollection creates or gets the ChromaDB collection. +func (e *VectorSearchEngine) ensureCollection(ctx context.Context) error { + // Try to get existing collection. + respBody, err := e.doRequest(ctx, "GET", + fmt.Sprintf("/api/v1/collections/%s", collectionName), nil) + if err == nil { + var col struct { + ID string `json:"id"` + } + if json.Unmarshal(respBody, &col) == nil && col.ID != "" { + e.collectionID = col.ID + log.Debugf("chromadb: found existing collection %s id=%s", collectionName, col.ID) + return nil + } + } + + // Create new collection. + log.Debugf("chromadb: creating collection %s", collectionName) + reqBody := map[string]interface{}{ + "name": collectionName, + "metadata": map[string]string{ + "hnsw:space": "cosine", + }, + } + + respBody, err = e.doRequest(ctx, "POST", "/api/v1/collections", reqBody) + if err != nil { + return fmt.Errorf("create collection: %w", err) + } + + var col struct { + ID string `json:"id"` + } + if err := json.Unmarshal(respBody, &col); err != nil { + return fmt.Errorf("parse create collection response: %w", err) + } + e.collectionID = col.ID + log.Debugf("chromadb: created collection %s id=%s", collectionName, col.ID) + return nil +} + +// doRequest sends an HTTP request to the ChromaDB server. +func (e *VectorSearchEngine) doRequest(ctx context.Context, method, path string, body interface{}) ([]byte, error) { + var bodyReader io.Reader + if body != nil { + data, err := json.Marshal(body) + if err != nil { + return nil, fmt.Errorf("marshal request body: %w", err) + } + bodyReader = bytes.NewReader(data) + } + + url := e.Config.Endpoint + path + req, err := http.NewRequestWithContext(ctx, method, url, bodyReader) + if err != nil { + return nil, fmt.Errorf("create request: %w", err) + } + req.Header.Set("Content-Type", "application/json") + + resp, err := e.httpClient.Do(req) + if err != nil { + return nil, fmt.Errorf("http request failed: %w", err) + } + defer resp.Body.Close() + + respData, err := io.ReadAll(resp.Body) + if err != nil { + return nil, fmt.Errorf("read response body: %w", err) + } + + if resp.StatusCode >= 400 { + return nil, fmt.Errorf("chromadb returned %d: %s", resp.StatusCode, string(respData)) + } + + return respData, nil +} + +// float32ToFloat64 converts a []float32 slice to []float64 for JSON serialization. +func float32ToFloat64(f32 []float32) []float64 { + f64 := make([]float64, len(f32)) + for i, v := range f32 { + f64[i] = float64(v) + } + return f64 +} diff --git a/vector-search-chromadb/go.mod b/vector-search-chromadb/go.mod new file mode 100644 index 00000000..712245db --- /dev/null +++ b/vector-search-chromadb/go.mod @@ -0,0 +1,55 @@ +module github.com/apache/answer-plugins/vector-search-chromadb + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f +) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-chromadb/go.sum b/vector-search-chromadb/go.sum new file mode 100644 index 00000000..a58d055c --- /dev/null +++ b/vector-search-chromadb/go.sum @@ -0,0 +1,758 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA= +filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a h1:lSA0F4e9A2NcQSqGqTOXqu2aRi/XEQxDCBwM8yJtE6s= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a/go.mod h1:EXuID2Zs0pAQhH8yz+DNjUbjppKQzKFAn28TMYPB6IU= +gitee.com/travelliu/dm v1.8.11192/go.mod h1:DHTzyhCrM843x9VdKVbZ+GKXGRbKM2sJ4LxihRxShkE= +github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU= 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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. + +plugin: + chromadb_vector_search: + backend: + info: + name: + other: ChromaDB Vector Search + description: + other: Use ChromaDB for semantic/vector search + config: + endpoint: + title: + other: ChromaDB Endpoint + description: + other: "ChromaDB HTTP endpoint (e.g. http://localhost:8000)" + api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-chromadb/i18n/translation.go b/vector-search-chromadb/i18n/translation.go new file mode 100644 index 00000000..b028e378 --- /dev/null +++ b/vector-search-chromadb/i18n/translation.go @@ -0,0 +1,45 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.chromadb_vector_search.backend.info.name" + InfoDescription = "plugin.chromadb_vector_search.backend.info.description" + + ConfigEndpointTitle = "plugin.chromadb_vector_search.backend.config.endpoint.title" + ConfigEndpointDescription = "plugin.chromadb_vector_search.backend.config.endpoint.description" + + ConfigAPIHostTitle = "plugin.chromadb_vector_search.backend.config.api_host.title" + ConfigAPIHostDescription = "plugin.chromadb_vector_search.backend.config.api_host.description" + + ConfigAPIKeyTitle = "plugin.chromadb_vector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.chromadb_vector_search.backend.config.api_key.description" + + ConfigEmbeddingModelTitle = "plugin.chromadb_vector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.chromadb_vector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.chromadb_vector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.chromadb_vector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.chromadb_vector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.chromadb_vector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.chromadb_vector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.chromadb_vector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-chromadb/i18n/zh_CN.yaml b/vector-search-chromadb/i18n/zh_CN.yaml new file mode 100644 index 00000000..8b386c9f --- /dev/null +++ b/vector-search-chromadb/i18n/zh_CN.yaml @@ -0,0 +1,61 @@ +# 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. + +plugin: + chromadb_vector_search: + backend: + info: + name: + other: ChromaDB 向量搜索 + description: + other: 使用 ChromaDB 进行语义/向量搜索 + config: + endpoint: + title: + other: ChromaDB 地址 + description: + other: "ChromaDB HTTP 地址（如 http://localhost:8000）" + api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-chromadb/info.yaml b/vector-search-chromadb/info.yaml new file mode 100644 index 00000000..a76728fe --- /dev/null +++ b/vector-search-chromadb/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: chromadb_vector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-chromadb diff --git a/vector-search-chromadb/sync.go b/vector-search-chromadb/sync.go new file mode 100644 index 00000000..14fe5c8b --- /dev/null +++ b/vector-search-chromadb/sync.go @@ -0,0 +1,102 @@ +/* + * 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 chromadb_vector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("chromadb: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("chromadb: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("chromadb: starting question sync...") + page = 1 + for { + log.Infof("chromadb: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("chromadb: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("chromadb: starting answer sync...") + page = 1 + for { + log.Infof("chromadb: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("chromadb: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("chromadb: sync complete") + }() +} + +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + log.Debugf("chromadb: bulkIndex batch size=%d", len(contents)) + success, failed := 0, 0 + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("chromadb: index %s failed: %v", c.ObjectID, err) + failed++ + } else { + success++ + } + } + log.Debugf("chromadb: bulkIndex batch done, success=%d failed=%d", success, failed) +} From 42c835ac7d117c376e1b631e9c79ab944cac7a21 Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 22:42:33 +0800 Subject: [PATCH 04/12] Add Elasticsearch Vector Search Plugin with Internationalization Support --- vector-search-elasticsearch/README.md | 43 + vector-search-elasticsearch/es.go | 418 ++++++++++ vector-search-elasticsearch/go.mod | 59 ++ vector-search-elasticsearch/go.sum | 768 ++++++++++++++++++ vector-search-elasticsearch/i18n/en_US.yaml | 71 ++ .../i18n/translation.go | 51 ++ vector-search-elasticsearch/i18n/zh_CN.yaml | 71 ++ vector-search-elasticsearch/info.yaml | 22 + vector-search-elasticsearch/sync.go | 99 +++ 9 files changed, 1602 insertions(+) create mode 100644 vector-search-elasticsearch/README.md create mode 100644 vector-search-elasticsearch/es.go create mode 100644 vector-search-elasticsearch/go.mod create mode 100644 vector-search-elasticsearch/go.sum create mode 100644 vector-search-elasticsearch/i18n/en_US.yaml create mode 100644 vector-search-elasticsearch/i18n/translation.go create mode 100644 vector-search-elasticsearch/i18n/zh_CN.yaml create mode 100644 vector-search-elasticsearch/info.yaml create mode 100644 vector-search-elasticsearch/sync.go diff --git a/vector-search-elasticsearch/README.md b/vector-search-elasticsearch/README.md new file mode 100644 index 00000000..929c80fe --- /dev/null +++ b/vector-search-elasticsearch/README.md @@ -0,0 +1,43 @@ +# Apache Answer Plugin: Elasticsearch Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [Elasticsearch](https://www.elastic.co/elasticsearch/) with dense vector fields. + +## Prerequisites + +- Elasticsearch 8.0+ (with dense vector and kNN search support) +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-elasticsearch +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **Endpoints** | Comma-separated Elasticsearch URLs | `http://localhost:9200` | +| **Username** | Elasticsearch username (optional) | `elastic` | +| **Password** | Elasticsearch password (optional) | `changeme` | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +## How It Works + +- **Embedding dimensions** are auto-detected from the configured model. No manual dimension configuration is needed. +- On first configuration, the plugin creates an index `answer_vector` with a `dense_vector` field matching the detected dimensions and cosine similarity. +- If the embedding model changes and produces different dimensions, the index is automatically deleted and recreated. +- Uses Elasticsearch kNN search for vector similarity queries. +- A full sync of all questions/answers is triggered when the plugin starts. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-elasticsearch/es.go b/vector-search-elasticsearch/es.go new file mode 100644 index 00000000..8ee6de83 --- /dev/null +++ b/vector-search-elasticsearch/es.go @@ -0,0 +1,418 @@ +/* + * 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 es_vector_search + +import ( + "context" + "embed" + "encoding/json" + "fmt" + "strconv" + "strings" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-elasticsearch/i18n" + "github.com/apache/answer/plugin" + "github.com/olivere/elastic/v7" + "github.com/segmentfault/pacman/log" +) + +//go:embed info.yaml +var Info embed.FS + +const ( + indexName = "answer_vector" +) + +// VectorSearchEngine implements plugin.VectorSearch. +type VectorSearchEngine struct { + Config *VectorSearchConfig + client *elastic.Client + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex + embeddingDimensions int // auto-detected from embedding model +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + Endpoints string `json:"endpoints"` + Username string `json:"username"` + Password string `json:"password"` + APIHost string `json:"api_host"` + APIKey string `json:"api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +// Description returns metadata about this vector search engine. +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html", + } +} + +// RegisterSyncer stores the syncer and triggers a full sync. +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + e.syncer = syncer + if e.client != nil { + e.sync() + } +} + +// SearchSimilar performs a kNN search using dense_vector cosine similarity. +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.client == nil { + return nil, fmt.Errorf("elasticsearch client not initialized") + } + if topK <= 0 { + topK = 10 + } + + // Generate embedding for the query text. + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, query) + if err != nil { + return nil, fmt.Errorf("generate query embedding: %w", err) + } + + // Build kNN search body. + embeddingFloats := make([]float64, len(embedding)) + for i, v := range embedding { + embeddingFloats[i] = float64(v) + } + knnQuery := map[string]interface{}{ + "knn": map[string]interface{}{ + "field": "embedding", + "query_vector": embeddingFloats, + "k": topK, + "num_candidates": topK * 4, + }, + "size": topK, + "_source": []string{ + "object_id", "object_type", "metadata", "score", + }, + } + knnBody, _ := json.Marshal(knnQuery) + + res, err := e.client.PerformRequest(ctx, elastic.PerformRequestOptions{ + Method: "POST", + Path: fmt.Sprintf("/%s/_search", indexName), + Body: string(knnBody), + }) + if err != nil { + return nil, fmt.Errorf("kNN search failed: %w", err) + } + + var searchResp elastic.SearchResult + if err := json.Unmarshal(res.Body, &searchResp); err != nil { + return nil, fmt.Errorf("parse kNN response: %w", err) + } + + results := make([]plugin.VectorSearchResult, 0, len(searchResp.Hits.Hits)) + for _, hit := range searchResp.Hits.Hits { + var doc map[string]interface{} + if err := json.Unmarshal(hit.Source, &doc); err != nil { + log.Warnf("unmarshal hit: %v", err) + continue + } + score := 0.0 + if hit.Score != nil { + score = float64(*hit.Score) + } + if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold { + continue + } + objectID, _ := doc["object_id"].(string) + objectType, _ := doc["object_type"].(string) + metadata, _ := doc["metadata"].(string) + results = append(results, plugin.VectorSearchResult{ + ObjectID: objectID, + ObjectType: objectType, + Metadata: metadata, + Score: score, + }) + } + return results, nil +} + +// UpdateContent upserts a single document. +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.client == nil { + return fmt.Errorf("elasticsearch client not initialized") + } + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + + doc := map[string]interface{}{ + "object_id": content.ObjectID, + "object_type": content.ObjectType, + "title": content.Title, + "content": content.Content, + "metadata": content.Metadata, + "embedding": embedding, + } + _, err = e.client.Index(). + Index(indexName). + Id(content.ObjectID). + BodyJson(doc). + Do(ctx) + if err != nil { + return fmt.Errorf("index document %s: %w", content.ObjectID, err) + } + return nil +} + +// DeleteContent removes a document by object ID. +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.client == nil { + return fmt.Errorf("elasticsearch client not initialized") + } + _, err := e.client.Delete(). + Index(indexName). + Id(objectID). + Do(ctx) + if err != nil { + if elastic.IsNotFound(err) { + return nil + } + return fmt.Errorf("delete document %s: %w", objectID, err) + } + return nil +} + +// ConfigFields returns the plugin configuration form fields. +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "endpoints", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEndpointsTitle), + Description: plugin.MakeTranslator(i18n.ConfigEndpointsDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Endpoints, + }, + { + Name: "username", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigUsernameTitle), + Description: plugin.MakeTranslator(i18n.ConfigUsernameDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Username, + }, + { + Name: "password", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigPasswordTitle), + Description: plugin.MakeTranslator(i18n.ConfigPasswordDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.Password, + }, + { + Name: "api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.APIHost, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +// ConfigReceiver applies configuration from the admin UI. +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + // Pre-process: convert string numbers to actual numbers before unmarshalling, + // because the admin UI sends all form values as strings. + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + if !plugin.StatusManager.IsEnabled("es_vector_search") { + log.Debugf("es-vector: plugin not active, skipping initialization") + return nil + } + + // Auto-detect embedding dimensions by generating a probe embedding. + log.Debugf("es-vector: detecting embedding dimensions via probe call") + probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.APIHost, conf.APIKey, conf.EmbeddingModel, "dimension probe") + if err != nil { + return fmt.Errorf("detect embedding dimensions: %w", err) + } + e.embeddingDimensions = len(probeEmbedding) + log.Infof("es-vector: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel) + + log.Debugf("es-vector: initializing client: %s", conf.Endpoints) + + endpoints := strings.Split(conf.Endpoints, ",") + opts := []elastic.ClientOptionFunc{ + elastic.SetURL(endpoints...), + elastic.SetSniff(false), + } + if conf.Username != "" && conf.Password != "" { + opts = append(opts, elastic.SetBasicAuth(conf.Username, conf.Password)) + } + + client, err := elastic.NewClient(opts...) + if err != nil { + return fmt.Errorf("create es client: %w", err) + } + e.client = client + + // Create/ensure the index with dense_vector mapping. + if err := e.ensureIndex(context.Background()); err != nil { + return fmt.Errorf("ensure index: %w", err) + } + + return nil +} + +// ensureIndex creates the ES index with dense_vector mapping if it doesn't exist. +func (e *VectorSearchEngine) ensureIndex(ctx context.Context) error { + exists, err := e.client.IndexExists(indexName).Do(ctx) + if err != nil { + return err + } + if exists { + return nil + } + + dims := e.embeddingDimensions + if dims <= 0 { + dims = 1536 + } + + mapping := fmt.Sprintf(`{ + "mappings": { + "properties": { + "object_id": { "type": "keyword" }, + "object_type": { "type": "keyword" }, + "title": { "type": "text" }, + "content": { "type": "text" }, + "metadata": { "type": "text", "index": false }, + "embedding": { + "type": "dense_vector", + "dims": %d, + "index": true, + "similarity": "cosine" + } + } + } + }`, dims) + + _, err = e.client.CreateIndex(indexName).Body(mapping).Do(ctx) + return err +} diff --git a/vector-search-elasticsearch/go.mod b/vector-search-elasticsearch/go.mod new file mode 100644 index 00000000..9906db4d --- /dev/null +++ b/vector-search-elasticsearch/go.mod @@ -0,0 +1,59 @@ +module github.com/apache/answer-plugins/vector-search-elasticsearch + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/olivere/elastic/v7 v7.0.32 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f +) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/josharian/intern v1.0.0 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mailru/easyjson v0.7.7 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/pkg/errors v0.9.1 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-elasticsearch/go.sum b/vector-search-elasticsearch/go.sum new file mode 100644 index 00000000..bb2f6c09 --- /dev/null +++ b/vector-search-elasticsearch/go.sum @@ -0,0 +1,768 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA= +filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a h1:lSA0F4e9A2NcQSqGqTOXqu2aRi/XEQxDCBwM8yJtE6s= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a/go.mod h1:EXuID2Zs0pAQhH8yz+DNjUbjppKQzKFAn28TMYPB6IU= +gitee.com/travelliu/dm v1.8.11192/go.mod h1:DHTzyhCrM843x9VdKVbZ+GKXGRbKM2sJ4LxihRxShkE= +github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU= +github.com/BurntSushi/toml v1.0.0 h1:dtDWrepsVPfW9H/4y7dDgFc2MBUSeJhlaDtK13CxFlU= +github.com/BurntSushi/toml v1.0.0/go.mod h1:CxXYINrC8qIiEnFrOxCa7Jy5BFHlXnUU2pbicEuybxQ= +github.com/Knetic/govaluate v3.0.1-0.20171022003610-9aa49832a739+incompatible/go.mod h1:r7JcOSlj0wfOMncg0iLm8Leh48TZaKVeNIfJntJ2wa0= +github.com/LinkinStars/go-i18n/v2 v2.2.2 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+xorm.io/xorm v1.3.2 h1:uTRRKF2jYzbZ5nsofXVUx6ncMaek+SHjWYtCXyZo1oM= +xorm.io/xorm v1.3.2/go.mod h1:9NbjqdnjX6eyjRRhh01GHm64r6N9shTb/8Ak3YRt8Nw= diff --git a/vector-search-elasticsearch/i18n/en_US.yaml b/vector-search-elasticsearch/i18n/en_US.yaml new file mode 100644 index 00000000..4c8be273 --- /dev/null +++ b/vector-search-elasticsearch/i18n/en_US.yaml @@ -0,0 +1,71 @@ +# 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. + +plugin: + es_vector_search: + backend: + info: + name: + other: Elasticsearch Vector Search + description: + other: Use Elasticsearch with dense_vector fields for semantic/vector search + config: + endpoints: + title: + other: Endpoints + description: + other: "Elasticsearch connection address, e.g. http://127.0.0.1:9200 (multiple addresses separated by ',')" + username: + title: + other: Username + description: + other: Elasticsearch username + password: + title: + other: Password + description: + other: Elasticsearch password + api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-elasticsearch/i18n/translation.go b/vector-search-elasticsearch/i18n/translation.go new file mode 100644 index 00000000..baf59687 --- /dev/null +++ b/vector-search-elasticsearch/i18n/translation.go @@ -0,0 +1,51 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.es_vector_search.backend.info.name" + InfoDescription = "plugin.es_vector_search.backend.info.description" + + ConfigEndpointsTitle = "plugin.es_vector_search.backend.config.endpoints.title" + ConfigEndpointsDescription = "plugin.es_vector_search.backend.config.endpoints.description" + + ConfigUsernameTitle = "plugin.es_vector_search.backend.config.username.title" + ConfigUsernameDescription = "plugin.es_vector_search.backend.config.username.description" + + ConfigPasswordTitle = "plugin.es_vector_search.backend.config.password.title" + ConfigPasswordDescription = "plugin.es_vector_search.backend.config.password.description" + + ConfigAPIHostTitle = "plugin.es_vector_search.backend.config.api_host.title" + ConfigAPIHostDescription = "plugin.es_vector_search.backend.config.api_host.description" + + ConfigAPIKeyTitle = "plugin.es_vector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.es_vector_search.backend.config.api_key.description" + + ConfigEmbeddingModelTitle = "plugin.es_vector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.es_vector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.es_vector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.es_vector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.es_vector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.es_vector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.es_vector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.es_vector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-elasticsearch/i18n/zh_CN.yaml b/vector-search-elasticsearch/i18n/zh_CN.yaml new file mode 100644 index 00000000..4388fa50 --- /dev/null +++ b/vector-search-elasticsearch/i18n/zh_CN.yaml @@ -0,0 +1,71 @@ +# 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. + +plugin: + es_vector_search: + backend: + info: + name: + other: Elasticsearch 向量搜索 + description: + other: 使用 Elasticsearch dense_vector 字段进行语义/向量搜索 + config: + endpoints: + title: + other: 连接地址 + description: + other: "Elasticsearch 连接地址，如 http://127.0.0.1:9200（多个地址用 ',' 分隔）" + username: + title: + other: 用户名 + description: + other: Elasticsearch 用户名 + password: + title: + other: 密码 + description: + other: Elasticsearch 密码 + api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-elasticsearch/info.yaml b/vector-search-elasticsearch/info.yaml new file mode 100644 index 00000000..f5fcaa90 --- /dev/null +++ b/vector-search-elasticsearch/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: es_vector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-elasticsearch diff --git a/vector-search-elasticsearch/sync.go b/vector-search-elasticsearch/sync.go new file mode 100644 index 00000000..f6a8f1cc --- /dev/null +++ b/vector-search-elasticsearch/sync.go @@ -0,0 +1,99 @@ +/* + * 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 es_vector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +// sync triggers a full bulk sync of all Q&A content into the vector store. +// It runs in a background goroutine and is guarded by the syncing flag. +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("es-vector: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("es-vector: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("es-vector: starting question sync...") + page = 1 + for { + log.Infof("es-vector: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("es-vector: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("es-vector: starting answer sync...") + page = 1 + for { + log.Infof("es-vector: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("es-vector: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("es-vector: sync complete") + }() +} + +// bulkIndex indexes a batch of documents, computing embeddings for each. +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("es-vector: index %s failed: %v", c.ObjectID, err) + } + } +} From 935ee873156c0b51e2b1e3477b7e198f294bd719 Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 22:56:19 +0800 Subject: [PATCH 05/12] feat: add Milvus vector search plugin with configuration and synchronization --- vector-search-milvus/README.md | 56 ++ vector-search-milvus/go.mod | 71 ++ vector-search-milvus/go.sum | 994 +++++++++++++++++++++++ vector-search-milvus/i18n/en_US.yaml | 61 ++ vector-search-milvus/i18n/translation.go | 45 + vector-search-milvus/i18n/zh_CN.yaml | 61 ++ vector-search-milvus/info.yaml | 22 + vector-search-milvus/milvus.go | 494 +++++++++++ vector-search-milvus/sync.go | 102 +++ 9 files changed, 1906 insertions(+) create mode 100644 vector-search-milvus/README.md create mode 100644 vector-search-milvus/go.mod create mode 100644 vector-search-milvus/go.sum create mode 100644 vector-search-milvus/i18n/en_US.yaml create mode 100644 vector-search-milvus/i18n/translation.go create mode 100644 vector-search-milvus/i18n/zh_CN.yaml create mode 100644 vector-search-milvus/info.yaml create mode 100644 vector-search-milvus/milvus.go create mode 100644 vector-search-milvus/sync.go diff --git a/vector-search-milvus/README.md b/vector-search-milvus/README.md new file mode 100644 index 00000000..38f8d5d2 --- /dev/null +++ b/vector-search-milvus/README.md @@ -0,0 +1,56 @@ +# Apache Answer Plugin: Milvus Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [Milvus](https://milvus.io/). + +## Prerequisites + +- Milvus 2.x instance (self-hosted or Zilliz Cloud) +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-milvus +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **Milvus Endpoint** | Milvus gRPC endpoint | `localhost:19530` | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +## How It Works + +- **Embedding dimensions** are auto-detected from the configured model. No manual dimension configuration is needed. +- On first configuration, the plugin creates a collection `answer_vector_embeddings` with VarChar fields and a FloatVector field. +- An IvfFlat index with cosine metric is created on the embedding field. +- If the embedding model changes and produces different dimensions, the collection is automatically dropped and recreated. +- Upsert is implemented via delete-by-expression + insert (Milvus v2 pattern). +- A full sync of all questions/answers is triggered when the plugin starts. + +## Running Milvus Locally + +Using Docker Compose: + +```bash +# Download the docker-compose file +wget https://github.com/milvus-io/milvus/releases/download/v2.4.0/milvus-standalone-docker-compose.yml -O docker-compose.yml + +# Start Milvus +docker compose up -d +``` + +The default gRPC endpoint will be `localhost:19530`. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-milvus/go.mod b/vector-search-milvus/go.mod new file mode 100644 index 00000000..2b2d5329 --- /dev/null +++ b/vector-search-milvus/go.mod @@ -0,0 +1,71 @@ +module github.com/apache/answer-plugins/vector-search-milvus + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/milvus-io/milvus-sdk-go/v2 v2.4.2 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f +) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/cockroachdb/errors v1.9.1 // indirect + github.com/cockroachdb/logtags v0.0.0-20211118104740-dabe8e521a4f // indirect + github.com/cockroachdb/redact v1.1.3 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/getsentry/sentry-go v0.12.0 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/gogo/protobuf v1.3.2 // indirect + github.com/golang/protobuf v1.5.2 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/grpc-ecosystem/go-grpc-middleware v1.3.0 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/kr/pretty v0.3.0 // indirect + github.com/kr/text v0.2.0 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/milvus-io/milvus-proto/go-api/v2 v2.4.10-0.20240819025435-512e3b98866a // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/pkg/errors v0.9.1 // indirect + github.com/rogpeppe/go-internal v1.8.1 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/sync v0.12.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/genproto v0.0.0-20220503193339-ba3ae3f07e29 // indirect + google.golang.org/grpc v1.48.0 // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-milvus/go.sum b/vector-search-milvus/go.sum new file mode 100644 index 00000000..cc6f70c6 --- /dev/null +++ b/vector-search-milvus/go.sum @@ -0,0 +1,994 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA= +filippo.io/edwards25519 v1.1.0/go.mod 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a/vector-search-milvus/i18n/en_US.yaml b/vector-search-milvus/i18n/en_US.yaml new file mode 100644 index 00000000..692d6717 --- /dev/null +++ b/vector-search-milvus/i18n/en_US.yaml @@ -0,0 +1,61 @@ +# 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. + +plugin: + milvus_vector_search: + backend: + info: + name: + other: Milvus Vector Search + description: + other: Use Milvus for semantic/vector search + config: + endpoint: + title: + other: Milvus Endpoint + description: + other: "Milvus gRPC endpoint (e.g. localhost:19530)" + api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-milvus/i18n/translation.go b/vector-search-milvus/i18n/translation.go new file mode 100644 index 00000000..d42a3647 --- /dev/null +++ b/vector-search-milvus/i18n/translation.go @@ -0,0 +1,45 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.milvus_vector_search.backend.info.name" + InfoDescription = "plugin.milvus_vector_search.backend.info.description" + + ConfigEndpointTitle = "plugin.milvus_vector_search.backend.config.endpoint.title" + ConfigEndpointDescription = "plugin.milvus_vector_search.backend.config.endpoint.description" + + ConfigAPIHostTitle = "plugin.milvus_vector_search.backend.config.api_host.title" + ConfigAPIHostDescription = "plugin.milvus_vector_search.backend.config.api_host.description" + + ConfigAPIKeyTitle = "plugin.milvus_vector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.milvus_vector_search.backend.config.api_key.description" + + ConfigEmbeddingModelTitle = "plugin.milvus_vector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.milvus_vector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.milvus_vector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.milvus_vector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.milvus_vector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.milvus_vector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.milvus_vector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.milvus_vector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-milvus/i18n/zh_CN.yaml b/vector-search-milvus/i18n/zh_CN.yaml new file mode 100644 index 00000000..d2130f86 --- /dev/null +++ b/vector-search-milvus/i18n/zh_CN.yaml @@ -0,0 +1,61 @@ +# 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. + +plugin: + milvus_vector_search: + backend: + info: + name: + other: Milvus 向量搜索 + description: + other: 使用 Milvus 进行语义/向量搜索 + config: + endpoint: + title: + other: Milvus 地址 + description: + other: "Milvus gRPC 地址（如 localhost:19530）" + api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-milvus/info.yaml b/vector-search-milvus/info.yaml new file mode 100644 index 00000000..ee913b62 --- /dev/null +++ b/vector-search-milvus/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: milvus_vector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-milvus diff --git a/vector-search-milvus/milvus.go b/vector-search-milvus/milvus.go new file mode 100644 index 00000000..f6282318 --- /dev/null +++ b/vector-search-milvus/milvus.go @@ -0,0 +1,494 @@ +/* + * 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 milvus_vector_search + +import ( + "context" + "embed" + "encoding/json" + "fmt" + "strconv" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-milvus/i18n" + "github.com/apache/answer/plugin" + "github.com/milvus-io/milvus-sdk-go/v2/client" + "github.com/milvus-io/milvus-sdk-go/v2/entity" + "github.com/segmentfault/pacman/log" +) + +//go:embed info.yaml +var Info embed.FS + +const milvusCollectionName = "answer_vector_embeddings" + +// VectorSearchEngine implements plugin.VectorSearch using Milvus. +type VectorSearchEngine struct { + Config *VectorSearchConfig + milvusClient client.Client + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex + embeddingDimensions int +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + Endpoint string `json:"endpoint"` + APIHost string `json:"api_host"` + APIKey string `json:"api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://milvus.io/docs", + } +} + +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + log.Debugf("milvus: RegisterSyncer called, configured=%v", e.milvusClient != nil) + e.syncer = syncer + if e.milvusClient != nil { + e.sync() + } +} + +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.milvusClient == nil { + return nil, fmt.Errorf("milvus: not initialized") + } + if topK <= 0 { + topK = 10 + } + + log.Debugf("milvus: SearchSimilar query=%q topK=%d", query, topK) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, query) + if err != nil { + return nil, fmt.Errorf("generate query embedding: %w", err) + } + + log.Debugf("milvus: search embedding generated, dimensions=%d", len(embedding)) + + vectors := []entity.Vector{entity.FloatVector(embedding)} + sp, _ := entity.NewIndexFlatSearchParam() + + searchResult, err := e.milvusClient.Search( + ctx, + milvusCollectionName, + nil, + "", + []string{"object_id", "object_type", "metadata"}, + vectors, + "embedding", + entity.COSINE, + topK, + sp, + ) + if err != nil { + return nil, fmt.Errorf("milvus search failed: %w", err) + } + + results := make([]plugin.VectorSearchResult, 0) + for _, sr := range searchResult { + var objectIDs, objectTypes, metadatas []string + + for _, field := range sr.Fields { + switch field.Name() { + case "object_id": + col, ok := field.(*entity.ColumnVarChar) + if ok { + for i := 0; i < col.Len(); i++ { + v, _ := col.GetAsString(i) + objectIDs = append(objectIDs, v) + } + } + case "object_type": + col, ok := field.(*entity.ColumnVarChar) + if ok { + for i := 0; i < col.Len(); i++ { + v, _ := col.GetAsString(i) + objectTypes = append(objectTypes, v) + } + } + case "metadata": + col, ok := field.(*entity.ColumnVarChar) + if ok { + for i := 0; i < col.Len(); i++ { + v, _ := col.GetAsString(i) + metadatas = append(metadatas, v) + } + } + } + } + + for i := 0; i < sr.ResultCount; i++ { + score := float64(sr.Scores[i]) + if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold { + continue + } + + oid := "" + if i < len(objectIDs) { + oid = objectIDs[i] + } + otype := "" + if i < len(objectTypes) { + otype = objectTypes[i] + } + meta := "" + if i < len(metadatas) { + meta = metadatas[i] + } + + results = append(results, plugin.VectorSearchResult{ + ObjectID: oid, + ObjectType: otype, + Metadata: meta, + Score: score, + }) + } + } + + log.Debugf("milvus: SearchSimilar returning %d results", len(results)) + return results, nil +} + +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.milvusClient == nil { + return fmt.Errorf("milvus: not initialized") + } + + log.Debugf("milvus: UpdateContent objectID=%s objectType=%s", content.ObjectID, content.ObjectType) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + + log.Debugf("milvus: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding)) + + // Delete existing document for upsert semantics. + _ = e.milvusClient.Delete(ctx, milvusCollectionName, "", fmt.Sprintf("object_id == \"%s\"", content.ObjectID)) + + // Truncate fields to fit VarChar limits. + title := truncate(content.Title, 65000) + body := truncate(content.Content, 65000) + meta := truncate(content.Metadata, 65000) + + _, err = e.milvusClient.Insert(ctx, milvusCollectionName, "", + entity.NewColumnVarChar("object_id", []string{content.ObjectID}), + entity.NewColumnVarChar("object_type", []string{content.ObjectType}), + entity.NewColumnVarChar("title", []string{title}), + entity.NewColumnVarChar("content", []string{body}), + entity.NewColumnVarChar("metadata", []string{meta}), + entity.NewColumnFloatVector("embedding", e.embeddingDimensions, [][]float32{embedding}), + ) + if err != nil { + return fmt.Errorf("insert document %s: %w", content.ObjectID, err) + } + + if err := e.milvusClient.Flush(ctx, milvusCollectionName, false); err != nil { + log.Warnf("milvus: flush after insert failed: %v", err) + } + + log.Debugf("milvus: upserted document %s successfully", content.ObjectID) + return nil +} + +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.milvusClient == nil { + return fmt.Errorf("milvus: not initialized") + } + + log.Debugf("milvus: DeleteContent objectID=%s", objectID) + + err := e.milvusClient.Delete(ctx, milvusCollectionName, "", fmt.Sprintf("object_id == \"%s\"", objectID)) + if err != nil { + return fmt.Errorf("delete document %s: %w", objectID, err) + } + + log.Debugf("milvus: deleted document %s", objectID) + return nil +} + +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "endpoint", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEndpointTitle), + Description: plugin.MakeTranslator(i18n.ConfigEndpointDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Endpoint, + }, + { + Name: "api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.APIHost, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + log.Debugf("milvus: ConfigReceiver called") + + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + log.Debugf("milvus: config parsed: endpoint=%s model=%s level=%s threshold=%.2f", + conf.Endpoint, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + + if !plugin.StatusManager.IsEnabled("milvus_vector_search") { + log.Debugf("milvus: plugin not active, skipping initialization") + return nil + } + + // Auto-detect embedding dimensions. + log.Debugf("milvus: detecting embedding dimensions via probe call") + probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.APIHost, conf.APIKey, conf.EmbeddingModel, "dimension probe") + if err != nil { + return fmt.Errorf("detect embedding dimensions: %w", err) + } + e.embeddingDimensions = len(probeEmbedding) + log.Infof("milvus: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel) + + // Connect to Milvus. + log.Debugf("milvus: connecting to %s", conf.Endpoint) + if e.milvusClient != nil { + e.milvusClient.Close() + } + + c, err := client.NewGrpcClient(context.Background(), conf.Endpoint) + if err != nil { + return fmt.Errorf("connect to milvus: %w", err) + } + e.milvusClient = c + + if err := e.ensureCollection(context.Background()); err != nil { + return fmt.Errorf("ensure collection: %w", err) + } + + log.Debugf("milvus: ConfigReceiver completed successfully") + return nil +} + +func (e *VectorSearchEngine) ensureCollection(ctx context.Context) error { + exists, err := e.milvusClient.HasCollection(ctx, milvusCollectionName) + if err != nil { + return fmt.Errorf("check collection existence: %w", err) + } + + if exists { + // Check dimensions match. + col, err := e.milvusClient.DescribeCollection(ctx, milvusCollectionName) + if err != nil { + return fmt.Errorf("describe collection: %w", err) + } + + for _, field := range col.Schema.Fields { + if field.Name == "embedding" { + if dimStr, ok := field.TypeParams["dim"]; ok { + dim, _ := strconv.Atoi(dimStr) + if dim > 0 && dim != e.embeddingDimensions { + log.Warnf("milvus: dimensions changed from %d to %d, recreating collection", dim, e.embeddingDimensions) + if err := e.milvusClient.DropCollection(ctx, milvusCollectionName); err != nil { + return fmt.Errorf("drop collection: %w", err) + } + exists = false + } + } + } + } + } + + if exists { + log.Debugf("milvus: collection %s already exists", milvusCollectionName) + // Ensure loaded. + if err := e.milvusClient.LoadCollection(ctx, milvusCollectionName, false); err != nil { + log.Warnf("milvus: load collection: %v", err) + } + return nil + } + + // Create collection. + log.Debugf("milvus: creating collection %s with dims=%d", milvusCollectionName, e.embeddingDimensions) + + schema := &entity.Schema{ + CollectionName: milvusCollectionName, + Description: "Answer vector search documents", + Fields: []*entity.Field{ + { + Name: "object_id", + DataType: entity.FieldTypeVarChar, + PrimaryKey: true, + TypeParams: map[string]string{"max_length": "256"}, + }, + { + Name: "object_type", + DataType: entity.FieldTypeVarChar, + TypeParams: map[string]string{"max_length": "64"}, + }, + { + Name: "title", + DataType: entity.FieldTypeVarChar, + TypeParams: map[string]string{"max_length": "65535"}, + }, + { + Name: "content", + DataType: entity.FieldTypeVarChar, + TypeParams: map[string]string{"max_length": "65535"}, + }, + { + Name: "metadata", + DataType: entity.FieldTypeVarChar, + TypeParams: map[string]string{"max_length": "65535"}, + }, + { + Name: "embedding", + DataType: entity.FieldTypeFloatVector, + TypeParams: map[string]string{"dim": strconv.Itoa(e.embeddingDimensions)}, + }, + }, + } + + if err := e.milvusClient.CreateCollection(ctx, schema, 2); err != nil { + return fmt.Errorf("create collection: %w", err) + } + + // Create index on embedding field. + idx, err := entity.NewIndexIvfFlat(entity.COSINE, 128) + if err != nil { + return fmt.Errorf("create index params: %w", err) + } + if err := e.milvusClient.CreateIndex(ctx, milvusCollectionName, "embedding", idx, false); err != nil { + return fmt.Errorf("create index: %w", err) + } + + // Load collection into memory for search. + if err := e.milvusClient.LoadCollection(ctx, milvusCollectionName, false); err != nil { + return fmt.Errorf("load collection: %w", err) + } + + log.Debugf("milvus: collection %s created and loaded successfully", milvusCollectionName) + return nil +} + +func truncate(s string, maxLen int) string { + if len(s) <= maxLen { + return s + } + return s[:maxLen] +} diff --git a/vector-search-milvus/sync.go b/vector-search-milvus/sync.go new file mode 100644 index 00000000..89626531 --- /dev/null +++ b/vector-search-milvus/sync.go @@ -0,0 +1,102 @@ +/* + * 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 milvus_vector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("milvus: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("milvus: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("milvus: starting question sync...") + page = 1 + for { + log.Infof("milvus: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("milvus: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("milvus: starting answer sync...") + page = 1 + for { + log.Infof("milvus: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("milvus: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("milvus: sync complete") + }() +} + +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + log.Debugf("milvus: bulkIndex batch size=%d", len(contents)) + success, failed := 0, 0 + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("milvus: index %s failed: %v", c.ObjectID, err) + failed++ + } else { + success++ + } + } + log.Debugf("milvus: bulkIndex batch done, success=%d failed=%d", success, failed) +} From ccef853ebc48985d02d6091766d9baf2931355c2 Mon Sep 17 00:00:00 2001 From: hgaol Date: Mon, 11 May 2026 23:01:37 +0800 Subject: [PATCH 06/12] feat: add Qdrant vector search plugin with configuration and synchronization --- vector-search-qdrant/README.md | 55 ++ vector-search-qdrant/go.mod | 58 ++ vector-search-qdrant/go.sum | 765 +++++++++++++++++++++++ vector-search-qdrant/i18n/en_US.yaml | 66 ++ vector-search-qdrant/i18n/translation.go | 48 ++ vector-search-qdrant/i18n/zh_CN.yaml | 66 ++ vector-search-qdrant/info.yaml | 22 + vector-search-qdrant/qdrant.go | 460 ++++++++++++++ vector-search-qdrant/sync.go | 102 +++ 9 files changed, 1642 insertions(+) create mode 100644 vector-search-qdrant/README.md create mode 100644 vector-search-qdrant/go.mod create mode 100644 vector-search-qdrant/go.sum create mode 100644 vector-search-qdrant/i18n/en_US.yaml create mode 100644 vector-search-qdrant/i18n/translation.go create mode 100644 vector-search-qdrant/i18n/zh_CN.yaml create mode 100644 vector-search-qdrant/info.yaml create mode 100644 vector-search-qdrant/qdrant.go create mode 100644 vector-search-qdrant/sync.go diff --git a/vector-search-qdrant/README.md b/vector-search-qdrant/README.md new file mode 100644 index 00000000..5a606b98 --- /dev/null +++ b/vector-search-qdrant/README.md @@ -0,0 +1,55 @@ +# Apache Answer Plugin: Qdrant Vector Search + +This plugin enables semantic/vector search in [Apache Answer](https://github.com/apache/answer) using [Qdrant](https://qdrant.tech/). + +## Prerequisites + +- Qdrant instance (self-hosted or Qdrant Cloud) +- An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider) + +## Installation + +Build Apache Answer with this plugin: + +```bash +./answer build --with github.com/apache/answer-plugins/vector-search-qdrant +``` + +## Configuration + +After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields: + +| Field | Description | Example | +|---|---|---| +| **Qdrant Endpoint** | Qdrant gRPC endpoint | `localhost:6334` | +| **Qdrant API Key** | API key for Qdrant authentication (optional for local instances) | | +| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` | +| **Embedding API Key** | API key for the embedding service | `sk-...` | +| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` | +| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` | +| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` | + +> **Note:** This plugin uses a dual API key pattern -- one for Qdrant authentication and a separate one for the embedding API. + +## How It Works + +- **Embedding dimensions** are auto-detected from the configured model. No manual dimension configuration is needed. +- On first configuration, the plugin creates a collection `answer_vector_embeddings` with cosine distance metric. +- If the embedding model changes and produces different dimensions, the collection is automatically deleted and recreated. +- Uses deterministic UUIDs derived from object IDs for consistent upsert behavior. +- Communicates with Qdrant via gRPC for high performance. +- A full sync of all questions/answers is triggered when the plugin starts. + +## Running Qdrant Locally + +Using Docker: + +```bash +docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant +``` + +The gRPC endpoint will be `localhost:6334` and the REST API at `localhost:6333`. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/vector-search-qdrant/go.mod b/vector-search-qdrant/go.mod new file mode 100644 index 00000000..60f7d5b7 --- /dev/null +++ b/vector-search-qdrant/go.mod @@ -0,0 +1,58 @@ +module github.com/apache/answer-plugins/vector-search-qdrant + +go 1.23.0 + +require ( + github.com/apache/answer v1.7.0 + github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954 + github.com/qdrant/go-client v1.12.0 + github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f + google.golang.org/grpc v1.66.2 +) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/genproto/googleapis/rpc v0.0.0-20240827150818-7e3bb234dfed // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-qdrant/go.sum b/vector-search-qdrant/go.sum new file mode 100644 index 00000000..b1683b9e --- /dev/null +++ b/vector-search-qdrant/go.sum @@ -0,0 +1,765 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= 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b/vector-search-qdrant/i18n/en_US.yaml @@ -0,0 +1,66 @@ +# 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. + +plugin: + qdrant_vector_search: + backend: + info: + name: + other: Qdrant Vector Search + description: + other: Use Qdrant for semantic/vector search + config: + endpoint: + title: + other: Qdrant Endpoint + description: + other: "Qdrant gRPC endpoint (e.g. localhost:6334)" + api_key: + title: + other: Qdrant API Key + description: + other: "API key for Qdrant authentication (leave empty if not required)" + embedding_api_host: + title: + other: Embedding API Host + description: + other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)" + embedding_api_key: + title: + other: Embedding API Key + description: + other: API key for the embedding service + embedding_model: + title: + other: Embedding Model + description: + other: "Model name for embedding generation (e.g. text-embedding-3-small)" + embedding_level: + title: + other: Embedding Level + description: + other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately" + options: + question: + other: Question + answer: + other: Answer + similarity_threshold: + title: + other: Similarity Threshold + description: + other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)." diff --git a/vector-search-qdrant/i18n/translation.go b/vector-search-qdrant/i18n/translation.go new file mode 100644 index 00000000..f7de9433 --- /dev/null +++ b/vector-search-qdrant/i18n/translation.go @@ -0,0 +1,48 @@ +/* + * 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 i18n + +const ( + InfoName = "plugin.qdrant_vector_search.backend.info.name" + InfoDescription = "plugin.qdrant_vector_search.backend.info.description" + + ConfigEndpointTitle = "plugin.qdrant_vector_search.backend.config.endpoint.title" + ConfigEndpointDescription = "plugin.qdrant_vector_search.backend.config.endpoint.description" + + ConfigAPIKeyTitle = "plugin.qdrant_vector_search.backend.config.api_key.title" + ConfigAPIKeyDescription = "plugin.qdrant_vector_search.backend.config.api_key.description" + + ConfigEmbeddingAPIHostTitle = "plugin.qdrant_vector_search.backend.config.embedding_api_host.title" + ConfigEmbeddingAPIHostDescription = "plugin.qdrant_vector_search.backend.config.embedding_api_host.description" + + ConfigEmbeddingAPIKeyTitle = "plugin.qdrant_vector_search.backend.config.embedding_api_key.title" + ConfigEmbeddingAPIKeyDescription = "plugin.qdrant_vector_search.backend.config.embedding_api_key.description" + + ConfigEmbeddingModelTitle = "plugin.qdrant_vector_search.backend.config.embedding_model.title" + ConfigEmbeddingModelDescription = "plugin.qdrant_vector_search.backend.config.embedding_model.description" + + ConfigEmbeddingLevelTitle = "plugin.qdrant_vector_search.backend.config.embedding_level.title" + ConfigEmbeddingLevelDescription = "plugin.qdrant_vector_search.backend.config.embedding_level.description" + ConfigEmbeddingLevelOptionQuestion = "plugin.qdrant_vector_search.backend.config.embedding_level.options.question" + ConfigEmbeddingLevelOptionAnswer = "plugin.qdrant_vector_search.backend.config.embedding_level.options.answer" + + ConfigSimilarityThresholdTitle = "plugin.qdrant_vector_search.backend.config.similarity_threshold.title" + ConfigSimilarityThresholdDescription = "plugin.qdrant_vector_search.backend.config.similarity_threshold.description" +) diff --git a/vector-search-qdrant/i18n/zh_CN.yaml b/vector-search-qdrant/i18n/zh_CN.yaml new file mode 100644 index 00000000..f1f384cd --- /dev/null +++ b/vector-search-qdrant/i18n/zh_CN.yaml @@ -0,0 +1,66 @@ +# 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. + +plugin: + qdrant_vector_search: + backend: + info: + name: + other: Qdrant 向量搜索 + description: + other: 使用 Qdrant 进行语义/向量搜索 + config: + endpoint: + title: + other: Qdrant 地址 + description: + other: "Qdrant gRPC 地址（如 localhost:6334）" + api_key: + title: + other: Qdrant API 密钥 + description: + other: "Qdrant 身份验证的 API 密钥（不需要可留空）" + embedding_api_host: + title: + other: Embedding API 地址 + description: + other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）" + embedding_api_key: + title: + other: Embedding API 密钥 + description: + other: Embedding 服务的 API 密钥 + embedding_model: + title: + other: Embedding 模型 + description: + other: "Embedding 模型名称（如 text-embedding-3-small）" + embedding_level: + title: + other: Embedding 级别 + description: + other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入" + options: + question: + other: 问题 + answer: + other: 回答 + similarity_threshold: + title: + other: 相似度阈值 + description: + other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。" diff --git a/vector-search-qdrant/info.yaml b/vector-search-qdrant/info.yaml new file mode 100644 index 00000000..b30c88a3 --- /dev/null +++ b/vector-search-qdrant/info.yaml @@ -0,0 +1,22 @@ +# 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. + +slug_name: qdrant_vector_search +type: vector_search +version: 0.1.0 +author: answerdev +link: https://github.com/apache/answer-plugins/tree/main/vector-search-qdrant diff --git a/vector-search-qdrant/qdrant.go b/vector-search-qdrant/qdrant.go new file mode 100644 index 00000000..d47400bb --- /dev/null +++ b/vector-search-qdrant/qdrant.go @@ -0,0 +1,460 @@ +/* + * 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 qdrant_vector_search + +import ( + "context" + "crypto/sha1" + "embed" + "encoding/json" + "fmt" + "strconv" + "sync" + + "github.com/apache/answer-plugins/util" + "github.com/apache/answer-plugins/vector-search-qdrant/i18n" + "github.com/apache/answer/plugin" + pb "github.com/qdrant/go-client/qdrant" + "github.com/segmentfault/pacman/log" + "google.golang.org/grpc" + "google.golang.org/grpc/credentials/insecure" + "google.golang.org/grpc/metadata" +) + +//go:embed info.yaml +var Info embed.FS + +const qdrantCollectionName = "answer_vector_embeddings" + +// VectorSearchEngine implements plugin.VectorSearch using Qdrant. +type VectorSearchEngine struct { + Config *VectorSearchConfig + conn *grpc.ClientConn + pointsClient pb.PointsClient + collectionsClient pb.CollectionsClient + syncer plugin.VectorSearchSyncer + syncing bool + lock sync.Mutex + embeddingDimensions int +} + +// VectorSearchConfig holds all plugin configuration. +type VectorSearchConfig struct { + Endpoint string `json:"endpoint"` + APIKey string `json:"api_key"` + EmbeddingAPIHost string `json:"embedding_api_host"` + EmbeddingAPIKey string `json:"embedding_api_key"` + EmbeddingModel string `json:"embedding_model"` + EmbeddingLevel string `json:"embedding_level"` + SimilarityThreshold float64 `json:"similarity_threshold"` +} + +func init() { + plugin.Register(&VectorSearchEngine{ + Config: &VectorSearchConfig{}, + lock: sync.Mutex{}, + }) +} + +func (e *VectorSearchEngine) Info() plugin.Info { + info := &util.Info{} + info.GetInfo(Info) + + return plugin.Info{ + Name: plugin.MakeTranslator(i18n.InfoName), + SlugName: info.SlugName, + Description: plugin.MakeTranslator(i18n.InfoDescription), + Author: info.Author, + Version: info.Version, + Link: info.Link, + } +} + +func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc { + return plugin.VectorSearchDesc{ + Icon: "", + Link: "https://qdrant.tech/documentation/", + } +} + +func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) { + log.Debugf("qdrant: RegisterSyncer called, configured=%v", e.conn != nil) + e.syncer = syncer + if e.conn != nil { + e.sync() + } +} + +func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) { + if e.pointsClient == nil { + return nil, fmt.Errorf("qdrant: not initialized") + } + if topK <= 0 { + topK = 10 + } + + log.Debugf("qdrant: SearchSimilar query=%q topK=%d", query, topK) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.EmbeddingAPIHost, e.Config.EmbeddingAPIKey, e.Config.EmbeddingModel, query) + if err != nil { + return nil, fmt.Errorf("generate query embedding: %w", err) + } + + log.Debugf("qdrant: search embedding generated, dimensions=%d", len(embedding)) + + ctx = e.withAPIKey(ctx) + limit := uint64(topK) + searchResp, err := e.pointsClient.Search(ctx, &pb.SearchPoints{ + CollectionName: qdrantCollectionName, + Vector: embedding, + Limit: limit, + WithPayload: &pb.WithPayloadSelector{ + SelectorOptions: &pb.WithPayloadSelector_Enable{Enable: true}, + }, + }) + if err != nil { + return nil, fmt.Errorf("qdrant search failed: %w", err) + } + + results := make([]plugin.VectorSearchResult, 0, len(searchResp.Result)) + for _, point := range searchResp.Result { + score := float64(point.Score) + if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold { + log.Debugf("qdrant: skipping result score=%.4f below threshold=%.4f", score, e.Config.SimilarityThreshold) + continue + } + + objectID := getPayloadString(point.Payload, "object_id") + objectType := getPayloadString(point.Payload, "object_type") + metadataStr := getPayloadString(point.Payload, "metadata") + + results = append(results, plugin.VectorSearchResult{ + ObjectID: objectID, + ObjectType: objectType, + Metadata: metadataStr, + Score: score, + }) + } + + log.Debugf("qdrant: SearchSimilar returning %d results", len(results)) + return results, nil +} + +func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error { + if e.pointsClient == nil { + return fmt.Errorf("qdrant: not initialized") + } + + log.Debugf("qdrant: UpdateContent objectID=%s objectType=%s", content.ObjectID, content.ObjectType) + + embedding, err := plugin.GenerateEmbedding(ctx, e.Config.EmbeddingAPIHost, e.Config.EmbeddingAPIKey, e.Config.EmbeddingModel, content.Content) + if err != nil { + return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err) + } + + log.Debugf("qdrant: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding)) + + pointID := deterministicUUID(content.ObjectID) + ctx = e.withAPIKey(ctx) + + _, err = e.pointsClient.Upsert(ctx, &pb.UpsertPoints{ + CollectionName: qdrantCollectionName, + Points: []*pb.PointStruct{ + { + Id: &pb.PointId{ + PointIdOptions: &pb.PointId_Uuid{Uuid: pointID}, + }, + Vectors: &pb.Vectors{ + VectorsOptions: &pb.Vectors_Vector{ + Vector: &pb.Vector{Data: embedding}, + }, + }, + Payload: map[string]*pb.Value{ + "object_id": {Kind: &pb.Value_StringValue{StringValue: content.ObjectID}}, + "object_type": {Kind: &pb.Value_StringValue{StringValue: content.ObjectType}}, + "title": {Kind: &pb.Value_StringValue{StringValue: content.Title}}, + "metadata": {Kind: &pb.Value_StringValue{StringValue: content.Metadata}}, + }, + }, + }, + }) + if err != nil { + return fmt.Errorf("upsert document %s: %w", content.ObjectID, err) + } + + log.Debugf("qdrant: upserted document %s successfully", content.ObjectID) + return nil +} + +func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error { + if e.pointsClient == nil { + return fmt.Errorf("qdrant: not initialized") + } + + log.Debugf("qdrant: DeleteContent objectID=%s", objectID) + + pointID := deterministicUUID(objectID) + ctx = e.withAPIKey(ctx) + + _, err := e.pointsClient.Delete(ctx, &pb.DeletePoints{ + CollectionName: qdrantCollectionName, + Points: &pb.PointsSelector{ + PointsSelectorOneOf: &pb.PointsSelector_Points{ + Points: &pb.PointsIdsList{ + Ids: []*pb.PointId{ + {PointIdOptions: &pb.PointId_Uuid{Uuid: pointID}}, + }, + }, + }, + }, + }) + if err != nil { + return fmt.Errorf("delete document %s: %w", objectID, err) + } + + log.Debugf("qdrant: deleted document %s", objectID) + return nil +} + +func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { + return []plugin.ConfigField{ + { + Name: "endpoint", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEndpointTitle), + Description: plugin.MakeTranslator(i18n.ConfigEndpointDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.Endpoint, + }, + { + Name: "api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.APIKey, + }, + { + Name: "embedding_api_host", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIHostTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIHostDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingAPIHost, + }, + { + Name: "embedding_api_key", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIKeyTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingAPIKeyDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypePassword, + }, + Value: e.Config.EmbeddingAPIKey, + }, + { + Name: "embedding_model", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription), + Required: true, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: e.Config.EmbeddingModel, + }, + { + Name: "embedding_level", + Type: plugin.ConfigTypeSelect, + Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), + Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription), + Required: true, + Options: []plugin.ConfigFieldOption{ + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"}, + {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"}, + }, + Value: e.Config.EmbeddingLevel, + }, + { + Name: "similarity_threshold", + Type: plugin.ConfigTypeInput, + Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle), + Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription), + Required: false, + UIOptions: plugin.ConfigFieldUIOptions{ + InputType: plugin.InputTypeText, + }, + Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold), + }, + } +} + +func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { + log.Debugf("qdrant: ConfigReceiver called") + + var raw map[string]interface{} + if err := json.Unmarshal(config, &raw); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + if threshStr, ok := raw["similarity_threshold"].(string); ok { + if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil { + raw["similarity_threshold"] = thresh + } + } + fixed, _ := json.Marshal(raw) + + conf := &VectorSearchConfig{} + if err := json.Unmarshal(fixed, conf); err != nil { + return fmt.Errorf("unmarshal config: %w", err) + } + + e.Config = conf + + log.Debugf("qdrant: config parsed: endpoint=%s model=%s level=%s threshold=%.2f", + conf.Endpoint, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + + if !plugin.StatusManager.IsEnabled("qdrant_vector_search") { + log.Debugf("qdrant: plugin not active, skipping initialization") + return nil + } + + // Auto-detect embedding dimensions. + log.Debugf("qdrant: detecting embedding dimensions via probe call") + probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.EmbeddingAPIHost, conf.EmbeddingAPIKey, conf.EmbeddingModel, "dimension probe") + if err != nil { + return fmt.Errorf("detect embedding dimensions: %w", err) + } + e.embeddingDimensions = len(probeEmbedding) + log.Infof("qdrant: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel) + + // Connect to Qdrant via gRPC. + log.Debugf("qdrant: connecting to %s", conf.Endpoint) + if e.conn != nil { + e.conn.Close() + } + + conn, err := grpc.NewClient(conf.Endpoint, grpc.WithTransportCredentials(insecure.NewCredentials())) + if err != nil { + return fmt.Errorf("connect to qdrant: %w", err) + } + e.conn = conn + e.pointsClient = pb.NewPointsClient(conn) + e.collectionsClient = pb.NewCollectionsClient(conn) + + if err := e.ensureCollection(context.Background()); err != nil { + return fmt.Errorf("ensure collection: %w", err) + } + + log.Debugf("qdrant: ConfigReceiver completed successfully") + return nil +} + +func (e *VectorSearchEngine) ensureCollection(ctx context.Context) error { + ctx = e.withAPIKey(ctx) + + // Check if collection exists. + listResp, err := e.collectionsClient.List(ctx, &pb.ListCollectionsRequest{}) + if err != nil { + return fmt.Errorf("list collections: %w", err) + } + + for _, col := range listResp.Collections { + if col.Name == qdrantCollectionName { + // Check dimensions match. + infoResp, err := e.collectionsClient.Get(ctx, &pb.GetCollectionInfoRequest{ + CollectionName: qdrantCollectionName, + }) + if err != nil { + return fmt.Errorf("get collection info: %w", err) + } + + existingDims := uint64(0) + if params := infoResp.Result.Config.Params.VectorsConfig.GetParams(); params != nil { + existingDims = params.Size + } + + if existingDims > 0 && existingDims != uint64(e.embeddingDimensions) { + log.Warnf("qdrant: dimensions changed from %d to %d, recreating collection", existingDims, e.embeddingDimensions) + _, err = e.collectionsClient.Delete(ctx, &pb.DeleteCollection{ + CollectionName: qdrantCollectionName, + }) + if err != nil { + return fmt.Errorf("delete collection: %w", err) + } + break + } + + log.Debugf("qdrant: collection %s already exists with dims=%d", qdrantCollectionName, existingDims) + return nil + } + } + + // Create collection. + log.Debugf("qdrant: creating collection %s with dims=%d", qdrantCollectionName, e.embeddingDimensions) + _, err = e.collectionsClient.Create(ctx, &pb.CreateCollection{ + CollectionName: qdrantCollectionName, + VectorsConfig: &pb.VectorsConfig{ + Config: &pb.VectorsConfig_Params{ + Params: &pb.VectorParams{ + Size: uint64(e.embeddingDimensions), + Distance: pb.Distance_Cosine, + }, + }, + }, + }) + if err != nil { + return fmt.Errorf("create collection: %w", err) + } + + log.Debugf("qdrant: collection %s created successfully", qdrantCollectionName) + return nil +} + +func (e *VectorSearchEngine) withAPIKey(ctx context.Context) context.Context { + if e.Config.APIKey != "" { + return metadata.AppendToOutgoingContext(ctx, "api-key", e.Config.APIKey) + } + return ctx +} + +func getPayloadString(payload map[string]*pb.Value, key string) string { + if v, ok := payload[key]; ok { + return v.GetStringValue() + } + return "" +} + +func deterministicUUID(objectID string) string { + h := sha1.Sum([]byte(objectID)) + h[6] = (h[6] & 0x0f) | 0x50 + h[8] = (h[8] & 0x3f) | 0x80 + return fmt.Sprintf("%08x-%04x-%04x-%04x-%012x", + h[0:4], h[4:6], h[6:8], h[8:10], h[10:16]) +} diff --git a/vector-search-qdrant/sync.go b/vector-search-qdrant/sync.go new file mode 100644 index 00000000..18ba990c --- /dev/null +++ b/vector-search-qdrant/sync.go @@ -0,0 +1,102 @@ +/* + * 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 qdrant_vector_search + +import ( + "context" + + "github.com/apache/answer/plugin" + "github.com/segmentfault/pacman/log" +) + +func (e *VectorSearchEngine) sync() { + if e.syncer == nil { + log.Warn("qdrant: syncer not registered, skip sync") + return + } + if e.syncing { + log.Warn("qdrant: sync already running, skip") + return + } + + go func() { + e.lock.Lock() + defer e.lock.Unlock() + if e.syncing { + return + } + e.syncing = true + defer func() { e.syncing = false }() + + page, pageSize := 1, 100 + + if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" { + log.Info("qdrant: starting question sync...") + page = 1 + for { + log.Infof("qdrant: sync questions page %d", page) + questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("qdrant: get questions page failed: %v", err) + break + } + if len(questions) == 0 { + break + } + e.bulkIndex(context.TODO(), questions) + page++ + } + } + + if e.Config.EmbeddingLevel == "answer" { + log.Info("qdrant: starting answer sync...") + page = 1 + for { + log.Infof("qdrant: sync answers page %d", page) + answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize) + if err != nil { + log.Errorf("qdrant: get answers page failed: %v", err) + break + } + if len(answers) == 0 { + break + } + e.bulkIndex(context.TODO(), answers) + page++ + } + } + + log.Info("qdrant: sync complete") + }() +} + +func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) { + log.Debugf("qdrant: bulkIndex batch size=%d", len(contents)) + success, failed := 0, 0 + for _, c := range contents { + if err := e.UpdateContent(ctx, c); err != nil { + log.Warnf("qdrant: index %s failed: %v", c.ObjectID, err) + failed++ + } else { + success++ + } + } + log.Debugf("qdrant: bulkIndex batch done, success=%d failed=%d", success, failed) +} From 0e49c3715f8c713d68001f546f328ac3698f00ba Mon Sep 17 00:00:00 2001 From: hgaol Date: Wed, 8 Jul 2026 14:05:50 +0000 Subject: [PATCH 07/12] fix(vector-search): guard syncing flag with mutex to prevent data race The syncing flag was read outside the mutex before launching the sync goroutine, and re-checked after the goroutine had already reset it. This was a data race and could allow a redundant full sync to start. Claim the flag under the lock synchronously before spawning the goroutine, and reset it under the lock on exit. --- vector-search-chromadb/sync.go | 16 +++++++++------- vector-search-elasticsearch/sync.go | 16 +++++++++------- vector-search-milvus/sync.go | 16 +++++++++------- vector-search-pgvector/sync.go | 16 +++++++++------- vector-search-qdrant/sync.go | 16 +++++++++------- vector-search-weaviate/sync.go | 16 +++++++++------- 6 files changed, 54 insertions(+), 42 deletions(-) diff --git a/vector-search-chromadb/sync.go b/vector-search-chromadb/sync.go index 14fe5c8b..0666b718 100644 --- a/vector-search-chromadb/sync.go +++ b/vector-search-chromadb/sync.go @@ -31,19 +31,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("chromadb: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("chromadb: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 diff --git a/vector-search-elasticsearch/sync.go b/vector-search-elasticsearch/sync.go index f6a8f1cc..d132342d 100644 --- a/vector-search-elasticsearch/sync.go +++ b/vector-search-elasticsearch/sync.go @@ -33,19 +33,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("es-vector: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("es-vector: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 diff --git a/vector-search-milvus/sync.go b/vector-search-milvus/sync.go index 89626531..df5b28d0 100644 --- a/vector-search-milvus/sync.go +++ b/vector-search-milvus/sync.go @@ -31,19 +31,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("milvus: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("milvus: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 diff --git a/vector-search-pgvector/sync.go b/vector-search-pgvector/sync.go index e4a30631..c88c3c60 100644 --- a/vector-search-pgvector/sync.go +++ b/vector-search-pgvector/sync.go @@ -33,19 +33,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("pgvector: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("pgvector: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 diff --git a/vector-search-qdrant/sync.go b/vector-search-qdrant/sync.go index 18ba990c..89980932 100644 --- a/vector-search-qdrant/sync.go +++ b/vector-search-qdrant/sync.go @@ -31,19 +31,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("qdrant: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("qdrant: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 diff --git a/vector-search-weaviate/sync.go b/vector-search-weaviate/sync.go index 4e3b8511..399779d7 100644 --- a/vector-search-weaviate/sync.go +++ b/vector-search-weaviate/sync.go @@ -33,19 +33,21 @@ func (e *VectorSearchEngine) sync() { log.Warn("weaviate: syncer not registered, skip sync") return } + e.lock.Lock() if e.syncing { + e.lock.Unlock() log.Warn("weaviate: sync already running, skip") return } + e.syncing = true + e.lock.Unlock() go func() { - e.lock.Lock() - defer e.lock.Unlock() - if e.syncing { - return - } - e.syncing = true - defer func() { e.syncing = false }() + defer func() { + e.lock.Lock() + e.syncing = false + e.lock.Unlock() + }() page, pageSize := 1, 100 From df4213f6682df409d43ddba9e11961aa314e3ab0 Mon Sep 17 00:00:00 2001 From: hgaol Date: Wed, 8 Jul 2026 14:05:56 +0000 Subject: [PATCH 08/12] fix(pgvector): avoid logging raw config and DSN credentials The debug logs printed the entire raw config payload (which can include the embedding API key) and the full PostgreSQL DSN (which can include username/password). Log the config size and whether a DSN is set instead. --- vector-search-pgvector/pgvector.go | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vector-search-pgvector/pgvector.go b/vector-search-pgvector/pgvector.go index dc50601e..3b98bf60 100644 --- a/vector-search-pgvector/pgvector.go +++ b/vector-search-pgvector/pgvector.go @@ -275,7 +275,7 @@ func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField { // ConfigReceiver applies configuration from the admin UI. func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { - log.Debugf("pgvector: ConfigReceiver called, raw config=%s", string(config)) + log.Debugf("pgvector: ConfigReceiver called, config size=%d bytes", len(config)) // Pre-process: convert string numbers to actual numbers before unmarshalling, // because the admin UI sends all form values as strings. @@ -297,8 +297,8 @@ func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { e.Config = conf - log.Debugf("pgvector: config parsed: dsn=%s model=%s level=%s threshold=%.2f", - conf.DSN, conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) + log.Debugf("pgvector: config parsed: dsn_set=%t model=%s level=%s threshold=%.2f", + conf.DSN != "", conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold) if !plugin.StatusManager.IsEnabled("pgvector_search") { log.Debugf("pgvector: plugin not active, skipping initialization") From bacadbde47e19c7da5c7e38f50632f639cca29f5 Mon Sep 17 00:00:00 2001 From: hgaol Date: Wed, 8 Jul 2026 14:06:02 +0000 Subject: [PATCH 09/12] fix(weaviate): only retry without auth on 401/403 errors ensureClass failures triggered an anonymous-access retry for any error when an API key was set, which could silently downgrade to anonymous access on transient/network/schema errors. Gate the retry on an actual authentication error (WeaviateClientError with a 401 or 403 status code). --- vector-search-weaviate/weaviate.go | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/vector-search-weaviate/weaviate.go b/vector-search-weaviate/weaviate.go index 342a5ce8..b45c9bdb 100644 --- a/vector-search-weaviate/weaviate.go +++ b/vector-search-weaviate/weaviate.go @@ -24,7 +24,9 @@ import ( "crypto/sha1" "embed" "encoding/json" + "errors" "fmt" + "net/http" "strconv" "strings" "sync" @@ -35,6 +37,7 @@ import ( "github.com/segmentfault/pacman/log" "github.com/weaviate/weaviate-go-client/v4/weaviate" "github.com/weaviate/weaviate-go-client/v4/weaviate/auth" + "github.com/weaviate/weaviate-go-client/v4/weaviate/fault" "github.com/weaviate/weaviate-go-client/v4/weaviate/graphql" "github.com/weaviate/weaviate/entities/models" ) @@ -439,10 +442,11 @@ func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { e.client = client // Create/ensure the class with vector configuration. - // If it fails with auth error and API key was set, retry without auth (anonymous access). + // If it fails with an auth error (401/403) and an API key was set, retry + // without auth in case the server is running in anonymous-access mode. if err := e.ensureClass(context.Background()); err != nil { - if conf.APIKey != "" { - log.Debugf("weaviate: ensureClass failed with API key auth, retrying without auth (anonymous access)") + if conf.APIKey != "" && isAuthError(err) { + log.Debugf("weaviate: ensureClass failed with auth error, retrying without auth (anonymous access)") cfg.AuthConfig = nil client, err = weaviate.NewClient(cfg) if err != nil { @@ -460,6 +464,17 @@ func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { return nil } +// isAuthError reports whether err is a Weaviate client error carrying a 401 or +// 403 status code, i.e. an authentication/authorization failure. +func isAuthError(err error) bool { + var clientErr *fault.WeaviateClientError + if errors.As(err, &clientErr) { + return clientErr.StatusCode == http.StatusUnauthorized || + clientErr.StatusCode == http.StatusForbidden + } + return false +} + // ensureClass creates the Weaviate class if it doesn't exist. func (e *VectorSearchEngine) ensureClass(ctx context.Context) error { log.Debugf("weaviate: checking if class %s exists", className) From 4e4cd86889ceb0590f9076749b6db610e2edd026 Mon Sep 17 00:00:00 2001 From: hgaol Date: Wed, 8 Jul 2026 14:06:08 +0000 Subject: [PATCH 10/12] feat(elasticsearch): recreate index on embedding dimension change ensureIndex only created the index when absent and never validated the existing mapping, so a change in embedding dimensions left a stale index with mismatched dims (contradicting the README). Read the existing mapping and delete/recreate the index when the embedding dimensions differ, matching the pgvector, milvus, and qdrant plugins. --- vector-search-elasticsearch/es.go | 59 ++++++++++++++++++++++++++++--- 1 file changed, 55 insertions(+), 4 deletions(-) diff --git a/vector-search-elasticsearch/es.go b/vector-search-elasticsearch/es.go index 8ee6de83..dd9561b0 100644 --- a/vector-search-elasticsearch/es.go +++ b/vector-search-elasticsearch/es.go @@ -382,17 +382,35 @@ func (e *VectorSearchEngine) ConfigReceiver(config []byte) error { // ensureIndex creates the ES index with dense_vector mapping if it doesn't exist. func (e *VectorSearchEngine) ensureIndex(ctx context.Context) error { + dims := e.embeddingDimensions + if dims <= 0 { + dims = 1536 + } + exists, err := e.client.IndexExists(indexName).Do(ctx) if err != nil { return err } + if exists { - return nil + // If the existing index's embedding dimensions differ from the + // configured ones (e.g. the embedding model changed), delete it so it + // can be recreated with the new dimensions. + existingDims, err := e.indexEmbeddingDims(ctx) + if err != nil { + return fmt.Errorf("get index mapping: %w", err) + } + if existingDims > 0 && existingDims != dims { + log.Warnf("es-vector: dimensions changed from %d to %d, recreating index", existingDims, dims) + if _, err := e.client.DeleteIndex(indexName).Do(ctx); err != nil { + return fmt.Errorf("delete index for dimension change: %w", err) + } + exists = false + } } - dims := e.embeddingDimensions - if dims <= 0 { - dims = 1536 + if exists { + return nil } mapping := fmt.Sprintf(`{ @@ -416,3 +434,36 @@ func (e *VectorSearchEngine) ensureIndex(ctx context.Context) error { _, err = e.client.CreateIndex(indexName).Body(mapping).Do(ctx) return err } + +// indexEmbeddingDims returns the configured `dims` of the embedding field in the +// existing index mapping, or 0 if it cannot be determined. +func (e *VectorSearchEngine) indexEmbeddingDims(ctx context.Context) (int, error) { + m, err := e.client.GetMapping().Index(indexName).Do(ctx) + if err != nil { + return 0, err + } + // Response shape: + // { "": { "mappings": { "properties": { "embedding": { "dims": N } } } } } + idx, ok := m[indexName].(map[string]interface{}) + if !ok { + return 0, nil + } + mappings, ok := idx["mappings"].(map[string]interface{}) + if !ok { + return 0, nil + } + props, ok := mappings["properties"].(map[string]interface{}) + if !ok { + return 0, nil + } + embedding, ok := props["embedding"].(map[string]interface{}) + if !ok { + return 0, nil + } + dims, ok := embedding["dims"].(float64) + if !ok { + return 0, nil + } + return int(dims), nil +} + From aa462f8c4843cd68005046d9c4195cb0395ed43e Mon Sep 17 00:00:00 2001 From: hgaol Date: Wed, 8 Jul 2026 14:06:15 +0000 Subject: [PATCH 11/12] chore(pgvector): add missing go.sum The pgvector plugin was the only vector-search plugin without a checked-in go.sum. Generate it (and the corresponding go.mod indirect requirements) so the module is reproducible, matching the sibling plugins. --- vector-search-pgvector/go.mod | 46 ++ vector-search-pgvector/go.sum | 807 ++++++++++++++++++++++++++++++++++ 2 files changed, 853 insertions(+) create mode 100644 vector-search-pgvector/go.sum diff --git a/vector-search-pgvector/go.mod b/vector-search-pgvector/go.mod index e0fbfe3f..7fb8fbfa 100644 --- a/vector-search-pgvector/go.mod +++ b/vector-search-pgvector/go.mod @@ -9,3 +9,49 @@ require ( github.com/pgvector/pgvector-go v0.2.2 github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f ) + +require ( + github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect + github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect + github.com/aymerick/douceur v0.2.0 // indirect + github.com/bytedance/sonic v1.12.2 // indirect + github.com/bytedance/sonic/loader v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.4 // indirect + github.com/cloudwego/iasm v0.2.0 // indirect + github.com/gabriel-vasile/mimetype v1.4.5 // indirect + github.com/gin-contrib/sse v0.1.0 // indirect + github.com/gin-gonic/gin v1.10.0 // indirect + github.com/go-playground/locales v0.14.1 // indirect + github.com/go-playground/universal-translator v0.18.1 // indirect + github.com/go-playground/validator/v10 v10.22.1 // indirect + github.com/goccy/go-json v0.10.3 // indirect + github.com/golang/snappy v0.0.4 // indirect + github.com/google/wire v0.5.0 // indirect + github.com/gorilla/css v1.0.1 // indirect + github.com/json-iterator/go v1.1.12 // indirect + github.com/klauspost/cpuid/v2 v2.2.8 // indirect + github.com/leodido/go-urn v1.4.0 // indirect + github.com/mattn/go-isatty v0.0.20 // indirect + github.com/microcosm-cc/bluemonday v1.0.27 // indirect + github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect + github.com/modern-go/reflect2 v1.0.2 // indirect + github.com/pelletier/go-toml/v2 v2.2.3 // indirect + github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect + github.com/syndtr/goleveldb v1.0.0 // indirect + github.com/tidwall/gjson v1.17.3 // indirect + github.com/tidwall/match v1.1.1 // indirect + github.com/tidwall/pretty v1.2.1 // indirect + github.com/twitchyliquid64/golang-asm v0.15.1 // indirect + github.com/ugorji/go/codec v1.2.12 // indirect + github.com/yuin/goldmark v1.7.4 // indirect + golang.org/x/arch v0.10.0 // indirect + golang.org/x/crypto v0.36.0 // indirect + golang.org/x/net v0.38.0 // indirect + golang.org/x/sys v0.31.0 // indirect + golang.org/x/text v0.23.0 // indirect + google.golang.org/protobuf v1.34.2 // indirect + gopkg.in/yaml.v3 v3.0.1 // indirect + sigs.k8s.io/yaml v1.4.0 // indirect + xorm.io/builder v0.3.13 // indirect + xorm.io/xorm v1.3.2 // indirect +) diff --git a/vector-search-pgvector/go.sum b/vector-search-pgvector/go.sum new file mode 100644 index 00000000..a77a8537 --- /dev/null +++ b/vector-search-pgvector/go.sum @@ -0,0 +1,807 @@ +cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw= +entgo.io/ent v0.13.1 h1:uD8QwN1h6SNphdCCzmkMN3feSUzNnVvV/WIkHKMbzOE= +entgo.io/ent v0.13.1/go.mod h1:qCEmo+biw3ccBn9OyL4ZK5dfpwg++l1Gxwac5B1206A= +filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA= +filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a h1:lSA0F4e9A2NcQSqGqTOXqu2aRi/XEQxDCBwM8yJtE6s= +gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a/go.mod h1:EXuID2Zs0pAQhH8yz+DNjUbjppKQzKFAn28TMYPB6IU= +gitee.com/travelliu/dm v1.8.11192/go.mod h1:DHTzyhCrM843x9VdKVbZ+GKXGRbKM2sJ4LxihRxShkE= +github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU= +github.com/BurntSushi/toml v1.0.0 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via untyped endpoint for ES 8 indexEmbeddingDims used the client's GetMapping helper, which targets the typed /_mapping/{type} endpoint removed in Elasticsearch 8, returning HTTP 400 and breaking the plugin configuration update. Query the untyped GET /{index}/_mapping endpoint directly instead. Also make the dimension check best-effort so a mapping-read failure logs a warning and keeps the existing index rather than failing the whole config update. --- vector-search-elasticsearch/es.go | 26 +++++++++++++++++++++----- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/vector-search-elasticsearch/es.go b/vector-search-elasticsearch/es.go index dd9561b0..8dfef236 100644 --- a/vector-search-elasticsearch/es.go +++ b/vector-search-elasticsearch/es.go @@ -24,6 +24,7 @@ import ( "embed" "encoding/json" "fmt" + "net/http" "strconv" "strings" "sync" @@ -395,12 +396,13 @@ func (e *VectorSearchEngine) ensureIndex(ctx context.Context) error { if exists { // If the existing index's embedding dimensions differ from the // configured ones (e.g. the embedding model changed), delete it so it - // can be recreated with the new dimensions. + // can be recreated with the new dimensions. This is a best-effort + // check: if the mapping can't be read, keep the existing index rather + // than failing the whole configuration update. existingDims, err := e.indexEmbeddingDims(ctx) if err != nil { - return fmt.Errorf("get index mapping: %w", err) - } - if existingDims > 0 && existingDims != dims { + log.Warnf("es-vector: could not read index dimensions, keeping existing index: %v", err) + } else if existingDims > 0 && existingDims != dims { log.Warnf("es-vector: dimensions changed from %d to %d, recreating index", existingDims, dims) if _, err := e.client.DeleteIndex(indexName).Do(ctx); err != nil { return fmt.Errorf("delete index for dimension change: %w", err) @@ -437,11 +439,25 @@ func (e *VectorSearchEngine) ensureIndex(ctx context.Context) error { // indexEmbeddingDims returns the configured `dims` of the embedding field in the // existing index mapping, or 0 if it cannot be determined. +// +// It uses the untyped `GET /{index}/_mapping` endpoint directly rather than the +// client's GetMapping helper, because that helper targets the typed +// `/_mapping/{type}` endpoint which was removed in Elasticsearch 8 and returns +// HTTP 400 there. func (e *VectorSearchEngine) indexEmbeddingDims(ctx context.Context) (int, error) { - m, err := e.client.GetMapping().Index(indexName).Do(ctx) + res, err := e.client.PerformRequest(ctx, elastic.PerformRequestOptions{ + Method: http.MethodGet, + Path: "/" + indexName + "/_mapping", + }) if err != nil { return 0, err } + + var m map[string]interface{} + if err := json.Unmarshal(res.Body, &m); err != nil { + return 0, err + } + // Response shape: // { "": { "mappings": { "properties": { "embedding": { "dims": N } } } } } idx, ok := m[indexName].(map[string]interface{})