vector-search-memory/memory.go - answer-plugins - Git at Google

 /*
  * 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 memory_vector_search

 import (
 	"context"
 	"embed"
 	"encoding/json"
 	"fmt"
 	"math"
 	"sort"
 	"strconv"
 	"sync"

 	"github.com/apache/answer-plugins/util"
 	"github.com/apache/answer-plugins/vector-search-memory/i18n"
 	"github.com/apache/answer/plugin"
 	"github.com/segmentfault/pacman/log"
 )

 //go:embed info.yaml
 var Info embed.FS

 type document struct {
 	objectID   string
 	objectType string
 	title      string
 	content    string
 	metadata   string
 	embedding  []float32
 }

 // VectorSearchEngine implements plugin.VectorSearch using an in-memory store.
 type VectorSearchEngine struct {
 	Config              *VectorSearchConfig
 	docs                map[string]*document
 	mu                  sync.RWMutex
 	syncer              plugin.VectorSearchSyncer
 	syncing             bool
 	lock                sync.Mutex
 	embeddingDimensions int
 	configured          bool
 }

 // VectorSearchConfig holds all plugin configuration.
 type VectorSearchConfig struct {
 	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{},
 		docs:   make(map[string]*document),
 		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: "",
 	}
 }

 func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) {
 	log.Debugf("memory: RegisterSyncer called, configured=%v", e.configured)
 	e.syncer = syncer
 	if e.configured {
 		e.sync()
 	}
 }

 func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) {
 	if !e.configured {
 		return nil, fmt.Errorf("memory: not initialized")
 	}
 	if topK <= 0 {
 		topK = 10
 	}

 	log.Debugf("memory: 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("memory: search embedding generated, dimensions=%d", len(embedding))

 	e.mu.RLock()
 	type scored struct {
 		doc   *document
 		score float64
 	}
 	candidates := make([]scored, 0, len(e.docs))
 	for _, doc := range e.docs {
 		score := cosineSimilarity(embedding, doc.embedding)
 		if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold {
 			continue
 		}
 		candidates = append(candidates, scored{doc: doc, score: score})
 	}
 	e.mu.RUnlock()

 	sort.Slice(candidates, func(i, j int) bool {
 		return candidates[i].score > candidates[j].score
 	})

 	if len(candidates) > topK {
 		candidates = candidates[:topK]
 	}

 	results := make([]plugin.VectorSearchResult, 0, len(candidates))
 	for _, c := range candidates {
 		results = append(results, plugin.VectorSearchResult{
 			ObjectID:   c.doc.objectID,
 			ObjectType: c.doc.objectType,
 			Metadata:   c.doc.metadata,
 			Score:      c.score,
 		})
 	}

 	log.Debugf("memory: SearchSimilar returning %d results", len(results))
 	return results, nil
 }

 func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error {
 	if !e.configured {
 		return fmt.Errorf("memory: not initialized")
 	}

 	log.Debugf("memory: 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("memory: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding))

 	e.mu.Lock()
 	e.docs[content.ObjectID] = &document{
 		objectID:   content.ObjectID,
 		objectType: content.ObjectType,
 		title:      content.Title,
 		content:    content.Content,
 		metadata:   content.Metadata,
 		embedding:  embedding,
 	}
 	e.mu.Unlock()

 	log.Debugf("memory: upserted document %s successfully (total docs=%d)", content.ObjectID, len(e.docs))
 	return nil
 }

 func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error {
 	if !e.configured {
 		return fmt.Errorf("memory: not initialized")
 	}

 	log.Debugf("memory: DeleteContent objectID=%s", objectID)

 	e.mu.Lock()
 	delete(e.docs, objectID)
 	e.mu.Unlock()

 	log.Debugf("memory: deleted document %s", objectID)
 	return nil
 }

 func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField {
 	return []plugin.ConfigField{
 		{
 			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("memory: 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("memory: config parsed: model=%s level=%s threshold=%.2f",
 		conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold)

 	if !plugin.StatusManager.IsEnabled("memory_vector_search") {
 		log.Debugf("memory: plugin not active, skipping initialization")
 		return nil
 	}

 	// Auto-detect embedding dimensions.
 	log.Debugf("memory: 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("memory: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel)

 	// Clear existing documents since model may have changed.
 	e.mu.Lock()
 	e.docs = make(map[string]*document)
 	e.mu.Unlock()

 	e.configured = true

 	log.Debugf("memory: ConfigReceiver completed successfully")
 	return nil
 }

 // cosineSimilarity computes the cosine similarity between two vectors.
 func cosineSimilarity(a, b []float32) float64 {
 	if len(a) != len(b) || len(a) == 0 {
 		return 0
 	}
 	var dot, normA, normB float64
 	for i := range a {
 		ai, bi := float64(a[i]), float64(b[i])
 		dot += ai * bi
 		normA += ai * ai
 		normB += bi * bi
 	}
 	if normA == 0 || normB == 0 {
 		return 0
 	}
 	return dot / (math.Sqrt(normA) * math.Sqrt(normB))
 }
	/*
	* 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 memory_vector_search

	import (
	"context"
	"embed"
	"encoding/json"
	"fmt"
	"math"
	"sort"
	"strconv"
	"sync"

	"github.com/apache/answer-plugins/util"
	"github.com/apache/answer-plugins/vector-search-memory/i18n"
	"github.com/apache/answer/plugin"
	"github.com/segmentfault/pacman/log"
	)

	//go:embed info.yaml
	var Info embed.FS

	type document struct {
	objectID string
	objectType string
	title string
	content string
	metadata string
	embedding []float32
	}

	// VectorSearchEngine implements plugin.VectorSearch using an in-memory store.
	type VectorSearchEngine struct {
	Config *VectorSearchConfig
	docs map[string]*document
	mu sync.RWMutex
	syncer plugin.VectorSearchSyncer
	syncing bool
	lock sync.Mutex
	embeddingDimensions int
	configured bool
	}

	// VectorSearchConfig holds all plugin configuration.
	type VectorSearchConfig struct {
	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{},
	docs: make(map[string]*document),
	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: "",
	}
	}

	func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) {
	log.Debugf("memory: RegisterSyncer called, configured=%v", e.configured)
	e.syncer = syncer
	if e.configured {
	e.sync()
	}
	}

	func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) {
	if !e.configured {
	return nil, fmt.Errorf("memory: not initialized")
	}
	if topK <= 0 {
	topK = 10
	}

	log.Debugf("memory: 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("memory: search embedding generated, dimensions=%d", len(embedding))

	e.mu.RLock()
	type scored struct {
	doc *document
	score float64
	}
	candidates := make([]scored, 0, len(e.docs))
	for _, doc := range e.docs {
	score := cosineSimilarity(embedding, doc.embedding)
	if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold {
	continue
	}
	candidates = append(candidates, scored{doc: doc, score: score})
	}
	e.mu.RUnlock()

	sort.Slice(candidates, func(i, j int) bool {
	return candidates[i].score > candidates[j].score
	})

	if len(candidates) > topK {
	candidates = candidates[:topK]
	}

	results := make([]plugin.VectorSearchResult, 0, len(candidates))
	for _, c := range candidates {
	results = append(results, plugin.VectorSearchResult{
	ObjectID: c.doc.objectID,
	ObjectType: c.doc.objectType,
	Metadata: c.doc.metadata,
	Score: c.score,
	})
	}

	log.Debugf("memory: SearchSimilar returning %d results", len(results))
	return results, nil
	}

	func (e VectorSearchEngine) UpdateContent(ctx context.Context, content plugin.VectorSearchContent) error {
	if !e.configured {
	return fmt.Errorf("memory: not initialized")
	}

	log.Debugf("memory: 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("memory: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding))

	e.mu.Lock()
	e.docs[content.ObjectID] = &document{
	objectID: content.ObjectID,
	objectType: content.ObjectType,
	title: content.Title,
	content: content.Content,
	metadata: content.Metadata,
	embedding: embedding,
	}
	e.mu.Unlock()

	log.Debugf("memory: upserted document %s successfully (total docs=%d)", content.ObjectID, len(e.docs))
	return nil
	}

	func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error {
	if !e.configured {
	return fmt.Errorf("memory: not initialized")
	}

	log.Debugf("memory: DeleteContent objectID=%s", objectID)

	e.mu.Lock()
	delete(e.docs, objectID)
	e.mu.Unlock()

	log.Debugf("memory: deleted document %s", objectID)
	return nil
	}

	func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField {
	return []plugin.ConfigField{
	{
	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("memory: 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("memory: config parsed: model=%s level=%s threshold=%.2f",
	conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold)

	if !plugin.StatusManager.IsEnabled("memory_vector_search") {
	log.Debugf("memory: plugin not active, skipping initialization")
	return nil
	}

	// Auto-detect embedding dimensions.
	log.Debugf("memory: 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("memory: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel)

	// Clear existing documents since model may have changed.
	e.mu.Lock()
	e.docs = make(map[string]*document)
	e.mu.Unlock()

	e.configured = true

	log.Debugf("memory: ConfigReceiver completed successfully")
	return nil
	}

	// cosineSimilarity computes the cosine similarity between two vectors.
	func cosineSimilarity(a, b []float32) float64 {
	if len(a) != len(b) \|\| len(a) == 0 {
	return 0
	}
	var dot, normA, normB float64
	for i := range a {
	ai, bi := float64(a[i]), float64(b[i])
	dot += ai * bi
	normA += ai * ai
	normB += bi * bi
	}
	if normA == 0 \|\| normB == 0 {
	return 0
	}
	return dot / (math.Sqrt(normA) * math.Sqrt(normB))
	}