vector-search-qdrant/qdrant.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 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])
 }
	/*
	* 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])
	}