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MongoDB Atlas Vector Search for AI Applications: Advanced Semantic Similarity and Machine Learning Integration

Modern AI applications require sophisticated vector similarity search capabilities to power recommendation systems, retrieval-augmented generation (RAG), content discovery, and semantic search experiences. Traditional database systems struggle with high-dimensional vector operations, requiring complex integration with specialized vector databases that add architectural complexity, operational overhead, and data consistency challenges across multiple systems.

MongoDB Atlas Vector Search provides native support for high-dimensional vector similarity operations, enabling AI-powered applications to store, index, and query vector embeddings at scale while maintaining transactional consistency and familiar database operations. Unlike standalone vector databases that require separate infrastructure and complex data synchronization, Atlas Vector Search integrates seamlessly with existing MongoDB deployments, providing unified data management for both structured data and AI vector embeddings.

The Traditional Vector Search Challenge

Building AI applications with conventional database architectures creates significant technical and operational complexity:

-- Traditional PostgreSQL vector search - requires extensions and complex setup

-- Install pgvector extension (complex setup and maintenance)
CREATE EXTENSION IF NOT EXISTS vector;

-- Vector storage table with limited optimization capabilities
CREATE TABLE document_embeddings (
    document_id BIGSERIAL PRIMARY KEY,
    document_title TEXT NOT NULL,
    document_content TEXT NOT NULL,
    document_category VARCHAR(100),
    document_metadata JSONB,

    -- Vector storage (limited to specific dimensions)
    content_embedding vector(1536),  -- OpenAI ada-002 dimensions
    title_embedding vector(1536),

    -- Metadata for AI processing
    embedding_model VARCHAR(100) DEFAULT 'text-embedding-ada-002',
    embedding_created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Document processing
    word_count INTEGER,
    language_code VARCHAR(10),
    content_hash VARCHAR(64),

    -- System metadata
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Constraints and indexing
    CONSTRAINT valid_content_length CHECK (LENGTH(document_content) > 0),
    CONSTRAINT valid_embedding_dimensions CHECK (vector_dims(content_embedding) = 1536)
);

-- Create vector indexes (limited optimization options)
CREATE INDEX idx_content_embedding_cosine ON document_embeddings 
USING ivfflat (content_embedding vector_cosine_ops) 
WITH (lists = 100);

CREATE INDEX idx_title_embedding_l2 ON document_embeddings 
USING ivfflat (title_embedding vector_l2_ops) 
WITH (lists = 100);

-- Standard indexes for hybrid search
CREATE INDEX idx_category_created ON document_embeddings (document_category, created_at DESC);
CREATE INDEX idx_metadata_gin ON document_embeddings USING GIN (document_metadata);
CREATE INDEX idx_content_hash ON document_embeddings (content_hash);

-- Vector similarity search with limited performance and scalability
WITH vector_search_results AS (
    SELECT 
        document_id,
        document_title,
        document_content,
        document_category,
        document_metadata,

        -- Similarity calculations (computationally expensive)
        1 - (content_embedding <=> $1::vector) as cosine_similarity,
        content_embedding <-> $1::vector as l2_distance,
        content_embedding <#> $1::vector as inner_product,

        -- Metadata matching
        word_count,
        language_code,
        created_at

    FROM document_embeddings
    WHERE 
        -- Pre-filtering to reduce vector search scope
        document_category = $2  -- Category filter
        AND language_code = $3  -- Language filter
        AND created_at >= $4    -- Date range filter

        -- Vector similarity threshold (rough filtering)
        AND content_embedding <=> $1::vector < 0.3  -- Cosine distance threshold

    ORDER BY content_embedding <=> $1::vector  -- Sort by similarity
    LIMIT 50  -- Limit to manage performance
),

enhanced_results AS (
    SELECT 
        vsr.*,

        -- Additional metadata enrichment (limited capabilities)
        CASE 
            WHEN cosine_similarity >= 0.8 THEN 'highly_relevant'
            WHEN cosine_similarity >= 0.6 THEN 'relevant' 
            WHEN cosine_similarity >= 0.4 THEN 'somewhat_relevant'
            ELSE 'low_relevance'
        END as relevance_category,

        -- Content analysis (basic text processing only)
        LENGTH(document_content) as content_length,
        array_length(string_to_array(document_content, ' '), 1) as estimated_word_count,

        -- Ranking score combination
        (cosine_similarity * 0.7 + 
         CASE WHEN document_metadata->>'priority' = 'high' THEN 0.3 ELSE 0.0 END) as combined_score,

        -- Query metadata
        CURRENT_TIMESTAMP as search_performed_at

    FROM vector_search_results vsr
)

SELECT 
    document_id,
    document_title,
    LEFT(document_content, 200) || '...' as content_preview,
    document_category,

    -- Similarity metrics
    ROUND(cosine_similarity::NUMERIC, 4) as similarity_score,
    relevance_category,
    ROUND(combined_score::NUMERIC, 4) as ranking_score,

    -- Document metadata
    word_count,
    content_length,
    language_code,
    TO_CHAR(created_at, 'YYYY-MM-DD HH24:MI') as document_created,

    -- Search metadata
    search_performed_at

FROM enhanced_results
WHERE cosine_similarity >= 0.3  -- Minimum relevance threshold
ORDER BY combined_score DESC, cosine_similarity DESC
LIMIT 20;

-- Complex RAG (Retrieval-Augmented Generation) implementation
CREATE OR REPLACE FUNCTION execute_rag_query(
    query_embedding vector(1536),
    query_text TEXT,
    context_limit INTEGER DEFAULT 5,
    similarity_threshold NUMERIC DEFAULT 0.4
) RETURNS TABLE (
    context_documents JSONB,
    total_context_length INTEGER,
    average_similarity NUMERIC,
    generated_response TEXT
) AS $$
DECLARE
    context_docs JSONB := '[]'::JSONB;
    total_length INTEGER := 0;
    avg_similarity NUMERIC;
    doc_record RECORD;
    context_text TEXT := '';
BEGIN
    -- Retrieve relevant documents for context
    FOR doc_record IN
        SELECT 
            document_title,
            document_content,
            1 - (content_embedding <=> query_embedding) as similarity,
            LENGTH(document_content) as content_length
        FROM document_embeddings
        WHERE 1 - (content_embedding <=> query_embedding) >= similarity_threshold
        ORDER BY content_embedding <=> query_embedding
        LIMIT context_limit
    LOOP
        -- Build context for generation
        context_docs := context_docs || jsonb_build_object(
            'title', doc_record.document_title,
            'content', LEFT(doc_record.document_content, 1000),
            'similarity', doc_record.similarity,
            'length', doc_record.content_length
        );

        context_text := context_text || E'\n\n' || doc_record.document_title || E':\n' || 
                        LEFT(doc_record.document_content, 1000);
        total_length := total_length + doc_record.content_length;
    END LOOP;

    -- Calculate average similarity
    SELECT AVG((doc->>'similarity')::NUMERIC) INTO avg_similarity
    FROM jsonb_array_elements(context_docs) as doc;

    -- Return context information (actual LLM generation would be external)
    RETURN QUERY SELECT 
        context_docs,
        total_length,
        COALESCE(avg_similarity, 0.0),
        'Generated response would be created by external LLM service using context: ' || 
        LEFT(context_text, 200) || '...' as generated_response;

END;
$$ LANGUAGE plpgsql;

-- Execute RAG query (requires external LLM integration)
SELECT * FROM execute_rag_query(
    $1::vector,  -- Query embedding
    'What are the best practices for machine learning?',  -- Original query
    5,  -- Context documents limit
    0.4 -- Similarity threshold
);

-- Problems with traditional vector search approaches:
-- 1. Limited vector dimensions and performance optimization
-- 2. Complex setup and maintenance of vector extensions
-- 3. Poor integration between vector search and document metadata
-- 4. Limited scaling capabilities for high-dimensional vectors
-- 5. No native support for multiple similarity metrics
-- 6. Complex hybrid search combining vector and traditional queries
-- 7. Limited machine learning pipeline integration
-- 8. Expensive computational overhead for similarity calculations
-- 9. No native support for embedding model versioning
-- 10. Difficult operational management of vector indexes

MongoDB Atlas Vector Search provides native, high-performance vector operations:

// MongoDB Atlas Vector Search - native AI-powered vector similarity with unified data management
const { MongoClient } = require('mongodb');

// Advanced Atlas Vector Search Manager
class AtlasVectorSearchManager {
  constructor(connectionString, vectorConfig = {}) {
    this.connectionString = connectionString;
    this.client = null;
    this.db = null;

    // Vector search configuration
    this.config = {
      // Embedding configuration
      defaultEmbeddingModel: vectorConfig.defaultEmbeddingModel || 'text-embedding-ada-002',
      embeddingDimensions: vectorConfig.embeddingDimensions || 1536,
      embeddingProvider: vectorConfig.embeddingProvider || 'openai',

      // Vector index configuration
      vectorIndexes: vectorConfig.vectorIndexes || {},
      similarityMetrics: vectorConfig.similarityMetrics || ['cosine', 'euclidean', 'dotProduct'],

      // Search optimization
      enableHybridSearch: vectorConfig.enableHybridSearch !== false,
      enableSemanticCaching: vectorConfig.enableSemanticCaching !== false,
      defaultSearchLimit: vectorConfig.defaultSearchLimit || 20,

      // Performance tuning
      numCandidates: vectorConfig.numCandidates || 100,
      searchThreads: vectorConfig.searchThreads || 4,

      // AI integration
      enableRAGPipeline: vectorConfig.enableRAGPipeline !== false,
      enableRecommendations: vectorConfig.enableRecommendations !== false,
      enableSemanticAnalytics: vectorConfig.enableSemanticAnalytics !== false
    };

    this.initializeVectorSearch();
  }

  async initializeVectorSearch() {
    console.log('Initializing Atlas Vector Search system...');

    try {
      // Connect to MongoDB Atlas
      this.client = new MongoClient(this.connectionString);
      await this.client.connect();
      this.db = this.client.db();

      // Setup collections and vector indexes
      await this.setupVectorInfrastructure();

      // Initialize AI integration services
      await this.setupAIIntegration();

      console.log('Atlas Vector Search system initialized successfully');

    } catch (error) {
      console.error('Error initializing vector search:', error);
      throw error;
    }
  }

  async setupVectorInfrastructure() {
    console.log('Setting up vector search infrastructure...');

    try {
      // Create collections with optimized configuration
      this.collections = {
        documents: this.db.collection('documents'),
        userInteractions: this.db.collection('user_interactions'),
        searchAnalytics: this.db.collection('search_analytics'),
        embeddingCache: this.db.collection('embedding_cache')
      };

      // Create vector search indexes
      await this.createVectorIndexes();

      // Create supporting indexes for hybrid search
      await this.createHybridSearchIndexes();

      console.log('Vector infrastructure setup completed');

    } catch (error) {
      console.error('Error setting up vector infrastructure:', error);
      throw error;
    }
  }

  async createVectorIndexes() {
    console.log('Creating optimized vector search indexes...');

    try {
      // Primary content vector index with multiple similarity metrics
      const contentVectorIndex = {
        name: "vector_index_content_embeddings",
        type: "vectorSearch",
        definition: {
          fields: [
            {
              type: "vector",
              path: "contentEmbedding",
              numDimensions: this.config.embeddingDimensions,
              similarity: "cosine"  // Primary similarity metric
            },
            {
              type: "filter",
              path: "category"
            },
            {
              type: "filter", 
              path: "language"
            },
            {
              type: "filter",
              path: "tags"
            },
            {
              type: "filter",
              path: "metadata.contentType"
            },
            {
              type: "filter",
              path: "isPublished"
            }
          ]
        }
      };

      // Title/summary vector index for heading-based search
      const titleVectorIndex = {
        name: "vector_index_title_embeddings",
        type: "vectorSearch",
        definition: {
          fields: [
            {
              type: "vector",
              path: "titleEmbedding", 
              numDimensions: this.config.embeddingDimensions,
              similarity: "cosine"
            },
            {
              type: "filter",
              path: "category"
            },
            {
              type: "filter",
              path: "metadata.priority"
            }
          ]
        }
      };

      // Multi-modal vector index for images and rich content
      const multiModalVectorIndex = {
        name: "vector_index_multimodal_embeddings",
        type: "vectorSearch", 
        definition: {
          fields: [
            {
              type: "vector",
              path: "imageEmbedding",
              numDimensions: 768,  // CLIP model dimensions
              similarity: "cosine"
            },
            {
              type: "vector",
              path: "textEmbedding",
              numDimensions: this.config.embeddingDimensions,
              similarity: "cosine"
            },
            {
              type: "filter",
              path: "mediaType"
            }
          ]
        }
      };

      // User behavior vector index for recommendations
      const userVectorIndex = {
        name: "vector_index_user_preferences",
        type: "vectorSearch",
        definition: {
          fields: [
            {
              type: "vector",
              path: "preferenceEmbedding",
              numDimensions: this.config.embeddingDimensions,
              similarity: "cosine"
            },
            {
              type: "filter",
              path: "userSegment"
            },
            {
              type: "filter",
              path: "isActive"
            }
          ]
        }
      };

      // Create the vector indexes
      const indexCreationTasks = [
        this.createCollectionIndex('documents', contentVectorIndex),
        this.createCollectionIndex('documents', titleVectorIndex),
        this.createCollectionIndex('documents', multiModalVectorIndex),
        this.createCollectionIndex('userInteractions', userVectorIndex)
      ];

      await Promise.all(indexCreationTasks);

      console.log('Vector indexes created successfully');

    } catch (error) {
      console.error('Error creating vector indexes:', error);
      throw error;
    }
  }

  async createCollectionIndex(collectionName, indexDefinition) {
    try {
      const collection = this.collections[collectionName];
      await collection.createIndex(
        indexDefinition.definition.fields.reduce((acc, field) => {
          if (field.type === 'vector') {
            acc[field.path] = 'vector';
          }
          return acc;
        }, {}),
        {
          name: indexDefinition.name,
          background: true
        }
      );
    } catch (error) {
      console.error(`Error creating index ${indexDefinition.name}:`, error);
    }
  }

  async performAdvancedVectorSearch(searchConfig) {
    console.log('Performing advanced vector search...');

    const searchStartTime = Date.now();

    try {
      // Build comprehensive vector search aggregation pipeline
      const vectorSearchPipeline = [
        // Stage 1: Vector similarity search
        {
          $vectorSearch: {
            index: searchConfig.indexName || "vector_index_content_embeddings",
            path: searchConfig.vectorPath || "contentEmbedding",
            queryVector: searchConfig.queryVector,
            numCandidates: searchConfig.numCandidates || this.config.numCandidates,
            limit: searchConfig.limit || this.config.defaultSearchLimit,

            // Advanced filtering for hybrid search
            filter: {
              $and: [
                ...(searchConfig.categoryFilter ? [{ category: { $in: searchConfig.categoryFilter } }] : []),
                ...(searchConfig.languageFilter ? [{ language: searchConfig.languageFilter }] : []),
                ...(searchConfig.dateRange ? [{
                  createdAt: {
                    $gte: searchConfig.dateRange.start,
                    $lte: searchConfig.dateRange.end
                  }
                }] : []),
                ...(searchConfig.tagsFilter ? [{ tags: { $in: searchConfig.tagsFilter } }] : []),
                ...(searchConfig.customFilters || [])
              ]
            }
          }
        },

        // Stage 2: Add similarity score and metadata enrichment
        {
          $addFields: {
            // Similarity scoring and ranking
            searchScore: { $meta: "vectorSearchScore" },
            searchRank: { $meta: "vectorSearchRank" },

            // Content analysis and metadata
            contentLength: { $strLenCP: "$content" },
            wordCount: {
              $size: {
                $split: ["$content", " "]
              }
            },

            // Relevance classification
            relevanceCategory: {
              $switch: {
                branches: [
                  {
                    case: { $gte: [{ $meta: "vectorSearchScore" }, 0.8] },
                    then: "highly_relevant"
                  },
                  {
                    case: { $gte: [{ $meta: "vectorSearchScore" }, 0.6] },
                    then: "relevant"
                  },
                  {
                    case: { $gte: [{ $meta: "vectorSearchScore" }, 0.4] },
                    then: "somewhat_relevant"
                  }
                ],
                default: "low_relevance"
              }
            },

            // Enhanced ranking with business logic
            enhancedScore: {
              $add: [
                { $meta: "vectorSearchScore" },

                // Boost for high-priority content
                {
                  $cond: [
                    { $eq: ["$metadata.priority", "high"] },
                    0.1,
                    0
                  ]
                },

                // Boost for recent content
                {
                  $cond: [
                    {
                      $gte: [
                        "$createdAt",
                        { $subtract: [new Date(), 7 * 24 * 60 * 60 * 1000] }
                      ]
                    },
                    0.05,
                    0
                  ]
                },

                // Boost for popular content
                {
                  $multiply: [
                    { $divide: [{ $ifNull: ["$analytics.viewCount", 0] }, 1000] },
                    0.02
                  ]
                }
              ]
            },

            // Search metadata
            searchMetadata: {
              queryProcessedAt: new Date(),
              indexUsed: searchConfig.indexName || "vector_index_content_embeddings",
              numCandidatesSearched: searchConfig.numCandidates || this.config.numCandidates
            }
          }
        },

        // Stage 3: Content enrichment and analysis
        {
          $lookup: {
            from: "user_interactions",
            let: { docId: "$_id" },
            pipeline: [
              {
                $match: {
                  $expr: {
                    $and: [
                      { $eq: ["$documentId", "$$docId"] },
                      { $gte: ["$interactionDate", { $subtract: [new Date(), 30 * 24 * 60 * 60 * 1000] }] }
                    ]
                  }
                }
              },
              {
                $group: {
                  _id: null,
                  totalInteractions: { $sum: 1 },
                  averageRating: { $avg: "$rating" },
                  interactionTypes: { $addToSet: "$interactionType" },
                  uniqueUsers: { $addToSet: "$userId" }
                }
              }
            ],
            as: "recentInteractions"
          }
        },

        // Stage 4: Related content discovery
        {
          $lookup: {
            from: "documents",
            let: { 
              currentCategory: "$category",
              currentTags: "$tags",
              currentId: "$_id"
            },
            pipeline: [
              {
                $match: {
                  $expr: {
                    $and: [
                      { $ne: ["$_id", "$$currentId"] },
                      { 
                        $or: [
                          { $eq: ["$category", "$$currentCategory"] },
                          { $in: ["$$currentTags", "$tags"] }
                        ]
                      }
                    ]
                  }
                }
              },
              {
                $sample: { size: 3 }
              },
              {
                $project: {
                  _id: 1,
                  title: 1,
                  category: 1,
                  tags: 1
                }
              }
            ],
            as: "relatedContent"
          }
        },

        // Stage 5: Final enrichment and formatting
        {
          $addFields: {
            // Interaction analytics
            interactionMetrics: {
              $cond: [
                { $gt: [{ $size: "$recentInteractions" }, 0] },
                {
                  totalInteractions: { $arrayElemAt: ["$recentInteractions.totalInteractions", 0] },
                  averageRating: { $arrayElemAt: ["$recentInteractions.averageRating", 0] },
                  uniqueUserCount: { $size: { $arrayElemAt: ["$recentInteractions.uniqueUsers", 0] } }
                },
                {
                  totalInteractions: 0,
                  averageRating: null,
                  uniqueUserCount: 0
                }
              ]
            },

            // Content summary for preview
            contentPreview: {
              $concat: [
                { $substr: ["$content", 0, 200] },
                "..."
              ]
            },

            // Final ranking score incorporating all factors
            finalScore: {
              $add: [
                "$enhancedScore",

                // Interaction quality boost
                {
                  $multiply: [
                    { $ifNull: ["$interactionMetrics.averageRating", 0] },
                    0.02
                  ]
                },

                // Engagement boost
                {
                  $multiply: [
                    { $divide: [{ $ifNull: ["$interactionMetrics.totalInteractions", 0] }, 100] },
                    0.03
                  ]
                }
              ]
            }
          }
        },

        // Stage 6: Final projection and cleanup
        {
          $project: {
            // Core content information
            title: 1,
            contentPreview: 1,
            category: 1,
            tags: 1,
            author: 1,
            createdAt: 1,
            language: 1,

            // Search relevance metrics
            searchScore: { $round: ["$searchScore", 4] },
            enhancedScore: { $round: ["$enhancedScore", 4] },
            finalScore: { $round: ["$finalScore", 4] },
            relevanceCategory: 1,
            searchRank: 1,

            // Content metrics
            contentLength: 1,
            wordCount: 1,

            // Engagement metrics
            interactionMetrics: 1,

            // Related content
            relatedContent: 1,

            // Metadata
            metadata: 1,
            searchMetadata: 1
          }
        },

        // Stage 7: Final sorting and ranking
        {
          $sort: {
            finalScore: -1,
            searchScore: -1,
            createdAt: -1
          }
        }
      ];

      // Execute the comprehensive vector search
      const searchResults = await this.collections.documents
        .aggregate(vectorSearchPipeline, {
          allowDiskUse: true,
          maxTimeMS: 30000
        })
        .toArray();

      const searchLatency = Date.now() - searchStartTime;

      // Log search analytics
      await this.logSearchAnalytics({
        queryVector: searchConfig.queryVector,
        resultsCount: searchResults.length,
        searchLatency: searchLatency,
        searchConfig: searchConfig,
        timestamp: new Date()
      });

      console.log(`Vector search completed: ${searchResults.length} results in ${searchLatency}ms`);

      return {
        success: true,
        results: searchResults,
        searchMetadata: {
          latency: searchLatency,
          resultsCount: searchResults.length,
          indexUsed: searchConfig.indexName || "vector_index_content_embeddings",
          numCandidatesSearched: searchConfig.numCandidates || this.config.numCandidates
        }
      };

    } catch (error) {
      console.error('Error performing vector search:', error);
      return {
        success: false,
        error: error.message,
        searchMetadata: {
          latency: Date.now() - searchStartTime
        }
      };
    }
  }

  async executeRAGPipeline(queryText, searchConfig = {}) {
    console.log('Executing RAG (Retrieval-Augmented Generation) pipeline...');

    try {
      // Generate query embedding (in production, this would call embedding API)
      const queryEmbedding = await this.generateEmbedding(queryText);

      // Perform vector search for relevant context
      const contextSearch = await this.performAdvancedVectorSearch({
        ...searchConfig,
        queryVector: queryEmbedding,
        limit: searchConfig.contextLimit || 5,
        numCandidates: searchConfig.numCandidates || 50
      });

      if (!contextSearch.success) {
        throw new Error(`Context retrieval failed: ${contextSearch.error}`);
      }

      // Build context for generation
      const contextDocuments = contextSearch.results;
      const contextText = contextDocuments
        .map((doc, index) => {
          return `Document ${index + 1} (Relevance: ${doc.relevanceCategory}):\nTitle: ${doc.title}\nContent: ${doc.contentPreview}`;
        })
        .join('\n\n');

      // Calculate context quality metrics
      const contextMetrics = {
        documentCount: contextDocuments.length,
        averageRelevance: contextDocuments.reduce((sum, doc) => sum + doc.searchScore, 0) / contextDocuments.length,
        totalContextLength: contextText.length,
        categories: [...new Set(contextDocuments.map(doc => doc.category))],
        languages: [...new Set(contextDocuments.map(doc => doc.language))]
      };

      // In production, this would call LLM API for generation
      const generatedResponse = await this.simulateResponseGeneration(queryText, contextText, contextMetrics);

      // Store RAG execution for analytics
      await this.logRAGExecution({
        query: queryText,
        contextMetrics: contextMetrics,
        responseGenerated: true,
        executionTime: Date.now(),
        contextDocuments: contextDocuments.map(doc => ({
          id: doc._id,
          title: doc.title,
          relevanceScore: doc.searchScore
        }))
      });

      return {
        success: true,
        query: queryText,
        contextDocuments: contextDocuments,
        contextMetrics: contextMetrics,
        generatedResponse: generatedResponse,
        searchMetadata: contextSearch.searchMetadata
      };

    } catch (error) {
      console.error('Error executing RAG pipeline:', error);
      return {
        success: false,
        error: error.message
      };
    }
  }

  async generateRecommendations(userId, recommendationConfig = {}) {
    console.log(`Generating recommendations for user: ${userId}`);

    try {
      // Get user interaction history and preferences
      const userProfile = await this.buildUserProfile(userId);

      if (!userProfile.success) {
        throw new Error(`Unable to build user profile: ${userProfile.error}`);
      }

      // Generate user preference embedding
      const userEmbedding = await this.generateUserPreferenceEmbedding(userProfile.data);

      // Find similar content based on user preferences
      const recommendationSearch = await this.performAdvancedVectorSearch({
        queryVector: userEmbedding,
        indexName: "vector_index_content_embeddings",
        limit: recommendationConfig.limit || 10,
        numCandidates: recommendationConfig.numCandidates || 100,

        // Filter out already interacted content
        customFilters: [
          {
            _id: {
              $nin: userProfile.data.interactedDocuments || []
            }
          }
        ]
      });

      // Find similar users for collaborative filtering
      const similarUsers = await this.findSimilarUsers(userId, userEmbedding);

      // Combine content-based and collaborative filtering
      const hybridRecommendations = await this.combineRecommendationStrategies(
        recommendationSearch.results,
        similarUsers,
        userProfile.data
      );

      return {
        success: true,
        userId: userId,
        recommendations: hybridRecommendations,
        recommendationMetadata: {
          contentBasedCount: recommendationSearch.results.length,
          collaborativeSignals: similarUsers.length,
          userProfileStrength: userProfile.data.profileStrength
        }
      };

    } catch (error) {
      console.error('Error generating recommendations:', error);
      return {
        success: false,
        error: error.message
      };
    }
  }

  async buildUserProfile(userId) {
    try {
      // Aggregate user interaction data
      const userInteractionPipeline = [
        {
          $match: {
            userId: userId,
            interactionDate: {
              $gte: new Date(Date.now() - 90 * 24 * 60 * 60 * 1000) // Last 90 days
            }
          }
        },
        {
          $lookup: {
            from: "documents",
            localField: "documentId",
            foreignField: "_id",
            as: "document"
          }
        },
        {
          $unwind: "$document"
        },
        {
          $group: {
            _id: "$userId",

            // Interaction patterns
            totalInteractions: { $sum: 1 },
            categories: { $addToSet: "$document.category" },
            tags: { $addToSet: "$document.tags" },
            languages: { $addToSet: "$document.language" },

            // Preference indicators
            averageRating: { $avg: "$rating" },
            favoriteCategories: {
              $push: {
                category: "$document.category",
                rating: "$rating",
                interactionType: "$interactionType"
              }
            },

            // Content characteristics
            preferredContentLength: { $avg: { $strLenCP: "$document.content" } },
            interactionTypes: { $addToSet: "$interactionType" },

            // Temporal patterns
            interactedDocuments: { $addToSet: "$documentId" },
            recentInteractions: {
              $push: {
                documentId: "$documentId",
                rating: "$rating",
                interactionDate: "$interactionDate"
              }
            }
          }
        }
      ];

      const userProfileData = await this.collections.userInteractions
        .aggregate(userInteractionPipeline)
        .toArray();

      if (userProfileData.length === 0) {
        return {
          success: false,
          error: 'No user interaction data found'
        };
      }

      const profile = userProfileData[0];

      // Calculate profile strength
      profile.profileStrength = Math.min(
        (profile.totalInteractions / 50) * 0.4 +
        (profile.categories.length / 10) * 0.3 +
        (profile.tags.flat().length / 20) * 0.3,
        1.0
      );

      return {
        success: true,
        data: profile
      };

    } catch (error) {
      return {
        success: false,
        error: error.message
      };
    }
  }

  async generateEmbedding(text) {
    // In production, this would call OpenAI or other embedding API
    // For demonstration, return a mock embedding
    return Array.from({ length: this.config.embeddingDimensions }, () => Math.random() - 0.5);
  }

  async generateUserPreferenceEmbedding(userProfile) {
    // In production, this would create embeddings based on user preferences
    // For demonstration, return a mock embedding based on user categories
    const categoryWeights = userProfile.favoriteCategories.reduce((acc, item) => {
      acc[item.category] = (acc[item.category] || 0) + (item.rating || 3);
      return acc;
    }, {});

    // Create weighted embedding (simplified approach)
    return Array.from({ length: this.config.embeddingDimensions }, () => Math.random() - 0.5);
  }

  async simulateResponseGeneration(queryText, contextText, contextMetrics) {
    // In production, this would call ChatGPT or another LLM
    return `Based on ${contextMetrics.documentCount} relevant documents with average relevance of ${contextMetrics.averageRelevance.toFixed(3)}, here's a comprehensive response to "${queryText}": [Generated response would appear here using the provided context from ${contextMetrics.categories.join(', ')} categories]`;
  }

  async logSearchAnalytics(searchData) {
    try {
      await this.collections.searchAnalytics.insertOne({
        ...searchData,
        createdAt: new Date()
      });
    } catch (error) {
      console.error('Error logging search analytics:', error);
    }
  }

  async logRAGExecution(ragData) {
    try {
      await this.collections.searchAnalytics.insertOne({
        type: 'rag_execution',
        ...ragData,
        createdAt: new Date()
      });
    } catch (error) {
      console.error('Error logging RAG execution:', error);
    }
  }

  async shutdown() {
    console.log('Shutting down Atlas Vector Search manager...');

    try {
      if (this.client) {
        await this.client.close();
      }
      console.log('Atlas Vector Search manager shutdown completed');
    } catch (error) {
      console.error('Error during shutdown:', error);
    }
  }
}

// Benefits of MongoDB Atlas Vector Search:
// - Native vector similarity search with automatic optimization
// - Seamless integration with existing MongoDB data and operations  
// - Advanced hybrid search combining vector similarity with traditional queries
// - Multiple similarity metrics (cosine, euclidean, dot product) in single platform
// - Automatic scaling and performance optimization for vector workloads
// - Built-in support for multi-modal embeddings and AI model integration
// - Real-time vector search with consistent results and ACID transactions
// - SQL-compatible vector operations through QueryLeaf integration
// - Enterprise-ready security, monitoring, and operational management
// - Native support for RAG pipelines and recommendation systems

module.exports = {
  AtlasVectorSearchManager
};

Understanding Atlas Vector Search Architecture

Advanced AI Integration Patterns

MongoDB Atlas Vector Search enables sophisticated AI application architectures with native vector operations:

// Enterprise AI Application Architecture with Atlas Vector Search
class EnterpriseAIVectorPlatform extends AtlasVectorSearchManager {
  constructor(connectionString, aiConfig) {
    super(connectionString, aiConfig);

    this.aiConfig = {
      ...aiConfig,
      enableMultiModalSearch: true,
      enableRealtimeRecommendations: true,
      enableSemanticAnalytics: true,
      enableContentGeneration: true,
      enableKnowledgeGraphs: true
    };

    this.setupEnterpriseAICapabilities();
  }

  async implementAdvancedAIWorkflows() {
    console.log('Implementing enterprise AI workflows...');

    const aiWorkflows = {
      // Multi-modal content processing
      multiModalProcessing: {
        textEmbeddings: true,
        imageEmbeddings: true,
        audioEmbeddings: true,
        videoEmbeddings: true
      },

      // Advanced recommendation engines
      recommendationSystems: {
        contentBasedFiltering: true,
        collaborativeFiltering: true,
        hybridRecommendations: true,
        realTimePersonalization: true
      },

      // Knowledge management and RAG
      knowledgeManagement: {
        documentRetrieval: true,
        contextGeneration: true,
        responseGeneration: true,
        knowledgeGraphIntegration: true
      },

      // Semantic search and discovery
      semanticCapabilities: {
        intentRecognition: true,
        entityExtraction: true,
        topicModeling: true,
        conceptualSearch: true
      }
    };

    return await this.deployAIWorkflows(aiWorkflows);
  }

  async setupRealtimePersonalization() {
    console.log('Setting up real-time personalization engine...');

    // Real-time user behavior processing
    const personalizationPipeline = [
      {
        $match: {
          timestamp: { $gte: new Date(Date.now() - 60000) } // Last minute
        }
      },
      {
        $lookup: {
          from: "user_profiles",
          localField: "userId", 
          foreignField: "userId",
          as: "userProfile"
        }
      },
      {
        $vectorSearch: {
          index: "vector_index_user_preferences",
          path: "userProfile.preferenceEmbedding",
          queryVector: "$behaviorEmbedding",
          numCandidates: 100,
          limit: 20
        }
      }
    ];

    return await this.deployPersonalizationEngine(personalizationPipeline);
  }
}

SQL-Style Vector Search Operations with QueryLeaf

QueryLeaf provides familiar SQL syntax for MongoDB Atlas Vector Search operations:

-- QueryLeaf advanced vector search operations with SQL-familiar syntax

-- Configure Atlas Vector Search capabilities  
CONFIGURE VECTOR_SEARCH
SET provider = 'mongodb_atlas',
    default_embedding_model = 'text-embedding-ada-002',
    embedding_dimensions = 1536,
    similarity_metrics = ['cosine', 'euclidean', 'dot_product'],
    enable_hybrid_search = true,
    enable_semantic_caching = true,
    default_num_candidates = 100,
    default_search_limit = 20;

-- Create vector-optimized table for AI applications
CREATE TABLE ai_documents (
    document_id UUID PRIMARY KEY,
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    category VARCHAR(100),
    language VARCHAR(10) DEFAULT 'en',

    -- Vector embeddings for semantic search
    content_embedding VECTOR(1536),
    title_embedding VECTOR(1536),
    summary_embedding VECTOR(1536),

    -- Multi-modal embeddings
    image_embedding VECTOR(768),   -- CLIP embeddings
    audio_embedding VECTOR(512),   -- Audio model embeddings

    -- Metadata for hybrid search
    tags TEXT[],
    author VARCHAR(200),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Content analysis
    word_count INTEGER,
    readability_score DECIMAL(5,2),
    sentiment_score DECIMAL(3,2),

    -- Engagement metrics
    view_count BIGINT DEFAULT 0,
    like_count BIGINT DEFAULT 0,
    share_count BIGINT DEFAULT 0,
    average_rating DECIMAL(3,2),

    -- AI metadata
    embedding_model VARCHAR(100) DEFAULT 'text-embedding-ada-002',
    embedding_version VARCHAR(20) DEFAULT 'v1',
    last_embedding_update TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

    -- Vector search optimization
    is_published BOOLEAN DEFAULT true,
    content_type VARCHAR(50),
    priority_level INTEGER DEFAULT 1

) WITH (
    vector_indexes = [
        {
            name: 'idx_content_vector_search',
            path: 'content_embedding', 
            similarity: 'cosine',
            dimensions: 1536,
            filters: ['category', 'language', 'is_published', 'content_type']
        },
        {
            name: 'idx_title_vector_search',
            path: 'title_embedding',
            similarity: 'cosine', 
            dimensions: 1536,
            filters: ['category', 'priority_level']
        },
        {
            name: 'idx_multimodal_vector_search',
            path: ['content_embedding', 'image_embedding'],
            similarity: 'cosine',
            filters: ['content_type', 'language']
        }
    ]
);

-- Advanced semantic search with hybrid filtering
WITH semantic_search_results AS (
  SELECT 
    document_id,
    title,
    content,
    category,
    language,
    tags,
    author,
    created_at,
    word_count,
    view_count,
    average_rating,

    -- Vector similarity scoring
    VECTOR_SIMILARITY(content_embedding, $1, 'cosine') as content_similarity,
    VECTOR_SIMILARITY(title_embedding, $1, 'cosine') as title_similarity,
    VECTOR_SIMILARITY(summary_embedding, $1, 'cosine') as summary_similarity,

    -- Distance metrics for different use cases
    VECTOR_DISTANCE(content_embedding, $1, 'euclidean') as euclidean_distance,
    VECTOR_DISTANCE(content_embedding, $1, 'manhattan') as manhattan_distance,

    -- Hybrid ranking factors
    LOG(view_count + 1) as popularity_score,
    COALESCE(average_rating, 3.0) as quality_score,

    -- Temporal relevance
    EXTRACT(DAYS FROM (CURRENT_TIMESTAMP - created_at)) as days_old,
    CASE 
      WHEN created_at >= CURRENT_TIMESTAMP - INTERVAL '7 days' THEN 0.1
      WHEN created_at >= CURRENT_TIMESTAMP - INTERVAL '30 days' THEN 0.05
      ELSE 0.0
    END as recency_boost

  FROM ai_documents
  WHERE 
    -- Vector search with advanced filtering
    VECTOR_SEARCH(
      content_embedding,
      $1,  -- Query embedding vector
      similarity_metric => 'cosine',
      num_candidates => 100,
      filters => {
        'category': $2,        -- Category filter
        'language': $3,        -- Language filter  
        'is_published': true,
        'content_type': $4     -- Content type filter
      }
    )
    AND created_at >= $5       -- Date range filter
    AND word_count >= $6       -- Minimum content length
    AND (tags && $7 OR $7 IS NULL)  -- Tag overlap filter
),

enhanced_ranking AS (
  SELECT 
    ssr.*,

    -- Multi-factor ranking calculation
    (
      content_similarity * 0.4 +           -- Primary semantic similarity
      title_similarity * 0.2 +             -- Title relevance
      summary_similarity * 0.1 +           -- Summary relevance
      (popularity_score / 10.0) * 0.1 +    -- Engagement factor
      (quality_score / 5.0) * 0.1 +        -- Quality factor
      recency_boost +                       -- Temporal relevance
      CASE 
        WHEN priority_level >= 5 THEN 0.1   -- Priority boost
        ELSE 0.0 
      END
    ) as composite_relevance_score,

    -- Content analysis and categorization
    CASE 
      WHEN content_similarity >= 0.8 THEN 'highly_relevant'
      WHEN content_similarity >= 0.6 THEN 'relevant'
      WHEN content_similarity >= 0.4 THEN 'somewhat_relevant'
      ELSE 'marginally_relevant'
    END as relevance_category,

    -- Semantic clustering for diverse results
    NTILE(5) OVER (ORDER BY content_similarity DESC) as relevance_tier,

    -- Content quality indicators
    CASE 
      WHEN word_count >= 2000 AND average_rating >= 4.0 THEN 'comprehensive_high_quality'
      WHEN word_count >= 1000 AND average_rating >= 3.5 THEN 'detailed_good_quality'
      WHEN word_count >= 500 AND average_rating >= 3.0 THEN 'standard_quality'
      ELSE 'basic_content'
    END as content_quality_tier,

    -- Engagement performance metrics
    (view_count * 0.3 + like_count * 0.4 + share_count * 0.3) as engagement_score,

    -- Search result preview
    LEFT(content, 300) || CASE WHEN LENGTH(content) > 300 THEN '...' ELSE '' END as content_preview

  FROM semantic_search_results ssr
),

diversity_optimization AS (
  SELECT 
    er.*,

    -- Category diversity to prevent over-concentration
    ROW_NUMBER() OVER (PARTITION BY category ORDER BY composite_relevance_score DESC) as category_rank,

    -- Author diversity for varied perspectives  
    ROW_NUMBER() OVER (PARTITION BY author ORDER BY composite_relevance_score DESC) as author_rank,

    -- Temporal diversity for balanced timeline coverage
    ROW_NUMBER() OVER (
      PARTITION BY DATE_TRUNC('month', created_at) 
      ORDER BY composite_relevance_score DESC
    ) as temporal_rank,

    -- Content length diversity
    CASE 
      WHEN word_count <= 500 THEN 'short'
      WHEN word_count <= 1500 THEN 'medium' 
      WHEN word_count <= 3000 THEN 'long'
      ELSE 'comprehensive'
    END as content_length_category,

    -- Similarity to previous results (prevents near-duplicates)
    LAG(content_similarity, 1) OVER (ORDER BY composite_relevance_score DESC) as prev_result_similarity

  FROM enhanced_ranking er
)

-- Final search results with comprehensive analytics
SELECT 
  document_id,
  title,
  content_preview,
  category,
  language,
  author,
  TO_CHAR(created_at, 'YYYY-MM-DD HH24:MI') as published_date,

  -- Relevance and ranking metrics
  ROUND(content_similarity::NUMERIC, 4) as semantic_similarity,
  ROUND(composite_relevance_score::NUMERIC, 4) as final_relevance_score,
  relevance_category,
  relevance_tier,

  -- Content characteristics
  word_count,
  content_quality_tier,
  content_length_category,

  -- Engagement and quality indicators
  view_count,
  average_rating,
  ROUND(engagement_score::NUMERIC, 1) as engagement_score,

  -- Diversity indicators
  category_rank,
  author_rank,
  temporal_rank,

  -- Metadata
  tags,
  ROUND(euclidean_distance::NUMERIC, 4) as euclidean_distance,
  days_old,

  -- Search quality indicators
  CASE 
    WHEN ABS(content_similarity - COALESCE(prev_result_similarity, 0)) < 0.05 THEN 'potential_duplicate'
    WHEN composite_relevance_score >= 0.8 THEN 'excellent_match'
    WHEN composite_relevance_score >= 0.6 THEN 'good_match'
    WHEN composite_relevance_score >= 0.4 THEN 'fair_match'
    ELSE 'weak_match'
  END as search_quality_assessment,

  -- Performance metadata
  CURRENT_TIMESTAMP as search_executed_at

FROM diversity_optimization
WHERE 
  -- Quality thresholds
  composite_relevance_score >= 0.3
  AND content_similarity >= 0.2

  -- Diversity constraints (ensure balanced results)
  AND category_rank <= 3        -- Max 3 results per category
  AND author_rank <= 2          -- Max 2 results per author
  AND temporal_rank <= 2        -- Max 2 results per month

ORDER BY 
  composite_relevance_score DESC,
  content_similarity DESC,
  engagement_score DESC
LIMIT 20;

-- Real-time recommendation engine with collaborative filtering
CREATE MATERIALIZED VIEW user_recommendation_profiles AS
WITH user_interaction_patterns AS (
  SELECT 
    user_id,

    -- Interaction behavior analysis
    COUNT(*) as total_interactions,
    COUNT(DISTINCT document_id) as unique_documents_viewed,
    COUNT(DISTINCT category) as categories_explored,
    AVG(interaction_rating) as average_rating,

    -- Preference extraction from interactions
    ARRAY_AGG(DISTINCT category ORDER BY COUNT(*) DESC) as preferred_categories,
    ARRAY_AGG(DISTINCT tags) as interacted_tags,

    -- Temporal patterns
    AVG(EXTRACT(HOUR FROM interaction_timestamp)) as preferred_interaction_hour,
    MODE() WITHIN GROUP (ORDER BY EXTRACT(DOW FROM interaction_timestamp)) as preferred_day_of_week,

    -- Content preferences
    AVG(word_count) as preferred_content_length,
    AVG(readability_score) as preferred_readability,

    -- Engagement patterns
    SUM(CASE WHEN interaction_type = 'like' THEN 1 ELSE 0 END) as likes_given,
    SUM(CASE WHEN interaction_type = 'share' THEN 1 ELSE 0 END) as shares_made,
    SUM(CASE WHEN interaction_type = 'bookmark' THEN 1 ELSE 0 END) as bookmarks_created

  FROM user_interactions ui
  JOIN ai_documents ad ON ui.document_id = ad.document_id  
  WHERE ui.interaction_timestamp >= CURRENT_TIMESTAMP - INTERVAL '90 days'
    AND ui.interaction_rating IS NOT NULL
  GROUP BY user_id
  HAVING COUNT(*) >= 5  -- Minimum interaction threshold
),

user_preference_vectors AS (
  SELECT 
    uip.user_id,
    uip.total_interactions,
    uip.preferred_categories,
    uip.average_rating,

    -- Generate user preference embedding from interaction patterns
    VECTOR_AGGREGATE(
      ad.content_embedding,
      weights => ui.interaction_rating,
      aggregation_method => 'weighted_average'
    ) as preference_embedding,

    -- Category preference strengths
    JSONB_OBJECT_AGG(
      ad.category,
      AVG(ui.interaction_rating)
    ) as category_preference_scores,

    -- Content characteristics preferences
    uip.preferred_content_length,
    uip.preferred_readability,

    -- Profile completeness and reliability
    LEAST(uip.total_interactions / 50.0, 1.0) as profile_completeness,
    CURRENT_TIMESTAMP as profile_generated_at

  FROM user_interaction_patterns uip
  JOIN user_interactions ui ON uip.user_id = ui.user_id
  JOIN ai_documents ad ON ui.document_id = ad.document_id
  WHERE ui.interaction_timestamp >= CURRENT_TIMESTAMP - INTERVAL '90 days'
  GROUP BY 
    uip.user_id, uip.total_interactions, uip.preferred_categories, 
    uip.average_rating, uip.preferred_content_length, uip.preferred_readability
);

-- Advanced recommendation generation with multiple strategies
WITH target_user_profile AS (
  SELECT * FROM user_recommendation_profiles 
  WHERE user_id = $1  -- Target user for recommendations
),

content_based_recommendations AS (
  SELECT 
    ad.document_id,
    ad.title,
    ad.category,
    ad.content_preview,
    ad.author,
    ad.created_at,
    ad.average_rating,
    ad.view_count,

    -- Content similarity to user preferences
    VECTOR_SIMILARITY(
      ad.content_embedding, 
      tup.preference_embedding, 
      'cosine'
    ) as content_similarity,

    -- Category preference alignment
    COALESCE(
      (tup.category_preference_scores->>ad.category)::NUMERIC,
      tup.average_rating
    ) as category_preference_score,

    -- Content characteristic matching
    ABS(ad.word_count - tup.preferred_content_length) / 1000.0 as length_mismatch,
    ABS(ad.readability_score - tup.preferred_readability) as readability_mismatch,

    'content_based' as recommendation_strategy

  FROM ai_documents ad
  CROSS JOIN target_user_profile tup
  WHERE ad.is_published = true
    AND ad.document_id NOT IN (
      -- Exclude already interacted content
      SELECT DISTINCT document_id 
      FROM user_interactions 
      WHERE user_id = $1
      AND interaction_timestamp >= CURRENT_TIMESTAMP - INTERVAL '30 days'
    )
  ORDER BY 
    content_similarity DESC,
    category_preference_score DESC
  LIMIT 50
),

collaborative_filtering AS (
  -- Find similar users based on preference vectors
  WITH similar_users AS (
    SELECT 
      urp.user_id as similar_user_id,
      VECTOR_SIMILARITY(
        urp.preference_embedding,
        tup.preference_embedding,
        'cosine'
      ) as user_similarity,
      urp.profile_completeness

    FROM user_recommendation_profiles urp
    CROSS JOIN target_user_profile tup
    WHERE urp.user_id != tup.user_id
      AND urp.profile_completeness >= 0.3  -- Reliable profiles only
    ORDER BY user_similarity DESC
    LIMIT 20  -- Top similar users
  ),

  collaborative_recommendations AS (
    SELECT 
      ad.document_id,
      ad.title,
      ad.category,
      ad.content_preview,
      ad.author,
      ad.created_at,
      ad.average_rating,
      ad.view_count,

      -- Weighted recommendation score from similar users
      AVG(ui.interaction_rating * su.user_similarity) as collaborative_score,
      COUNT(*) as similar_user_interactions,
      'collaborative_filtering' as recommendation_strategy

    FROM similar_users su
    JOIN user_interactions ui ON su.similar_user_id = ui.user_id
    JOIN ai_documents ad ON ui.document_id = ad.document_id
    WHERE ad.is_published = true
      AND ui.interaction_rating >= 3  -- Positive interactions only
      AND ui.interaction_timestamp >= CURRENT_TIMESTAMP - INTERVAL '60 days'
      AND ad.document_id NOT IN (
        -- Exclude target user's interactions
        SELECT DISTINCT document_id 
        FROM user_interactions 
        WHERE user_id = $1
      )
    GROUP BY 
      ad.document_id, ad.title, ad.category, ad.content_preview,
      ad.author, ad.created_at, ad.average_rating, ad.view_count
    HAVING COUNT(*) >= 2  -- Multiple similar users recommended
    ORDER BY collaborative_score DESC
    LIMIT 30
  )

  SELECT * FROM collaborative_recommendations
),

hybrid_recommendations AS (
  -- Combine content-based and collaborative filtering
  SELECT 
    COALESCE(cb.document_id, cf.document_id) as document_id,
    COALESCE(cb.title, cf.title) as title,
    COALESCE(cb.category, cf.category) as category,
    COALESCE(cb.content_preview, cf.content_preview) as content_preview,
    COALESCE(cb.author, cf.author) as author,
    COALESCE(cb.created_at, cf.created_at) as created_at,
    COALESCE(cb.average_rating, cf.average_rating) as average_rating,
    COALESCE(cb.view_count, cf.view_count) as view_count,

    -- Hybrid scoring
    COALESCE(cb.content_similarity, 0.0) * 0.6 +
    COALESCE(cf.collaborative_score, 0.0) * 0.4 as hybrid_score,

    cb.content_similarity,
    cf.collaborative_score,
    cf.similar_user_interactions,

    -- Recommendation diversity factors
    ROW_NUMBER() OVER (PARTITION BY COALESCE(cb.category, cf.category) ORDER BY 
      (COALESCE(cb.content_similarity, 0.0) * 0.6 + COALESCE(cf.collaborative_score, 0.0) * 0.4) DESC
    ) as category_rank,

    -- Final recommendation strategy
    CASE 
      WHEN cb.document_id IS NOT NULL AND cf.document_id IS NOT NULL THEN 'hybrid'
      WHEN cb.document_id IS NOT NULL THEN 'content_based'
      WHEN cf.document_id IS NOT NULL THEN 'collaborative'
    END as recommendation_source

  FROM content_based_recommendations cb
  FULL OUTER JOIN collaborative_filtering cf ON cb.document_id = cf.document_id
)

-- Final personalized recommendations
SELECT 
  document_id,
  title,
  content_preview,
  category,
  author,
  TO_CHAR(created_at, 'YYYY-MM-DD') as published_date,

  -- Recommendation scoring
  ROUND(hybrid_score::NUMERIC, 4) as recommendation_score,
  ROUND(content_similarity::NUMERIC, 4) as content_match,
  ROUND(collaborative_score::NUMERIC, 4) as social_signal,
  recommendation_source,

  -- Content quality indicators
  average_rating,
  view_count,

  -- Diversity indicators
  category_rank,

  -- Confidence metrics
  CASE 
    WHEN recommendation_source = 'hybrid' AND hybrid_score >= 0.7 THEN 'high_confidence'
    WHEN hybrid_score >= 0.5 THEN 'medium_confidence'
    WHEN hybrid_score >= 0.3 THEN 'low_confidence'
    ELSE 'experimental'
  END as confidence_level,

  -- Recommendation explanation
  CASE recommendation_source
    WHEN 'content_based' THEN 'Recommended based on your content preferences'
    WHEN 'collaborative' THEN 'Recommended by users with similar interests'
    WHEN 'hybrid' THEN 'Recommended based on content analysis and user behavior'
  END as recommendation_explanation,

  CURRENT_TIMESTAMP as recommended_at

FROM hybrid_recommendations
WHERE 
  hybrid_score >= 0.2  -- Minimum recommendation threshold
  AND category_rank <= 2  -- Max 2 recommendations per category for diversity
ORDER BY 
  hybrid_score DESC,
  average_rating DESC NULLS LAST
LIMIT 15;

-- RAG (Retrieval-Augmented Generation) pipeline for question answering
CREATE FUNCTION execute_rag_pipeline(
    query_text TEXT,
    context_limit INTEGER DEFAULT 5,
    similarity_threshold DECIMAL DEFAULT 0.4,
    language_preference VARCHAR DEFAULT 'en'
) RETURNS TABLE (
    context_documents JSONB,
    context_summary TEXT,
    generated_response TEXT,
    confidence_score DECIMAL,
    sources_cited INTEGER,
    processing_metadata JSONB
) AS $$
DECLARE
    query_embedding VECTOR(1536);
    context_docs JSONB := '[]'::JSONB;
    context_text TEXT := '';
    total_context_length INTEGER := 0;
    avg_relevance DECIMAL;
    processing_start_time TIMESTAMP := CURRENT_TIMESTAMP;
BEGIN
    -- Generate embedding for the query (in production, call embedding API)
    query_embedding := GENERATE_EMBEDDING(query_text);

    -- Retrieve relevant context using vector search
    WITH context_retrieval AS (
        SELECT 
            document_id,
            title,
            content,
            category,
            author,
            created_at,
            average_rating,
            VECTOR_SIMILARITY(content_embedding, query_embedding, 'cosine') as relevance_score,
            word_count

        FROM ai_documents
        WHERE VECTOR_SEARCH(
            content_embedding,
            query_embedding,
            similarity_metric => 'cosine',
            num_candidates => 50,
            filters => {
                'language': language_preference,
                'is_published': true
            }
        )
        AND VECTOR_SIMILARITY(content_embedding, query_embedding, 'cosine') >= similarity_threshold
        ORDER BY relevance_score DESC
        LIMIT context_limit
    )

    -- Build context for generation
    SELECT 
        JSONB_AGG(
            JSONB_BUILD_OBJECT(
                'document_id', document_id,
                'title', title,
                'content', LEFT(content, 1000),
                'category', category,
                'author', author,
                'relevance_score', relevance_score,
                'word_count', word_count
            ) ORDER BY relevance_score DESC
        ),
        STRING_AGG(
            title || E':\n' || LEFT(content, 800) || E'\n\n',
            '' ORDER BY relevance_score DESC
        ),
        SUM(LENGTH(content)),
        AVG(relevance_score)
    INTO context_docs, context_text, total_context_length, avg_relevance
    FROM context_retrieval;

    -- Generate response (in production, call LLM API)
    -- For demonstration, return structured information
    RETURN QUERY SELECT 
        COALESCE(context_docs, '[]'::JSONB) as context_documents,
        CONCAT(
            'Based on ', COALESCE(JSONB_ARRAY_LENGTH(context_docs), 0), 
            ' relevant documents from categories: ',
            STRING_AGG(DISTINCT category, ', ')
        ) as context_summary,
        CONCAT(
            'Generated response to "', query_text, 
            '" based on the retrieved context. Average relevance: ',
            ROUND(COALESCE(avg_relevance, 0), 3)
        ) as generated_response,
        COALESCE(avg_relevance, 0.0) as confidence_score,
        COALESCE(JSONB_ARRAY_LENGTH(context_docs), 0) as sources_cited,
        JSONB_BUILD_OBJECT(
            'processing_time_ms', EXTRACT(MILLISECONDS FROM (CURRENT_TIMESTAMP - processing_start_time)),
            'context_length', total_context_length,
            'language_used', language_preference,
            'similarity_threshold', similarity_threshold,
            'timestamp', CURRENT_TIMESTAMP
        ) as processing_metadata
    FROM (
        SELECT DISTINCT category 
        FROM JSONB_TO_RECORDSET(context_docs) AS x(category TEXT)
    ) cat;

END;
$$ LANGUAGE plpgsql;

-- Execute RAG pipeline for question answering
SELECT * FROM execute_rag_pipeline(
    'What are the best practices for implementing microservices architecture?',
    5,    -- context_limit
    0.4,  -- similarity_threshold  
    'en'  -- language_preference
);

-- QueryLeaf provides comprehensive Atlas Vector Search capabilities:
-- 1. Native SQL syntax for vector similarity operations and embedding management
-- 2. Advanced hybrid search combining vector similarity with traditional filters
-- 3. Multi-modal vector search supporting text, image, and audio embeddings
-- 4. Intelligent recommendation systems with content-based and collaborative filtering
-- 5. Production-ready RAG pipeline implementation with context optimization
-- 6. Real-time personalization based on user behavior and preference vectors
-- 7. Comprehensive analytics and performance monitoring for AI applications
-- 8. Enterprise-ready vector indexing with automatic scaling and optimization
-- 9. SQL-familiar semantic search operations accessible to traditional database teams
-- 10. Native integration with MongoDB Atlas infrastructure and security features

Best Practices for Production Vector Search Implementation

Vector Index Design and Optimization

Essential practices for effective Atlas Vector Search deployment:

  1. Embedding Strategy: Choose appropriate embedding models and dimensions based on use case requirements and performance constraints
  2. Index Configuration: Design vector indexes with optimal similarity metrics, candidate limits, and filter combinations for query patterns
  3. Hybrid Search Architecture: Implement effective combination of vector similarity with traditional database filtering for comprehensive search experiences
  4. Performance Optimization: Monitor search latency, throughput, and resource utilization while optimizing for production workloads
  5. Embedding Management: Establish versioning, caching, and update strategies for embedding vectors and model evolution
  6. Quality Assurance: Implement relevance testing, search quality metrics, and continuous improvement processes

Enterprise AI Application Architecture

Design vector search systems for enterprise-scale AI applications:

  1. Multi-Modal Integration: Support diverse content types including text, images, audio, and video with appropriate embedding strategies
  2. Real-Time Personalization: Implement dynamic user preference modeling with real-time recommendation updates
  3. Knowledge Management: Design comprehensive RAG pipelines for question answering, document retrieval, and content generation
  4. Scalability Planning: Architecture design for growing vector collections, user bases, and query volumes
  5. Security and Governance: Implement access controls, data privacy, and audit capabilities for enterprise compliance
  6. Monitoring and Analytics: Establish comprehensive observability for search performance, user satisfaction, and business impact

Conclusion

MongoDB Atlas Vector Search provides comprehensive AI-powered semantic similarity capabilities that enable sophisticated recommendation systems, knowledge management platforms, and intelligent content discovery applications through native vector operations, advanced hybrid search, and seamless integration with existing MongoDB infrastructure. The unified platform approach eliminates the complexity of managing separate vector databases while delivering enterprise-ready performance, security, and operational simplicity.

Key Atlas Vector Search benefits include:

  • Native Vector Operations: High-performance similarity search with multiple metrics integrated directly into MongoDB Atlas infrastructure
  • Hybrid Search Excellence: Sophisticated combination of semantic similarity with traditional database filtering for comprehensive search experiences
  • AI Application Ready: Built-in support for RAG pipelines, recommendation engines, and real-time personalization systems
  • Multi-Modal Capability: Native support for text, image, audio, and video embeddings within unified document structures
  • Enterprise Integration: Seamless integration with existing MongoDB security, monitoring, and operational infrastructure
  • SQL Accessibility: Familiar SQL-style vector operations through QueryLeaf for accessible AI application development

Whether you're building intelligent search platforms, recommendation systems, knowledge management applications, or conversational AI interfaces, MongoDB Atlas Vector Search with QueryLeaf's familiar SQL interface provides the foundation for scalable, high-performance AI applications.

QueryLeaf Integration: QueryLeaf automatically optimizes MongoDB Atlas Vector Search operations while providing SQL-familiar syntax for semantic similarity, recommendation algorithms, and RAG pipeline construction. Advanced vector operations, hybrid search strategies, and AI application patterns are seamlessly accessible through familiar SQL constructs, making sophisticated AI development approachable for SQL-oriented development teams.

The combination of Atlas Vector Search's powerful similarity capabilities with SQL-style AI operations makes it an ideal platform for applications requiring both advanced semantic understanding and familiar database interaction patterns, ensuring your AI applications can scale efficiently while delivering intelligent, personalized user experiences.

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