interface IEmbedding {
  createEmbedding(text: string, options?: any): any;
  createBatchEmbedding(text: string[], options?: any): any;
}

interface IVectorDatabase {
  listIndexes(options?: any): any;
  createIndex(options: any): any;
  deleteIndex(indexID: any, options?: any): any;
  editIndex(indexID: any, options?: any): any;
}

interface IVectorIndex {
  createRecord(record: any, options?: any): any;
  createRecords(records: any[], options?: any): any;
  getRecord(recordID: any, options?: any): any;
  getRecords(recordIDs: any[], options?: any): any;
  updateRecord(record: any, options?: any): any;
  updateRecords(records: any[], options?: any): any;
  deleteRecord(recordID: any, options?: any): any;
  deleteRecords(recordIDs: any[], options?: any): any;
}

type PageRecordsParams = {
  EntityID: string | number;        // The ID of the entity to get records from
  PageNumber: number;               // Page number (1-based)
  PageSize: number;                 // Number of records per page
  ResultType: "entity_object" | "simple" | "count_only";  // Type of result
  Filter?: string;                  // Optional SQL filter
}

import { VectorBase, PageRecordsParams } from '@memberjunction/ai-vectors';
import { BaseEntity } from '@memberjunction/core';

export class MyVectorProcessor extends VectorBase {
  async processEntityRecords(entityId: string): Promise<void> {
    // Get all entity records
    const records = await this.GetRecordsByEntityID(entityId);
    
    // Process each record
    for (const record of records) {
      // Your vector processing logic here
      console.log(`Processing ${record.EntityInfo.Name} record ${record.ID}`);
    }
  }
  
  async paginatedProcess(entityId: string): Promise<void> {
    // Process records page by page
    const params: PageRecordsParams = {
      EntityID: entityId,
      PageNumber: 1,
      PageSize: 100,
      ResultType: 'entity_object' as const
    };
    
    const records = await this.PageRecordsByEntityID<BaseEntity>(params);
    // Process paged records
    console.log(`Retrieved ${records.length} records`);
  }
}

import { IEmbedding } from '@memberjunction/ai-vectors';

export class OpenAIEmbedding implements IEmbedding {
  constructor(private apiKey: string) {}
  
  async createEmbedding(text: string): Promise<number[]> {
    // Implementation using OpenAI's embedding API
  }
  
  async createBatchEmbedding(texts: string[]): Promise<number[][]> {
    // Batch implementation
  }
}

import { VectorBase, PageRecordsParams } from '@memberjunction/ai-vectors';
import { BaseEntity } from '@memberjunction/core';
import { AIModelEntityExtended, VectorDatabaseEntity } from '@memberjunction/core-entities';

class EntityVectorizer extends VectorBase {
  async vectorizeEntities(entityId: string): Promise<void> {
    // Get AI model for embeddings (defaults to first embedding model if no ID provided)
    const embeddingModel: AIModelEntityExtended = this.GetAIModel();
    console.log(`Using embedding model: ${embeddingModel.Name}`);
    
    // Get vector database (defaults to first configured vector DB)
    const vectorDb: VectorDatabaseEntity = this.GetVectorDatabase();
    console.log(`Using vector database: ${vectorDb.Name}`);
    
    // Process records in pages for memory efficiency
    let pageNumber = 1;
    let hasMoreRecords = true;
    
    while (hasMoreRecords) {
      const params: PageRecordsParams = {
        EntityID: entityId,
        PageNumber: pageNumber,
        PageSize: 50,
        ResultType: 'entity_object' as const,
        Filter: "IsActive = 1"  // Optional: add custom filtering
      };
      
      const records = await this.PageRecordsByEntityID<BaseEntity>(params);
      hasMoreRecords = records.length === params.PageSize;
      pageNumber++;
      
      // Process the current page of records
      for (const record of records) {
        // Your vectorization logic here
        // Example: Generate embeddings for record content
      }
    }
  }
  
  async saveVectorizedEntity(entity: BaseEntity): Promise<boolean> {
    // SaveEntity automatically adds user context
    return await this.SaveEntity(entity);
  }
}

import { VectorBase } from '@memberjunction/ai-vectors';
import { CompositeKey } from '@memberjunction/core';

class AdvancedVectorProcessor extends VectorBase {
  async getSpecificRecords(entityId: string): Promise<void> {
    // Build composite keys for specific records
    const compositeKeys: CompositeKey[] = [
      {
        KeyValuePairs: [
          { FieldName: 'Status', Value: 'Active' },
          { FieldName: 'CategoryID', Value: '123' }
        ]
      },
      {
        KeyValuePairs: [
          { FieldName: 'Status', Value: 'Pending' },
          { FieldName: 'CategoryID', Value: '456' }
        ]
      }
    ];
    
    // This will generate: (Status = 'Active' AND CategoryID = '123') OR (Status = 'Pending' AND CategoryID = '456')
    const records = await this.GetRecordsByEntityID(entityId, compositeKeys);
    console.log(`Found ${records.length} matching records`);
  }
}

import { VectorBase } from '@memberjunction/ai-vectors';
import { AIEngine } from '@memberjunction/aiengine';
import { Metadata, RunView } from '@memberjunction/core';

// Your vector processing will have access to:
// - Entity metadata
// - AI models configuration
// - Vector database configuration
// - User context

try {
  const model = this.GetAIModel('specific-model-id');
} catch (error) {
  // Will throw if no embedding model is configured
  console.error('No AI Model Entity found');
}

try {
  const records = await this.GetRecordsByEntityID('invalid-id');
} catch (error) {
  // Will throw if entity ID doesn't exist
  console.error(`Entity with ID invalid-id not found.`);
}