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This package provides a complete solution for building RAG systems with Supabase and OpenAI.\n\n## Features\n\n- **Document Indexing**: Process and index documents from Supabase storage into vector databases\n- **Context-Aware Vectors**: Generate context-aware vector embeddings for improved retrieval quality\n- **Memory-Efficient Processing**: Handles large documents without running into memory limitations\n- **Query Classification**: Classify user queries into predefined categories\n- **Question Answering**: Generate accurate answers from your document collection\n- **FAQ Generation**: Automatically generate FAQs from your documents\n\n## Installation\n\n```bash\nnpm install @coworker-agency/rag\n```\n\n## Quick Start\n\n```javascript\nimport { init } from '@coworker-agency/rag';\n\n// Initialize the library with your configuration\nconst rag = init({\n  supabaseUrl: 'https://your-project.supabase.co',\n  supabaseKey: 'your-supabase-key',\n  supabaseSecretKey: 'your-supabase-service-role-key',\n  supabaseBucket: 'documents',\n  tableName: 'vector_documents',\n  openaiApiKey: 'your-openai-api-key',\n  openaiLlmModel: 'gpt-4o',\n  openaiEmbeddingModel: 'text-embedding-3-small'\n});\n\n// Process documents from Supabase storage\nconst result = await rag.processDocuments();\nconsole.log(`Processed ${result.processed} documents`);\n\n// Search for relevant information\nconst faqs = await rag.searchFaq('What is context-aware retrieval?', 3);\nconsole.log(faqs.questions);\n\n// Get specific answer to a question\nconst answer = await rag.getAnswer('How do vector embeddings work?');\nconsole.log(answer);\n```\n\n## API Reference\n\n### init(options)\n\nInitializes the RAG library with configuration options.\n\n```javascript\nconst rag = init({\n  supabaseUrl,         // Supabase project URL (required)\n  supabaseKey,         // Supabase public key (optional if supabaseSecretKey is provided)\n  supabaseSecretKey,   // Supabase service role key (required)\n  supabaseBucket,      // Supabase storage bucket name (required)\n  tableName,           // Vector store table name (default: 'vector_documents')\n  openaiApiKey,        // OpenAI API key (required)\n  openaiLlmModel,      // OpenAI LLM model (default: 'gpt-4o')\n  openaiEmbeddingModel // OpenAI Embedding model (default: 'text-embedding-3-small')\n});\n```\n\n### processDocuments(options)\n\nProcesses documents from Supabase storage and indexes them into the vector store.\n\n```javascript\nconst result = await rag.processDocuments({\n  batchSize: 10,        // Number of files to process in parallel\n  maxFileSize: 20971520, // 20MB max file size\n  skipExisting: true     // Skip already indexed files\n});\n```\n\n### searchFaq(query, limit)\n\nSearch vector store and generate FAQ responses based on the query.\n\n```javascript\nconst faqs = await rag.searchFaq('How do I implement RAG?', 5);\n// Returns { questions: [{ question, answer, links }] }\n```\n\n### getAnswer(question)\n\nGet a specific answer to a question using the vector store.\n\n```javascript\nconst result = await rag.getAnswer('What are embeddings?');\n// Returns { question, answer: { text, links } }\n```\n\n### queryClassifier(query, classifications, options)\n\nClassify a query into one of the provided categories.\n\n```javascript\nconst category = await rag.queryClassifier(\n  'How do I reset my password?',\n  [\n    { category: 'account', description: 'Account management questions', temperature: 0.8 },\n    { category: 'technical', description: 'Technical or implementation questions', temperature: 0.7 },\n    { category: 'billing', description: 'Billing or payment questions', temperature: 0.6 }\n  ],\n  { memory: ['Previous user message'] }\n);\n```\n\n### getContextAwareVectors(documentContent, options)\n\nGenerate context-aware vectors from document content.\n\n```javascript\nconst vectors = await rag.getContextAwareVectors(documentText, {\n  chunkSize: 1000,\n  chunkOverlap: 200\n});\n// Returns array of { context, content, vector }\n```\n\n## Memory-Efficient Processing\n\nThis package includes special handling for large documents to avoid memory issues:\n\n- Automatic detection of large files (>100K chars)\n- Split processing into manageable chunks\n- Memory-efficient batch processing\n- Optional LLM refinement skipping for very large files\n- Document summarization and context extraction\n\n## Supabase Setup\n\nThis package requires a properly configured Supabase instance with:\n\n1. A vector-enabled table (e.g., `vector_documents`)\n2. Storage bucket for document files\n3. Optional RPC function for vector search\n\nExample SQL for creating the vector table:\n\n```sql\n-- Enable pgvector extension\nCREATE EXTENSION IF NOT EXISTS vector;\n\n-- Create table for document vectors\nCREATE TABLE vector_documents (\n  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n  content TEXT NOT NULL,\n  metadata JSONB,\n  embedding VECTOR(1536),  -- For text-embedding-3-small (1536 dimensions)\n  created_at TIMESTAMPTZ DEFAULT NOW()\n);\n\n-- Create function for similarity search\nCREATE OR REPLACE FUNCTION match_documents(\n  query_embedding VECTOR(1536),\n  match_threshold FLOAT,\n  match_count INT\n) RETURNS TABLE (\n  id UUID,\n  content TEXT,\n  metadata JSONB,\n  similarity FLOAT\n) LANGUAGE SQL STABLE AS $$\n  SELECT\n    id,\n    content,\n    metadata,\n    1 - (embedding <=> query_embedding) AS similarity\n  FROM vector_documents\n  WHERE 1 - (embedding <=> query_embedding) > match_threshold\n  ORDER BY similarity DESC\n  LIMIT match_count;\n$$;\n```\n\n## License\n\nMIT","readmeFilename":"README.md","author":{"name":"Coworker Agency"}}