How It Works
Your documents become searchable knowledge for any AI assistant
How RAG Actually Works
Retrieval-Augmented Generation (RAG) is not just data storage; it's an intelligent retrieval system that understands the semantic meaning of your content.
The RAG Pipeline
- 1Document Processing: Your PDFs are parsed and split into 200-token chunks with 20-token overlap to preserve context at boundaries.
- 2Embedding Generation: Each chunk is transformed into a 768-dimensional vector using an advanced multilingual embedding model, capturing semantic meaning across 100+ languages.
- 3Vector Storage: Embeddings are stored in PostgreSQL with pgvector extension, indexed using HNSW (Hierarchical Navigable Small World) for sub-100ms retrieval.
- 4Hybrid Search: Queries use both vector similarity (semantic) and PostgreSQL full-text search (keyword matching) for optimal recall.
- 5Reranking: An advanced reranking service refines top-k results with cross-encoder model, improving precision by 15-30% for complex queries.
What Makes awRAG Particularly Sophisticated
Advanced Embedding Model
- • Advanced multilingual embedding model (1024 dimensions)
- • 100+ languages with cross-language search capability
- • Trained on 1B English + 500M non-English pairs
- • Float32 precision for maximum accuracy
Dual-Index Vector Strategy
- • HNSW (primary): <100ms query time, 95%+ recall
- • IVFFlat (fallback): Ensures query availability
- • M=16, ef_construction=64 for optimal performance
- • Cosine similarity for normalized embeddings
Industry-Standard Hybrid Search
- • Vector similarity for semantic understanding
- • PostgreSQL full-text for keyword matching
- • Universal language support (no detection overhead)
- • Same approach as Pinecone, Weaviate, Elasticsearch
Production Optimizations
- • Token-based chunking (gpt-tokenizer, cl100k_base)
- • Batch embedding (up to 96 chunks per API call)
- • Memory-efficient processing (<100MB for 50MB PDFs)
- • Automatic deduplication (hash-based detection)
What Makes awRAG Special
Cross-Language Semantic Retrieval
Ask in English, get answers from German docs. Search in Chinese, find French content. 100+ languages with true cross-language understanding that competitors don't offer.
Upload Once, Search from ANY AI
Upload your documents once, and every AI assistant (Claude, GPT, Gemini, future models) can instantly search through all of them. With Google Drive, iCloud & Co, you're locked inside their ecosystem with basic file access.
Lightning Fast
Get answers in under 1 second, even across millions of documents. Production-ready performance.
Enterprise Security
Your documents are protected with enterprise-grade security and SOC 2-compliant audit logging.
✅ awRAG: True cross-language search 🌍
Ask in English, get answers from German docs. Search in Chinese, find French content. 100+ languages.
❌ Google Drive, iCloud & Co: Keyword-only search
No understanding of content. No cross-language capability. Files you open and read yourself.
Google Drive, iCloud & Co store bytes:
- • Files you open and read yourself
- • Keyword-only search, no understanding of content
- • Locked inside their ecosystem
- • Data scanned, sold, or handed over
awRAG stores meaning — Knowledge Storage:
- • Direct, cited answers from your documents
- • Semantic search that understands what you mean
- • Available everywhere - Gmail, Slack, Docs, any AI assistant
- • EU servers only, never scanned or sold
Why This Matters for You
Google Drive stores bytes. awRAG stores meaning. This is the technology behind Knowledge Storage — turning your documents into answers, context, and citations, accessible from anywhere on the web, all on EU servers that never scan or sell your data.
Performance: ~700ms average query time • Scalability: Millions of documents
Security: Enterprise-grade RLS + OAuth 2.0 • Compliance: SOC 2 audit logging