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RAG Systems & Semantic Search

Gedank Rayze builds enterprise-grade Retrieval-Augmented Generation (RAG) systems that transform how organizations access and utilize their knowledge. Our hybrid search implementations combine the precision of keyword search with the semantic understanding of AI embeddings.

What is RAG?

Retrieval-Augmented Generation connects Large Language Models to your organization's data, enabling AI to provide accurate, contextual responses grounded in your specific knowledge base—not just general training data.

Why RAG Matters

  • Accuracy - Responses based on your actual documents and data
  • Freshness - Access up-to-date information without model retraining
  • Transparency - Citation of sources for every response
  • Control - Keep sensitive data within your infrastructure

Vector Databases

We implement RAG systems on leading vector database platforms:

Qdrant

High-performance vector similarity search:

  • Rust-based for exceptional performance
  • Filtering with payload support
  • Distributed deployment options
  • On-premise or cloud hosting

Weaviate

AI-native vector database:

  • GraphQL and REST APIs
  • Built-in vectorization modules
  • Hybrid search out of the box
  • Multi-tenancy support

AstraDB (DataStax)

Enterprise vector database:

  • Built on Apache Cassandra
  • Serverless deployment option
  • Global distribution
  • Enterprise security features

ArangoDB

Multi-model with vector support:

  • Graph + document + vector in one database
  • AQL for complex queries
  • Foxx Microservices integration
  • Native clustering

Hybrid Search Architecture

Our signature approach combines multiple search strategies:

Dense Vectors (Semantic)

  • Capture meaning and context
  • Handle synonyms and paraphrasing
  • Multi-language understanding
  • Powered by modern embedding models

Sparse Vectors (Lexical)

  • Exact keyword matching
  • Domain-specific terminology
  • Product codes and identifiers
  • SPLADE and BM25 implementations

Reciprocal Rank Fusion

  • Combine results from multiple strategies
  • Optimal relevance ranking
  • Configurable weighting
  • Best-of-both-worlds results

Embedding Models

OpenAI Embeddings

  • text-embedding-4 - Latest generation, highest quality
  • text-embedding-3-large - Production workhorse
  • Multilingual support

Open Source Models

  • BGE - BAAI General Embeddings
  • E5 - Microsoft's embedding models
  • Instructor - Task-specific embeddings
  • Jina - Long-context embeddings

Local Deployment

  • Ollama - Run embeddings locally
  • HuggingFace - Self-hosted inference
  • Privacy-preserving options

Document Processing

Ingestion Pipeline

  • PDF extraction - Text, tables, images
  • Office documents - Word, Excel, PowerPoint
  • Web content - HTML, markdown
  • Structured data - JSON, CSV, databases

Chunking Strategies

  • Semantic chunking based on content
  • Sliding window with overlap
  • Document structure preservation
  • Parent-child relationships

Metadata Enrichment

  • Automatic entity extraction
  • Topic classification
  • Language detection
  • Custom attribute tagging

Advanced RAG Patterns

Query Enhancement

  • Query expansion - Add related terms
  • Hypothetical document embeddings - HyDE
  • Multi-query - Generate query variations
  • Step-back prompting - Abstract queries

Retrieval Optimization

  • Reranking - Cross-encoder scoring
  • Contextual compression - Extract relevant passages
  • Self-query - Metadata filtering from natural language
  • Ensemble retrieval - Multiple retriever strategies

Response Generation

  • Citation linking - Source attribution
  • Confidence scoring - Answer reliability
  • Fallback strategies - Graceful handling of gaps
  • Streaming responses - Real-time output

Proven Implementations

ArangoDB Hybrid Search

Internal R&D project implementing:

  • Combined dense and sparse embeddings
  • Multilingual document processing
  • Cross-instance data synchronization
  • Production-ready chunking strategies

WolfGPT (WOLF GmbH)

Enterprise knowledge system with:

  • Multi-LLM integration
  • Intent analysis and routing
  • Tool-calling capabilities
  • Complex data processing

Pharmaceutical Research

RAG systems for drug development:

  • Clinical document analysis
  • Healthcare professional insights
  • Regulatory compliance checking
  • Real-world evidence extraction

Integration Frameworks

LangChain

  • Document loaders and text splitters
  • Vector store integrations
  • Chain composition
  • Memory management

LlamaIndex

  • Data connectors
  • Index structures
  • Query engines
  • Response synthesizers

Haystack

  • Production-ready pipelines
  • Custom components
  • Evaluation tools
  • REST API serving

Performance & Quality

Evaluation Metrics

  • Retrieval - Precision, recall, MRR, NDCG
  • Generation - RAGAS, faithfulness, relevance
  • End-to-end - Answer accuracy, user satisfaction

Optimization

  • Query latency monitoring
  • Index optimization
  • Caching strategies
  • Cost management