vrin 0.3.2

Enhanced Hybrid RAG SDK with multi-hop reasoning, cross-document synthesis, and expert-level analysis capabilities

VRIN Hybrid RAG SDK v0.2.3

Optimized Hybrid RAG SDK with smart deduplication, enhanced fact extraction, and competitive performance.

🚀 New in v0.2.3 - Latest Optimizations

• 🎯 Smart Deduplication - Prevents duplicate facts and chunks, reduces storage by 40-60%
• ⚡ Enhanced Performance - Sub-3s queries with 7+ combined graph + vector results
• 🧠 Improved Fact Quality - 0.8+ confidence facts with self-updating system
• 💾 Storage Optimization - Only stores unique, fact-rich content with transparency
• 🔄 Self-Improving - Higher confidence facts automatically update existing ones
• 📊 Competitive Edge - Outperforms pure RAG and basic GraphRAG systems
• 🔧 Updated Import - Now use from vrin import VRINClient

🚀 Core Features

• ⚡ Hybrid RAG Architecture - Graph reasoning + Vector similarity search
• 🧠 Intelligent Entity Matching - AI-powered compound entity recognition
• 📊 Advanced Fact Extraction - High-confidence structured knowledge extraction
• 🔍 Sub-3s Query Response - Optimized retrieval with comprehensive coverage
• 🎯 AI-Powered Summaries - Natural language answers with cited sources
• 📈 Enterprise-Ready - User isolation, authentication, and production scaling

📦 Installation

pip install vrin==0.2.3

🔧 Quick Start

from vrin import VRINClient

# Initialize with your API key
client = VRINClient(api_key="your_vrin_api_key")

# Insert knowledge with automatic fact extraction and optimization
result = client.insert(
    content="Python is a high-level programming language created by Guido van Rossum in 1991. It emphasizes code readability and supports multiple programming paradigms.",
    title="Python Programming Language",
    tags=["programming", "python", "language"]
)

print(f"✅ Extracted {result['facts_extracted']} facts")
print(f"📦 Chunk stored: {result['chunk_stored']}")
print(f"💾 Storage details: {result['storage_details']}")

# Query with intelligent hybrid search
response = client.query("Who created Python and when?")
print(f"📝 Answer: {response['summary']}")
print(f"⚡ Performance: {response['total_facts']} graph + {response['total_chunks']} vector = {response['combined_results']} results")
print(f"🔍 Query time: {response['search_time']}")

📊 Performance (v0.2.3 Optimized)

• Fact Extraction: 3-8 high-quality facts per insertion (0.8+ confidence)
• Query Response: Sub-3s with 7+ combined graph + vector results
• Hybrid Coverage: 2-5 graph facts + 3-5 vector chunks per query
• Storage Efficiency: 40-60% reduction through smart deduplication
• Self-Improvement: Facts automatically update with higher confidence versions
• Competitive Advantage: Outperforms single-method RAG systems

🏗️ Architecture

VRIN uses a sophisticated Hybrid RAG architecture:

1. Smart Fact Extraction - LLM-powered extraction with deduplication
2. Graph Storage - Facts stored as knowledge graph in Neptune
3. Vector Storage - Semantic embeddings in OpenSearch with optimization
4. Hybrid Retrieval - Combines graph traversal + vector similarity
5. Result Fusion - Intelligent ranking and result combination
6. AI Summarization - Natural language response generation
7. Storage Optimization - Prevents duplicates and optimizes efficiency

🔐 Authentication & Setup

1. Sign up at VRIN Console (when available)
2. Get your API key from account dashboard
3. Use the API key to initialize your client

client = VRINClient(api_key="vrin_your_api_key_here")

📄 License

MIT License - see LICENSE file for details.

📈 Latest Updates (August 08, 2025)

• Smart Deduplication (40-60% storage reduction)
• Storage Transparency with detailed explanations
• Content similarity detection
• High-confidence fact extraction (0.8+)
• Sub-3s hybrid query performance
• Enhanced entity matching with compound entities

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Built with ❤️ by the VRIN Team

Last updated: August 08, 2025 (Auto-generated)