vrin 0.3.4

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

VRIN Hybrid RAG SDK v0.3.3

Enterprise-grade Hybrid RAG system with user-defined AI specialization, multi-hop reasoning, and production-ready performance.

🚀 New in v0.3.3 - Production Ready

• 🧠 User-Defined Specialization - Create custom AI experts for any domain
• 🔗 Multi-Hop Reasoning - Cross-document synthesis with reasoning chains
• ⚡ Enhanced Graph Retrieval - Fixed Neptune storage, now finding 36-50 facts vs 0
• 🎯 Expert-Level Performance - 8.5/10 validation against professional analysis
• 🏗️ Production Infrastructure - 11 Lambda functions deployed on AWS
• 💾 Smart Storage - 40-60% reduction through intelligent deduplication
• 🔒 Enterprise Security - Bearer token auth, user isolation, compliance ready

🚀 Core Features

• ⚡ Hybrid RAG Architecture - Graph reasoning + Vector similarity search
• 🧠 User-Defined AI Experts - Customize reasoning for any domain
• 🔗 Multi-Hop Reasoning - Cross-document synthesis and pattern detection
• 📊 Advanced Fact Extraction - High-confidence structured knowledge extraction
• 🔍 Expert-Level Analysis - Professional-grade insights with reasoning chains
• 📈 Enterprise-Ready - User isolation, authentication, and production scaling

📦 Installation

pip install vrin==0.3.3

🔧 Quick Start

from vrin import VRINClient

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

# STEP 1: Define your custom AI expert
result = client.specialize(
    custom_prompt="You are a senior M&A legal partner with 25+ years experience...",
    reasoning_focus=["cross_document_synthesis", "causal_chains"],
    analysis_depth="expert"
)

# STEP 2: Insert knowledge with automatic fact extraction
result = client.insert(
    content="Complex M&A legal document content...",
    title="Strategic M&A Assessment"
)
print(f"✅ Extracted {result['facts_count']} facts")
print(f"💾 Storage: {result['storage_details']}")

# STEP 3: Query with expert-level reasoning
response = client.query("What are the strategic litigation opportunities?")
print(f"📝 Expert Analysis: {response['summary']}")
print(f"🔗 Multi-hop Chains: {response['multi_hop_chains']}")
print(f"📊 Cross-doc Patterns: {response['cross_document_patterns']}")
print(f"⚡ Performance: {response['search_time']}")

📊 Performance (v0.3.3 Production)

• Expert Queries: < 20s for multi-hop analysis with reasoning chains
• Graph Retrieval: Now finding 36-50 facts (fixed from 0 facts)
• Multi-hop Reasoning: 1-10 reasoning chains per complex query
• Cross-document Patterns: 2+ patterns detected per expert analysis
• Storage Efficiency: 40-60% reduction through intelligent deduplication
• Expert Validation: 8.5/10 performance on professional M&A analysis
• Infrastructure: 11 Lambda functions, sub-second API response

🏗️ Architecture

VRIN uses enterprise-grade Hybrid RAG with user-defined specialization:

1. User Specialization - Custom AI experts defined by users
2. Enhanced Fact Extraction - Fixed Neptune storage with proper edge relationships
3. Multi-hop Reasoning - Cross-document synthesis with reasoning chains
4. Hybrid Retrieval - Graph traversal + vector similarity (36-50 facts)
5. Expert Synthesis - Domain-specific analysis using custom prompts
6. Production Infrastructure - 11 Lambda functions on AWS
7. Enterprise Security - Bearer token auth, user isolation, compliance

🔐 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")

🏢 Production Ready Features

• Custom AI Experts: Define domain-specific reasoning for any field
• Multi-hop Analysis: Cross-document synthesis with evidence chains
• Working Graph Facts: Fixed Neptune storage now retrieving real relationships
• Expert Validation: 8.5/10 performance against professional analysis
• Production APIs: Bearer token auth, 99.5% uptime, enterprise ready
• Smart Deduplication: 40-60% storage optimization with transparency

🎯 Use Cases

• Legal Analysis: M&A risk assessment, contract review, litigation strategy
• Financial Research: Investment analysis, market research, due diligence
• Technical Documentation: API analysis, architecture review, compliance
• Strategic Planning: Competitive analysis, market intelligence, decision support

🌟 What Makes VRIN Different

vs. Basic RAG Systems

• ✅ Multi-hop reasoning across knowledge graphs
• ✅ User-defined specialization instead of rigid templates
• ✅ Cross-document synthesis with pattern detection
• ✅ Expert-level performance validated against professionals

vs. Enterprise AI Platforms

• ✅ Complete customization - users define their own AI experts
• ✅ Production-ready AWS infrastructure with full authentication
• ✅ Temporal knowledge graphs with provenance tracking
• ✅ Open SDK with transparent operations and full API access

📄 License

MIT License - see LICENSE file for details.

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Built with ❤️ by the VRIN Team

Last updated: August 13, 2025 - Production v0.3.3