vrin 0.8.1

Enterprise Hybrid RAG SDK with entity-centric extraction, zero-loss architecture, complete source attribution, constraint solver, temporal consistency, and multi-cloud deployment

VRIN Hybrid RAG SDK v0.7.0

Enterprise-grade Hybrid RAG system with hybrid cloud architecture, multi-hop constraint solver, temporal fact consistency, conversation state, user-defined AI specialization, and blazing-fast performance.

🏗️ Hybrid Cloud Architecture

VRIN supports two deployment models:

• General Users (vrin_ API keys): Cost-effective shared infrastructure
• Enterprise Users (vrin_ent_ API keys): 100% private infrastructure with data sovereignty

🛡️ Data Sovereignty Guarantee: Enterprise customer data NEVER leaves their cloud account

🚀 New in v0.7.0 - Multi-Hop Constraint Solver with Temporal Fact Consistency

• 🧩 LLM-BASED CONSTRAINT EXTRACTION - Intelligent identification of temporal, numerical, entity, comparison, and aggregation constraints
• ⏰ TEMPORAL FACT VALIDITY - Facts with temporal metadata (valid_from, valid_to, status, version)
• 🔄 AUTOMATIC CONFLICT RESOLUTION - Smart fact supersession with version history
• 📅 TEMPORAL FILTERING - Query facts valid at specific points in time or date ranges
• 🎯 MULTI-CONSTRAINT QUERIES - Handle complex queries with multiple simultaneous constraints
• 📊 FIRST-IN-INDUSTRY - First RAG system with explicit temporal fact validity tracking

What This Solves

• ✅ "What was Cadence stock value in 2010 and 2011?" - Multi-year temporal queries
• ✅ "Calculate percentage increase from 2010 to 2015" - Date range aggregations
• ✅ "Show revenue greater than $50M in Q2 2023" - Temporal + numerical constraints
• ✅ Automatic handling of conflicting facts (old values superseded by new ones)
• ✅ Complete fact history with version tracking

🚀 v0.6.0 Features - Conversation State & Context Maintenance

• 💬 CONVERSATION STATE - Multi-turn conversations with automatic context maintenance
• 🔄 Session Management - Stateful conversations like ChatGPT/Claude
• 🧠 Entity Tracking - Entities tracked across conversation turns
• 📝 Context Awareness - Natural follow-up questions with full context
• ⏱️ Session Persistence - 24-hour conversation sessions with auto-compression
• 🎯 Improved Retrieval - Better results using conversation context

🚀 v0.4.0 Features - Hybrid Cloud & Performance Breakthrough

• 🏗️ HYBRID CLOUD COMPLETE - Enterprise private infrastructure with data sovereignty
• 🔄 API Key Routing - vrin_ shared vs vrin_ent_ private infrastructure
• ☁️ Azure Integration - CosmosDB with Gremlin API for enterprise customers
• 🏢 Enterprise Portal - Organization, user, and API key management
• ⚡ Performance Revolution - Raw fact retrieval in <2s (96.3% faster than full analysis)
• 🚀 Dual-Speed Processing - Fast website display + comprehensive expert analysis
• 🧠 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 - 7 Lambda functions optimized (Python 3.12)
• 💾 Smart Storage - 40-60% reduction through intelligent deduplication
• 🔒 Enterprise Security - Bearer token auth, user isolation, compliance ready

🚀 Core Features

• 💬 Conversation State - Multi-turn conversations with automatic context maintenance (NEW v0.6.0)
• 🏗️ Hybrid Cloud Architecture - Customer choice of infrastructure (shared vs private)
• 🛡️ Data Sovereignty - Enterprise data stays in customer cloud account
• ⚡ 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 Portal - Complete organization and user management
• 📈 Enterprise-Ready - User isolation, authentication, and production scaling

📦 Installation

pip install vrin==0.7.0

🔧 Quick Start

from vrin import VRINClient

# For general users (shared infrastructure)
client = VRINClient(api_key="vrin_your_api_key")

# For enterprise users (private infrastructure with data sovereignty)
from vrin import VRINEnterpriseClient
enterprise_client = VRINEnterpriseClient(api_key="vrin_ent_your_enterprise_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 (NEW v0.7.0: temporal metadata)
result = client.insert(
    content="In 2010, Cadence stock was $100. In 2011, it increased to $150.",
    title="Financial Data with Temporal Facts"
)
print(f"✅ Extracted {result['facts_count']} facts with temporal metadata")
print(f"💾 Storage: {result['storage_details']}")
print(f"🔄 Conflicts handled: {result.get('storage_result', {}).get('updated_facts', 0)} superseded")

# STEP 3A: Fast fact retrieval for website display (v0.4.0)
raw_response = client.get_raw_facts_only("What are strategic insights?")
print(f"⚡ Lightning-fast retrieval: {raw_response['search_time']}")  # ~0.7-2s
print(f"📊 Facts found: {raw_response['total_facts']}")

# STEP 3B: Complete expert analysis for comprehensive reports
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"⚡ Full Analysis: {response['search_time']}")  # ~15-20s

# NEW v0.7.0: Temporal and constraint-based queries
temporal_response = client.query("What was Cadence stock value in 2010 and 2011?")
print(f"📅 Temporal Query Results:")
print(f"  Constraints identified: {temporal_response['constraints_applied']}")
print(f"  Temporal filtering applied: {temporal_response['temporal_filtering_applied']}")
print(f"  Facts before filtering: {temporal_response['facts_before_filtering']}")
print(f"  Facts after filtering: {temporal_response['facts_after_filtering']}")
print(f"  Summary: {temporal_response['summary'][:100]}...")

# Multi-constraint query example
complex_response = client.query("Calculate revenue percentage increase from Q2 2010 to Q4 2015")
print(f"🧩 Multi-Constraint Query:")
print(f"  Constraint types: {list(complex_response['constraints'].keys())}")
print(f"  Temporal range: {complex_response['constraints'].get('temporal', [])}")
print(f"  Aggregation: {complex_response['constraints'].get('aggregation', [])}")

# NEW v0.6.0: Multi-turn conversations with context
client.start_conversation()

response1 = client.continue_conversation("What was Cadence's 2010 stock value?")
print(f"Turn 1: {response1['summary'][:100]}...")

response2 = client.continue_conversation("What about 2011?")  # Context maintained!
print(f"Turn 2: {response2['summary'][:100]}...")

response3 = client.continue_conversation("Calculate the percentage increase")
print(f"Turn 3: {response3['summary'][:100]}...")

client.end_conversation()
print(f"Session: {response3['session_id']}, Total turns: {response3['conversation_turn']}")

📊 Performance & Validation (v0.7.0)

Production Performance

• ⚡ Raw Fact Retrieval: 0.7-2s (96.3% faster than full analysis)
• 🧠 Expert Analysis: 15-20s for comprehensive multi-hop reasoning
• ⏰ Constraint Extraction: ~200-500ms for LLM-based constraint identification (NEW v0.7.0)
• 📅 Temporal Filtering: ~50-100ms for fact validity filtering (NEW v0.7.0)
• 💬 Conversation State: ~50ms session creation, ~100ms context retrieval
• 📊 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: 7 Lambda functions optimized (Python 3.12), sub-second API response

Benchmark Validation (September 2025)

VRIN v0.7.0 validated against industry-standard RAG benchmarks:

Benchmark	v0.6.0 Accuracy	v0.7.0 Expected	Status
RGB (Noise Robustness)	97.9% ✅	97.9%	Core retrieval validated
FRAMES (Multi-hop)	28.6%	60%+ 🎯	Constraint solver improves multi-constraint queries
BEIR SciFact	22.2%	25%+	Scientific claim verification
RAGBench FinQA	11.1%	40%+ 🎯	Temporal + numerical constraint handling

Key Finding: v0.7.0 Multi-Hop Constraint Solver addresses the multi-constraint challenges identified in FRAMES and RAGBench FinQA. Temporal fact consistency enables accurate time-based queries.

v0.7.0 Constraint Solver Capabilities

• ✅ Temporal Constraints: Year, quarter, month, date ranges, relative time
• ✅ Numerical Constraints: Greater than, less than, between, specific values
• ✅ Entity Constraints: Specific entities, properties, relationships
• ✅ Comparison Constraints: A vs B, differences, changes over time
• ✅ Aggregation Constraints: Total, average, sum, percentage changes
• ✅ Multi-Constraint Queries: Handle 2-5 simultaneous constraints
• ✅ Temporal Filtering: Facts valid at specific points in time
• ✅ Conflict Resolution: Automatic fact supersession with version history

See: docs/BENCHMARK_TESTING_RESULTS.md for comprehensive analysis

🏗️ Hybrid Cloud Architecture

VRIN uses enterprise-grade Hybrid RAG with hybrid cloud architecture:

🔄 API Key Routing

• vrin_ keys → VRIN shared infrastructure (cost-effective)
• vrin_ent_ keys → Customer private infrastructure (data sovereignty)

📊 Database Support

• Neptune (AWS) - For general users and AWS enterprise deployments
• CosmosDB (Azure) - For Azure enterprise deployments with Gremlin API
• Automatic routing based on API key type and enterprise configuration

🏢 Enterprise Portal

• Organization and user management
• API key provisioning and management
• Infrastructure configuration (Azure/AWS)
• Usage monitoring and analytics

🏗️ System Flow

1. API Key Authentication - Routes to appropriate infrastructure
2. User Specialization - Custom AI experts defined by users
3. Enhanced Fact Extraction - Multi-cloud database storage
4. Multi-hop Reasoning - Cross-document synthesis with reasoning chains
5. Hybrid Retrieval - Graph traversal + vector similarity (36-50 facts)
6. Expert Synthesis - Domain-specific analysis using custom prompts
7. Production Infrastructure - 11 Lambda functions with hybrid routing
8. Enterprise Security - Bearer token auth, user isolation, compliance

🔐 Authentication & Setup

General Users (Shared Infrastructure)

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

client = VRINClient(api_key="vrin_your_api_key_here")

Enterprise Users (Private Infrastructure)

1. Contact VRIN Enterprise Sales for onboarding
2. Deploy VRIN infrastructure in your Azure/AWS account
3. Get your vrin_ent_ API key from enterprise portal
4. Configure your infrastructure via enterprise portal

enterprise_client = VRINEnterpriseClient(api_key="vrin_ent_your_enterprise_key")

🏢 Production Ready Features

🔄 Hybrid Cloud

• Data Sovereignty: Enterprise data never leaves customer infrastructure
• Multi-Cloud Support: AWS Neptune and Azure CosmosDB
• Intelligent Routing: Automatic infrastructure routing by API key type
• Enterprise Portal: Complete organization and user management

🧠 AI Capabilities

• Custom AI Experts: Define domain-specific reasoning for any field
• Multi-hop Analysis: Cross-document synthesis with evidence chains
• Working Graph Facts: Fixed Neptune/CosmosDB storage now retrieving real relationships
• Expert Validation: 8.5/10 performance against professional analysis

🏗️ Infrastructure

• Production APIs: Bearer token auth, 99.5% uptime, enterprise ready
• Smart Deduplication: 40-60% storage optimization with transparency
• Hybrid Database: Seamless Neptune/CosmosDB routing
• Enterprise Security: VPC isolation, private endpoints, compliance ready

🎯 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
• ✅ Hybrid cloud architecture - customer choice of infrastructure
• ✅ 100% data sovereignty - enterprise data never leaves customer infrastructure
• ✅ Multi-cloud support - AWS and Azure with seamless routing
• ✅ Enterprise portal - complete organization and user management
• ✅ Production-ready infrastructure with full authentication
• ✅ Temporal knowledge graphs with provenance and graceful fallback handling
• ✅ Resilient connectivity - Neptune/CosmosDB fallback ensures service continuity
• ✅ 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: September 30, 2025 - Production v0.7.0 with Multi-Hop Constraint Solver, Temporal Fact Consistency, Conversation State & Hybrid Cloud Architecture