# VRIN Hybrid RAG SDK v0.3.4
Enterprise-grade Hybrid RAG system with user-defined AI specialization, multi-hop reasoning, and blazing-fast performance optimization.
## 🚀 New in v0.3.4 - Performance Breakthrough
- ⚡ **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
- ⚡ **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
```bash
pip install vrin==0.3.4
```
## 🔧 Quick Start
```python
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 3A: Fast fact retrieval for website display (NEW v0.3.4)
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
```
## 📊 Performance (v0.3.4 Breakthrough)
- **⚡ Raw Fact Retrieval**: 0.7-2s (96.3% faster than full analysis)
- **🧠 Expert Analysis**: 15-20s for comprehensive multi-hop reasoning
- **📊 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
## 🏗️ 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](https://console.vrin.ai) (when available)
2. Get your API key from account dashboard
3. Use the API key to initialize your client
```python
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 and graceful fallback handling
- ✅ **Resilient connectivity** - Neptune fallback ensures service continuity
- ✅ **Open SDK** with transparent operations and full API access
## 📄 License
MIT License - see LICENSE file for details.
---
**Built with ❤️ by the VRIN Team**
*Last updated: August 13, 2025 - Production v0.3.3*
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"description": "# VRIN Hybrid RAG SDK v0.3.4\n\nEnterprise-grade Hybrid RAG system with user-defined AI specialization, multi-hop reasoning, and blazing-fast performance optimization.\n\n## \ud83d\ude80 New in v0.3.4 - Performance Breakthrough\n\n- \u26a1 **Performance Revolution** - Raw fact retrieval in <2s (96.3% faster than full analysis)\n- \ud83d\ude80 **Dual-Speed Processing** - Fast website display + comprehensive expert analysis \n- \ud83e\udde0 **User-Defined Specialization** - Create custom AI experts for any domain\n- \ud83d\udd17 **Multi-Hop Reasoning** - Cross-document synthesis with reasoning chains\n- \ud83d\udcca **Enhanced Graph Retrieval** - Fixed Neptune storage, now finding 36-50 facts vs 0\n- \ud83c\udfaf **Expert-Level Performance** - 8.5/10 validation against professional analysis\n- \ud83c\udfd7\ufe0f **Production Infrastructure** - 7 Lambda functions optimized (Python 3.12)\n- \ud83d\udcbe **Smart Storage** - 40-60% reduction through intelligent deduplication\n- \ud83d\udd12 **Enterprise Security** - Bearer token auth, user isolation, compliance ready\n\n## \ud83d\ude80 Core Features\n\n- \u26a1 **Hybrid RAG Architecture** - Graph reasoning + Vector similarity search\n- \ud83e\udde0 **User-Defined AI Experts** - Customize reasoning for any domain\n- \ud83d\udd17 **Multi-Hop Reasoning** - Cross-document synthesis and pattern detection\n- \ud83d\udcca **Advanced Fact Extraction** - High-confidence structured knowledge extraction\n- \ud83d\udd0d **Expert-Level Analysis** - Professional-grade insights with reasoning chains\n- \ud83d\udcc8 **Enterprise-Ready** - User isolation, authentication, and production scaling\n\n## \ud83d\udce6 Installation\n\n```bash\npip install vrin==0.3.4\n```\n\n## \ud83d\udd27 Quick Start\n\n```python\nfrom vrin import VRINClient\n\n# Initialize with your API key\nclient = VRINClient(api_key=\"your_vrin_api_key\")\n\n# STEP 1: Define your custom AI expert\nresult = client.specialize(\n custom_prompt=\"You are a senior M&A legal partner with 25+ years experience...\",\n reasoning_focus=[\"cross_document_synthesis\", \"causal_chains\"],\n analysis_depth=\"expert\"\n)\n\n# STEP 2: Insert knowledge with automatic fact extraction\nresult = client.insert(\n content=\"Complex M&A legal document content...\",\n title=\"Strategic M&A Assessment\"\n)\nprint(f\"\u2705 Extracted {result['facts_count']} facts\")\nprint(f\"\ud83d\udcbe Storage: {result['storage_details']}\")\n\n# STEP 3A: Fast fact retrieval for website display (NEW v0.3.4)\nraw_response = client.get_raw_facts_only(\"What are strategic insights?\")\nprint(f\"\u26a1 Lightning-fast retrieval: {raw_response['search_time']}\") # ~0.7-2s\nprint(f\"\ud83d\udcca Facts found: {raw_response['total_facts']}\")\n\n# STEP 3B: Complete expert analysis for comprehensive reports\nresponse = client.query(\"What are the strategic litigation opportunities?\")\nprint(f\"\ud83d\udcdd Expert Analysis: {response['summary']}\")\nprint(f\"\ud83d\udd17 Multi-hop Chains: {response['multi_hop_chains']}\")\nprint(f\"\ud83d\udcca Cross-doc Patterns: {response['cross_document_patterns']}\")\nprint(f\"\u26a1 Full Analysis: {response['search_time']}\") # ~15-20s\n```\n\n## \ud83d\udcca Performance (v0.3.4 Breakthrough)\n\n- **\u26a1 Raw Fact Retrieval**: 0.7-2s (96.3% faster than full analysis)\n- **\ud83e\udde0 Expert Analysis**: 15-20s for comprehensive multi-hop reasoning\n- **\ud83d\udcca Graph Retrieval**: Now finding 36-50 facts (fixed from 0 facts)\n- **\ud83d\udd17 Multi-hop Reasoning**: 1-10 reasoning chains per complex query \n- **\ud83d\udccb Cross-document Patterns**: 2+ patterns detected per expert analysis\n- **\ud83d\udcbe Storage Efficiency**: 40-60% reduction through intelligent deduplication\n- **\ud83c\udfaf Expert Validation**: 8.5/10 performance on professional M&A analysis\n- **\ud83c\udfd7\ufe0f Infrastructure**: 7 Lambda functions optimized (Python 3.12), sub-second API response\n\n## \ud83c\udfd7\ufe0f Architecture\n\nVRIN uses enterprise-grade Hybrid RAG with user-defined specialization:\n\n1. **User Specialization** - Custom AI experts defined by users\n2. **Enhanced Fact Extraction** - Fixed Neptune storage with proper edge relationships\n3. **Multi-hop Reasoning** - Cross-document synthesis with reasoning chains\n4. **Hybrid Retrieval** - Graph traversal + vector similarity (36-50 facts)\n5. **Expert Synthesis** - Domain-specific analysis using custom prompts\n6. **Production Infrastructure** - 11 Lambda functions on AWS\n7. **Enterprise Security** - Bearer token auth, user isolation, compliance\n\n## \ud83d\udd10 Authentication & Setup\n\n1. 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