vrin 0.2.0

Enterprise-grade Hybrid RAG SDK with optimized knowledge graph traversal and vector search

VRIN Hybrid RAG SDK v0.2.0

Enterprise-grade Hybrid RAG SDK with optimized knowledge graph traversal and vector search.

🚀 Features

• ⚡ Optimized Hybrid RAG - Combines graph knowledge with vector similarity search
• 🧠 Intelligent Entity Extraction - AI-powered entity recognition and graph traversal
• 📊 Advanced Fact Extraction - High-confidence fact extraction with scoring
• 🔍 Sub-2s Query Response - Lightning-fast hybrid retrieval performance
• 🎯 AI-Powered Summaries - Natural language answers with cited facts
• 📈 Enterprise-Ready - User isolation, authentication, and production scaling

📦 Installation

pip install vrin

🔧 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
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")
# Output: ✅ Extracted 7 facts

# Query with intelligent hybrid search
response = client.query("Who created Python and when?")
print(f"📝 Answer: {response['summary']}")
print(f"⚡ Performance: {response['total_facts']} facts in {response['search_time']}")

# Output: 
# 📝 Answer: Python was created by Guido van Rossum in 1991. It is a high-level programming language known for its simplicity and readability...
# ⚡ Performance: 7 facts in 1.60s

🔍 Advanced Usage

Raw Results Access

# Get detailed graph facts and vector results
details = client.get_raw_results("Python programming language")

print("📊 Graph Facts:")
for fact in details['graph_facts']:
    confidence = fact['confidence']
    print(f"  • {fact['subject']} {fact['predicate']} {fact['object']} (confidence: {confidence:.2f})")

print(f"\n🔎 Vector Chunks: {len(details['vector_results'])}")
print(f"🧠 Entities Found: {details['entities_found']}")

Knowledge Graph Visualization

# Get knowledge graph data for visualization
graph = client.get_knowledge_graph()
print(f"📈 Graph: {len(graph['data']['nodes'])} nodes, {len(graph['data']['edges'])} edges")

Batch Processing

# Insert multiple documents efficiently
documents = [
    {"content": "Machine learning is a subset of AI...", "title": "ML Basics"},
    {"content": "Neural networks consist of layers...", "title": "Neural Networks"},
    {"content": "Deep learning uses multiple layers...", "title": "Deep Learning"}
]

for doc in documents:
    result = client.insert(doc['content'], doc['title'])
    print(f"Processed: {doc['title']} -> {result['facts_extracted']} facts")

🎯 API Reference

VRINClient

__init__(api_key: str)

Initialize the VRIN client with your API key.

insert(content: str, title: str = None, tags: List[str] = None) -> Dict

Insert knowledge into the system with automatic fact extraction.

Returns:

• success: Whether the operation succeeded
• facts_extracted: Number of facts extracted
• chunk_id: Unique identifier for the content
• message: Status message

query(query: str, include_summary: bool = True, include_raw_results: bool = False) -> Dict

Query the knowledge base with optimized hybrid retrieval.

Parameters:

• query: Search query string
• include_summary: Include AI-generated summary (default: True)
• include_raw_results: Include detailed graph facts and vector results

Returns:

• success: Whether the query succeeded
• summary: AI-generated comprehensive answer
• search_time: Query execution time
• entities_found: Extracted entities from the query
• total_facts: Number of graph facts found
• total_chunks: Number of vector chunks found

get_raw_results(query: str) -> Dict

Get complete raw results including graph facts and vector chunks.

get_knowledge_graph() -> Dict

Get knowledge graph visualization data with nodes and edges.

🔐 Authentication

1. Sign up at VRIN Console
2. Create a new API key
3. Use the API key to initialize your client

client = VRINClient(api_key="vrin_your_api_key_here")

🏗️ Architecture

VRIN uses a sophisticated Hybrid RAG architecture:

1. Fact Extraction - LLM-powered extraction of structured facts
2. Graph Storage - Facts stored as a knowledge graph in Neptune
3. Vector Storage - Semantic embeddings in OpenSearch
4. Hybrid Retrieval - Combines graph traversal + vector similarity
5. Result Fusion - Intelligent ranking and result combination
6. AI Summarization - Natural language response generation

📊 Performance

• Fact Extraction: ~7-10 facts per 100 words
• Query Response: Sub-2s for typical queries
• Confidence Scoring: 0.85-0.95 for high-quality facts
• Scalability: Handles millions of facts per user

🛠️ Requirements

• Python 3.8+
• Active internet connection
• VRIN API key

📄 License

MIT License - see LICENSE file for details.

🤝 Support

• 📧 Email: [e-mail]
• 📚 Documentation: https://docs.vrin.ai
• 🐛 Issues: https://github.com/vrin-ai/vrin-sdk/issues

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Built with ❤️ by the VRIN Team