vrin 0.1.5

AI-powered knowledge management and retrieval system

VRIN Hybrid RAG SDK

A powerful Python SDK for interacting with the VRIN Hybrid RAG (Retrieval Augmented Generation) system. Built with NVIDIA-inspired hybrid search capabilities, combining dense vector search, sparse keyword search, and graph-based context enhancement.

🚀 Features

• Hybrid RAG Search: Combines dense vector and sparse keyword search for optimal results
• Graph-Based Context: Enhances results with knowledge graph relationships
• Asynchronous Processing: Submit documents and process them in the background
• Batch Operations: Process multiple documents efficiently
• Real-time Queries: Get instant search results with sub-2-second latency
• Type Safety: Full type hints and data validation
• Error Handling: Comprehensive error handling with custom exceptions

📦 Installation

pip install vrin

🔑 Quick Start

1. Get Your API Key

First, you'll need to get an API key from VRIN. Contact us at [e-mail] to get started.

2. Basic Usage

from vrin import VRINClient

# Initialize the client
client = VRINClient(api_key="your_api_key_here")

# Insert knowledge into the system
job = client.insert(
    content="Machine learning is a subset of artificial intelligence that enables computers to learn and make decisions without being explicitly programmed.",
    title="Introduction to Machine Learning",
    tags=["AI", "ML", "technology"],
    source="vrin-docs"
)

# Or insert plain text quickly
job = client.insert_text("Python is a programming language created by Guido van Rossum.")

# Wait for processing to complete
client.wait_for_job(job.job_id)

# Query the knowledge base
results = client.query("What is machine learning?")

# Print results
for result in results:
    print(f"Content: {result.content}")
    print(f"Score: {result.score}")
    print(f"Source: {result.title}")
    print("---")

3. Advanced Usage

from vrin import VRINClient, Document

# Initialize client
client = VRINClient(api_key="your_api_key_here")

# Create multiple documents
documents = [
    Document(
        content="Python is a high-level programming language known for its simplicity and readability.",
        title="Python Programming",
        tags=["programming", "python", "language"],
        source="tutorial"
    ),
    Document(
        content="Data science combines statistics, programming, and domain expertise to extract insights from data.",
        title="Data Science Fundamentals",
        tags=["data-science", "statistics", "analytics"],
        source="course"
    )
]

# Insert knowledge items in batch
jobs = client.batch_insert(documents)

# Wait for all jobs to complete
completed_jobs = client.batch_wait_for_jobs([job.job_id for job in jobs])

# Query with different search types
sparse_results = client.query("programming language", search_type="sparse")
hybrid_results = client.query("data analysis", search_type="hybrid")
dense_results = client.query("machine learning algorithms", search_type="dense")

# Get personalized results
user_results = client.query("python programming", user_id="user123")

📚 API Reference

VRINClient

The main client class for interacting with the VRIN system.

Methods

insert(content, title=None, tags=None, source=None, user_id=None, document_type="text")

Insert knowledge into the system for processing and indexing.

Parameters:

• content (str): The knowledge content to insert (text, facts, information, etc.)
• title (str, optional): Title for the knowledge
• tags (List[str], optional): List of tags for categorization
• source (str, optional): Source of the knowledge
• user_id (str, optional): User ID for ownership
• document_type (str): Type of content (default: "text")

Returns: JobStatus object

query(query, user_id=None, max_results=10, search_type="hybrid")

Query the knowledge base using hybrid RAG search.

Parameters:

• query (str): The search query
• user_id (str, optional): User ID for personalized results
• max_results (int): Maximum number of results to return
• search_type (str): Type of search ("hybrid", "sparse", "dense")

Returns: List of QueryResult objects

wait_for_job(job_id, timeout=300, poll_interval=5)

Wait for a job to complete.

Parameters:

• job_id (str): The job ID to wait for
• timeout (int): Maximum time to wait in seconds
• poll_interval (int): Time between status checks in seconds

Returns: JobStatus object

insert_and_wait(content, title=None, tags=None, source=None, user_id=None, document_type="text", timeout=300)

Insert knowledge and wait for it to complete processing.

Returns: JobStatus object

Data Models

Document

Represents a document to be processed and indexed.

Document(
    content="Your document content",
    title="Document Title",
    tags=["tag1", "tag2"],
    source="source-name",
    user_id="user123",
    document_type="text"
)

QueryResult

Represents a search result from the knowledge base.

QueryResult(
    content="Retrieved content",
    score=0.95,
    search_type="hybrid",
    metadata={"title": "Document Title", "tags": ["tag1"]},
    chunk_id="chunk_123",
    graph_context={"related_chunks": [], "graph_score": 0.1}
)

JobStatus

Represents the status of a processing job.

JobStatus(
    job_id="job_123",
    status="completed",
    message="Job completed successfully",
    progress=100,
    timestamp=1234567890
)

🔧 Configuration

Environment Variables

You can configure the SDK using environment variables:

export VRIN_API_KEY="your_api_key_here"
export VRIN_BASE_URL="https://api.vrin.ai/v1"

Custom Base URL

For development or custom deployments:

client = VRINClient(
    api_key="your_api_key",
    base_url="https://your-custom-domain.com/api"
)

🚨 Error Handling

The SDK provides comprehensive error handling:

from vrin import VRINClient, VRINError, JobFailedError, TimeoutError

try:
    client = VRINClient(api_key="your_api_key")
    results = client.query("your query")
except VRINError as e:
    print(f"VRIN error: {e}")
except JobFailedError as e:
    print(f"Job failed: {e}")
except TimeoutError as e:
    print(f"Operation timed out: {e}")

📊 Performance

• Query Latency: < 2 seconds for most queries
• Document Processing: Asynchronous with real-time status updates
• Batch Processing: Efficient handling of multiple documents
• Scalability: Built on AWS infrastructure for enterprise-scale workloads

🔒 Security

• API Key Authentication: Secure API key-based authentication
• HTTPS: All communications are encrypted
• VPC Isolation: Backend services run in isolated VPC
• Data Privacy: Your data remains private and secure

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Documentation: https://docs.vrin.ai
• Email: [e-mail]
• Issues: GitHub Issues

🏗️ Architecture

The VRIN Hybrid RAG system is built on:

• AWS Lambda: Serverless API endpoints
• Amazon OpenSearch: Vector and keyword search
• Amazon Neptune: Knowledge graph storage
• Amazon ECS: Document processing
• Amazon SQS: Job queue management
• Amazon DynamoDB: Job status tracking

🔄 Version History

• v0.1.0: Initial release with basic functionality
  • Document submission and processing
  • Hybrid RAG search
  • Job status tracking
  • Batch operations