vector-vault 5.7.3

Quickly create ChatGPT RAG apps and Unleash the full potential of GenAI with Vector Vault

Vector Vault

Vector Vault is a cutting-edge, cloud-native and RAG-native vector database solution that revolutionizes AI integration in applications. Our platform seamlessly combines vector databases, similarity search, and AI model interactions into a single, easy-to-use service.

Key Features

• RAG-Native Architecture: Perform Retrieval-Augmented Generation in one line of code.
• Unparalleled Simplicity: Implement sophisticated AI features with minimal code.
• Full-Stack Integration: Use our Python package for backend operations and our JavaScript package for easy front-end integration.
• Cloud-Engine: Our service handles vector search, retrieval, and AI model interactions, simplifying your architecture.
• One-Line Operations: Save to the cloud vector database and generate RAG responses in one line of code.
• Developer-Centric: Focus on your application logic rather than complex AI and front-end integrations.
• Unlimited Isolated Databases: Create and access an infinite number of vector databases, ideal for multi-tenant applications.

Quick Start

Install Vector Vault:

pip install vector-vault

Basic usage:

from vectorvault import Vault

vault = Vault(user='YOUR_EMAIL',
              api_key='YOUR_API_KEY', 
              openai_key='YOUR_OPENAI_KEY',
              vault='NAME_OF_VAULT')

# Add data to your vault
vault.add('some text')
vault.get_vectors()
vault.save()

# Get AI-powered RAG responses
rag_response = vault.get_chat("Your question here", get_context=True)
print(rag_response)

Key Concepts

• Vaults: Isolated serverless Vector databases. No limits, inifitely scalable.
• RAG-Native: Vector Similarity Search Retrieval Augmented Generation by default - fully customizable with params
• Cloud Engine: We process operations and AI references in the Vector Vault cloud, making it easy for you to integrate to the front end and build real applications

Advanced Features

• Metadata Management: Easily add and retrieve metadata for your vector entries.
• Streaming Responses: Use get_chat_stream() for interactive chat experiences.
• Custom Prompts and Personalities: Tailor AI responses to your specific needs.

Use Cases

• AI-powered customer service chatbots
• Semantic search in large document collections
• Personalized content recommendations
• Intelligent chatbots with access to vast knowledge bases
• Multi-tenant systems needing isolated vector databases

Why Vector Vault?

• Simplicity: Easier to use than traditional vector databases and AI integrations.
• RAG Optimization: Built from the ground up for Retrieval-Augmented Generation workflows.
• Customization: Add specific knowledge to your Vault and tailor AI responses to your needs.
• Scalability: Fully serverless platform offering unparalleled scalability.
• Time and Resource Saving: Dramatically reduce development time for AI feature integration.

Getting Started

1. Sign up for a 30-day free trial at VectorVault.io to get your API key.
2. Install the vectorvault package: pip install vector-vault
3. Explore our examples folder for tutorials and practical applications.

Learn More

• Full API Documentation: Link to API docs
• Interactive Dashboard: app.vectorvault.io
• Join our Discord community for support and discussions.

Start building with Vector Vault today and experience the future of RAG-native, cloud-native vector databases!