Metadata-Version: 2.1
Name: vector_vault
Version: 5.7.5
Summary: Quickly create ChatGPT RAG apps and Unleash the full potential of GenAI with Vector Vault
Home-page: https://github.com/John-Rood/VectorVault
Author: VectorVault.io
Author-email: john@johnrood.com
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
License-File: LICENSE

# Vector Vault

![Vector Vault Header](https://images.squarespace-cdn.com/content/646ad2edeaaf682a9bbc36da/297fde6c-f5b4-4076-83bc-81dcfdbffebe/Vector+Vault+Header+5000.jpg)

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:
```bash
pip install vector-vault
```

Basic usage:
```python
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](https://vectorvault.io) to get your API key.
2. Install the `vectorvault` package: `pip install vector-vault`
3. Explore our [examples folder](https://github.com/John-Rood/VectorVault/tree/main/examples) for tutorials and practical applications.

## Learn More

- Full API Documentation: [Link to API docs](https://github.com/John-Rood/VectorVault/tree/main/fulldocs.md)
- Interactive Dashboard: [app.vectorvault.io](https://app.vectorvault.io)
- Join our [Discord community](https://discord.gg/AkMsP9Uq) for support and discussions.

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