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

• Simple: Implement sophisticated AI features with minimal code.
• Full-Stack: Use our Python package for backend operations and our JavaScript package for easy front-end integration.
• RAG-Native: Perform Retrieval-Augmented Generation in one line of code.
• Cloud Engine: Our service handles vector search, retrieval, and AI model interactions, simplifying your architecture.
• Platform-Agnostic: Change models without changing your code (OpenAI, Anthropic, Groq, etc.)
• Unlimited Isolated Databases: Create and access an infinite number of vector databases, ideal for multi-tenant applications.

Quick Start

Install:

pip install vector-vault

Basic Usage:

from vectorvault import Vault

# Initialize Vault
vault = Vault(
    user='YOUR_EMAIL',
    api_key='YOUR_VECTOR_VAULT_API_KEY', 
    openai_key='YOUR_OPENAI_API_KEY',
    vault='MY_NEW_VAULT',
    verbose=True
)

# Build your vault
vault.add('some text') # automatic chunk sizing
vault.get_vectors() # generate vectors for the all data 
vault.save() # save data and vectors to the cloud

# Get AI-powered RAG responses
rag_response = vault.get_chat("What is this vault about?", get_context=True)
print(rag_response)

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Platform Agnostic:

Vector Vault supports multiple AI model platforms - OpenAI, Anthropic, Groq, Grok, and more - all under the same interface. Simply pass in the appropriate API keys upon initialization:

vault = Vault(
    user='YOUR_EMAIL',
    api_key='YOUR_VECTOR_VAULT_API_KEY', 
    openai_key='YOUR_OPENAI_API_KEY',      
    anthropic_key='YOUR_ANTHROPIC_API_KEY', # optional 
    groq_key='YOUR_GROQ_API_KEY',           # optional 
    grok_key='YOUR_GROK_API_KEY',           # optional 
    vault='MY_NEW_VAULT',
    verbose=True
)

No matter which provider you choose, downstream methods like get_chat(...) remain the same. You can seamlessly switch providers later without rewriting your code.

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Adding Personality & Custom Prompts

Vector Vault allows you to define a global “personality” for your AI responses, as well as custom prompts for both context-based and non-context-based queries. This is extremely helpful for brand consistency, specialized tones, or role-playing scenario

Setting a Personality

# Define your brand’s or chatbot’s personality
personality_text = """You are an enthusiastic and helpful assistant 
that always uses uplifting language and friendly emojis 😄."""
vault.save_personality_message(personality_text)

Once saved, this personality is automatically used in all future responses from this vault.

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Custom Prompts

You can also set custom prompts that will wrap your user’s message before sending to the model.

# For RAG responses, vector similar data is injected into `context`, and `content` is the user's message 
context_prompt = """You have access to the following context: {context}
Answer using a formal tone:
{content}"""
vault.save_custom_prompt(context_prompt, context=True)

Now, whenever you do:

response = vault.get_chat("What's new in the world of data science?", get_context=True)

Vector Vault automatically uses your context_prompt before sending to the LLM. By editing this custom_prompt, you can ensure your RAG responses come out perfect every time.

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Key Concepts

• Vaults: Serverless vector databases. Create as many as you need.
• RAG-Native: Add data, ask questions, retrieve relevant context from your Vault, and generate AI responses in one step.
• Cloud Engine: Our backend handles heavy lifting and integrates seamlessly with multiple AI providers.
• Personality & Custom Prompts: Easily store, retrieve, and modify custom roles/tones/prompts for brand consistency.
• Provider Agnosticism: Switch from OpenAI to Anthropic or any other platform by changing a single parameter. The rest of your code stays the same.

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: More straightforward than rolling your own vector database or hooking up multiple AI integrations.
• RAG Optimization: Built from the ground up for Retrieval-Augmented Generation workflows.
• Customization: Override prompts, personalities, or entire models with minimal code.
• Scalability: Serverless approach means no scaling overhead. Build prototypes or enterprise apps all the same.
• Time and Resource Saving: Drastically reduce your AI development lifecycle.

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!