Metadata-Version: 2.1
Name: vector-vault
Version: 7.3.7.1
Summary: Quickly create RAG apps, Agents, and Unleash the full power of AI 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
Requires-Dist: numpy
Requires-Dist: requests
Requires-Dist: bs4
Requires-Dist: google-cloud-storage
Requires-Dist: annoy
Requires-Dist: openai
Requires-Dist: tiktoken
Requires-Dist: cerebras-cloud-sdk
Requires-Dist: anthropic
Requires-Dist: pymupdf
Requires-Dist: google-genai

# Vector Vault: A Foundational Platform for Autonomous AI Agents

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

**Vector Vault is a foundational platform for building, deploying, and operating autonomous AI agents.** While most tools focus on creating chatbots, we're engineering the production-grade infrastructure for **persistent, stateful agents** that execute complex tasks over time—with or without human supervision.

This isn't just a better vector database. It's the **execution layer for the agentic future.**

## 🚀 Beyond Chatbots: Welcome to Autonomous AI

The AI industry is rapidly moving from simple request-response systems to **autonomous digital workers.** Vector Vault provides the core infrastructure to build agents that:

- **Persist and adapt** across multiple, asynchronous interactions.
- **Execute complex, multi-step workflows** independently.
- **Maintain state** while scaling in a serverless environment.
- **Learn and evolve** from every interaction.
- **Operate autonomously** for hours, days, or indefinitely.

### Vector Flow: Visual Agent Construction
Build sophisticated AI agents visually at **[app.vectorvault.io/vector-flow](https://app.vectorvault.io/vector-flow)**.

- **Drag-and-drop agent design** with advanced reasoning patterns.
- **Multi-platform AI integration** (OpenAI, Claude, Grok, Groq, Gemini).
- **Python execution** in secure, sandboxed containers (run code inside your flows).
- **API integrations** and external tool access.
- **Real-time deployment** with instant production updates.

### Built on PAR (Persistent Agentic Runtime)
Vector Flow runs on a **Persistent Agentic Runtime**. Compute remains **serverless & stateless**, but each agent’s state is stored durably in the cloud.  
• **Continuous state** – agents pick up exactly where they left off, no context rebuilding.  
• **Temporal autonomy** – agents respond to events over minutes, hours, or days.  
• **Scalable execution** – state lives in PAR while stateless workers spin up on-demand to process steps.

This architecture is what lets Vector Vault move beyond chatbots and power long-running, auditable AI systems.

## ⚡ Quick Start: Your First Autonomous Agent

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

#### Build an Intelligent Agent in Minutes:

```python
from vectorvault import Vault

# Initialize with multi-platform AI support
vault = Vault(
    user='YOUR_EMAIL',
    api_key='YOUR_VECTOR_VAULT_API_KEY', 
    openai_key='YOUR_OPENAI_API_KEY',
    anthropic_key='YOUR_ANTHROPIC_KEY',  # optional
    vault='MY_AGENT_VAULT'
)

# Build your agent's knowledge base
vault.add('Your domain expertise, technical docs, and procedures...')
vault.get_vectors()
vault.save()

# Deploy autonomous workflows
agent_response = vault.run_flow(
    'intelligent_assistant',
    'Process this new customer inquiry',
    customer_data={"tier": "premium", "history": [...]},
    escalation_rules={"urgent": True}
)

# Get context-aware responses with smart history
response = vault.get_chat(
    "What about that issue we discussed earlier?",
    history=conversation_history,
    get_context=True,
    smart_history_search=True,  # AI generates contextual search queries
    model="claude-sonnet-4-0"   # Switch models seamlessly
)
```

## 🧠 Platform-Agnostic AI Intelligence

Vector Vault supports all leading AI platforms under one interface. Switch between OpenAI, Claude, Grok and Gemini **mid-conversation** without changing your code:

```python
# Start with OpenAI for analysis
response = vault.get_chat("Analyze this data", model="gpt-4o")

# Switch to Claude for reasoning  
response = vault.get_chat("What's your recommendation based on that?", model="claude-sonnet-4-0")

# Use Grok for creative tasks
response = vault.get_chat("Now, generate some innovative solutions", model="grok-4")
```

## 🎯 Smart History Search: Context That Actually Works

Traditional RAG fails when users say "Tell me more about that" or "How do I fix that?" Our **Smart History Search** solves this by using AI to generate a contextual search query based on the conversation history.

```python
# User: "I'm getting database timeout errors in PostgreSQL"
# AI: "Here are some common causes..."
# User: "How do I fix that?"

# WITHOUT smart search: Searches "how do I fix that" → returns random, generic results
# WITH smart search: Searches "PostgreSQL database timeout errors fix" → returns specific solutions

response = vault.get_chat(
    "How do I fix that?",  # Vague, contextual query
    history=conversation_history,
    get_context=True,
    smart_history_search=True
)
```

## 🏗️ Advanced Agent Capabilities

### Multimodal Intelligence
Build agents that can see and understand images and documents.
```python
response = vault.get_chat(
    "Analyze the key takeaways from this financial report",
    image_path="/path/to/report.pdf", 
    get_context=True
)
```

### Real-Time Streaming
Create interactive and responsive agent experiences.
```python
# Console applications
response = vault.print_stream(
    vault.get_chat_stream("Research the latest AI trends", get_context=True)
)

# Web applications (Server-Sent Events)
@app.route('/agent-stream')
def agent_chat():
    return Response(
        vault.cloud_stream(vault.get_chat_stream(user_message, get_context=True)),
        mimetype='text/event-stream'
    )
```

- **Context payload behavior**:
  - **get_chat**: if you pass `return_context=True`, it returns a dict: `{'response': str, 'context': list}`.
  - **get_chat_stream**: if you pass `return_context=True`, after token streaming finishes, a single JSON string is yielded just before `!END` with the same shape: `{"response": "<full_response>", "context": [...]}`.
  - **Inline context streaming** happens only when you also provide metatag parameters.

- **How to catch the final context payload (server-side streaming to frontend)**:
```python
# Capture JSON context payload (do not append or emit)
if isinstance(word, str) and word.startswith('{"response":') and word.endswith('}'):
    try:
        context_data = json.loads(word)
        collected_context = context_data.get('context')
        continue
    except Exception:
        pass
else:
    # handle normal token output
    ...
```

### Cross-Vault Retrieval
Search multiple vaults at once and get the absolute most similar results overall. Results are merged across all listed vaults and globally sorted by distance.

```python
# Chat with cross-vault context
response = vault.get_chat(
    "Where is the refund policy and API rate limits documented?",
    get_context=True,
    n_context=6,
    vaults=["docs", "customer_support", "developer_portal"]
)

# Or use the low-level API directly
results = vault.get_similar_from_vaults(
    "refund policy",
    n=8,
    vaults=["docs", "customer_support", "legal"]
)

# Enforce per-vault minimums (remaining slots fill from best overall)
results = vault.get_similar_from_vaults(
    "refund policy",
    n=5,
    vaults={"legal": 2, "docs": 1}
)
```

Notes:
- You can pass a single vault as a string to target only that vault
- You can enforce per-vault minimums with a dict: `vaults={"legal": 2, "docs": 1}`; if sum(minima) > n, n automatically expands to the sum of minima; otherwise the remainder fills from best overall

## 🌟 Why Vector Vault for Autonomous Agents?

### Build True Agents, Not Just Chatbots
- Our **Persistent Agentic Runtime** is built for stateful, long-running tasks.
- Use our **visual flow builder** to design complex reasoning patterns.
- Achieve **real-time deployment** and enable continuous agent learning.

### Deploy with Confidence
- **Serverless scaling** from prototype to enterprise-grade applications.
- **Multi-platform AI support** with automatic provider detection.
- **Comprehensive logging** and observability for every agent action.

### Accelerate Your Development
- Execute complex AI workflows with **one-line operations**.
- Go from idea to deployed agent in minutes with the **visual agent builder**.
- **Instant deployment** with zero infrastructure management.

### Build on a Future-Proof Platform
- **Unlimited isolated databases** for multi-tenant agent systems.
- **Advanced RAG** with smart contextual search that actually works.
- **Continuous innovation** in agentic capabilities to keep you ahead.

## 🚀 The Agentic Future Starts Here

Vector Vault isn't just keeping up with the AI revolution—we're **defining it**. While others build better chatbots, we're creating the infrastructure for **digital workers** that think, persist, and execute autonomously.

### Get Started Today:
1. **30-day free trial**: [VectorVault.io](https://vectorvault.io)
2. **Visual agent builder**: [app.vectorvault.io/vector-flow](https://app.vectorvault.io/vector-flow)  
3. **Install the platform**: `pip install vector-vault`

### Learn More:
- **Full Documentation**: [API Reference](https://github.com/John-Rood/VectorVault/tree/main/vectorvault/documentation/fulldocs.md)
- **Vector Flow Guide**: [Agent Building Documentation](https://github.com/John-Rood/VectorVault/tree/main/documentation/vectoflow_docs)
- **Chat Functions**: [RAG & Streaming Guide](https://github.com/John-Rood/VectorVault/tree/main/vectorvault/documentation/get_chat_docs.md)
- **Community**: [Discord](https://discord.com/channels/1111817087007084544/1111817087451676835)
- **JavaScript SDK**: [VectorVault-js](https://github.com/John-Rood/VectorVault-js)

**The age of autonomous AI agents is here. Build yours with Vector Vault.**
