chatvat 0.1.5

The Universal RAG Chatbot Factory. Zero-dependency AI deployments.

ChatVat 🏭

The Universal RAG Chatbot Factory

---

🌟 The Vision

ChatVat is not just another chatbot script. It is a Manufacturing Plant for self-contained AI systems.

It solves the "It works on my machine" problem by adhering to a strict "Zero-Dependency" philosophy. ChatVat takes your raw data sources—websites, APIs, and documents—and fuses them with a production-grade RAG engine into a sealed Docker container. This "capsule" contains everything needed to run: the code, the database, the browser, and the API server.

You can deploy a ChatVat bot anywhere: from a MacBook Air to an air-gapped server in Antarctica, without installing Python or git.

Core Philosophy

• Production Parity: The bot you test locally is bit-for-bit identical to the bot you deploy.
• Source Injection: The engine code is "injected" directly into the container during the build. No external git clones or PyPI downloads are required inside the image.
• Self-Healing: Built-in deduplication (MD5 Hashing), crash recovery, and "Ghost Entry" prevention.

---

⚡ Quick Start

1. Installation

Install the ChatVat CLI from the source (or your private registry).

pip install chatvat

2. Initialize the Assembly Line

Create a clean directory for your new bot and run the configuration wizard.

mkdir my-crypto-bot
cd my-crypto-bot
chatvat init

The wizard will guide you through:

• Naming your bot
• Setting up AI Brain (Groq Llama-3 + HuggingFace Embeddings)
• Connecting Data Sources (URLs, APIs, or Local Files)
• Defining Deployment Ports

3. Build the Capsule

Compile your configuration and the ChatVat engine into a Docker Image.

chatvat build

What happens here? > The CLI locates the chatvat library on your system, copies the core engine code into a build context, injects your chatvat.config.json, and triggers a Docker build. The result is a sealed image containing your specific bot.

4. Deploy Anywhere

Run your bot using standard Docker commands. It injects your API keys at runtime for security.

# Example: Running on Port 8000
docker run -d \
  -p 8000:8000 \
  --env-file .env \
  --name crypto-bot \
  chatvat-bot

---

🧠 Architecture Deep Dive

ChatVat implements a modular RAG (Retrieval-Augmented Generation) pipeline designed for resilience.

The Components

Component	Role	Description
The Cortex	Intelligence	Powered by Groq for ultra-fast inference using the model of your choice and HuggingFace for embeddings.
The Memory	Vector Store	A persistent, thread-safe ChromaDB instance. It uses MD5 hashing to prevent duplicate data entry.
The Eyes	Crawler	A headless Chromium browser (via Crawl4AI/Playwright) capable of reading dynamic JS-heavy websites.
The Nervous System	Ingestor	A background worker that auto-updates knowledge every X minutes (configurable).
The API	Interface	A high-performance FastAPI server exposing REST endpoints.

The "Source Injection" Workflow

Unlike traditional builds that pip install libraries from the internet, ChatVat performs Source Injection:

1. Locate: The CLI finds where chatvat is installed on your host machine.
2. Extract: It copies the raw Python source code of the engine.
3. Inject: It places this code into the /app directory of the Docker container.
4. Seal: The Dockerfile sets PYTHONPATH=/app, making the injected code instantly executable without installation.

---

🛠️ Configuration Guide

Your bot is defined by chatvat.config.json. You can edit this file manually after running init.

{
    "bot_name": "ChatVatBot",
    "port": 8000,
    "refresh_interval_minutes": 60,
    "system_prompt": "You are a helpful assistant for the .....",
    "llm_model": "llama-3.1-70b-versatile",
    "embedding_model": "all-MiniLM-L6-v2",
    "sources": [
        {
            "type": "static_url",
            "target": "[https://www.amazon.com/gp/bestsellers/books/ref=zg_bs_nav](https://www.amazon.com/gp/bestsellers/books/ref=zg_bs_nav)"
        },
        {
            "type": "dynamic_json",
            "target": "[https://YOUR_API_ENDPOINT](https://YOUR_API_ENDPOINT)",
            "headers": {
                "Authorization": "Bearer ${API_KEY}"
            }
        },
        {
            "type": "local_file",
            "target": "./policy_docs.pdf"
        }
    ]
}

• refresh_interval_minutes: Set to 0 to disable auto-updates.
• sources: Supports static_url (Websites), dynamic_json (REST APIs), and local_file (PDF/TXT).
• headers: Can use ${VAR_NAME} syntax to reference environment variables from your .env file.

---

📚 API Reference

Once the container is running, interact with it via HTTP REST API.

1. Health Check

Used by cloud balancers (AWS/Render) to verify the bot is alive.

GET /health

Response:

{
  "status": "healthy",
  "version": "0.1.0"
}

2. Chat Interface

The main endpoint for sending queries.

POST /chat

Payload:

{
  "message": "What events are happening on Day 1?"
}

Response:

{
  "message": "On Day 1, the opening ceremony starts at 10 AM..."
}

---

⚖️ License & Proprietary Notice

Copyright © 2025 Madhav Kapila. All Rights Reserved.

This software is Proprietary (Closed Source).

• Usage: You are granted a limited license to use this tool for educational, private testing, or internal business purposes.
• Restrictions:
  • You MAY NOT copy, modify, redistribute, sell, or lease any part of the chatvat source code.
  • You MAY NOT reverse engineer or attempt to extract the source code.
  • You MAY NOT upload this package to public registries (like PyPI) or public repositories (like GitHub) without prior written permission.

All intellectual property rights remain exclusively with Madhav Kapila.

---

Built with ❤️ by the ChatVat Factory.