README WIP

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- ✨ [Key Features](#-key-features)
- 🎯 [Why TrustGraph?](#-why-trustgraph)
- 🚀 [Getting Started](#-getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- 🔧 [Configuration Builder](#-configuration-builder)
- [Core Concepts](#-core-concepts)
- 📐 [Architecture](#-architecture)
- 🧩 [Integrations](#-integrations)
- 📊 [Observability & Telemetry](#-observability--telemetry)
- 🤝 [Contributing](#-contributing)
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## What is TrustGraph?
**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences.
![architecture](TG-layer-diagram.svg)
## ✨ Key Features
- 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md`
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TrustGraph is designed to be modular to support as many LLMs and environments as possible. A natural fit for a modular architecture is to decompose functions into a set of modules connected through a pub/sub backbone. [Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker managing data processing queues connected to procesing modules.
## 📐 Architecture
**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences.
![architecture](TG-layer-diagram.svg)
### Pulsar Workflows
- For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.