Update quickstart

docs/README.md

@@ -0,0 +1,89 @@
+# TrustGraph Documentation
+
+Welcome to the TrustGraph documentation. This directory contains comprehensive guides for using TrustGraph's APIs and command-line tools.
+
+## Documentation Overview
+
+### 📚 [API Documentation](apis/README.md)
+Complete reference for TrustGraph's APIs, including REST, WebSocket, Pulsar, and Python SDK interfaces. Learn how to integrate TrustGraph services into your applications.
+
+### 🖥️ [CLI Documentation](cli/README.md)
+Comprehensive guide to TrustGraph's command-line interface. Includes detailed documentation for all CLI commands, from system administration to knowledge graph management.
+
+### 🚀 [Quick Start Guide](README.quickstart-docker-compose.md)
+Step-by-step guide to get TrustGraph running using Docker Compose. Perfect for first-time users who want to quickly deploy and test TrustGraph.
+
+## Getting Started
+
+If you're new to TrustGraph, we recommend starting with the
+[Compose - Quick Start Guide](README.quickstart-docker-compose.md)
+to get a working system up and running quickly.
+
+For developers integrating TrustGraph into applications, check out the
+[API Documentation](apis/README.md) to understand the available interfaces.
+
+For system administrators and power users, the
+[CLI Documentation](cli/README.md) provides detailed information about all
+command-line tools.
+
+## Ways to deploy
+
+If you haven't deployed TrustGraph before, the 'compose' deployment 
+mentioned above is going to be the least commitment of setting things up:
+See [Quick Start Guide](README.quickstart-docker-compose.md)
+
+Other deployment mechanisms include:
+- [Scaleway Kubernetes deployment using Pulumi](https://github.com/trustgraph-ai/pulumi-trustgraph-scaleway)
+- [Intel Gaudi and GPU](https://github.com/trustgraph-ai/trustgraph-tiber-cloud) - tested on Intel Tiber cloud
+- [Azure Kubernetes deployment using Pulumi](https://github.com/trustgraph-ai/pulumi-trustgraph-aks)
+- [AWS EC2 single instance deployment using Pulumi](https://github.com/trustgraph-ai/pulumi-trustgraph-ec2)
+- [GCP GKE cloud deployment using Pulumi](https://github.com/trustgraph-ai/pulumi-trustgraph-gke)
+- [RKE Kubernetes on AWS deployment using Pulumi](https://github.com/trustgraph-ai/pulumi-trustgraph-aws-rke)
+- It should be possible to deploy on AWS EKS, but we haven't been able to
+  script anything reliable so far.
+
+## What is TrustGraph?
+
+TrustGraph is a comprehensive knowledge graph and retrieval-augmented generation (RAG) platform that enables:
+
+- **Knowledge Extraction**: Extract structured knowledge from documents and text
+- **Graph RAG**: Advanced retrieval-augmented generation using knowledge graphs
+- **Multi-Model Support**: Integration with various AI models (OpenAI, Claude, Ollama, etc.)
+- **Scalable Architecture**: Built on Apache Pulsar for high-performance processing
+- **Flexible Deployment**: Docker Compose files for various configurations
+
+## Key Features
+
+- **Graph-Based Knowledge Representation**: Store and query knowledge as interconnected graphs
+- **Document Processing**: Extract knowledge from PDFs, text files, and other documents
+- **Agent-Based Interactions**: Conversational AI with access to knowledge graphs
+- **Multi-Modal APIs**: REST, WebSocket, Pulsar, and Python SDK interfaces
+- **Comprehensive CLI**: Command-line tools for all operations
+- **Monitoring & Observability**: Built-in metrics and monitoring capabilities
+
+## Architecture
+
+TrustGraph consists of several key components:
+
+- **Processing Pipeline**: Document ingestion, chunking, vectorization, and knowledge extraction
+- **Storage Systems**: Vector databases, graph databases (Neo4j/Cassandra), and document storage
+- **AI Services**: Text completion, embeddings, and specialized processing services
+- **API Gateway**: REST and WebSocket interfaces for external integration
+- **Message System**: Apache Pulsar for reliable, scalable message processing
+
+## Support
+
+For questions, issues, or contributions:
+
+- **GitHub Issues**: Report bugs and feature requests
+- **Documentation**: This documentation covers most use cases
+- **Community**: Join discussions and share experiences
+
+## Related Resources
+
+- [TrustGraph GitHub Repository](https://github.com/trustgraph-ai/trustgraph)
+- [Docker Hub Images](https://hub.docker.com/u/trustgraph)
+- [Example Notebooks](https://github.com/trustgraph-ai/example-notebooks) -
+  shows some example use of various APIs.
+  
+

docs/README.quickstart-docker-compose.md

@@ -1,6 +1,8 @@
 
 # Getting Started
 
+## Preparation
+
 > [!TIP]
 > Before launching `TrustGraph`, be sure to have the `Docker Engine`  or `Podman Machine` installed and running on the host machine. 
 > 
@@ -13,16 +15,29 @@
 > [!TIP]
 > If using `Podman`, the only change will be to substitute `podman` instead of `docker` in all commands.
 
-All `TrustGraph` components are deployed through a `Docker Compose` file. There are **16** `Docker Compose` files to choose from, depending on the desired model deployment and choosing between the graph stores `Cassandra` or `Neo4j` or `FalkorDB`:
+## Create the configuration
 
-- `AzureAI` serverless endpoint for deployed models in Azure
-- `Bedrock` API for models deployed in AWS Bedrock
-- `Claude` through Anthropic's API
-- `Cohere` through Cohere's API
-- `Mix` for mixed model deployments
-- `Ollama` for local model deployments
-- `OpenAI` for OpenAI's API
-- `VertexAI` for models deployed in Google Cloud
+This guide talks you through the Compose file launch, which is the easiest
+way to lauch on a standalone machine, or a single cloud instance.
+See [README](README.md) for links to other deployment mechanisms.
+
+To create the deployment configuration, go to the
+[deployment portal](https://config-ui.demo.trustgraph.ai/) and follow the
+instructions.
+- Select Docker Compose or Podman Compose as the deployment
+  mechanism.
+- Use Cassandra for the graph store, it's easiest and most tested.
+- Use Qdrant for the vector store, it's easiest and most tested.
+- Chunker: Recursive, chunk size of 1000, 50 overlap should be fine.
+- Pick your favourite LLM model:
+  - If you have enough horsepower in a local GPU, LMStudio is an easy
+    starting point for a local model deployment.  Ollama is fairly easy.
+  - VertexAI on Google is relatively straightforward for a cloud
+    model-as-a-service LLM, and you can get some free credits.
+- Max output tokens as per the model, 2048 is safe.
+- Customisation, check LLM Prompt Manager and Agent Tools.
+- Finish deployment, Generate and download the deployment bundle.
+  Read the extra deploy steps on that page.
 
 `Docker Compose` enables the following functions: