From 9d8450441c17e1f908514db96921f79a78040b5a Mon Sep 17 00:00:00 2001 From: Cyber MacGeddon Date: Thu, 3 Jul 2025 14:00:46 +0100 Subject: [PATCH] Update quickstart --- docs/README.md | 89 ++++++++++++++++++++++++ docs/README.quickstart-docker-compose.md | 33 ++++++--- 2 files changed, 113 insertions(+), 9 deletions(-) create mode 100644 docs/README.md diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 00000000..fd5b10a5 --- /dev/null +++ b/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. + + diff --git a/docs/README.quickstart-docker-compose.md b/docs/README.quickstart-docker-compose.md index 76f7e1f5..91a9c2f6 100644 --- a/docs/README.quickstart-docker-compose.md +++ b/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: