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trustgraph/trustgraph_configurator/resources/dialog/trustgraph-flow.yaml
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# TrustGraph Configuration Builder - Dialog Flow (State Machine)
# Each step declares its own transitions to the next state(s)
flow:
id: "trustgraph-config"
title: "TrustGraph Configuration Builder"
version: "1.0"
start: version
steps:
# ─────────────────────────────────────────────────────────────
# Version Selection
# ─────────────────────────────────────────────────────────────
version:
title: "Which TrustGraph version?"
state_key: version
input:
type: select
options:
- label: "TrustGraph 2.2"
value: "2.2"
description: "Additional document format support"
badge: pre-release
recommended: false
- label: "TrustGraph 2.1"
value: "2.1"
description: "Explainability and provenance support"
badge: stable
recommended: true
- label: "TrustGraph 1.9"
value: "1.9"
description: "Improvements to ontology and knowledge extraction prompts"
badge: pre-release
recommended: false
- label: "TrustGraph 1.8"
value: "1.8"
description: "Pluggable messaging fabric, switched to Garage for object store"
badge: stable
recommended: false
transitions:
- next: platform
# ─────────────────────────────────────────────────────────────
# Platform Selection
# ─────────────────────────────────────────────────────────────
platform:
title: "Which platform?"
description: "Choose how you'll run the TrustGraph containers"
state_key: platform
input:
type: select
options:
- label: "Docker Compose"
value: "docker-compose"
icon: "docker"
description: "Easy to install on MacOS and Linux. Great for evaluation and learning. May not be well suited to production deployments."
recommended: true
- label: "Podman Compose"
value: "podman-compose"
icon: "podman"
description: "Feature-compatible with Docker Compose, default for most Linux systems. May not be well suited to production deployments."
- label: "Minikube"
value: "minikube"
icon: "minikube"
description: "Stand-alone Kubernetes for small-to-medium deployments. Good for learning Kubernetes. Single node cluster limits production use."
- label: "Google Kubernetes Engine (GKE)"
value: "gke"
icon: "google-cloud"
description: "Managed Kubernetes on Google Cloud. Production-ready with full observability and scalability."
- label: "AWS EKS"
value: "eks"
icon: "aws"
description: "Elastic Kubernetes Service on AWS infrastructure. Suitable for production deployment."
- label: "Azure AKS"
value: "aks"
icon: "azure"
description: "Azure Kubernetes Service on Microsoft infrastructure. Suitable for production deployment."
- label: "Scaleway Kubernetes"
value: "scw"
icon: "scaleway"
description: "Managed Kubernetes on Scaleway European cloud infrastructure. Suitable for production deployment."
- label: "OVHCloud Kubernetes"
value: "ovh"
icon: "ovhcloud"
description: "Managed Kubernetes on OVHCloud European infrastructure. Suitable for production deployment."
transitions:
- next: graph-store
# ─────────────────────────────────────────────────────────────
# Graph Store
# ─────────────────────────────────────────────────────────────
graph-store:
title: "Which graph store?"
description: "Where TrustGraph stores the knowledge graph"
state_key: graph_store
input:
type: select
options:
- label: "Apache Cassandra"
value: "cassandra"
icon: "cassandra"
description: "NoSQL database with fast read/write at scale. TrustGraph integrates a graph schema."
recommended: true
# - label: "Neo4j"
# value: "neo4j"
# icon: "neo4j"
# description: "Native graph database with Cypher query language"
# - label: "Memgraph"
# value: "memgraph"
# icon: "memgraph"
# description: "In-memory graph database with Cypher query language. High performance for real-time analytics."
# - label: "FalkorDB"
# value: "falkordb"
# icon: "falkordb"
# description: "High-performance graph database optimized for low-latency queries. Based on GraphBLAS."
transitions:
- next: vector-db
# ─────────────────────────────────────────────────────────────
# Vector Database
# ─────────────────────────────────────────────────────────────
vector-db:
title: "Which vector database?"
description: "Stores embeddings for semantic search and RAG"
state_key: vector_db
input:
type: select
options:
- label: "Qdrant"
value: "qdrant"
icon: "qdrant"
description: "High RPS, minimal latency, fast indexing with accuracy control"
recommended: true
# - label: "Milvus"
# value: "milvus"
# icon: "milvus"
# description: "Open-source vector database built for scale"
# - label: "Pinecone"
# value: "pinecone"
# icon: "pinecone"
# description: "Managed vector database service (requires API key)"
transitions:
- next: row-store
# ─────────────────────────────────────────────────────────────
# Row Store
# ─────────────────────────────────────────────────────────────
row-store:
title: "Which row data store?"
description: "Storage for row/tabular data"
state_key: row_store
input:
type: select
options:
- label: "Apache Cassandra"
value: "cassandra"
icon: "cassandra"
description: "NoSQL database with fast read/write performance at scale. Used for row storage in TrustGraph."
transitions:
- next: model-deployment
# ─────────────────────────────────────────────────────────────
# Model Deployment
# ─────────────────────────────────────────────────────────────
model-deployment:
title: "How will you run the LLM?"
description: "Choose where the language model runs"
state_key: model_deployment
input:
type: select
options:
# Local / Self-hosted
- label: "Ollama"
value: "ollama"
icon: "ollama"
description: "Run LLMs locally. Bundles model weights, configs, and data into a single package. No external API dependencies."
- label: "Llamafile"
value: "llamafile"
icon: "llamafile"
description: "Single-file LLM distribution with embedded inference server. Runs on most computers with no dependencies."
- label: "LM Studio"
value: "lm-studio"
icon: "lm-studio"
description: "Easy local LLM running. Very usable, great support. Commercial, free for personal use."
# Cloud API Providers
- label: "OpenAI"
value: "openai"
icon: "openai"
description: "Industry-leading models (GPT-4, etc). Requires API key."
- label: "Claude (Anthropic)"
value: "claude"
icon: "anthropic"
description: "Helpful, honest, and harmless AI models. Designed to be trustworthy and reliable. Requires API key."
- label: "Mistral"
value: "mistral"
icon: "mistral"
description: "Efficient models excelling in multilingual tasks, code generation, and reasoning. Strong performance relative to size. Requires API key."
- label: "Cohere"
value: "cohere"
icon: "cohere"
description: "Cloud-agnostic AI with high security, privacy, and customization. Supports on-premises and private cloud. Requires API key."
# Cloud Platform Services
- label: "Azure AI"
value: "azure"
icon: "azure"
description: "Azure Endpoint Services for building, deploying, and managing AI applications. Requires Azure subscription."
- label: "Azure OpenAI"
value: "azure-openai"
icon: "azure"
description: "OpenAI models hosted on Azure infrastructure. Requires Azure subscription."
- label: "Amazon Bedrock"
value: "bedrock"
icon: "aws"
description: "Fully managed service for generative AI on AWS. Multiple model providers available. Requires AWS account."
- label: "Google AI Studio"
value: "google-ai-studio"
icon: "google"
description: "Integrated environment to prototype and experiment with Google's generative AI models. Requires Google account."
- label: "Vertex AI"
value: "vertex-ai"
icon: "google-cloud"
description: "Google Cloud platform for training and deploying ML models. Requires GCP account."
# Self-hosted inference servers
- label: "vLLM"
value: "vllm"
icon: "vllm"
description: "High-throughput LLM serving engine. Self-hosted with OpenAI-compatible API."
- label: "Text Generation Inference (TGI)"
value: "tgi"
icon: "huggingface"
description: "Hugging Face's production-ready inference server. Self-hosted with high performance."
transitions:
- next: max-output-tokens
# ─────────────────────────────────────────────────────────────
# Max Output Tokens
# ─────────────────────────────────────────────────────────────
max-output-tokens:
title: "Maximum output tokens?"
description: "Limits the length of LLM responses"
state_key: max_output_tokens
input:
type: number
default: 4096
min: 256
max: 65536
step: 256
transitions:
- next: ocr-enabled
# ─────────────────────────────────────────────────────────────
# OCR Pipelines Toggle
# ─────────────────────────────────────────────────────────────
ocr-enabled:
title: "Enable OCR processing?"
description: "Replace standard PDF decoding with Optical Character Recognition for scanned documents and images"
state_key: ocr.enabled
input:
type: toggle
default: false
transitions:
- when: "ocr.enabled = true"
next: ocr-engine
- next: embeddings-enabled
# ─────────────────────────────────────────────────────────────
# OCR Engine (conditional)
# ─────────────────────────────────────────────────────────────
ocr-engine:
title: "Which OCR engine?"
state_key: ocr.engine
input:
type: select
options:
- label: "PDF Decode"
value: "pdf-decode"
description: "Default configuration. Extracts text from PDFs with structured text, but does not perform OCR on images or scanned documents."
recommended: true
- label: "Tesseract"
value: "tesseract"
description: "Free, open-source embedded OCR engine. Best-in-class for free/open-source. Use with PDF documents containing image scans."
- label: "Mistral"
value: "mistral"
description: "Best-in-class commercial OCR service. Requires a Mistral subscription. Use with PDF documents containing image scans."
transitions:
- next: embeddings-enabled
# ─────────────────────────────────────────────────────────────
# Embeddings Configuration Toggle
# ─────────────────────────────────────────────────────────────
embeddings-enabled:
title: "Configure embeddings engine?"
description: "Customize the embeddings model used for semantic search"
state_key: embeddings.enabled
input:
type: toggle
default: false
transitions:
# Version gate: embeddings config not available before 1.6.0
- when: "version < '1.6.0'"
next: review
- when: "embeddings.enabled = true"
next: embeddings-engine
- next: review
# ─────────────────────────────────────────────────────────────
# Embeddings Engine (conditional)
# ─────────────────────────────────────────────────────────────
embeddings-engine:
title: "Which embeddings engine?"
state_key: embeddings.engine
input:
type: select
options:
- label: "FastEmbed"
value: "fastembed"
icon: "fastembed"
description: "Lightweight, fast Python library for embedding generation. Small container image and quick start time."
recommended: true
- label: "HuggingFace sentence-transformers"
value: "huggingface-sentence-transformers"
icon: "huggingface"
description: "Support for a large number of open/community models. Use for non-standard models. Larger container image and longer start time due to PyTorch dependencies."
- label: "Ollama"
value: "ollama"
icon: "ollama"
description: "Use Ollama for embeddings. Requires Ollama to be running with an embedding model loaded."
transitions:
- next: review
# ─────────────────────────────────────────────────────────────
# Review & Generate (terminal)
# ─────────────────────────────────────────────────────────────
review:
title: "Review your configuration"
type: review
actions:
- id: generate
label: "Generate"
icon: "rocket"
description: "Generate the configuration package as a ZIP file"
transitions: [] # terminal state
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