FlowX.AI platform setup

This guide provides essential information for clients who need to configure and connect to the FlowX.AI Platform services.

Service endpoints

Java services

Service Name	Default Port	Protocol	Purpose
Connected Graph	9100	GraphQL	Knowledge graph queries
Agents	9101	gRPC	Agent management
Binaries	9102	gRPC	File storage
Conversations	9103	gRPC	Conversation management
Models	9104	gRPC	AI model configuration
Tenants	9105	gRPC	Multi-tenant management
Knowledge Graph (KAG)	9106	gRPC	Knowledge ingestion

Python services

Service Name	Default Port	Protocol	Purpose
Planner	9150	gRPC	Task orchestration
AI Developer	9151	REST	Code generation
AI Analyst	9152	REST	Process analysis
AI Designer	9153	REST	UI generation

Production Note: In production deployments, all services run on port 9100. The ports listed above are for local development using docker-compose.

Required environment variables

Service discovery configuration

These variables control how services locate each other:

Variable	Type	Default	Description
`GRPC_HOST_RESOLVER`	String	`k8s`	Service discovery method (`k8s` or `host`)
`GRPC_HOST_RESOLVER_HELM_CHART`	String	-	Helm chart name (required when `GRPC_HOST_RESOLVER=k8s`)
`GRPC_HOST_RESOLVER_FIXED_IP`	String	`ai-platform`	Fixed IP for services (required when `GRPC_HOST_RESOLVER=host`)

Configuration Examples: For Kubernetes deployment:

GRPC_HOST_RESOLVER=k8s
GRPC_HOST_RESOLVER_HELM_CHART=ai-platform

For local/Docker deployment:

GRPC_HOST_RESOLVER=host
GRPC_HOST_RESOLVER_FIXED_IP=localhost

Service port configuration

Variable	Type	Default	Description
`SERVICE_PORT`	Integer	9100	Port the service listens on

Service endpoint overrides

For custom service locations:

Variable Pattern	Example	Description
`AI_SERVICE_<service_id>_ENDPOINT`	`AI_SERVICE_MODELS_ENDPOINT=host.docker.internal:9104`	Override specific service endpoint

Common Service IDs:

MODELS - AI Models service
CONVERSATIONS - Conversations service
AGENTS - Agents service
BINARIES - Binaries service
TENANTS - Tenants service
CONNECTED_GRAPH - Connected Graph service
KAG - Knowledge Graph service

Python-specific variables

Variable	Type	Default	Description
`DEBUG`	Boolean	`false`	Enable debug logging

Authentication configuration

Variable	Type	Description
`SECURITY_OAUTH2_BASE_SERVER_URL`	String	OAuth2 server base URL
`SECURITY_OAUTH2_REALM`	String	OAuth2 realm name
`SECURITY_OAUTH2_CLIENT_ID`	String	OAuth2 client ID

Client connection examples

Testing service connectivity

Use grpcurl to test gRPC services:

# List available services
grpcurl --plaintext localhost:9104 list

# List service methods
grpcurl --plaintext localhost:9104 list ai.flowx.ai.platform.services.internal.models.grpc.ModelsService

# Test connectivity
grpcurl --plaintext \
    -d '{"message": "hello"}' \
    localhost:9104 ai.flowx.ai.platform.services.internal.models.grpc.ModelsService.SayHello

Docker Compose client configuration

For clients running alongside the AI Platform stack:

version: '3.8'
services:
  your-client-app:
    image: your-app:latest
    environment:
      # Connect to AI Platform services
      AI_PLATFORM_MODELS_URL: "ai-platform-models:9104"
      AI_PLATFORM_CONVERSATIONS_URL: "ai-platform-conversations:9103"
      AI_PLATFORM_GRAPHQL_URL: "http://ai-platform-connected-graph:9100/graphql"
      
      # For REST services
      AI_DEVELOPER_URL: "http://ai-platform-developer:9151"
      AI_ANALYST_URL: "http://ai-platform-analyst:9152"
      AI_DESIGNER_URL: "http://ai-platform-designer:9153"
    networks:
      - ai-platform-network

networks:
  ai-platform-network:
    external: true

Kubernetes client configuration

For clients deployed in the same Kubernetes cluster:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: your-client-app
spec:
  template:
    spec:
      containers:
      - name: app
        image: your-app:latest
        env:
        # Service discovery via Kubernetes DNS
        - name: GRPC_HOST_RESOLVER
          value: "k8s"
        - name: GRPC_HOST_RESOLVER_HELM_CHART
          value: "ai-platform"
        
        # Direct service URLs (alternative approach)
        - name: AI_PLATFORM_MODELS_URL
          value: "ai-platform-ai-models.default.svc.cluster.local:9100"
        - name: AI_PLATFORM_GRAPHQL_URL
          value: "http://ai-platform-connected-graph.default.svc.cluster.local:9100/graphql"

Prerequisites for clients

Java clients

If integrating with Java-based services:

Java 17 or higher
gRPC client libraries
Protocol Buffer definitions (available from the platform team)

Python clients

If integrating with Python-based services:

Python 3.12.9 or higher
HTTP client libraries (requests, httpx, etc.)
WebSocket client for streaming (optional)

General requirements

Network access to AI Platform services
Valid OAuth2 credentials
SSL/TLS certificates for production deployments

Common integration patterns

Service health checks

Before making requests, verify service availability:

# Check if gRPC service is responsive
grpcurl --plaintext ${SERVICE_HOST}:${SERVICE_PORT} grpc.health.v1.Health/Check

# Check HTTP service health (for REST services)
curl http://${SERVICE_HOST}:${SERVICE_PORT}/health

Load balancing

For production deployments, clients should:

Use service discovery rather than hardcoded IPs
Implement retry logic with exponential backoff
Configure connection pooling for gRPC clients
Monitor service health and route around unhealthy instances

Security considerations

Always use HTTPS/TLS in production
Rotate OAuth2 tokens regularly
Implement proper timeout handling
Use connection limits to prevent resource exhaustion

Troubleshooting

Connection issues

Verify environment variables are set correctly
Check network connectivity between client and services
Validate OAuth2 configuration and token validity
Review service logs for authentication/authorization errors

Service discovery problems

# For Kubernetes deployments
kubectl get services -l app=ai-platform

# Check if Helm chart is deployed
helm list -n ai-platform

# Verify DNS resolution
nslookup ai-platform-ai-models.default.svc.cluster.local

Docker network issues

# List Docker networks
docker network ls

# Inspect AI Platform network
docker network inspect ai-platform-network

# Check container connectivity
docker exec -it your-container ping ai-platform-models

Support

When requesting support, please provide:

Environment configuration (environment variables)
Deployment method (Docker, Kubernetes, local)
Client application details (language, framework)
Error messages and logs
Network topology if using custom networking

For technical issues, include the output of:

# Service connectivity test
grpcurl --plaintext ${SERVICE_HOST}:${SERVICE_PORT} list

# Environment variables (sanitized)
env | grep -E "(GRPC_|AI_SERVICE_|SECURITY_)" | sed 's/=.*/=***/'

​Service endpoints

​Java services

​Python services

​Required environment variables

​Service discovery configuration

​Service port configuration

​Service endpoint overrides

​Python-specific variables

​Authentication configuration

​Client connection examples

​Testing service connectivity

​Docker Compose client configuration

​Kubernetes client configuration

​Prerequisites for clients

​Java clients

​Python clients

​General requirements

​Common integration patterns

​Service health checks

​Load balancing

​Security considerations

​Troubleshooting

​Connection issues

​Service discovery problems

​Docker network issues

​Support