Documentation Index Fetch the complete documentation index at: https://docs.emergence.ai/llms.txt
Use this file to discover all available pages before exploring further.
Langfuse Setup Guide This guide walks through deploying Langfuse and connecting it to your CRAFT solutions. Langfuse provides LLM call tracing, evaluation, and prompt management for Data Insights, Data Governance, and Semiconductor solutions.
Prerequisites
A running CRAFT deployment (any version)
Docker and Docker Compose (for Docker deployment) or a Kubernetes cluster
Domain name for the Langfuse instance (recommended for production)
Step 1: Deploy Langfuse Docker Compose
Kubernetes (Helm)
# Clone the Langfuse repository (provided in your deployment kit) git clone < langfuse-repo-ur l > cd langfuse cp .env .env.local # Edit .env.local with your configuration
Edit .env.local with your configuration: # Required NEXTAUTH_URL=https://langfuse.your-domain.com NEXTAUTH_SECRET=<run: openssl rand -hex 32> SALT=<run: openssl rand -hex 32> # Database (auto-provisioned by compose) DATABASE_URL=postgresql://postgres:postgres@langfuse-db:5432/langfuse # Encryption ENCRYPTION_KEY=<run: openssl rand -hex 32>
Start the services: docker compose up -d docker compose ps # verify all services are healthy
Langfuse is now available at http://localhost:3000 (or your configured domain). helm repo add langfuse https://langfuse.github.io/langfuse-k8s helm repo update # Create secrets kubectl create secret generic langfuse-secrets \ --namespace langfuse \ --from-literal=nextauth-secret=$( openssl rand -hex 32 ) \ --from-literal=salt=$( openssl rand -hex 32 ) \ --from-literal=encryption-key=$( openssl rand -hex 32 ) \ --from-literal=pg-password= < your-pg-password > # Install helm install langfuse langfuse/langfuse \ --namespace langfuse \ --create-namespace \ --set langfuse.nextauth.url="https://langfuse.example.com" \ --set langfuse.nextauth.existingSecret=langfuse-secrets \ --set langfuse.nextauth.existingSecretKey=nextauth-secret \ --set langfuse.salt.existingSecret=langfuse-secrets \ --set langfuse.salt.existingSecretKey=salt
Step 2: Create a Langfuse Project
Navigate to your Langfuse instance
Sign up for an account (or log in with the admin account)
Create a new Project for each solution you want to trace:
data-insights, for Data Insights tracesdata-governance, for Data Governance tracessemiconductor, for Semiconductor traces
In each project, go to Settings → API Keys and copy the Public Key and Secret Key
Step 3: Store Credentials Store Langfuse credentials as Kubernetes Secrets (or in your Secrets backend):
# Create one secret per solution (use separate Langfuse projects for isolation): kubectl create secret generic langfuse-api-keys \ --namespace em-runtime \ --from-literal=public-key= < data-insights-public-key > \ --from-literal=secret-key= < data-insights-secret-key > kubectl create secret generic langfuse-api-keys-dr \ --namespace em-runtime \ --from-literal=public-key= < data-governance-public-key > \ --from-literal=secret-key= < data-governance-secret-key > kubectl create secret generic langfuse-api-keys-semi \ --namespace em-runtime \ --from-literal=public-key= < semiconductor-public-key > \ --from-literal=secret-key= < semiconductor-secret-key >
Set the Langfuse environment variables in each solution’s Helm values:
Data Insights # Helm values key for em-talk2data (Data Insights solution) em-talk2data : extraEnvVars : - name : LANGFUSE_HOST value : "https://langfuse.example.com" - name : LANGFUSE_PUBLIC_KEY valueFrom : secretKeyRef : name : langfuse-api-keys key : public-key - name : LANGFUSE_SECRET_KEY valueFrom : secretKeyRef : name : langfuse-api-keys key : secret-key
Data Governance Same configuration, referencing the data-governance project keys:
# Helm values key for em-data-readiness (Data Governance solution) em-data-readiness : extraEnvVars : - name : LANGFUSE_HOST value : "https://langfuse.example.com" - name : LANGFUSE_PUBLIC_KEY valueFrom : secretKeyRef : name : langfuse-api-keys-dr key : public-key - name : LANGFUSE_SECRET_KEY valueFrom : secretKeyRef : name : langfuse-api-keys-dr key : secret-key
Semiconductor Apply to both the Backend API and AI Agent services:
# Helm values key for em-semi (Semiconductor solution) em-semi : extraEnvVars : - name : LANGFUSE_HOST value : "https://langfuse.example.com" - name : LANGFUSE_PUBLIC_KEY valueFrom : secretKeyRef : name : langfuse-api-keys-semi key : public-key - name : LANGFUSE_SECRET_KEY valueFrom : secretKeyRef : name : langfuse-api-keys-semi key : secret-key
Step 5: Verify Traces After deploying the configuration, trigger an LLM call in any solution:
In Data Insights: ask a natural language question
In Data Governance: run a profiling job
In Semiconductor: send a chat message to the AI agent
Then check the Traces tab in Langfuse, you should see the call appear within seconds.
Troubleshooting
No traces appearing in Langfuse
Verify LANGFUSE_HOST, LANGFUSE_PUBLIC_KEY, and LANGFUSE_SECRET_KEY are set correctly in the pod
Check that the Langfuse host is reachable from the pod: kubectl exec -it <pod> -- curl -I https://langfuse.example.com
Check solution logs for Langfuse callback errors
Verify the public key corresponds to the correct Langfuse project
Langfuse UI not accessible
Check pod status: kubectl get pods -n langfuse
Check worker logs: kubectl logs -n langfuse -l app=langfuse-worker
Verify the NEXTAUTH_URL matches the URL you’re accessing
