Conductor AI Workflow Examples

This folder contains ready-to-use workflow examples demonstrating the AI capabilities of Conductor.

Prerequisites

1. Start Conductor Server

Ensure Conductor is running with AI integrations enabled:

bash

# From the conductor root directory
./gradlew bootRun

2. Configure AI Providers

Add the following to your conductor server configuration file:

properties

# Enable AI integrations
conductor.integrations.ai.enabled=true

# OpenAI (required for most examples)
conductor.ai.openai.apiKey=sk-your-openai-api-key

# Anthropic (optional, for RAG examples)
conductor.ai.anthropic.apiKey=sk-ant-your-anthropic-key

# Google Vertex AI (optional, for Gemini/Veo video examples)
conductor.ai.gemini.project-id=your-gcp-project
conductor.ai.gemini.location=us-central1

# PostgreSQL Vector DB (for RAG/embedding examples)
conductor.vectordb.instances[0].name=postgres-prod
conductor.vectordb.instances[0].type=postgres
conductor.vectordb.instances[0].postgres.datasourceURL=jdbc:postgresql://localhost:5432/vectors
conductor.vectordb.instances[0].postgres.user=conductor
conductor.vectordb.instances[0].postgres.password=secret
conductor.vectordb.instances[0].postgres.dimensions=1536

3. MCP Weather Server (for MCP examples)

Install and start the MCP weather server:

bash

# Install the MCP weather server
pip install mcp-server-fetch

# Start the server in streamable HTTP mode
python3 -m mcp_server_fetch --mode streamable-http --host localhost --port 3001 --stateless

The server will be available at http://localhost:3001/mcp.

Available Examples

File	Description	Requirements
`01-chat-completion.json`	Basic chat with GPT-4o-mini	OpenAI
`02-generate-embeddings.json`	Generate text embeddings	OpenAI
`03-image-generation.json`	Generate images with DALL-E 3	OpenAI
`04-audio-generation.json`	Text-to-speech with OpenAI TTS	OpenAI
`05-semantic-search.json`	Index and search documents	OpenAI, PostgreSQL
`06-rag-basic.json`	Basic RAG with search + answer	OpenAI/Anthropic, PostgreSQL
`07-rag-complete.json`	Full RAG demo (index + search + answer)	OpenAI, PostgreSQL
`08-mcp-list-tools.json`	List tools from MCP server	MCP Server
`09-mcp-call-tool.json`	Call MCP tool (weather)	MCP Server
`10-mcp-ai-agent.json`	AI agent with MCP tools	OpenAI/Anthropic, MCP Server
`11-video-openai-sora.json`	Generate video with OpenAI Sora-2 (async)	OpenAI
`12-video-gemini-veo.json`	Generate video with Google Veo-3 (async)	Google Vertex AI
`13-image-to-video-pipeline.json`	Image + video generation pipeline	OpenAI
`14-stabilityai-image.json`	Image generation with Stability AI (SD3.5)	Stability AI
`15-pdf-generation.json`	Generate PDF from markdown content	None (built-in)
`16-llm-to-pdf-pipeline.json`	LLM generates report → convert to PDF	OpenAI

Quick Start

Step 1: Register a Workflow

bash

# Register the chat completion workflow
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @01-chat-completion.json

Step 2: Execute the Workflow

bash

# Run the workflow (no input needed for hardcoded examples)
curl -X POST 'http://localhost:8080/api/workflow/chat_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

Step 3: Check the Result

bash

# Get workflow execution status (replace {workflowId} with the returned ID)
curl -X GET 'http://localhost:8080/api/workflow/{workflowId}'

Example Commands

1. Chat Completion

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @01-chat-completion.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/chat_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

2. Generate Embeddings

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @02-generate-embeddings.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/embedding_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

3. Image Generation

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @03-image-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/image_gen_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

4. Audio Generation (TTS)

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @04-audio-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/tts_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

5. Semantic Search

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @05-semantic-search.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/semantic_search_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

6. RAG (Basic)

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @06-rag-basic.json

# Execute with a question
curl -X POST 'http://localhost:8080/api/workflow/rag_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"question": "What is Conductor?"}'

7. RAG (Complete Demo)

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @07-rag-complete.json

# Execute (no input needed - fully self-contained)
curl -X POST 'http://localhost:8080/api/workflow/complete_rag_demo' \
  -H 'Content-Type: application/json' \
  -d '{}'

8. MCP List Tools

bash

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @08-mcp-list-tools.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/mcp_list_tools_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

9. MCP Call Tool (Weather)

bash

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @09-mcp-call-tool.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/mcp_weather_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

10. MCP AI Agent

bash

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @10-mcp-ai-agent.json

# Execute with a task
curl -X POST 'http://localhost:8080/api/workflow/mcp_ai_agent_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"task": "Get the current weather in San Francisco"}'

11. Video Generation (OpenAI Sora)

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @11-video-openai-sora.json

# Execute (async -- returns workflowId immediately, polls internally until video is ready)
curl -X POST 'http://localhost:8080/api/workflow/video_gen_openai_sora' \
  -H 'Content-Type: application/json' \
  -d '{}'

12. Video Generation (Google Gemini Veo)

bash

# Requires Google Vertex AI credentials (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @12-video-gemini-veo.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/video_gen_gemini_veo' \
  -H 'Content-Type: application/json' \
  -d '{}'

13. Image-to-Video Pipeline

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @13-image-to-video-pipeline.json

# Execute (generates a DALL-E image first, then a Sora video)
curl -X POST 'http://localhost:8080/api/workflow/image_to_video_pipeline' \
  -H 'Content-Type: application/json' \
  -d '{}'

14. Image Generation (Stability AI)

bash

# Requires STABILITY_API_KEY environment variable

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @14-stabilityai-image.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/image_gen_stabilityai' \
  -H 'Content-Type: application/json' \
  -d '{}'

15. PDF Generation (Markdown to PDF)

bash

# No external API keys required -- uses built-in PDFBox renderer

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @15-pdf-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/pdf_generation_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

16. LLM-to-PDF Pipeline (Report Generation)

bash

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @16-llm-to-pdf-pipeline.json

# Execute with a topic and audience
curl -X POST 'http://localhost:8080/api/workflow/llm_to_pdf_pipeline' \
  -H 'Content-Type: application/json' \
  -d '{"topic": "Cloud Migration Best Practices", "audience": "CTO and engineering leadership"}'

Register All Workflows at Once

bash

# Register all example workflows
for f in *.json; do
  echo "Registering $f..."
  curl -s -X POST 'http://localhost:8080/api/metadata/workflow' \
    -H 'Content-Type: application/json' \
    -d @"$f"
  echo ""
done

Troubleshooting

"VectorDB not found: postgres-prod"

Ensure you have configured the PostgreSQL vector database in your application.properties:

properties

conductor.vectordb.instances[0].name=postgres-prod
conductor.vectordb.instances[0].type=postgres
conductor.vectordb.instances[0].postgres.datasourceURL=jdbc:postgresql://localhost:5432/vectors
conductor.vectordb.instances[0].postgres.user=conductor
conductor.vectordb.instances[0].postgres.password=secret
conductor.vectordb.instances[0].postgres.dimensions=1536

"No configuration found for: openai"

Ensure you have set the OpenAI API key:

properties

conductor.ai.openai.apiKey=sk-your-openai-api-key

MCP Server Connection Refused

Verify the MCP server is running:
bash
```
curl http://localhost:3001/mcp
```
Check the server logs for errors
Ensure you're using the correct port in the workflow (default: 3001)

PostgreSQL Vector Extension Not Found

Ensure the pgvector extension is installed in your PostgreSQL database:

sql

CREATE EXTENSION IF NOT EXISTS vector;

Conductor AI Workflow Examples

Conductor AI Workflow Examples

Prerequisites

1. Start Conductor Server

2. Configure AI Providers

3. MCP Weather Server (for MCP examples)

Available Examples

Quick Start

Step 1: Register a Workflow

Step 2: Execute the Workflow

Step 3: Check the Result

Example Commands

1. Chat Completion

2. Generate Embeddings

3. Image Generation

4. Audio Generation (TTS)

5. Semantic Search

6. RAG (Basic)

7. RAG (Complete Demo)

8. MCP List Tools

9. MCP Call Tool (Weather)

10. MCP AI Agent

11. Video Generation (OpenAI Sora)

12. Video Generation (Google Gemini Veo)

13. Image-to-Video Pipeline

14. Image Generation (Stability AI)

15. PDF Generation (Markdown to PDF)

16. LLM-to-PDF Pipeline (Report Generation)

Register All Workflows at Once

Troubleshooting

"VectorDB not found: postgres-prod"

"No configuration found for: openai"

MCP Server Connection Refused

PostgreSQL Vector Extension Not Found

License