cxblueprint 0.0.post33

Python DSL for Amazon Connect contact flow generation

CxBlueprint

Programmatic Amazon Connect contact flow generation using Python. Allows for Ai models to generate contact flows based on human languages way easier than writing JSON as majority of the time it will fail to generate a valid contact flow from scratch.

Simple Example

from cxblueprint import Flow

flow = Flow.build("Burger Order")

welcome = flow.play_prompt("Welcome to Burger Palace!")
menu = flow.get_input("Press 1 for Classic Burger or 2 for Veggie Burger", timeout=10)
welcome.then(menu)

classic = flow.play_prompt("You selected Classic Burger. Your order is confirmed!")
veggie = flow.play_prompt("You selected Veggie Burger. Your order is confirmed!")
error_msg = flow.play_prompt("Invalid selection. Goodbye.")

disconnect = flow.disconnect()

# Chain menu options with error handling
menu.when("1", classic) \
    .when("2", veggie) \
    .otherwise(error_msg) \
    .on_error("InputTimeLimitExceeded", error_msg) \
    .on_error("NoMatchingCondition", error_msg) \
    .on_error("NoMatchingError", error_msg)

# Or without chaining:
# menu.when("1", classic)
# menu.when("2", veggie)
# menu.otherwise(error_msg)
# menu.on_error("InputTimeLimitExceeded", error_msg)
# menu.on_error("NoMatchingCondition", error_msg)
# menu.on_error("NoMatchingError", error_msg)

classic.then(disconnect)
veggie.then(disconnect)
error_msg.then(disconnect)

flow.compile_to_file("burger_order.json")

Terraform Template Example

Use placeholders for dynamic resource ARNs:

from cxblueprint import Flow

flow = Flow.build("Counter Flow")

welcome = flow.play_prompt("Thank you for calling!")
invoke_counter = flow.invoke_lambda(
    function_arn="${COUNTER_LAMBDA_ARN}",  # Resolved by Terraform
    timeout_seconds="8"
)
welcome.then(invoke_counter)

say_count = flow.play_prompt("You are caller number $.External.count")
invoke_counter.then(say_count)

disconnect = flow.disconnect()
say_count.then(disconnect)
invoke_counter.on_error("NoMatchingError", disconnect)

flow.compile_to_file("counter_flow.json")

Generated Flow Examples

Here's what the generated flows look like in the Amazon Connect console:

Features

• Fluent Python API for building flows
• Naive block positioning for AWS Connect visual canvas
• Automatic UUID generation for blocks
• Error/Conditional handling support
• Integration with AWS Lambda and lexv2 bots
• Template placeholder support for Terraform/IaC
• Decompile existing flows to Python
• Majority of Amazon Connect block types supported
• Shell scripts to download, validate, and test flows against Connect

Possible Future Uses/Ideas:

• Compliance Checking, check flow structures for best practices, encryption, etc.
• Optimization Suggestions, analyze flows for efficiency improvements.
• Output to diagram formats for other visualization tools.
• Template library of common flow patterns.

Quick Start

# See Terraform example
cd terraform_example
python flow_generator.py

# Deploy with Terraform
cd terraform
terraform init
terraform apply

Project Structure

src/
  flow_builder.py       # Main builder API
  decompiler.py         # JSON to Python
  blocks/               # All Connect block types
    contact_actions/    # Actions like CreateTask
      readme.md         # Contains progress on supported blocks
     flow_control_actions/ # Flow control blocks
      readme.md         # Contains progress on supported blocks
     interactions/      # Interaction blocks
      readme.md         # Contains progress on supported blocks
     participant_actions/  # Participant blocks
      readme.md         # Contains progress on supported blocks
examples/               # Sample flows
terraform_example/      # Complete deployment example
docs/                   # API reference

Documentation

• API Reference
• Terraform Example

Requirements

• Python 3.11+
• AWS credentials (for deployment)
• Terraform (optional, for infrastructure)