Building A Smart Planner Service: Intent To Plan

Hey there, tech enthusiasts! Ever wondered how a machine understands your simple requests and transforms them into complex actions? Today, we're diving deep into the fascinating world of building a Planner Service that takes natural language intents and converts them into actionable plans. This process, often referred to as Intent to Plan Conversion, is a core component of many intelligent systems, from personal assistants to automated robots. Let's break down how we can build one!

Understanding the Core Concepts: Intent, Plan, and Capabilities

Before we jump into the code, let's get our terminology straight. This helps in the Planner Service.

• Intent: This is what you, the user, actually want to accomplish. It's the high-level goal expressed in natural language. For example, “Book me a flight to Paris” or “Turn on the living room lights.”
• Plan: This is the structured representation of the steps needed to fulfill the intent. It's an abstract sequence of tasks, often visualized as a Directed Acyclic Graph (DAG), outlining the capabilities needed and their dependencies.
• Capabilities: These are the atomic actions or services that can be executed. Think of them as individual building blocks. Examples include a flight booking API, a smart light control service, or a weather data retrieval function. The system will search for the relevant capabilities through the registry.

Our Planner Service acts as the brain of the operation, translating the user’s intent into a series of actionable steps. This conversion is crucial for any system that aims to understand and respond to human language.

The Anatomy of Our Planner Service

Now, let's explore the key components we'll be building. In order to build this planner service, we should first add some python files.

backend/planner/intent_parser.py: Decoding User Intent

This module is the linguistic guru of our system. Its primary job is to analyze the user's input (the intent) and extract crucial information. This includes:

• Task Type: What the user wants to do (e.g., book, turn on, find).
• Entities: The objects involved in the task (e.g., flight, lights, weather).
• Constraints: Any specific requirements or limitations (e.g., destination, time, color).

This module will likely use Natural Language Processing (NLP) techniques. Libraries like spaCy or NLTK can be helpful for tasks like named entity recognition and intent classification. The output of the intent_parser.py will be structured data that the plan_builder.py can then use.

backend/planner/plan_builder.py: Crafting the Abstract Plan

This module takes the parsed intent data from the intent_parser.py and constructs an abstract plan. It's like an architect drawing up blueprints before the construction crew gets to work. This plan is often a Directed Acyclic Graph (DAG) that outlines the necessary capabilities and their dependencies.

For example, to book a flight, the plan might include steps like:

1. Find available flights (capability: flight search).
2. Select a flight (capability: flight selection).
3. Provide passenger details (capability: passenger information).
4. Confirm the booking (capability: booking confirmation).

The plan_builder.py is responsible for generating this high-level structure, ensuring that the necessary steps are in the correct order.

/plan/abstract and /plan/concrete Endpoints: The API Interface

These endpoints in planner/main.py will provide the interface for interacting with our Planner Service. They allow external systems to submit intents and receive structured plans in return.

• /plan/abstract: This endpoint takes the user's intent as input and returns the abstract plan generated by the plan_builder.py. The plan will be a structured JSON format, detailing the steps required but without specifying the specific services to use.
• /plan/concrete: This endpoint takes the abstract plan and resolves the capabilities. This requires integration with a Registry Service (more on this below). The endpoint then returns a concrete plan. The concrete plan has specific services to use.

Integration with Registry /search: Capability Resolution

This is where the magic happens! Our Planner Service needs to know which capabilities (services) are available to fulfill the tasks in the plan. This is where the Registry Service comes in. The /search endpoint of the Registry allows the planner to lookup available services. The planner will query the registry with the desired capability types (e.g., flight search) and any relevant constraints (e.g., destination, dates). The registry will return a list of registered services that can fulfill the request.

This integration allows our planner to dynamically discover and use available services, making the system flexible and adaptable to new capabilities as they become available.

Building and Testing Our Planner

Docker Compose and Deployment

We will need to use Docker Compose to run the Planner Service. Docker Compose allows us to define and manage multi-container applications easily. It ensures that all the necessary components (the planner, the registry, etc.) are running and communicating correctly.

Writing Minimal Tests

Testing is crucial to ensure that our Planner Service works as expected. We need to write tests to validate the output structure and verify the registry integration. The tests should cover the following points:

• Output Schema Validation: Does the /plan/abstract endpoint return a JSON plan with the expected structure? Are the required fields present, and do the data types match?
• Registry Integration: Does the /plan/concrete endpoint correctly use the registry to resolve capabilities? Does it choose the appropriate services based on the intent and constraints?

Automated tests will allow us to catch issues early and ensure that our system is robust and reliable.

Putting It All Together: The Flow of the Planner Service

Let's summarize how the Planner Service works:

1. User Input: The user expresses their intent (e.g.,