Here's the revised version with financial examples changed and formatted properly in markdown:
Below is a template version of the solution document. Each section includes a brief description of its purpose as well as example values (drawn from the original solution) that you can adjust to fit your project.
This document provides a complete technical solution design for the [Project Name].
Example:
Detailed Technical Solution Design: Recipe Recommendation Engine
Description:
This section gives a high-level summary of your solution, highlighting key features and design choices such as operational mode, UI framework, data acquisition, and logging.
Example:
- Generates Recommendations On-Demand: The system is triggered by user requests.
- Operates Locally: Designed to run on a local machine without cloud deployment.
- Lightweight Interactive Interface: Implements a simple UI using Streamlit.
- Utilizes pydantic AI Agents: Leverages agent-based processing with validated inputs.
- Acquires Data via Recipe API: Fetches live and historical recipe data.
- Implements Basic Logging: Uses Python's logging module for troubleshooting.
Description:
Include a diagram that visualizes the overall architecture of your system. It should map out major components and how they interact.
Example:
flowchart TD
A[User: Home Cook]
B[Streamlit UI (Local)]
C[AI Agent Controller (pydantic AI)]
D[Recipe Evaluation Module]
E[Nutrition Calculation Module]
F[Data Acquisition Module (Recipe API)]
G[Data Processing Module]
H[Recommendation Generator]
I[Output Formatter]
A --> B
B --> C
C --> F
F --> G
G --> C
C --> D
D --> E
E --> H
H --> I
I --> B
Description:
Show a diagram that details the flow of a user's request through the system—from the initial input to the final output.
Example:
flowchart TD
A[User initiates chat in Streamlit UI]
B[User input: "Recommend recipes for vegetarian dinner"]
C[LLM via pydantic AI Agent processes request]
D[Agent validates input with Pydantic models]
E[Agent selects necessary tools]
F[Tool: Fetch Recipe Details via Recipe API]
G[Tool: Retrieve Ingredient Data]
H[Tool: Compute Nutritional Information]
I[Aggregate data in Data Processing Module]
J[Evaluate recipes in Recipe Evaluation Module]
K[Compute nutrition metrics in Nutrition Calculation Module]
L[Aggregate and generate ranked recommendations]
M[Return response to user in Streamlit UI]
A --> B
B --> C
C --> D
D --> E
E --> F
E --> G
E --> H
F --> I
G --> I
H --> I
I --> J
J --> K
K --> L
L --> M
Description:
Outline your project's folder structure, detailing where each module or component is located along with a brief description of each file's purpose.
Example:
/recipe_recommendation_engine
├── agents
│ ├── __init__.py
│ └── ai_agent.py # Implements the pydantic AI Agent.
├── data
│ ├── __init__.py
│ └── recipe_client.py # Fetches recipe data using Recipe API.
├── models
│ ├── __init__.py
│ └── recipe_evaluator.py # Evaluates recipe strategies.
├── nutrition
│ ├── __init__.py
│ └── nutrition_calculator.py # Computes nutrition metrics.
├── ui
│ ├── __init__.py
│ └── streamlit_ui.py # Implements the UI using Streamlit.
├── utils
│ ├── __init__.py
│ └── config.py # Central configuration.
├── logs
│ └── app.log # Log file.
├── main.py # Application entry point.
└── requirements.txt # Python dependencies.
Description:
List and briefly describe the technologies, programming languages, libraries, and tools used to build the solution.
Example:
- Programming Language: Python 3.9+
- Data Acquisition: Recipe API
- AI Framework: pydantic AI
- User Interface: Streamlit
- Data Processing: Pandas, NumPy
- Logging: Python's built-in logging module
- Deployment: Local machine deployment (no Docker or cloud)
- Version Control: Git
Description:
Provide an in-depth explanation of each major component or module in your system. This section should detail the role, functionality, and rationale for each part of the solution.
Description:
Explain how the module retrieves recipe data. Mention connection setup, subscription to data feeds, and data fetching methods.
Example:
- Role: Connects to Recipe API.
- Functionality: Establishes a connection with credentials, subscribes to necessary data feeds, and retrieves recipe data.
Description:
Detail the AI agent responsible for processing user queries and routing them to the correct tools.
Example:
- Role: Serves as the central controller using pydantic AI.
- Functionality: Validates user input, registers tools, and decides whether to provide a conversational response or invoke a specific tool.
Description:
Describe how the module cleans and processes raw data, computes nutritional information, and prepares data for further analysis.
Example:
- Role: Processes raw recipe data using Pandas and NumPy.
- Functionality: Normalizes data, computes calorie content, macronutrients, etc.
Description:
Explain how the module evaluates different recipes based on the processed data.
Example:
- Role: Scores and ranks recipes.
- Functionality: Compares recipes using nutritional metrics and configurable parameters.
Description:
Outline how the module calculates nutrition metrics and integrates them into the final recommendations.
Example:
- Role: Computes nutrition-adjusted metrics such as calorie content and macronutrient ratios.
- Functionality: Applies nutritional guidelines to recipe outputs.
Description:
Describe the process of aggregating outputs from the recipe evaluation and nutrition calculation modules to produce final recommendations.
Example:
- Role: Aggregates and ranks recommendations.
- Functionality: Merges scores from various modules and formats the final output for display.
Description:
Detail how the user interface is implemented, including its role in accepting queries and displaying responses.
Example:
- Role: Provides a chat-like interface using Streamlit.
- Functionality: Accepts natural language queries and displays the corresponding responses from the AI agent.
Description:
Provides configuration details for the AI agent, including an overview, configuration, and tool integration. Include a sample code snippet to demonstrate usage.
Description:
Summarize the agent's purpose, identifier, and key characteristics.
Example:
- Agent Name: Recipe Recommendation Agent
- Agent ID: RRA-001
- Description: Orchestrates user interactions and routes requests based on validated input.
Description:
Describe how the agent is configured, including input/output schemas, session state management, and logging.
Example:
- Input Schema: Defines fields such as
queryanduser_id. - Output Schema: Specifies fields like
response_textandtool_used.
Description:
List the tools registered with the agent, along with their respective input and output schemas and configurations.
Example:
| Tool Name | Description | Input Schema | Output Schema | Configuration / Attributes |
|---|---|---|---|---|
| Recipe Lookup Tool | Retrieves recipe details | { recipe_name: str } |
{ recipe: dict, timestamp: str } |
Uses Recipe API; basic error handling. |
| Ingredient Retrieval Tool | Fetches ingredient data for a specified recipe | { recipe_name: str } |
{ ingredients: List[dict] } |
Configured for various cuisines. |
| Nutritional Information Calculator | Computes nutritional information | { ingredients: List[dict] } |
{ nutrition: dict } |
Uses USDA database. |
| Recipe Evaluation Tool | Scores and ranks various recipes | { nutrition: dict, preferences: dict } |
{ recipe_scores: dict } |
Uses configurable evaluation metrics. |
| Nutrition Calculation Tool | Computes nutrition-adjusted metrics | { recipe_output: dict, nutrition_config: dict } |
{ nutrition_metrics: dict } |
Applies predefined nutritional guidelines. |
Description:
Show a sample snippet demonstrating how the agent processes a query.
Example:
from pydantic import BaseModel
from pydantic_ai import Agent, Tool
# Agent Models
class AgentInput(BaseModel):
query: str
user_id: str
class AgentOutput(BaseModel):
response_text: str
tool_used: str
# Tool Models for Recipe Lookup
class RecipeLookupInput(BaseModel):
recipe_name: str
class RecipeLookupOutput(BaseModel):
recipe: dict
timestamp: str
def lookup_recipe(input_data: RecipeLookupInput) -> RecipeLookupOutput:
# Replace with actual Recipe API call logic.
return RecipeLookupOutput(
recipe={"name": "Vegetarian Lasagna", "ingredients": ["pasta", "tomato sauce", "vegetables"]},
timestamp="2025-02-17T14:30:00Z"
)
# Register the Recipe Lookup Tool with the agent.
recipe_lookup_tool = Tool(
name="Recipe Lookup Tool",
func=lookup_recipe,
input_model=RecipeLookupInput,
output_model=RecipeLookupOutput
)
def process_query(agent_input: AgentInput) -> AgentOutput:
if "recipe" in agent_input.query.lower():
recipe_name = agent_input.query.split()[-1]
tool_input = RecipeLookupInput(recipe_name=recipe_name)
tool_output = recipe_lookup_tool.run(tool_input)
return AgentOutput(
response_text=f"Here's the recipe for {recipe_name}: {tool_output.recipe}",
tool_used=recipe_lookup_tool.name
)
else:
return AgentOutput(
response_text="Hello! How can I assist you with recipes today?",
tool_used="None"
)
# Example usage:
input_example = AgentInput(query="Get recipe for Vegetarian Lasagna", user_id="cook_001")
agent_response = process_query(input_example)
print(agent_response.json())Description:
Explain the reasoning behind your design choices and the selection of each component. Discuss how these choices support maintainability, scalability, and performance.
Example:
- Modularity & pydantic AI Integration: Simplifies tool registration and input validation.
- Recipe API for Data Acquisition: Ensures reliable recipe data access.
- Lightweight UI via Streamlit: Provides a user-friendly interface for local use.
- Clear Separation of Concerns: Facilitates easier maintenance and future enhancements.
Description:
Summarize the overall design and its benefits. Reinforce the key aspects of your solution and how they meet the project objectives.
Example:
This comprehensive design document outlines the blueprint for implementing the Recipe Recommendation Engine on a local machine. By leveraging pydantic AI for intelligent tool routing and Recipe API for reliable recipe data access, the solution achieves a modular, maintainable, and user-friendly architecture.