# Technical Specification Document for Food Order Bot
**Version:** 1.0
**Date:** February 22, 2025
**Prepared by:** Grok 3 (xAI)
## 1. Introduction
### 1.1 Purpose
This Technical Specification Document (TSD) provides a detailed technical blueprint for the Food Order Bot, a conversational AI system for food ordering. It defines the software architecture, technology stack, components, interfaces, requirements, and acceptance criteria to guide development, ensure consistency, and verify completion.
### 1.2 Scope
The Food Order Bot enables users to interact via a chat interface (Gradio) to log in, browse restaurant menus, place orders, manage orders, and query multi-city availability and price constraints using a Mistral LLM. The system targets small to medium-sized food delivery startups, aiming for scalability, reliability, and user satisfaction.
## 2. Software Architecture and Tech Stack
### 2.1 Architecture Overview
The Food Order Bot follows a microservices-inspired architecture with a clear separation of concerns:
- **Frontend (Client):** Gradio-based web interface for user interaction.
- **Backend (API Server):** FastAPI-based server for business logic and API endpoints.
- **LLM Integration:** Mistral LLM for natural language processing and tool calls.
- **Persistence Layer:** SQLite for storing orders and sessions.
- **Configuration and Utilities:** Centralized configuration and logging for maintainability.
#### 2.1.1 Components
- **Gradio Client (`gradio_client.py`):**
- Handles user input (text/voice) and displays responses in a chat interface.
- Communicates with the backend via HTTP/REST APIs.
- **FastAPI Server (`mock_server.py`):**
- Exposes endpoints for login, menu retrieval, orders, and query processing.
- Integrates with `order_processor.py` for business logic.
- **Order Processor (`order_processor.py`):**
- Executes LLM tool calls, manages order logic, and interacts with the database.
- **LLM Module (`llm.py`):**
- Processes natural language inputs using Mistral, invoking tool calls for backend actions.
- **Database (`db.py`):**
- Manages SQLite storage for sessions and orders.
- **Configuration (`config.py`):**
- Loads settings from `.env` for flexibility.
- **Logging (`logging_config.py`):**
- Provides structured logging to `food_order_bot.log`.
- **Models (`models.py`):**
- Defines Pydantic models for request/response validation.
#### 2.1.2 Interfaces
- **Gradio ↔ FastAPI:** HTTP/REST API calls (e.g., POST `/process_order`, GET `/menu`).
- Format: JSON (e.g., `{ "session_id": "uuid", "user_input": "login user1 password123" }`).
- **FastAPI ↔ LLM:** Internal function calls to `llm.process_with_tools` for tool call generation.
- Format: LLM response with tool calls (e.g., `{"tool_calls": [{"name": "login", "arguments": {"username": "user1", "password": "password123"}}]}`).
- **FastAPI ↔ Database:** SQLite queries via `db.py` for CRUD operations.
- Format: Python dictionaries or lists (e.g., `{"order_id": "uuid", "user_id": "user1", "items": [...]`).
- **FastAPI ↔ Mistral API:** HTTP/REST calls to Mistral API for LLM processing.
- Format: JSON requests/responses as per Mistral’s `/v1/chat/completions` (e.g., `{"model": "mistral-large-latest", "messages": [...], "tools": [...]`).
### 2.2 Technology Stack
- **Programming Language:** Python 3.10+
- **Web Framework:** FastAPI for backend, Gradio for frontend.
- **LLM:** Mistral API (via mistralai SDK, version 1.0.0+).
- **Database:** SQLite (via sqlite3).
- **HTTP Clients:** `httpx` for async HTTP requests, `requests` for sync calls.
- **Testing:** `pytest` for unit and integration tests.
- **Code Quality:** `flake8` for linting, `black` for formatting.
- **Logging:** Custom logging with logging module.
- **Configuration:** `python-decouple` or custom `config.py` with `.env`.
- **Version Control:** Git.
## 3. Requirements
### 3.1 Functional Requirements (Derived from High-Level Product Specification)
The high-level product specification likely includes a scalable, user-friendly food ordering system with LLM-driven natural language processing. Based on this, the functional requirements are:
- **User Authentication:**
- Users can log in with a username and password (e.g., `login user1 password123`).
- System generates and manages JWT tokens for authenticated sessions.
- **Menu Browsing:**
- Users can view restaurant menus by city (default: Bengaluru) via `menu` or `list restaurants in <city>`.
- Supports multi-city queries (e.g., New York, Los Angeles, Chicago, Houston, Phoenix).
- **Order Management:**
- Users can place orders (e.g., `1 butter idli, add 2 ghee paddu from Rameshwaram Cafe`).
- Supports order modification (e.g., `remove butter idli`), review (`review order`), confirmation (`confirm`), and cancellation (`cancel`).
- Handles price constraints (e.g., `order items with price less than 300 Rs`).
- **Natural Language Processing:**
- LLM processes natural language inputs via tool calls, mapping to backend functions/APIs (e.g., `login`, `add_to_order`).
- Supports complex queries (e.g., city filtering, price filtering).
- **Order Persistence:**
- Orders and sessions persist in SQLite, retrievable via `/orders/{order_id}` and `/orders/history` (future milestone).
- **Multi-City Support:**
- Lists cities (`which cities are available?`) and filters restaurants by city.
- Defaults to Bengaluru unless specified.
### 3.2 Non-Functional Requirements
- **Performance:**
- Handle 1,000 concurrent users with an average response time < 2 seconds per request.
- Implement rate limiting (100 requests/hour/user) to prevent abuse.
- **Scalability:**
- Support horizontal scaling via cloud deployment (e.g., AWS/GCP) with Docker.
- Use SQLite for prototyping, with plans for PostgreSQL or MongoDB in production.
- **Reliability:**
- Achieve 99.9% uptime in production, with error logging and recovery mechanisms.
- Handle LLM API outages gracefully with fallback responses.
- **Security:**
- Use JWT for authentication, stored securely in sessions.
- Encrypt sensitive data (e.g., passwords) and log only non-sensitive information.
- **Usability:**
- Provide an intuitive Gradio interface with clear responses and no formatting issues (e.g., `\n` rendering).
- Support voice input for accessibility.
- **Maintainability:**
- Adhere to PEP 8 with `flake8` and `black` for code quality.
- Use modular design with clear documentation and version control (Git).
- **Testing:**
- Achieve 80% test coverage with `pytest` for unit, integration, and end-to-end tests.
- Conduct weekly integration tests and final user testing before launch.
## 4. Definition of Done (DoD)
A feature or milestone is considered "Done" when:
- **Code Complete:**
- All code is written, reviewed, and merged into the main branch.
- Code passes `flake8` linting and `black` formatting checks.
- **Testing Complete:**
- Unit tests cover 80% of new code, and integration tests verify functionality.
- End-to-end tests confirm user flows (e.g., login → order → confirm) work as expected.
- **Documentation Updated:**
- Technical specification, milestone document, and inline code comments are updated.
- API documentation (OpenAPI/Swagger) is generated and reviewed.
- **Deployment Ready:**
- Code is deployable to a staging environment with Docker.
- CI/CD pipeline (GitHub Actions) runs successfully, including linting, testing, and build.
- **Stakeholder Approval:**
- Demonstrated to the engineering team and stakeholders, with feedback incorporated.
- Meets acceptance criteria (see below).
## 5. Acceptance Criteria
A feature or milestone is accepted when:
- **Functional Requirements Met:**
- Users can log in, browse menus, place orders, and manage them via natural language inputs.
- Multi-city and price-filtering queries work correctly (e.g., list restaurants in New York, order items < 300 Rs).
- LLM tool calls trigger the correct backend functions/APIs (e.g., login → `/login`, add_to_order → `/process_order`).
- **Non-Functional Requirements Met:**
- Response time < 2 seconds for 95% of requests under 1,000 concurrent users.
- Rate limiting prevents abuse (100 requests/hour/user).
- System uptime > 99.9% in staging, with logged errors handled gracefully.
- Gradio interface is intuitive, with no formatting issues (e.g., `\n` renders as line breaks).
- Security measures (JWT, encryption) are verified by penetration testing.
- **Testing Verified:**
- All tests pass (unit, integration, end-to-end) with 80% coverage.
- No critical bugs reported during final user testing.
- **Documentation Verified:**
- Technical specification, milestone document, and API docs are clear and complete.
- Stakeholders approve the feature/milestone based on demonstrations.
- **Deployment Tested:**
- Staging deployment succeeds, and the system performs as expected under load.
- CI/CD pipeline confirms build and deployment readiness.
## 6. Components and Interfaces (Detailed)
### 6.1 Components
- **Gradio Client:**
- Input: User text/voice via `gr.Textbox/gr.Audio`.
- Output: Chat responses in `gr.Chatbot`.
- Interface: HTTP/REST to `/process_order` (JSON payloads).
- **FastAPI Server:**
- Endpoints:
- `/login` (POST): Authenticate user, return JWT.
- `/menu` (GET): Return restaurant menus.
- `/orders/{order_id}` (GET): Retrieve an order.
- `/orders` (POST): Place an order.
- `/process_order` (POST): Handle all LLM-driven queries.
- Logic: Delegates to `order_processor.py` for tool execution.
- **Order Processor:**
- Functions: `generate_order_summary`, `remove_item_from_order`, `display_menu`, `execute_tool_call`, `process_order`.
- Interface: Calls `llm.process_with_tools`, interacts with `db.py`, and makes HTTP calls to Mistral API.
- **LLM Module:**
- Function: `process_with_tools` processes inputs, returns tool calls.
- Interface: HTTP to Mistral API, internal calls to `order_processor.py`.
- **Database:**
- Tables: `sessions`, `orders` in SQLite.
- Interface: Python functions (`save_order`, `load_order`, `save_state`, `load_state`).
- **Configuration:**
- File: `.env` parsed by `config.py`.
- Interface: Python dictionary access in all modules.
- **Logging:**
- File: `food_order_bot.log`.
- Interface: logging module calls from all components.
### 6.2 Interfaces
- **Gradio ↔ FastAPI:** HTTP/REST (JSON) via `httpx` or `requests`.
- **FastAPI ↔ LLM:** Python function calls (`process_with_tools`).
- **FastAPI ↔ Database:** SQLite queries via `db.py`.
- **FastAPI ↔ Mistral API:** HTTP/REST (JSON) via `httpx`.
## 7. Notes
- **Risks:** Dependencies on Mistral API availability, Gradio UI rendering bugs, or SQLite scaling limits.
- **Future Work:** Transition to PostgreSQL for production, implement caching for performance, and add real-time analytics.
## 8. Contact Information
- **Author:** Grok 3 (xAI)
- **Email:** support@xai.com (for simulation purposes)
- **Version Control:** Use Git for tracking changes.