# Technical Blueprint for Innovative Idea Generator Chatbot (Updated)
## Table of Contents
1. [Introduction](#introduction)
2. [High-Level Overview](#high-level-overview)
- [Purpose and Goals](#purpose-and-goals)
- [Key Functionalities](#key-functionalities)
3. [System Architecture](#system-architecture)
- [Components](#components)
- [Data Flow](#data-flow)
4. [Detailed Module Design](#detailed-module-design)
- [Chatbot Interaction Module](#chatbot-interaction-module)
- [Stage-Based Interaction](#stage-based-interaction)
- [Dynamic Follow-ups](#dynamic-follow-ups)
- [Context Preservation](#context-preservation)
- [Data Collection and Refinement Module](#data-collection-and-refinement-module)
- [Input Parsing](#input-parsing)
- [Data Storage](#data-storage)
- [Data Refinement](#data-refinement)
- [Stage Management Module](#stage-management-module)
- [Stage Buttons Interface](#stage-buttons-interface)
- [Function Modularity](#function-modularity)
- [Web Search Integration Module](#web-search-integration-module)
- [LLM-Triggered Web Searches](#llm-triggered-web-searches)
- [User-Triggered Web Searches](#user-triggered-web-searches)
- [Database Management Module](#database-management-module)
- [Data Storage Format](#data-storage-format)
- [Dynamic Editing](#dynamic-editing)
5. [Implementation Details](#implementation-details)
- [Python and Gradio Usage](#python-and-gradio-usage)
- [Module and Function Organization](#module-and-function-organization)
- [Error Handling and Validation](#error-handling-and-validation)
- [LLM Context and Memory Management](#llm-context-and-memory-management)
- [User Interface Enhancements](#user-interface-enhancements)
6. [Additional Considerations](#additional-considerations)
- [Scalability and Extensibility](#scalability-and-extensibility)
- [Security and Privacy](#security-and-privacy)
- [Testing and Validation](#testing-and-validation)
7. [Conclusion](#conclusion)
---
## Introduction
This blueprint outlines the technical design for the **Innovative Idea Generator Chatbot**, an application aimed at transforming user ideas into well-defined project proposals ready for prototyping. The chatbot guides users through a structured, stage-based workflow, refining their inputs, and dynamically storing data for further editing and retrieval. The application is developed using **Python** and **Gradio**, adhering strictly to Python-based frameworks without the use of JavaScript, Next.js, or similar technologies.
---
## High-Level Overview
### Purpose and Goals
- **Objective**: Assist users in refining raw ideas into structured project proposals.
- **Outcome**: Generate a comprehensive proposal by collecting, refining, and storing user inputs through guided, stage-by-stage interactions.
### Key Functionalities
1. **Stage-Based Interaction**: Users navigate through the idea development process using individual stage buttons.
2. **Dynamic Follow-ups**: Trigger additional questions based on user responses for clarification.
3. **Data Refinement**: Rewrite user inputs for clarity and store them dynamically.
4. **Context Preservation**: Maintain conversation history for seamless interactions.
5. **Web Search Integration**: Allow both LLM and users to perform web searches during the interaction.
6. **Data Storage and Editing**: Store all collected data in a dynamic database accessible for modifications.
7. **Stage Navigation with Buttons**: Replace progress tracking bar with a checklist of clickable stage buttons.
8. **Stage-Specific Processing**: LLM processes one stage at a time based on user-activated buttons.
9. **Customizable AI Settings**: Allow users to select models and adjust generation parameters.
10. **API Key Management**: Enable users to securely input their Hugging Face API keys for model interactions.
---
## System Architecture
### Components
1. **User Interface (UI)**: Built with Gradio, providing a stage-based button interface and corresponding text fields.
2. **Chatbot Engine**: Manages conversations, including stage-specific prompts and response handling.
3. **Language Model (LLM) Interaction**: Utilizes an LLM for generating responses and processing inputs.
4. **Data Storage**: A database (SQL or CSV) for storing refined user inputs and conversation history.
5. **Web Search Module**: Integrates web search capabilities using APIs (e.g., DuckDuckGo Search).
6. **API Management Module**: Handles secure input and storage of user-provided API keys.
7. **Stage Management Interface**: Manages the display and interaction of stage buttons and corresponding text fields.
### Data Flow
1. **User Activation**: User clicks a stage button to initiate a specific stage.
2. **LLM Processing**: Chatbot focuses on the selected stage, prompting the user for relevant information.
3. **User Input**: User provides input for the stage.
4. **Data Refinement**: LLM refines the input for clarity and coherence.
5. **Form Population**: User presses the 'Fill Out Form' button to populate the corresponding text field with refined data.
6. **Data Storage**: Refined inputs are stored in the database.
7. **Web Search**: Triggered by the LLM or user, with results incorporated into the conversation.
8. **Stage Completion**: User marks the stage as complete before moving to the next stage.
9. **Feedback Loop**: Process repeats until all stages are completed.
---
## Detailed Module Design
### Chatbot Interaction Module
#### Stage-Based Interaction
- **Stage Buttons**: Ten individual buttons represent each main question/stage. Users click these buttons to activate and work on specific stages.
- **Exclusive Focus**: Upon activation of a stage button (e.g., 'Idea Name'), the LLM concentrates solely on tasks related to that stage.
- **Sequential Processing**: Users can navigate stages in any order, allowing flexibility in the workflow.
#### Dynamic Follow-ups
- **Conditional Questions**: Based on user responses within a stage, the chatbot may ask additional questions for further clarification.
- **LLM Analysis**: The LLM analyzes responses to determine if more information is needed.
- **Implementation**:
- Use conditional logic to assess the completeness of user inputs.
- Trigger follow-up prompts when necessary to ensure comprehensive data collection.
#### Context Preservation
- **Conversation History**: Maintain a log of all interactions to provide context for each stage.
- **LLM Access**: Pass relevant conversation history to the LLM for consistent and informed responses.
- **Implementation**:
- Utilize a list or database to store message histories.
- Ensure that the LLM receives pertinent history relevant to the current stage without exceeding token limits.
### Data Collection and Refinement Module
#### Input Parsing
- **Structured Data Extraction**: Parse LLM responses to extract relevant information specific to each stage.
- **Tags and Markers**: Use specific markers (e.g., ``) to delineate structured data within responses.
#### Data Storage
- **Database Choice**: Utilize SQL or CSV files for robust data storage.
- **Dynamic Updates**: Allow real-time updates to the stored data as users complete or modify stages.
- **Implementation**:
- Define a data model using Pydantic or similar for validation.
- Implement functions to read and write data to the database seamlessly.
#### Data Refinement
- **LLM Rewriting**: The LLM refines user inputs for clarity, coherence, and conciseness within each stage.
- **Validation**: Ensure the refined data aligns with the expected format and requirements of the stage.
- **Implementation**:
- Employ the LLM to generate refined versions of user inputs.
- Update the database with the refined data upon user confirmation.
### Stage Management Module
#### Stage Buttons Interface
- **Checklist of Stages**: Replace the traditional progress tracking bar with a checklist comprising individual stage buttons.
- **User Activation**: Users manually activate each stage by clicking the corresponding button, initiating focused processing by the LLM.
- **'Fill Out Form' Button**: After completing a stage, users press the 'Fill Out Form' button to populate the associated text field with refined data.
- **Implementation**:
- Design the UI to display ten stage buttons, each linked to a specific question.
- Ensure that each button triggers the LLM to handle only the tasks pertinent to that stage.
#### Function Modularity
- **Compartmentalization**: Each stage is implemented as a separate function or class method to ensure modularity.
- **Independent Modification**: Allows individual stages to be updated or modified without impacting others.
- **Implementation**:
- Define separate methods for each stage within the chatbot class.
- Utilize inheritance or composition for shared functionalities across stages.
### Web Search Integration Module
#### LLM-Triggered Web Searches
- **Automatic Searches**: The LLM determines when external information is necessary and triggers web searches accordingly.
- **Search Execution**: Perform searches using APIs and integrate the results into the conversation context.
- **Implementation**:
- LLM outputs a special token or command when a web search is needed.
- Chatbot captures this command and performs the search using the designated API.
#### User-Triggered Web Searches
- **User Commands**: Users can initiate searches by typing '@' followed by their query.
- **Query Optimization**: The LLM rewrites user queries to enhance search effectiveness.
- **Implementation**:
- Detect messages starting with '@' to identify user-initiated searches.
- Pass the query through the LLM for optimization before performing the search.
### Database Management Module
#### Data Storage Format
- **Structured Data**: Store data in well-defined tables or a structured SQLite database to ensure data integrity.
- **Fields**: Include all necessary fields corresponding to the ten main questions/stages.
- **Implementation**:
- Define a comprehensive schema for the SQL database.
- Use an Object-Relational Mapping (ORM) tool like SQLAlchemy for efficient database interactions.
#### Dynamic Editing
- **User Modifications**: Allow users to edit previous inputs through the UI, updating the corresponding text fields.
- **Real-Time Updates**: Reflect changes immediately in the database to maintain data consistency.
- **Implementation**:
- Provide editable text fields beneath each stage button.
- Implement functions to update the database upon user edits, ensuring that the refined data remains accurate and up-to-date.
---
## Implementation Details
### Python and Gradio Usage
- **Framework**: Utilize Gradio for the UI, providing a web-based interface with stage buttons and corresponding text fields.
- **Libraries**:
- **LLM Interaction**: Use `huggingface_hub` for LLM API calls.
- **Data Models**: Use `pydantic` for data validation and modeling.
- **Web Search**: Use `duckduckgo_search` API for web searches.
- **Environment Variables**: Use `python-dotenv` for managing API keys and configuration.
### Module and Function Organization
- **Main Modules**:
- `main.py`: Entry point of the application.
- `chatbot.py`: Contains the `InnovativeIdeaChatbot` class and related functions.
- `data_models.py`: Defines data models using Pydantic.
- `utils.py`: Utility functions for parsing, web search, etc.
- `config.py`: Configuration settings and constants.
- **Function Structure**:
- Each function should have clear input/output parameters.
- Include docstrings with type hints for clarity and maintainability.
- Ensure that each stage's function handles only its specific tasks to maintain modularity.
### Error Handling and Validation
- **User Input Validation**: Check for empty, invalid, or malformed inputs before processing.
- **API Error Handling**: Catch exceptions from API calls and provide user-friendly error messages or prompts.
- **Data Validation**: Use Pydantic models to validate data before storage to ensure data integrity.
- **Implementation**:
- Utilize try-except blocks around critical sections, especially around API interactions.
- Provide default values or prompts to guide users in case of errors or invalid inputs.
- Implement validation checks before updating the database with refined data.
### LLM Context and Memory Management
- **Conversation Length**: Manage the size of the conversation history to stay within LLM token limits, ensuring efficient processing.
- **Contextual Responses**: Ensure the LLM receives sufficient context relevant to the current stage without overwhelming it with unnecessary information.
- **Implementation**:
- Implement a sliding window mechanism to include only recent and relevant messages in the LLM prompt.
- Summarize older parts of the conversation if necessary to maintain context within token limits.
- Ensure that each stage's context is isolated to prevent cross-stage contamination.
### User Interface Enhancements
- **Stage Buttons**: Design intuitive and clearly labeled buttons for each of the ten stages, allowing easy navigation.
- **Text Fields**: Place editable text fields beneath each stage button to display and allow modifications of the refined data.
- **'Fill Out Form' Button**: Include a dedicated button for users to populate the corresponding text field with the refined data after completing a stage.
- **Visual Indicators**: Implement visual cues (e.g., color changes, checkmarks) to indicate the completion status of each stage.
- **Responsive Design**: Ensure the UI is responsive and user-friendly across different devices and screen sizes.
- **Implementation**:
- Utilize Gradio's layout features to arrange buttons and text fields logically.
- Incorporate CSS styling within Gradio to enhance the visual appeal and usability of the interface.
- Ensure that the UI updates dynamically based on user interactions and stage completions.
### API Key Management
- **Secure Input**: Allow users to input their Hugging Face API keys securely through the UI.
- **Environment Variables**: Store API keys using environment variables to prevent exposure and enhance security.
- **Implementation**:
- Provide a secure input field in the UI for users to enter their API keys.
- Utilize `python-dotenv` to manage and access API keys within the application without hardcoding them.
- Ensure that API keys are never exposed in logs or error messages.
---
# Appendices
## Appendix A: Data Model Definition
### InnovativeIdeaForm (Pydantic BaseModel)
Defines the structure for storing refined user inputs.
```python
from pydantic import BaseModel, Field
from typing import List, Optional
class InnovativeIdeaForm(BaseModel):
idea_name: Optional[str] = Field(None, description="Name of the innovative idea")
idea_overview: Optional[str] = Field(None, description="Simple overview of the idea")
problem_solution: Optional[str] = Field(None, description="Customer problem and how the idea addresses it")
target_customers: Optional[str] = Field(None, description="Potential customers and markets")
market_value: Optional[str] = Field(None, description="Estimated market value")
existing_solutions: Optional[str] = Field(None, description="Similar existing ideas or products")
unique_value: Optional[str] = Field(None, description="How the idea is better or different")
technical_challenges: Optional[str] = Field(None, description="Difficult technical problems")
legal_barriers: Optional[str] = Field(None, description="Possible legal issues")
data_dependencies: Optional[str] = Field(None, description="Required data types")
team_roles: Optional[List[str]] = Field(None, description="Required job roles")
timeline: Optional[str] = Field(None, description="Estimated time to build main components")
additional_info: Optional[str] = Field(None, description="Any additional information")
```
## Appendix B: Function Signatures
### Chatbot Class Methods
```python
from typing import List, Dict, Tuple, Optional, Union
class InnovativeIdeaChatbot:
def __init__(self):
"""
Initializes the chatbot with necessary configurations and data storage.
"""
pass
def get_initial_greeting(self) -> str:
"""
Returns the initial greeting message to the user.
"""
pass
def activate_stage(self, stage_name: str) -> Optional[str]:
"""
Activates the specified stage and returns the prompt for that stage.
Args:
stage_name (str): The name of the stage to activate.
Returns:
Optional[str]: The prompt message for the activated stage.
"""
pass
def process_stage_input(self, stage_name: str, message: str) -> Tuple[str, Optional[str]]:
"""
Processes the user's input for a specific stage and returns the refined data.
Args:
stage_name (str): The name of the current stage.
message (str): The user's input message.
Returns:
Tuple[str, Optional[str]]: A tuple containing the refined data and an optional follow-up prompt.
"""
pass
def fill_out_form(self, stage_name: str) -> Optional[str]:
"""
Populates the corresponding text field with refined data for the specified stage.
Args:
stage_name (str): The name of the stage to fill out.
Returns:
Optional[str]: The refined data to be displayed in the text field.
"""
pass
def update_idea_form(self, stage_name: str, form_data: str):
"""
Updates the idea form with the provided refined data for a specific stage.
Args:
stage_name (str): The name of the stage being updated.
form_data (str): The refined data to update in the form.
"""
pass
def reset(self):
"""
Resets the chatbot state for a new session, clearing all stored data and history.
"""
pass
```
## Appendix C: Sample System Prompt Template
```text
You are Myamoto, an AI assistant designed to guide users through the innovative idea development process. Your task is to help refine and expand their ideas, focusing strictly on one stage at a time. Be encouraging, creative, and analytical in your responses. Remember to:
1. Analyze the user's input carefully and extract relevant information for the current stage only.
2. Provide constructive feedback and suggestions to improve the idea, specific to the current stage.
3. If the user's response is insufficient, ask follow-up questions to gather more details.
4. Do not move to the next stage automatically. The user must explicitly indicate when they want to move on.
5. Maintain a supportive and enthusiastic tone throughout the conversation.
6. Always include a section in your response with the extracted information for the current stage.
7. If the user tries to skip ahead or provide information for future stages, gently redirect them to the current stage.
8. Use analogies, examples, or thought-provoking questions to help users think more deeply about their ideas.
9. When you feel the current stage has been thoroughly explored, explicitly state "stage complete" or "ready to move on" in your response.
10. Break down your thinking process into steps, enclosed in tags.
11. Provide a reflection on your thought process, enclosed in tags.
12. If you think additional information from a web search might be helpful, suggest it to the user. Ask them to use '@' followed by their search query to perform a web search.
Current stage: {current_stage}
Stage prompt: {stage_prompt}
```