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Conversations Database Viewer

This project provides a comprehensive interface for managing and analyzing conversation data stored in a SQLite database. The application is built with Flask and supports various utilities such as viewing, linking, and searching through conversation records.

Table of Contents

  1. Project Structure
  2. Setup Instructions
  3. Usage
  4. Scripts Overview
  5. Key Features
  6. Data Schema
  7. Requirements
  8. Troubleshooting

Project Structure

The project is organized into the following main folders and files:

├── app/
│   ├── __init__.py               # Flask initialization
│   ├── app.py                    # Main app setup and blueprint registration
│   ├── db.py                     # Database connection utility
│   ├── parsers.py                # Parsing JSON data for conversation details
│   ├── routes.py                 # Route definitions for web interface
│   ├── utils.py                  # Utility functions (e.g., timestamp formatting)
│   ├── helpers.py                # Helper functions to fetch additional data
├── debug_scripts/
│   ├── add_timestamp.py          # Migrates and updates 'timestamp' fields in Conversations table
│   ├── find.py                   # Script to search for specific IDs in database tables
│   ├── link_orphan_db.py         # Script to link orphaned messages to conversations
│   ├── timestamp_fix.py          # Script to fix missing timestamps in conversation records
├── static/
│   └── style.css                 # CSS for styling the web interface
├── templates/                    # HTML templates for the app views
│   ├── index.html
│   ├── base.html
│   ├── review_orphaned_messages.html
│   ├── export_template.html
│   ├── message_detail.html
│   ├── conversation.html
├── data/
│   └── search_history.json       # JSON file to log search history
├── GPT_conversations_database.db # SQLite database file 
├── requirements.txt              # Python dependencies
└── run.py                        # Script to run the Flask application

Setup Instructions

  1. Clone the Repository
    Clone this repository to your local machine:

    git clone https://github.com/sulaimonao/Conversations--Database-Search-App.git

  2. Install Dependencies
    Install the required Python packages by running:

    pip install -r requirements.txt

  3. Database Setup
    Ensure that the GPT_conversations_database.db file is present in the project root. This SQLite database stores the conversation records.

    • If you encounter issues with missing or outdated columns, run the debug_scripts/add_timestamp.py script to migrate and update the timestamp column.

Usage

Running the Application

Start the Flask server with:

python run.py

The application will start in development mode and be accessible at http://127.0.0.1:5000.

Navigating the Interface

  • Home (/): Displays a list of conversations with options to filter by date and search by keywords.
  • View Conversation: Click on a conversation to view detailed messages and metadata.
  • Review Orphaned Messages (/review_orphaned_messages): A page to review and link messages lacking a conversation ID.

Scripts Overview

run.py

The main entry point to run the Flask application. This script imports the app instance and lists all registered endpoints.

app/

Contains the core application files, including:

  • parsers.py: Defines parse_conversation_data() to process conversation and message metadata.
  • routes.py: Provides endpoints for viewing, searching, and exporting conversation data.
  • db.py: Supplies a reusable database connection via get_db_connection().

debug_scripts/

Scripts to assist with database maintenance:

  • add_timestamp.py: Adds and populates the timestamp column in the Conversations table.
  • link_orphan_db.py: Links orphaned messages to appropriate conversations based on timestamps.
  • timestamp_fix.py: Fixes timestamp data in cases where it is null.

Key Features

  1. Conversation Search and Filter: Search conversations by keywords and filter by date.
  2. Detailed View: Inspect conversation messages, metadata, and additional insights.
  3. Orphaned Message Linking: Automatically link orphaned messages to potential conversations.
  4. Export Options: Export conversations as JSON or HTML files.
  5. Database Maintenance Tools: Scripts for troubleshooting and updating the database.

Data Schema

Key Tables:

  1. Conversations

    • Columns: conversation_id, title, create_time, update_time, timestamp

  2. Messages

    • Columns: message_id, conversation_id, content, author_role, create_time

  3. Feedback

    • Stores user feedback linked to specific messages.

  4. ModelComparisons

    • Logs model-generated comparison data.

Requirements

  • Python 3.10+
  • Flask==2.2.5
  • pandas==1.5.3

Troubleshooting

  1. Database Issues

    • Run debug_scripts/add_timestamp.py if the timestamp column is missing or outdated.
    • Ensure GPT_conversations_database.db is in the root directory.

  2. Parsing Errors

    • Confirm all conversation data adheres to the expected schema with valid conversation_data.

  3. Search Issues

    • Validate the search_history.json file format and its path in the data/ directory.