step2_outfit_with_switch

Outfit Recommendation with Switch Example

This example demonstrates how to use the framework for outfit recommendation tasks with switch_case functionality. The example code can be found in the examples/step2_outfit_with_switch directory.

   cd examples/step2_outfit_with_switch

Overview

This example implements an outfit recommendation workflow that uses switch-case functionality to conditionally include weather information in the recommendation process. The workflow consists of the following key components:

1. Input Interface

   • Handles user input containing clothing requests and image data
   • Processes and caches any uploaded images
   • Extracts the user's outfit request instructions

2. Weather Decision Logic

   • WeatherDecider: Analyzes the user's request to determine if weather information is needed
   • Makes a binary decision (0 or 1) based on context in the user's request
   • Controls whether weather data should be fetched

3. Conditional Weather Search

   • WeatherSearcher: Only executes if WeatherDecider returns 0 (weather info needed)
   • Uses web search functionality to fetch current weather conditions
   • Integrates weather data into the recommendation context

4. Outfit Recommendation

   • Generates final clothing suggestions based on:
     • User's original request
     • Weather information (if available)
     • Any provided image context
   • Provides complete outfit recommendations

The workflow follows this sequence:

Prerequisites

• Python 3.10+
• Required packages installed (see requirements.txt)
• Access to OpenAI API or compatible endpoint (see configs/llms/gpt.yml)
• Access to Bing API key for web search functionality to search real-time weather information for outfit recommendations (see configs/tools/websearch.yml)
• Redis server running locally or remotely
• Conductor server running locally or remotely

Configuration

The container.yaml file is a configuration file that manages dependencies and settings for different components of the system, including Conductor connections, Redis connections, and other service configurations. To set up your configuration:

1. Generate the container.yaml file:

   python compile_container.py

   This will create a container.yaml file with default settings under examples/step2_outfit_with_switch.

2. Configure your LLM settings in configs/llms/gpt.yml and configs/llms/text_res.yml:

   • Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file

   export custom_openai_key="your_openai_api_key"
   export custom_openai_endpoint="your_openai_endpoint"

   • Configure other model settings like temperature as needed through environment variable or by directly modifying the yml file

3. Configure your Bing Search API key in configs/tools/websearch.yml:

   • Set your Bing API key through environment variable or by directly modifying the yml file

   export bing_api_key="your_bing_api_key"

4. Update settings in the generated container.yaml:

   • Modify Redis connection settings:
     • Set the host, port and credentials for your Redis instance
     • Configure both redis_stream_client and redis_stm_client sections
   • Update the Conductor server URL under conductor_config section
   • Adjust any other component settings as needed

Running the Example

3. Run the outfit recommendation with switch example:

   For terminal/CLI usage:

   python run_cli.py

   For app/GUI usage:

   python run_app.py

Troubleshooting

If you encounter issues:

• Verify Conductor and Redis are running and accessible

• Check your OpenAI API key and Bing API key are valid

• Check Redis Stream client and Redis STM client configuration

• Ensure all dependencies are installed correctly

• Review logs for any error messages

Building the Example

Coming soon! This section will provide detailed instructions for building the step2_outfit_with_switch example step by step.