Skip to content

Outfit Recommendation with Long-Term Memory Example

This example demonstrates how to use the framework for outfit recommendation tasks with long-term memory functionality. The example code can be found in the examples/step4_outfit_with_ltm directory.

   cd examples/step4_outfit_with_ltm

Overview

This example implements an outfit recommendation system with long-term memory capabilities through two main workflows:

  1. Image Storage Workflow
  2. ImageIndexListener: Monitors and captures new clothing images
  3. OutfitImagePreprocessor: Processes and prepares images for storage
  4. Stores processed images in Milvus long-term memory (LTM) for future retrieval

  5. Workflow sequence: Image Listening -> Preprocessing -> LTM Storage

  6. Outfit Recommendation Workflow

  7. OutfitQA: Conducts interactive Q&A to understand user preferences
  8. OutfitDecider: Determines if sufficient information is collected
  9. Uses DoWhileTask for iterative refinement until decision is positive
  10. OutfitGeneration: Generates outfit recommendations using stored image data
  11. OutfitConclusion: Presents final recommendations with explanations

The system leverages both short-term memory (Redis STM) and long-term memory (Milvus LTM) for: - Efficient image storage and retrieval - Persistent clothing item database - Context-aware outfit recommendations - Interactive preference refinement - Stateful conversation management

  1. Workflow Architecture Image Storage: Listen -> Preprocess -> Store in LTM Recommendation: QA Loop (QA + Decision) -> Generation -> Conclusion

The system uses Redis for state management, Milvus for long-term image storage, and Conductor for workflow orchestration. This architecture enables: - Scalable image database management - Intelligent outfit recommendations based on stored items - Interactive preference gathering - Persistent clothing knowledge base - Efficient retrieval of relevant items

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
  • Milvus vector database (will be started automatically when workflow runs)
  • Sufficient storage space for image database
  • Install Git LFS by git lfs intall, then pull sample images by git lfs pull

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, Milvus connections and other service configurations. To set up your configuration:

  1. Generate the container.yaml files: ```bash # For image storage workflow python image_storage/compile_container.py

# For outfit recommendation workflow python outfit_from_storage/compile_container.py `` This will create two container.yaml files with default settings underimage_storageandoutfit_from_storagedirectories: -image_storage/container.yaml: Configuration for the image storage workflow -outfit_from_storage/container.yaml`: Configuration for the outfit recommendation workflow

  1. Configure your LLM settings in configs/llms/gpt.yml and configs/llms/text_res.yml in the two workflow directories:
  2. Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file bash export custom_openai_key="your_openai_api_key" export custom_openai_endpoint="your_openai_endpoint"

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

  4. Configure your Bing Search API key in configs/tools/websearch.yml in the two workflow directories:

  5. Set your Bing API key through environment variable or by directly modifying the yml file bash export bing_api_key="your_bing_api_key"
  6. Configure your text encoder settings in configs/llms/text_encoder.yml in the two workflow directories:
  7. Set your OpenAI text encoder endpoint and API key through environment variable or by directly modifying the yml file bash export custom_openai_text_encoder_key="openai_text_encoder_key" export custom_openai_text_encoder_endpoint="your_openai_endpoint"
  8. The default text encoder configuration uses OpenAI text embedding v3 with 3072 dimensions, make sure you change the dim value of MilvusLTM in container.yaml

  9. Adjust the embedding dimension and other settings as needed through environment variable or by directly modifying the yml file

  10. Update settings in the generated container.yaml:

  11. Modify Redis connection settings:
    • Set the host, port and credentials for your Redis instance
    • Configure both redis_stream_client and redis_stm_client sections
  12. Update the Conductor server URL under conductor_config section
  13. Configure MilvusLTM in components section:
    • Set the storage_name and dim for MilvusLTM
    • Adjust other settings as needed
  14. Adjust any other component settings as needed

Running the Example

  1. Run the image storage workflow first:

For terminal/CLI usage: bash python image_storage/run_image_storage_cli.py For app usage: bash python image_storage/run_image_storage_app.py

This workflow will store outfit images in the Milvus database.

  1. Run the outfit recommendation workflow in a separate terminal:

For terminal/CLI usage: bash python outfit_from_storage/run_outfit_recommendation_cli.py

For app/GUI usage: bash python outfit_from_storage/run_outfit_recommendation_app.py

This workflow will retrieve outfit recommendations from the stored images.

Troubleshooting

If you encounter issues: - Verify Redis is running and accessible - Check your OpenAI API key and Bing API key are valid - Ensure all dependencies are installed correctly - Review logs for any error messages - Confirm Conductor server is running and accessible - Check Redis Stream client and Redis STM client configuration

Building the Example

Coming soon! This section will provide detailed instructions for building the step4_outfit_with_ltm example step by step.