EmbedSelection 1.0

Embedding selection: A tool for selecting the best embedding model for your use case

Embedding Selector Framework

- This framework helps you automatically select the most suitable text embedding model for a given downstream use case.

- It analyzes task requirements (e.g., retrieval, classification, summarization), matches them against available embedding models, and evaluates performance on relevant benchmarks.

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Features

• Use Case–Driven Selection: Takes a natural-language description of a use case and extracts structured metadata (e.g., languages, token limits, complexity).
• Metadata Extraction: Uses advanced LLM models to normalize requirements into a standardized schema (parameters, memory, licensing, etc.).
• Model Matching: Filters embedding models based on attributes like size, efficiency, license, and language coverage.
• Task Alignment: Selects relevant evaluation tasks from MTEB (Massive Text Embedding Benchmark).
• Performance Evaluation: Loads benchmark results and computes average scores per candidate model.

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How It Works

The pipeline runs in sequential steps:

• Use Case Selection Choose from predefined scenarios (chatbots, legal retrieval, recommendations, sentiment analysis, summarization, etc.) or provide your own description.
• Requirement Extraction (LLM Agent) GPT-4o parses the description into structured metadata, including: Supported languages Max token length Memory usage & parameter limits Task/domain classification
• Model Filtering Candidate models from MTEB are filtered according to the extracted attributes.
• Task Evaluation Candidate models are benchmarked on the most relevant MTEB tasks (retrieval, classification, summarization, etc.).
• Ranking & Export Models are ranked by performance (with ties broken by efficiency) and exported to CSV for inspection.

Usage

To use the tool, follow these steps:

  pip install EmbedSelection

  EmbedSelection 

Contributing

Contributions to improve the tool are welcome! Feel free to open issues for bugs or feature requests, or submit pull requests for enhancements.

Acknowledgements

This project utilizes MTEB benchmark from huggingface : https://huggingface.co/spaces/mteb/leaderboard