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# Embedding Model Benchmark 2024 ## Executive Summary This comprehensive benchmark evaluates 15 popular embedding models across multiple dimensions including retrieval quality, processing speed, memory usage, and cost. Results are based on evaluation across 5 diverse datasets totaling 2M+ documents and 50K queries. ## Models Evaluated ### OpenAI Models - **text-embedding-ada-002** (1536 dim) - Latest general-purpose model - **text-embedding-3-small** (1536 dim) - Optimized for speed/cost - *
How to Use
Recommended: Install to project (local)
mkdir -p .claude/skills
curl -o .claude/skills/embedding_model_benchmark.md \
https://raw.githubusercontent.com/alirezarezvani/claude-skills/main/engineering/rag-architect/references/embedding_model_benchmark.mdSkill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.
Alternative: Clone full repo
git clone https://github.com/alirezarezvani/claude-skillsThen reference at engineering/rag-architect/references/embedding_model_benchmark.md
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