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📈 Increase the quality of your RAG with a simple Linear Layer! No need to change your embedding model (keep that old OpenAI API).
📈 Increase the quality of your RAG with a simple Linear Layer! No need to change your embedding model (keep that old OpenAI API).
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Introducing EmbeddingAlign RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) systems.
Introducing EmbeddingAlign RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) systems.
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Key highlights:
Key highlights:
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- Uses a simple linear transformation on existing embeddings
- Uses a simple linear transformation on existing embeddings
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- Boosts hit rate from 89% to 95% on real-world examples
- Boosts hit rate from 89% to 95% on real-world examples
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- Minor increase on latency (less than 10ms)
- Minor increase on latency (less than 10ms)
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- Works on top of blackbox embedding models (Mistral AI, OpenAI, Cohere,...)
- Works on top of blackbox embedding models (Mistral AI, OpenAI, Cohere,...)
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- No dataset needed (just your documents)
- No dataset needed (just your documents)
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- Train easily on CPU
- Train easily on CPU
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🤗 Read the full article here on HF: 
🤗 Read the full article here on HF: 
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https://huggingface.co/blog/PLB/embedding-align-rag
https://huggingface.co/blog/PLB/embedding-align-rag
📈 Increase the quality of your RAG with a simple Linear Layer! No need to change your embedding model (keep that old OpenAI API).

Introducing EmbeddingAlign RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) systems.

Key highlights:
- Uses a simple linear transformation on existing embeddings
- Boosts hit rate from 89% to 95% on real-world examples
- Minor increase on latency (less than 10ms)
- Works on top of blackbox embedding models (Mistral AI, OpenAI, Cohere,...)
- No dataset needed (just your documents)
- Train easily on CPU

🤗 Read the full article here on HF: https://huggingface.co/blog/PLB/embedding-align-rag
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John6666
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Ersi Ni
nilbot
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Pierre-Louis B
PLB
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