RAG From Scratch in Python
📰 Dev.to · Puneet Gupta
Learn to build Retrieval-Augmented Generation (RAG) from scratch in Python and understand its benefits over larger context windows
Action Steps
- Build a chunking algorithm to split input text into smaller pieces
- Create embeddings for each chunk using a library like Hugging Face's Transformers
- Implement a hand-rolled cosine-similarity vector search to find relevant chunks
- Rerank the retrieved chunks based on their relevance to the input prompt
- Compare the performance of RAG with a larger context window to understand its advantages
Who Needs to Know This
NLP engineers and researchers can benefit from this tutorial to improve their language models' performance and efficiency
Key Insight
💡 RAG can outperform larger context windows by efficiently retrieving and re-ranking relevant information
Share This
🤖 Build RAG from scratch in Python and improve your language models' performance! #RAG #NLP #Python
Key Takeaways
Learn to build Retrieval-Augmented Generation (RAG) from scratch in Python and understand its benefits over larger context windows
Full Article
Building retrieval-augmented generation from first principles in Python: chunking, embeddings, a hand-rolled cosine-similarity vector search, reranking, and when RAG beats a bigger context window.
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