The Over Engineered RAG Architecture
📰 Medium · RAG
Learn to build a basic RAG architecture by chunking documents, embedding, and retrieving top-k results
Action Steps
- Chunk your documents into smaller pieces using a library like Hugging Face's transformers
- Embed the chunked documents using a sentence embedding model like sentence-transformers
- Store the embedded documents in a vector store like Faiss or Pinecone
- Retrieve the top-k most relevant documents at query time using a similarity search algorithm
- Test and evaluate your RAG architecture using a dataset like Natural Questions or TriviaQA
Who Needs to Know This
NLP engineers and data scientists can benefit from this article to improve their RAG architecture design and implementation
Key Insight
💡 A basic RAG architecture can be built by chunking documents, embedding them, and retrieving top-k results at query time
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🤖 Build a basic RAG architecture in 5 steps! Chunk, embed, store, retrieve, and test your way to better question answering #RAG #NLP
Key Takeaways
Learn to build a basic RAG architecture by chunking documents, embedding, and retrieving top-k results
Full Article
Everyone’s first RAG looks the same. You chunk your documents, embed them, throw them into a vector store, retrieve the top-k at query… Continue reading on Medium »
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