RAG

Retrieval-Augmented Generation (RAG)

1. Retriever-Reader Architecture

   • Frameworks for combining retrieval systems with LLMs in generation tasks.

2. Vector Databases

   • Using vector databases (like Pinecone, Weaviate) for efficient information retrieval.

3. Document Retrieval Techniques

   • BM25, Dense Passage Retrieval (DPR), and FAISS-based retrieval.

4. Hybrid Retrieval Methods

   • Combining sparse and dense retrieval methods for enhanced recall.

5. Real-time RAG Applications

   • Implementing RAG for dynamic data environments and question answering.

6. Indexing Techniques in RAG

   • Indexing strategies for large corpora, including inverted indices and k-means clustering.

7. Filtering and Scoring Retrieval Results

   • Methods for scoring and selecting relevant documents for generation.