GENERATION PIPELINE (SGRAG)

2024

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AI-generated summary

1) This guide explains the core retrieval-augmented generation (RAG) pipeline, breaking down how retrieval, augmentation, and generation work together to produce contextual LLM responses.
2)
- Explains the three core RAG processes: retrieval from knowledge bases, prompt augmentation, and LLM generation.
- Provides Python code examples using FAISS and OpenAI embeddings for context retrieval and prompt construction.
- Outlines the complete end-to-end RAG architecture, covering indexing, connectors, extractors, and model selection criteria.
3) Tags: RAG, LLM, Retrieval, Augmentation, Python

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