a172cf893d
Add 5 markdown documents that users can index to reproduce evaluation results. Changes: - Add sample_documents/ folder with 5 markdown files covering LightRAG features - Update sample_dataset.json with 3 improved, specific test questions - Shorten and correct evaluation README (removed outdated info about mock responses) - Add sample_documents reference with expected ~95% RAGAS score Test Results with sample documents: - Average RAGAS Score: 95.28% - Faithfulness: 100%, Answer Relevance: 96.67% - Context Recall: 88.89%, Context Precision: 95.56%
21 lines
1.5 KiB
Markdown
21 lines
1.5 KiB
Markdown
# RAG System Architecture
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## Main Components of RAG Systems
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A RAG system consists of three main components that work together to provide intelligent responses.
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### Component 1: Retrieval System
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The retrieval system is the first component of a RAG system. A retrieval system finds relevant documents from large document collections. Vector databases serve as the primary storage for the retrieval system. Search engines can also function as retrieval systems in RAG architectures.
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### Component 2: Embedding Model
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The embedding model is the second component of a RAG system. An embedding model converts text into vector representations for similarity search. The embedding model transforms documents and queries into numerical vectors. These vector representations enable semantic similarity matching between queries and documents.
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### Component 3: Large Language Model
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The large language model is the third component of a RAG system. An LLM generates responses based on retrieved context from documents. The large language model synthesizes information from multiple sources into coherent answers. LLMs provide natural language generation capabilities for the RAG system.
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## How Components Work Together
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The retrieval system fetches relevant documents for a user query. The embedding model enables similarity matching between query and documents. The LLM generates the final response using retrieved context. These three components collaborate to provide accurate, contextual responses.
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