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| """RAG Engine: A clear, educational Retrieval-Augmented Generation system. | |
| Designed to illustrate how a production-grade RAG pipeline works: | |
| - Ingestion & Text Extraction (PDF / Text) | |
| - Semantic Text Chunking | |
| - Dense Vector Embeddings (with offline Hash fallback) | |
| - Sparse BM25 Keyword Search | |
| - Hybrid Retrieval with Reciprocal Rank Fusion (RRF) | |
| - Augmented Generation with Source Citations | |
| - Faithfulness and Relevancy Evaluation | |
| """ | |
| from rag_engine.documents import Document, load_text_file, load_pdf_file | |
| from rag_engine.chunking import TextChunk, chunk_document, chunk_documents | |
| from rag_engine.embeddings import HashEmbeddings, get_embedding_model | |
| from rag_engine.vector_store import VectorStore | |
| from rag_engine.bm25_search import BM25SearchIndex | |
| from rag_engine.hybrid_search import HybridRetriever, reciprocal_rank_fusion | |
| from rag_engine.generator import generate_answer | |
| from rag_engine.eval import evaluate_faithfulness, evaluate_relevancy, evaluate_response | |
| from rag_engine.pipeline import RAGPipeline | |
| __all__ = [ | |
| "Document", | |
| "load_text_file", | |
| "load_pdf_file", | |
| "TextChunk", | |
| "chunk_document", | |
| "chunk_documents", | |
| "HashEmbeddings", | |
| "get_embedding_model", | |
| "VectorStore", | |
| "BM25SearchIndex", | |
| "HybridRetriever", | |
| "reciprocal_rank_fusion", | |
| "generate_answer", | |
| "evaluate_faithfulness", | |
| "evaluate_relevancy", | |
| "evaluate_response", | |
| "RAGPipeline", | |
| ] | |