ddc789b231
- All examples now have --embedding-mode parameter (unified interface benefit) - Default is 'sentence-transformers' (consistent with original behavior) - Users can now use OpenAI or MLX embeddings with any data source - Maintains functional equivalence with original scripts
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
"""
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Document RAG example using the unified interface.
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Supports PDF, TXT, MD, and other document formats.
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"""
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import sys
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from pathlib import Path
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# Add parent directory to path for imports
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sys.path.insert(0, str(Path(__file__).parent))
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from base_rag_example import BaseRAGExample, create_text_chunks
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from llama_index.core import SimpleDirectoryReader
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class DocumentRAG(BaseRAGExample):
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"""RAG example for document processing (PDF, TXT, MD, etc.)."""
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def __init__(self):
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super().__init__(
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name="Document",
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description="Process and query documents (PDF, TXT, MD, etc.) with LEANN",
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default_index_name="test_doc_files",
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)
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def _add_specific_arguments(self, parser):
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"""Add document-specific arguments."""
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doc_group = parser.add_argument_group("Document Parameters")
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doc_group.add_argument(
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"--data-dir",
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type=str,
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default="examples/data",
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help="Directory containing documents to index (default: examples/data)",
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)
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doc_group.add_argument(
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"--file-types",
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nargs="+",
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default=[".pdf", ".txt", ".md"],
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help="File types to process (default: .pdf .txt .md)",
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)
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doc_group.add_argument(
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"--chunk-size", type=int, default=256, help="Text chunk size (default: 256)"
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)
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doc_group.add_argument(
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"--chunk-overlap", type=int, default=128, help="Text chunk overlap (default: 128)"
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)
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async def load_data(self, args) -> list[str]:
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"""Load documents and convert to text chunks."""
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print(f"Loading documents from: {args.data_dir}")
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print(f"File types: {args.file_types}")
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# Check if data directory exists
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data_path = Path(args.data_dir)
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if not data_path.exists():
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raise ValueError(f"Data directory not found: {args.data_dir}")
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# Load documents
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documents = SimpleDirectoryReader(
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args.data_dir,
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recursive=True,
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encoding="utf-8",
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required_exts=args.file_types,
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).load_data(show_progress=True)
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if not documents:
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print(f"No documents found in {args.data_dir} with extensions {args.file_types}")
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return []
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print(f"Loaded {len(documents)} documents")
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# Convert to text chunks
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all_texts = create_text_chunks(
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documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
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)
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# Apply max_items limit if specified
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if args.max_items > 0 and len(all_texts) > args.max_items:
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print(f"Limiting to {args.max_items} chunks (from {len(all_texts)})")
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all_texts = all_texts[: args.max_items]
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return all_texts
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if __name__ == "__main__":
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import asyncio
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# Example queries for document RAG
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print("\n📄 Document RAG Example")
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print("=" * 50)
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print("\nExample queries you can try:")
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print("- 'What are the main techniques LEANN uses?'")
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print("- 'Summarize the key findings in these papers'")
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print("- 'What is the storage reduction achieved by LEANN?'")
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print("\nOr run without --query for interactive mode\n")
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rag = DocumentRAG()
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asyncio.run(rag.run())
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