274bbb19ea
- Add --chunk-size and --chunk-overlap parameters to all RAG examples - Preserve original default values for each data source: - Document: 256/128 (optimized for general documents) - Email: 256/25 (smaller overlap for email threads) - Browser: 256/128 (standard for web content) - WeChat: 192/64 (smaller chunks for chat messages) - Make --file-types optional filter instead of restriction in document_rag - Update README to clarify interactive mode and parameter usage - Fix LLM default model documentation (gpt-4o, not gpt-4o-mini)
106 lines
3.5 KiB
Python
106 lines
3.5 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=None,
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help="Filter by file types (e.g., .pdf .txt .md). If not specified, all supported types are processed",
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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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if args.file_types:
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print(f"Filtering by file types: {args.file_types}")
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else:
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print("Processing all supported 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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reader_kwargs = {
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"recursive": True,
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"encoding": "utf-8",
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}
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if args.file_types:
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reader_kwargs["required_exts"] = args.file_types
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documents = SimpleDirectoryReader(args.data_dir, **reader_kwargs).load_data(
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show_progress=True
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)
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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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