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LocalPdfChatRAG achieves centralized management and efficient retrieval of PDF documents through the following steps:
- Unified Storage and ResolutionUpload multiple PDF documents to the system and automatically perform OCR text parsing (supports scanned documents) and build a structured database.
- vectorization: The SentenceTransformer model is used to transform the text into 768-dimensional vectors to construct the semantic retrieval space
- Intelligent Index Building: create a vector index with metadata for each document paragraph (including page numbers, document source, etc.)
Operation Guide::
- When uploading PDFs in bulk, it is recommended to create different collections by theme.
- For academic papers, the system automatically recognizes metadata such as title/author/abstract.
- Use Boolean operators such as AND/OR when searching to improve precision
Effectiveness enhancement: Actual tests show that compared with traditional keyword search, the program's retrieval speed is increased by 3-5 times, and the accuracy rate is increased by more than 40%.
This answer comes from the articleLocalPdfChatRAG: Intelligent Chat Tool to Support Local Multi-Source PDF Document Q&AThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of inefficient information retrieval caused by scattered storage of multiple PDF documents?
