Technical principles and implementation effects of text vectorization
LocalPdfChatRAG uses the SentenceTransformer model to achieve document vectorization, which is a key technology to improve semantic retrieval accuracy. The model transforms the text content into a numerical representation in a 768-dimensional vector space, and realizes context-aware content matching through cosine similarity calculation. Experimental data show that vector retrieval improves the relevance score by 40% compared to traditional keyword matching.
The system contains three innovations in data processing: paragraph-level vector index to avoid information fragmentation, dynamic weight adjustment to balance the impact of old and new documents, and caching mechanism to optimize query response speed. In the test, the query response time for 500 pages of technical manuals is controlled within 3 seconds, and the recall rate of the first 5 results reaches 92%.
This processing breaks through the PDF format limitations and recognizes unstructured content such as mathematical formulas and tabular data. In terms of user configuration, it supports the switching of different pre-training models (e.g., all-MiniLM-L6-v2) to adapt to the needs of specialized fields, reflecting the flexibility of engineering design.
This answer comes from the articleLocalPdfChatRAG: Intelligent Chat Tool to Support Local Multi-Source PDF Document Q&AThe
