Ways to optimize the performance of large document processing
The following optimization strategies can be adopted for the performance problems encountered when processing large documents:
Hardware level optimization:
- Upgrade memory to 16GB or more, especially for documents with more than 100 pages
- Using NVIDIA GPUs and installing the faiss-gpu library to accelerate vector computation
- Configuring Swap Space for Temporary Memory Shortages
Document processing optimization:
- Split large documents into multiple small files for separate upload processing
- Adjust the text segmentation parameters to control the block size in the desired range (usually 256-512 words)
- Turn off the live preview feature to save resources
System configuration adjustments:
- Choose a smaller model version (e.g. 1.5B instead of 7B)
- exist
.envReduce the number of segments returned by the search in - Set processing timeout to avoid jamming
For very large document sets, it is recommended to process them in batches: first store the documents in a vector database, and then quiz them based on the existing database to avoid repeated processing of the same documents.
This answer comes from the articleDeepSeek-RAG-Chatbot: a locally running DeepSeek RAG chatbotThe
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