Methods to enhance the retrieval effectiveness of RAG systems
Using Simba improves retrieval in three dimensions:
- model optimization: high performance embedding models such as BAAI/bge-base-en-v1.5 can be selected in config.yaml
- Vector Storage Optimization: Support FAISS and other efficient vector databases, set collection_name to manage different knowledge collections.
- parameter tuning: Adjust the k value of retrieval section to control the number of results returned (default 5 chunks).
Implementation Steps:
- Modify llm configurations before running on the backend to choose the appropriate OpenAI or Ollama model
- Set the appropriate chunk_size (default 512) and chunk_overlap (default 200)
- GPU acceleration can be specified when deploying via Docker-compose (change device parameter to cuda)
- Regularly test retrieval time-consumption and accuracy via the /api/v1 interface
This answer comes from the articleSimba: Knowledge management system for organizing documents, seamlessly integrated into any RAG systemThe
May not be reproduced without permission:AI productivity tools " How to improve the search accuracy and efficiency of the RAG system?
