To optimize the retrieval results of NodeRAG, you can start from the following aspects:
- Data preparation: Ensure that the input data contains rich meta-information (e.g., author, date, etc.); the recommended format is structured JSON.
- Retrieve settings: Appropriately increase the depth of search (2-3 jumps), for complex queries you can enable the "graph enhancement" or "graph enrichment" function in the advanced settings.
- Configuration adjustments: Advanced users can optimize the retrieval strategy by editing the config.yaml file to adjust parameters such as node weights and edge types.
- Incremental Updates: Regularly importing new data keeps the graph structure current, especially when working with dynamic content.
- Model Integration: Reasonable configuration of APIs or local paths for large models such as LLaMA, GPT, etc. to improve the quality of generated content.
If you encounter retrieval inaccuracies, it is recommended to check the data quality, increase the depth of retrieval, or refer to the official documentation for performance optimization suggestions.
This answer comes from the articleNodeRAG: A Heterogeneous Graph-Based Tool for Accurate Information Retrieval and GenerationThe
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