Enhancing the Accuracy of the RAG Chat System
To improve the quality of responses, the following dimensions need to be optimized:
- Vector Search Optimization: Adjust the indexing configuration of Upstash Vector, it is recommended to use embeddings model with more than 768 dimensions and set a suitable similarity threshold (usually 0.78-0.85).
- Tip Engineering: Inject domain-specific prompt templates via chatProps.systemPrompt to explicitly limit the scope and style of responses.
- context window: Control the contextWindowSize parameter (3-5 history messages recommended) to avoid overly verbose context interference.
- Data preprocessing: Cleaning and chunking of incoming document data (chunk size recommended 512-1024 tokens) to ensure complete retrieval of segments.
Surveillance SolutionsFor specialized domain scenarios, Together AI's base model can be fine-tuned or a domain-specific LoRA adapter can be plugged in.
This answer comes from the articleAdding a RAG-driven online chat tool to Next.js applicationsThe
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