How does Dify's knowledge base feature work? How to optimize knowledge base retrieval performance?

Dify's knowledge base feature is based on vector retrieval technology, working as follows:

1. Document Upload and Processing

   • Supports multiple formats: TXT, PDF, Markdown, Word, CSV, etc.
   • Automatic chunking: Splits long documents into retrieval-friendly chunks
   • Text cleaning: Removes irrelevant characters and formatting

2. Vectorization

   • Uses embedding models to convert text to vectors
   • Supports multiple embedding models (e.g., OpenAI embeddings, HuggingFace models)
   • Vectors stored in vector databases (e.g., Milvus, Weaviate)

3. Retrieval Process

   • User question converted to query vector
   • Calculates similarity between query vector and document chunks
   • Returns most relevant document chunks

4. Answer Generation

   • Uses retrieved relevant document chunks as context
   • Combines with user question, uses LLM to generate answer

Optimization suggestions:

• Reasonably set chunk size and overlap
• Choose appropriate embedding model
• Regularly update knowledge base content
• Add metadata tags to improve retrieval precision

Candidates should understand the basic principles of vector retrieval and how to optimize knowledge base retrieval performance.