Technical Guide2026-05-1720 min Read
RAG Implementation Guide with Chinese LLMs
Best practices for building Retrieval-Augmented Generation systems using DeepSeek, Qwen, Kimi and other Chinese AI models.
RAGKnowledge BaseVector DatabaseImplementation
RAG Architecture Overview
RAG combines retrieval systems with LLM generation for accurate, up-to-date answers with source attribution.
- Document chunking and embedding generation
- Vector storage and similarity search
- Context injection and prompt engineering
- Response generation and citation
Embedding Model Selection
Choose embedding models optimized for Chinese text:
| Model | Dimensions | Chinese Performance | Speed |
|---|---|---|---|
| text-embedding-3-large | 3072 | Excellent | Fast |
| BGE-large-zh | 1024 | Best-in-class | Medium |
| M3E | 768 | Good | Fast |
Chunking Strategies
Optimal chunking depends on your use case:
- 512 tokens: General purpose Q&A
- 256 tokens: Precise fact retrieval
- 1024+ tokens: Document summarization
- Hybrid: Mix small and large chunks
Chinese LLM Recommendations for RAG
Top model choices for RAG applications:
- Kimi 128K: Best for long documents and complex queries
- DeepSeek V3: Excellent reasoning at low cost
- Qwen Plus: Balanced performance and price
- ERNIE 4.0: Strong enterprise features