Build a RAG Pipeline with Chinese LLMs: Qwen + DeepSeek, Step by Step

Retrieval-augmented generation is where Chinese LLMs quietly shine: long context windows and strong Mandarin comprehension make them excellent at answering over Chinese document sets. This is a minimal, runnable blueprint.

1. Chunk and embed

Split your source into overlapping chunks. For Chinese text, sentence- or paragraph-based splitting beats fixed character counts.

def chunk(text, max_len=512, overlap=64):
    out, start = [], 0
    while start < len(text):
        out.append(text[start:start+max_len])
        start += max_len - overlap
    return out

2. Retrieve

Store chunks in a vector store and pull the top-k by similarity at query time. Keep k small (3–5) to stay within context and reduce noise.

3. Generate with a reasoning model

Use a fast model (Qwen3) for drafting and a reasoning model (DeepSeek-R1) when the question is analytical. Route both through one endpoint:

curl https://tidelink.xyz/v1/chat/completions \
  -H "Authorization: Bearer $TIDELINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-r1","messages":[
        {"role":"system","content":"Answer only from the context."},
        {"role":"user","content":"Context:\n'"$CONTEXT"'\n\nQuestion: '"$QUESTION"'"}
      ]}'

4. Evaluate before shipping

Measure faithfulness (does the answer match the retrieved context?) and answer relevance on a held-out set. Chinese RAG fails most often on retrieval recall, not generation — so invest in chunking and embedding quality first. For more production patterns (agents, translation, summarization, content generation), see Chinese LLM use cases, and grab a free API key to test the pipeline above.