📚 RAG API — Build Smarter Apps with Chinese LLMs

Combine retrieval-augmented generation with cost-effective Chinese LLMs. DeepSeek for answers, Qwen for embeddings — 90% cheaper than OpenAI.

Start Building RAG →

Why Chinese LLMs for RAG?

💰 90% Cost Savings

Embeddings from $0.02/M tokens, generation from $0.28/M. Process millions of documents without breaking the bank.

🌏 Superior Chinese NLP

Chinese LLMs understand Chinese documents 3x better than Western models. Perfect for Asian market applications.

📏 128K Context

All models support 128K context windows, allowing retrieval of more relevant passages for better answers.

🔧 LangChain Compatible

Works with ChatOpenAI and OpenAIEmbeddings — just change the base URL. Full RAG pipeline support.

Complete RAG Example

# pip install langchain langchain-openai faiss-cpu

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA

# Use Qwen Flash for cheap embeddings
embeddings = OpenAIEmbeddings(
    model="qwen3.6-flash",
    openai_api_key="your-key",
    openai_api_base="https://eaf9553505eeb8f5-115-190-107-107.serveousercontent.com/v1"
)

# Use DeepSeek V4 for generation
llm = ChatOpenAI(
    model="deepseek-v4",
    openai_api_key="your-key",
    openai_api_base="https://eaf9553505eeb8f5-115-190-107-107.serveousercontent.com/v1"
)

# Build your RAG pipeline
vectorstore = FAISS.from_documents(docs, embeddings)
qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5})
)

# Ask questions about your documents
answer = qa.run("What is the refund policy?")

Cost Comparison: RAG at Scale

OperationOpenAIAI Token (Chinese)Savings
1M embedding tokens$0.10$0.0280%
1M generation tokens$2.50$0.2889%
10K queries/month~$300~$3588%
Build Your RAG App Today →