Integrating LlamaIndex with Chinese LLMs: Building Advanced RAG Systems

About LlamaIndex

LlamaIndex is a framework designed specifically for building RAG systems, providing a complete toolchain for data ingestion, vector indexing, retrieval, and generation.

Integrating with ChinaWHAPI

from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="qwen3.6-plus",
    api_key="your_chinawhapi_key",
    api_base="https://chinawhapi.com/v1",
    is_chat_model=True,
)

Building a RAG Pipeline

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(model="qwen3.6-plus", api_key="key", api_base="https://chinawhapi.com/v1")

documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine(llm=llm)
response = query_engine.query("Summarize the key points of this document")

Advanced Features

LlamaIndex supports multimodal indexing (images + text), query rewriting, hybrid search, reranking, and other advanced RAG features.