LangChain / LlamaIndex

Use 200+ models in LangChain and LlamaIndex by pointing the OpenAI-compatible API endpoint to Chuizi.AI.

Prerequisites

Python 3.9+
A Chuizi.AI account with an API key (starts with ck-)

LangChain Configuration

Installation

terminal

bash

pip install langchain-openai

Basic Usage

example.py

python

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    openai_api_base="https://api.chuizi.ai/v1",
    openai_api_key="ck-your-key-here",
    model="anthropic/claude-sonnet-4-6",
)

response = llm.invoke("What is RAG?")
print(response.content)

Using in a Chain

example.py

python

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful AI assistant."),
    ("user", "{input}"),
])

chain = prompt | llm
response = chain.invoke({"input": "Explain LangChain"})
print(response.content)

Next Steps

Embeddings API — vector embedding reference for RAG pipelines
Choose a Model — compare models for RAG and agent tasks
Structured Output — get JSON responses for data extraction