Using with LangChain

Because Qevron is OpenAI-compatible, you can point LangChain's OpenAI components at Qevron by just changing base_url.

Setup

pip install langchain langchain-openai
export QEVRON_API_KEY="sk-..."

Chat model

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="verinova",
    api_key=os.environ["QEVRON_API_KEY"],
    base_url="https://app.qevron.ai/v1",
)

print(llm.invoke("What is RAG, in one sentence?").content)

Embeddings

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="veriEmbedding",
    api_key=os.environ["QEVRON_API_KEY"],
    base_url="https://app.qevron.ai/v1",
)

vec = embeddings.embed_query("Qevron is an AI gateway.")
print(len(vec))

Chain example

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an assistant that answers briefly."),
    ("user", "{question}"),
])

chain = prompt | llm
print(chain.invoke({"question": "What is an embedding good for?"}).content)

Other frameworks

The same idea works in any tool that accepts base_url/api_base:

# LlamaIndex
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="verinova", api_base="https://app.qevron.ai/v1", api_key="sk-...")

// Vercel AI SDK
import { createOpenAI } from "@ai-sdk/openai";
const qevron = createOpenAI({
  baseURL: "https://app.qevron.ai/v1",
  apiKey: process.env.QEVRON_API_KEY,
});

Related: OpenAI Compatibility.