LlamaIndex

RAG and data tool. Uses ApiMira as its model source.

LlamaIndex takes an OpenAI-compatible address straight in the LLM constructor — no extra package needed.

What you need

Base URL

https://apimira.com/v1

API key

am-YOUR_KEYCreated in the dashboard, the API keys section. The full value is shown once.

Model ID

google/gemini-3.7-flashCopy it from the catalogue in full, including the prefix before the slash.

How to connect

1. 1Install llama-index-llms-openai.
2. 2Create the LLM with api_base, your key and a Model ID.
3. 3Pass it to your index or query engine as usual.

# pip install llama-index-llms-openai
from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="google/gemini-3.7-flash",
    api_base="https://apimira.com/v1",
    api_key="am-YOUR_KEY",
    is_chat_model=True,
    context_window=128000,
)

print(llm.complete("Hello"))

is_chat_model=True is required: without it LlamaIndex falls back to the legacy /v1/completions path, which the gateway does not serve. Set context_window to the window of the model you picked from the catalogue.

Function calling is supported, so agentic modes work. Strict JSON mode (response_format) and the retired functions format are rejected with an unsupported_parameter error — the parameter is never silently dropped. Image input is accepted by models marked "Sees images" on the Models page: a content part of type image_url with a data:image/png;base64,… URL (PNG, JPEG or WebP, up to 5 MB each, up to 50 per request). Remote URLs are not fetched, and a model without that mark answers with unsupported_capability — both errors name the reason outright.