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Connect LlamaIndex to Gonka Broker

LlamaIndex is a framework for building RAG pipelines and agents over your own data. Its OpenAI-compatible LLM class accepts a custom base URL, so you can point it at Gonka Broker and run retrieval and agents on open-source models — the only change from a standard OpenAI setup is the base URL, the key, and the model id.

The integration is the same in Python and JavaScript/TypeScript; only the package name and the constructor differ. Pick your language below.

  • A Gonka API key (starts with gnk-prx-). See Create a Gonka API Key.
  • Python 3.9+ or Node.js 18+, depending on your stack.

Install the OpenAI-compatible LLM integration:

Terminal window
pip install llama-index-llms-openai-like

Point OpenAILike at Gonka Broker:

from llama_index.llms.openai_like import OpenAILike
llm = OpenAILike(
model="moonshotai/Kimi-K2.6",
api_base="https://proxy.gonkabroker.com/v1",
api_key="gnk-prx-your-api-key",
is_chat_model=True,
context_window=128000,
)
response = llm.complete("Say hello from Gonka Broker.")
print(response)

Use OpenAILike (not the plain OpenAI class) — the standard class only accepts OpenAI’s own model names. Keep is_chat_model=True so calls go to the chat endpoint, and set context_window to your model’s window.

The model must match a Gonka-supported id exactly — for example moonshotai/Kimi-K2.6 or MiniMaxAI/MiniMax-M2.7 (see Supported Models). Once the LLM is configured, pass it to a Settings.llm / index / query engine and the rest of LlamaIndex works unchanged.

Run the snippet above. A printed reply confirms LlamaIndex is reaching Gonka Broker through your key.

  • 401 / invalid API key — wrong or paused key. Create a fresh one from Create a Gonka API Key.
  • Model not found / unsupported — the model must match a model Gonka Broker serves exactly (see Supported Models).
  • Connection errors — confirm the base URL is exactly https://proxy.gonkabroker.com/v1.
  • Output is truncated — raise context_window (Python) to match your model; the default is small.
  • Visible <think> reasoning in the output — reasoning models such as MiniMaxAI/MiniMax-M2.7 emit their thinking inline, which LlamaIndex returns verbatim in the response text. The example uses moonshotai/Kimi-K2.6, which returns clean text; strip the <think> block yourself if you prefer a reasoning model.