> ## Documentation Index
> Fetch the complete documentation index at: https://docs.radium.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex

> RAG and query engines via OpenAILike

<Error>
  This page contains an unverified test date marked in <span style="color:red">red</span>. Needs verification (Vijay, Adam, Alex, Product/Legal).
</Error>

# LlamaIndex

LlamaIndex provides `OpenAILike` for third-party OpenAI-compatible APIs. Use it to connect a LlamaIndex application to Radium.

## Before you start

You need Python 3.10 or newer, a Radium API key, and a terminal or PowerShell window.

## 1. Create a project and install LlamaIndex

<CodeGroup>
  ```bash macOS or Linux theme={null}
  mkdir radium-llamaindex && cd radium-llamaindex
  python3 -m venv .venv
  source .venv/bin/activate
  python -m pip install llama-index-llms-openai-like
  ```

  ```powershell Windows PowerShell theme={null}
  mkdir radium-llamaindex; cd radium-llamaindex
  py -m venv .venv
  .venv\Scripts\Activate.ps1
  python -m pip install llama-index-llms-openai-like
  ```
</CodeGroup>

## 2. Set your Radium credentials

<CodeGroup>
  ```bash macOS or Linux theme={null}
  export RADIUM_API_KEY="your-radium-api-key"
  export RADIUM_MODEL="hal-1.0"
  ```

  ```powershell Windows PowerShell theme={null}
  $env:RADIUM_API_KEY = "your-radium-api-key"
  $env:RADIUM_MODEL = "hal-1.0"
  ```
</CodeGroup>

## 3. Create main.py

```python theme={null}
import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai_like import OpenAILike

api_key = os.getenv("RADIUM_API_KEY")
if not api_key:
    raise SystemExit("Set RADIUM_API_KEY before running this program.")

llm = OpenAILike(
    model=os.getenv("RADIUM_MODEL", "hal-1.0"),
    api_key=api_key,
    api_base="https://api.radium.cloud/v1",
    is_chat_model=True,
    max_tokens=512,
    temperature=0,
    timeout=90,
)

response = llm.chat(
    [
        ChatMessage(role="system", content="Follow the user's instruction exactly."),
        ChatMessage(role="user", content="Reply with exactly: Radium connected."),
    ]
)

content = response.message.content
if not content or not content.strip():
    raise RuntimeError("Radium returned no visible text.")
print(f"RADIUM_RESPONSE: {content.strip()}")
```

<Note>
  LlamaIndex calls the endpoint setting `api_base`, not `base_url`.
</Note>

## 4. Run it

```bash theme={null}
python main.py
```

Expected output:

```
RADIUM_RESPONSE: Radium connected.
```

## Choose a model

```bash theme={null}
export RADIUM_MODEL="clarke-1.0"  # or hal-1.0 or tycho-1.0
python main.py
```

## Migrate an existing LlamaIndex app

Replace its current LLM object:

```python theme={null}
llm = OpenAILike(
    model="hal-1.0",
    api_key=os.environ["RADIUM_API_KEY"],
    api_base="https://api.radium.cloud/v1",
    is_chat_model=True,
    max_tokens=512,
)
```

Pass `llm` to the existing index, query engine, workflow, or agent. A RAG application still needs a separate embedding model; do not use these chat model IDs as embedding model IDs.

## Troubleshooting

| Problem | Fix |
| - | - |
| Authentication errors | Check `RADIUM_API_KEY` in the active terminal |
| `404` | Keep `/v1` in `api_base` and use an exact Radium model ID |
| Empty output | Use at least 512 output tokens |
| Import errors | Activate `.venv` and reinstall the pinned package |

## Validation

<Check>
  These instructions were verified with Python 3.12.7 and `llama-index-llms-openai-like==0.7.2` on <span style="color:red">August 20, 2026</span>. You do not need that exact package version.
</Check>

Text generation and `FunctionAgent` tool calling passed with `hal-1.0`, `clarke-1.0`, and `tycho-1.0`.

## Next steps

<CardGroup cols={2}>
  <Card title="API quickstart" icon="bolt" href="/quickstart">Call Radium directly</Card>
  <Card title="Tool calling" icon="wrench" href="/core-concepts/tool-calling">The tool\_use and tool\_result contract</Card>
</CardGroup>


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