# LlamaIndex

URL: https://docs.hanzo.ai/docs/guides/integrations/llamaindex

> OpenAILike takes api_base — not base_url — and needs is_chat_model set, because it defaults to the completions endpoint.

`OpenAILike` takes **`api\_base`**. Note the spelling — it is not `base\_url\`. The class ships in its own package and is a thin wrapper over the OpenAI model, made compatible with third-party endpoints.

```bash
pip install llama-index-llms-openai-like
```

```python
from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="zen6",
    api_base="https://api.hanzo.ai/v1",
    api_key="sk-...",
    context_window=1000000,
    is_chat_model=True,
    is_function_calling_model=True,
)

print(llm.complete("Hello"))
```

Lands on `POST /v1/chat/completions`.

Three defaults have to be overridden or the wrapper is wrong about the model. \*\*`is_chat_model` defaults to `False`\*\*, which sends `complete()` to the completions endpoint rather than chat. `is_function_calling_model` defaults to `False`, so an agent built on the model will produce prose where you expected a tool call. And `context_window` defaults to OpenAI's, not the model's — take the real one from that model's record in `GET /v1/models`.

`llama-index-embeddings-openai-like` is the matching package for the retrieval side, and reaches `POST /v1/embeddings`.

## MCP

`BasicMCPClient` takes the server URL as its first positional argument and infers the transport from it.

```bash
pip install llama-index-tools-mcp
```

```python
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

client = BasicMCPClient("https://api.hanzo.ai/v1/mcp")
tools = await McpToolSpec(client=client).to_tool_list_async()
```

A URL ending `/mcp` is streamable HTTP, one ending `/sse` is SSE, and a first argument that is a command with `args` is a local process.