# LlamaIndex (/integrations/llamaindex)



Zumik speaks the OpenAI wire format at `https://api.zumik.ai/v1`, so the LlamaIndex `Zumik` LLM is a thin configuration of LlamaIndex's `OpenAILike` LLM: the base URL is pinned and the key is read from `ZUMIK_API_KEY`. Use it anywhere a LlamaIndex `LLM` is accepted - query engines, chat engines, agents.

## Install [#install]

```bash
pip install llama-index-llms-zumik
```

The package is `llama-index-llms-zumik` `0.1.0` (Apache-2.0), pinning `llama-index-core==0.12.52`, `llama-index-llms-openai-like==0.4.0`, and `llama-index-llms-openai==0.4.7`.

```bash
export ZUMIK_API_KEY="zk_..."
# optional, for staging / self-hosted:
# export ZUMIK_BASE_URL="https://api.zumik.ai/v1"
```

## Basic completion [#basic-completion]

`model` is a Zumik alias (`code.fast`, `auto.balanced`, `reasoning.best`, ...) or a concrete provider model; it defaults to `auto.balanced`. Aliases are chat models, so `is_chat_model` defaults to `True`.

```python
from llama_index_llms_zumik import Zumik

llm = Zumik(model="code.fast")          # reads ZUMIK_API_KEY
print(llm.complete("Summarize the CAP theorem in one sentence."))
```

<Note>
  `context_window` defaults to a conservative 128,000. Pass the real value if your alias targets a larger window.
</Note>

## Query engine over documents [#query-engine-over-documents]

Set `Zumik` as `Settings.llm` and build an index. For embeddings you can run a local model or use Zumik's OpenAI-compatible `/v1/embeddings` through a LlamaIndex `OpenAILike` embedding.

```python
from llama_index.core import Settings, VectorStoreIndex, Document
from llama_index.embeddings.openai_like import OpenAILikeEmbedding
from llama_index_llms_zumik import Zumik, DEFAULT_API_BASE
import os

Settings.llm = Zumik(model="code.fast")

# Embeddings over the same Zumik /v1 surface.
Settings.embed_model = OpenAILikeEmbedding(
    model_name="auto.balanced",
    api_base=DEFAULT_API_BASE,
    api_key=os.environ["ZUMIK_API_KEY"],
)

docs = [
    Document(text="Zumik resolves model aliases like code.fast to a pinned provider release."),
    Document(text="QoS classes are interactive, standard, background, and batch."),
    Document(text="Every response reports the resolved release and reuse on its headers."),
]

index = VectorStoreIndex.from_documents(docs)
engine = index.as_query_engine()
print(engine.query("What QoS classes does Zumik expose?"))
```

<Note>
  The embeddings snippet needs `llama-index-embeddings-openai-like`, which is not a dependency of this package. Install it separately if you want Zumik-backed embeddings. `DEFAULT_API_BASE` is exported by the package and equals `https://api.zumik.ai/v1`.
</Note>

## Documents over a native session [#documents-over-a-native-session]

The `Zumik` LLM rides the OpenAI-compatible `/v1` surface, which resends the document context on every query. To reuse a long-lived document prefix instead, pin it to a native `/v2` [session](/concepts/sessions) with the [Python SDK](/sdk/python): build the prefix once as a bundle of `document` artifacts, open a session over it, then run queries against that session so the compiled context is reused.

```python
from zumik import ZumikClient   # first-party Zumik SDK
import os

zk = ZumikClient(api_key=os.environ["ZUMIK_API_KEY"])

docs = [
    "Zumik resolves aliases to pinned provider releases.",
    "Reuse is reported on response headers, not guessed.",
]
artifacts = [zk.create_artifact("document", text) for text in docs]
prefix = zk.create_bundle(
    bundle_type="agent_prefix",
    items=[{"artifact_id": a["id"], "role": "context"} for a in artifacts],
)
session = zk.create_session(base_bundle_ids=[prefix["id"]])

# Each query rides the same session, so the document prefix is compiled once and reused.
for q in ("How is reuse reported?", "What does an alias resolve to?"):
    answer = zk.create_native_response(
        model="code.fast",
        input=q,
        session_id=session["id"],
        branch_id=session["default_branch_id"],
    )
    print(answer["output_text"])
```

See the [RAG example](/examples/rag) for the full retrieval-over-sessions reference and [artifacts](/concepts/artifacts) for the object model.

<CardGroup cols="2">
  <Card title="LangChain" icon="link" href="/integrations/langchain">
    `ChatZumik` and `ZumikEmbeddings` for LangChain chains.
  </Card>

  <Card title="RAG example" icon="book" href="/examples/rag">
    Documents pinned to a session and reused across queries.
  </Card>
</CardGroup>