---
title: Oracle Cloud Infrastructure Generative AI
 | Developer Documentation
---

Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API. Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available **[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)** and **[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)**.

This notebook explains how to use OCI’s Genrative AI embedding models with LlamaIndex.

## Setup

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-oci-genai
```

```
!pip install llama-index
```

You will also need to install the OCI sdk

```
!pip install -U oci
```

## Basic Usage

```
from llama_index.embeddings.oci_genai import OCIGenAIEmbeddings


embedding = OCIGenAIEmbeddings(
    model_name="cohere.embed-english-light-v3.0",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID",
)


e1 = embedding.get_text_embedding("This is a test document")
print(e1[-5:])


e2 = embedding.get_query_embedding("This is a test document")
print(e2[-5:])


docs = ["This is a test document", "This is another test document"]
e3 = embedding.get_text_embedding_batch(docs)
print(e3)
```