Oracle Cloud Infrastructure Generative AI¶

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 and here.

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 🦙.

In [ ]:

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

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

In [ ]:

!pip install llama-index

!pip install llama-index

You will also need to install the OCI sdk

In [ ]:

!pip install -U oci

!pip install -U oci

Basic Usage¶

In [ ]:

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)

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)