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 are 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 models with LangChain.

Prerequisite

We will need to install the oci sdk

!pip install -U oci

OCI Generative AI API endpoint

https://inference.generativeai.us-chicago-1.oci.oraclecloud.com

Authentication

The authentication methods supported for this langchain integration are:

1. API Key
2. Session token
3. Instance principal
4. Resource principal

These follows the standard SDK authentication methods detailed here.

Usage

from langchain_community.embeddings import OCIGenAIEmbeddings

# use default authN method API-key
embeddings = OCIGenAIEmbeddings(
    model_id="MY_EMBEDDING_MODEL",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID",
)


query = "This is a query in English."
response = embeddings.embed_query(query)
print(response)

documents = ["This is a sample document", "and here is another one"]
response = embeddings.embed_documents(documents)
print(response)

API Reference:OCIGenAIEmbeddings

# Use Session Token to authN
embeddings = OCIGenAIEmbeddings(
    model_id="MY_EMBEDDING_MODEL",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID",
    auth_type="SECURITY_TOKEN",
    auth_profile="MY_PROFILE",  # replace with your profile name
)


query = "This is a sample query"
response = embeddings.embed_query(query)
print(response)

documents = ["This is a sample document", "and here is another one"]
response = embeddings.embed_documents(documents)
print(response)