langchain-oci 0.2.0

An integration package connecting OCI and LangChain

langchain-oci

This package contains the LangChain integrations with oci.

Installation

pip install -U langchain-oci

All integrations in this package assume that you have the credentials setup to connect with oci services.

---

Quick Start

This repository includes two main integration categories:

• OCI Generative AI
• OCI Data Science (Model Deployment)

---

OCI Generative AI Examples

1. Use a Chat Model

ChatOCIGenAI class exposes chat models from OCI Generative AI.

from langchain_oci import ChatOCIGenAI

llm = ChatOCIGenAI(
        model_id="MY_MODEL_ID",
        service_endpoint="MY_SERVICE_ENDPOINT",
        compartment_id="MY_COMPARTMENT_ID",
        model_kwargs={"max_tokens": 1024}, # Use max_completion_tokens instead of max_tokens for OpenAI models
        auth_profile="MY_AUTH_PROFILE",
        is_stream=True,
        auth_type="SECURITY_TOKEN"
llm.invoke("Sing a ballad of LangChain.")

2. Use a Completion Model

OCIGenAI class exposes LLMs from OCI Generative AI.

from langchain_oci import OCIGenAI

llm = OCIGenAI()
llm.invoke("The meaning of life is")

3. Use an Embedding Model

OCIGenAIEmbeddings class exposes embeddings from OCI Generative AI.

from langchain_oci import OCIGenAIEmbeddings

embeddings = OCIGenAIEmbeddings()
embeddings.embed_query("What is the meaning of life?")

4. Use Structured Output

ChatOCIGenAI supports structured output.

_{Note: The default method is function_calling. If default method returns None (e.g. for Gemini models), try json_schema or json_mode.}

from langchain_oci import ChatOCIGenAI
from pydantic import BaseModel

class Joke(BaseModel):
    setup: str
    punchline: str

llm = ChatOCIGenAI()
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about programming")

5. Use OpenAI Responses API

ChatOCIOpenAI supports OpenAI Responses API.

from oci_openai import (
    OciSessionAuth,
)
from langchain_oci import ChatOCIOpenAI
client = ChatOCIOpenAI(
        auth=OciSessionAuth(profile_name="MY_PROFILE_NAME"),
        compartment_id="MY_COMPARTMENT_ID",
        region="us-chicago-1",
        model="openai.gpt-4.1",
        conversation_store_id="MY_CONVERSATION_STORE_ID"
    )
messages = [
        (
            "system",
            "You are a helpful translator. Translate the user sentence to French.",
        ),
        ("human", "I love programming."),
    ]
response = client.invoke(messages)

NOTE: By default store argument is set to True which requires passing conversation_store_id. You can set store to False and not pass conversation_store_id.

from oci_openai import (
    OciSessionAuth,
)
from langchain_oci import ChatOCIOpenAI
client = ChatOCIOpenAI(
        auth=OciSessionAuth(profile_name="MY_PROFILE_NAME"),
        compartment_id="MY_COMPARTMENT_ID",
        region="us-chicago-1",
        model="openai.gpt-4.1",
        store=False
    )
messages = [
        (
            "system",
            "You are a helpful translator. Translate the user sentence to French.",
        ),
        ("human", "I love programming."),
    ]
response = client.invoke(messages)

OCI Data Science Model Deployment Examples

1. Use a Chat Model

You may instantiate the OCI Data Science model with the generic ChatOCIModelDeployment or framework specific class like ChatOCIModelDeploymentVLLM.

from langchain_oci.chat_models import ChatOCIModelDeployment, ChatOCIModelDeploymentVLLM

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri with your own
endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]

chat = ChatOCIModelDeployment(
    endpoint=endpoint,
    streaming=True,
    max_retries=1,
    model_kwargs={
        "temperature": 0.2,
        "max_tokens": 512,
    },  # other model params...
    default_headers={
        "route": "/v1/chat/completions",
        # other request headers ...
    },
)
chat.invoke(messages)

chat_vllm = ChatOCIModelDeploymentVLLM(endpoint=endpoint)
chat_vllm.invoke(messages)

2. Use a Completion Model

You may instantiate the OCI Data Science model with OCIModelDeploymentLLM or OCIModelDeploymentVLLM.

from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri and model name with your own
endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"

llm = OCIModelDeploymentLLM(
    endpoint=endpoint,
    model="odsc-llm",
)
llm.invoke("Who is the first president of United States?")

vllm = OCIModelDeploymentVLLM(
    endpoint=endpoint,
)
vllm.invoke("Who is the first president of United States?")

3. Use an Embedding Model

You may instantiate the OCI Data Science model with the OCIModelDeploymentEndpointEmbeddings.

from langchain_oci.embeddings import OCIModelDeploymentEndpointEmbeddings

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri with your own
endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"

embeddings = OCIModelDeploymentEndpointEmbeddings(
    endpoint=endpoint,
)

query = "Hello World!"
embeddings.embed_query(query)

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