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furqan-shaikh-devFurqan Shaikh
furqan-shaikh-dev
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Furqan Shaikh
authored
Add support for OCI OpenAI Responses API with Langchain/Langgraph (#61)
* Add OCIChatOpenAI for supporting OpenAI Responses API * remove oci values from example file * incorporate pr review comments * refactoring, adding openai dependency * add oci-openai as a dependency * move ChatOCIOpenAI to oci_generative_ai class, address other comments * address review comments * make conversation store id optional * fix lint errors * fix lint errors * fix lint errors in python 3.12 * fixing import error * fixing incorrect requires mark * add _resolve_base_url in try..except block --------- Co-authored-by: Furqan Shaikh <[email protected]>
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‎libs/oci/README.md‎

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@@ -79,6 +79,53 @@ structured_llm = llm.with_structured_output(Joke)
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structured_llm.invoke("Tell me a joke about programming")
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```
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### 5. Use OpenAI Responses API
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`ChatOCIOpenAI` supports OpenAI Responses API.
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```python
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from oci_openai import (
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OciSessionAuth,
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)
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from langchain_oci import ChatOCIOpenAI
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client = ChatOCIOpenAI(
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auth=OciSessionAuth(profile_name="MY_PROFILE_NAME"),
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compartment_id="MY_COMPARTMENT_ID",
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region="us-chicago-1",
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model="openai.gpt-4.1",
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conversation_store_id="MY_CONVERSATION_STORE_ID"
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)
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messages = [
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(
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"system",
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"You are a helpful translator. Translate the user sentence to French.",
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),
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("human", "I love programming."),
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]
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response = client.invoke(messages)
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```
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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`.
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```python
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from oci_openai import (
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OciSessionAuth,
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)
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from langchain_oci import ChatOCIOpenAI
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client = ChatOCIOpenAI(
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auth=OciSessionAuth(profile_name="MY_PROFILE_NAME"),
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compartment_id="MY_COMPARTMENT_ID",
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region="us-chicago-1",
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model="openai.gpt-4.1",
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store=False
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)
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messages = [
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(
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"system",
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"You are a helpful translator. Translate the user sentence to French.",
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),
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("human", "I love programming."),
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]
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response = client.invoke(messages)
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```
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## OCI Data Science Model Deployment Examples
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‎libs/oci/examples/__init__.py‎

Whitespace-only changes.

‎libs/oci/examples/chat_models/__init__.py‎

Whitespace-only changes.

‎libs/oci/examples/chat_models/langchain_oci_openai_responses_api_example.py‎

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from langchain_core.prompts import ChatPromptTemplate
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from oci_openai import OciSessionAuth
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from pydantic import BaseModel, Field
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from rich import print
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from langchain_oci import ChatOCIOpenAI
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COMPARTMENT_ID = "ocid1.compartment.oc1..aaaaaaaaexample"
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CONVERSATION_STORE_ID = (
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"ocid1.generativeaiconversationstore.oc1.us-chicago-1.aaaaaaaaexample"
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)
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SERVICE_ENDPOINT = "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
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REGION = "us-chicago-1"
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MODEL = "openai.gpt-4o"
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PROFILE_NAME = "oc1"
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def get_oci_openai_client():
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return ChatOCIOpenAI(
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auth=OciSessionAuth(profile_name=PROFILE_NAME),
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compartment_id=COMPARTMENT_ID,
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service_endpoint=SERVICE_ENDPOINT,
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model=MODEL,
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conversation_store_id=CONVERSATION_STORE_ID,
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)
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def do_model_invoke():
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client = get_oci_openai_client()
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messages = [
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(
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"system",
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"You are a helpful translator. Translate the user sentence to French.",
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),
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("human", "I love programming."),
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]
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response = client.invoke(messages)
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return response
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def do_prompt_chaining():
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client = get_oci_openai_client()
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful assistant that translates {input_language}"
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" to {output_language}.",
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),
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("human", "{input}"),
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]
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)
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chain = prompt | client
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response = chain.invoke(
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{
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"input_language": "English",
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"output_language": "German",
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"input": "I love programming.",
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}
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)
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return response
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def do_function_calling():
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class GetWeather(BaseModel):
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"""Get the current weather in a given location"""
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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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client = get_oci_openai_client()
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llm_with_tools = client.bind_tools([GetWeather])
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response = llm_with_tools.invoke(
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"what is the weather like in San Francisco",
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)
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return response
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def do_web_search():
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client = get_oci_openai_client()
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tool = {"type": "web_search"}
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llm_with_tools = client.bind_tools([tool])
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response = llm_with_tools.invoke("What was a positive news story from today?")
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return response
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def do_hosted_mcp_calling():
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client = get_oci_openai_client()
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llm_with_mcp_tools = client.bind_tools(
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[
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{
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"type": "mcp",
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"server_label": "deepwiki",
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"server_url": "https://mcp.deepwiki.com/mcp",
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"require_approval": "never",
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}
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]
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)
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response = llm_with_mcp_tools.invoke(
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"What transport protocols does the 2025-03-26 version of the MCP "
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"spec (modelcontextprotocol/modelcontextprotocol) support?"
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)
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return response
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def main():
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print(do_model_invoke())
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print(do_prompt_chaining())
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print(do_function_calling())
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print(do_web_search())
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print(do_hosted_mcp_calling())
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if __name__ == "__main__":
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main()

‎libs/oci/langchain_oci/__init__.py‎

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ChatOCIModelDeploymentTGI,
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ChatOCIModelDeploymentVLLM,
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)
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from langchain_oci.chat_models.oci_generative_ai import ChatOCIGenAI
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from langchain_oci.chat_models.oci_generative_ai import ChatOCIGenAI, ChatOCIOpenAI
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from langchain_oci.embeddings.oci_data_science_model_deployment_endpoint import (
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OCIModelDeploymentEndpointEmbeddings,
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)
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"OCIModelDeploymentLLM",
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"OCIModelDeploymentTGI",
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"OCIModelDeploymentVLLM",
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"ChatOCIOpenAI",
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]

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