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LangChain Python

Use ChatOpenAI from langchain-openai for Aurora's standard Chat Completions interface. Supply Aurora's base URL explicitly and disable Responses API routing.

Select Inference Endpoint
Choose preferred region.

Install and prepare​

Use a supported Python version in a virtual environment:

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade langchain-openai
export AURORA_API_TOKEN="<your-token>"

On Windows, activate with .venv\Scripts\Activate.ps1 in PowerShell. See Getting started to obtain the token.

Invoke the chat model​

Save as aurora_langchain.py:

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
  model="deepseek/deepseek-v4-flash",
  base_url="",
  api_key=os.environ["AURORA_API_TOKEN"],
  use_responses_api=False,
  stream_usage=True,
)

response = llm.invoke([
  ("system", "You are a concise technical writing assistant."),
  ("human", "Explain what an inference API does in one sentence."),
])
print(response.content)
print(response.usage_metadata)
python aurora_langchain.py

Expect an explanation and usage metadata. The model ID is sent as-is, including its author prefix. The base URL includes /v1 and excludes /chat/completions.

Stream a response​

Replace the invocation and print statements with:

for chunk in llm.stream("Explain streaming in three sentences."):
    if isinstance(chunk.content, str):
        print(chunk.content, end="", flush=True)
    if chunk.usage_metadata:
        print("\nUsage:", chunk.usage_metadata)
print()

For an async application, use await llm.ainvoke(...) or async for over llm.astream(...) with the same configuration.

Compatibility and troubleshooting​

  • Responses-only features: Do not attach OpenAI-hosted web search, file search, or code-interpreter tools. Local function tools require model tool support and a client-side execution loop.
  • Reasoning metadata: ChatOpenAI targets standard OpenAI response fields. LangChain does not guarantee preservation of provider-specific fields such as reasoning_content. This recipe covers text and standard usage; evaluate metadata handling before building reasoning-dependent agent loops.
  • Wrong credentials or endpoint: Pass api_key and base_url explicitly as shown instead of depending on unrelated OpenAI environment variables.
  • Structured-output errors: Check the model's schema support and explicitly choose a compatible structured-output method rather than assuming defaults.

See shared troubleshooting and the LangChain ChatOpenAI reference.