OpenAI integration

You are currently on a page documenting the use of OpenAI text completion models. The latest and most popular OpenAI models are chat completion models.Unless you are specifically using gpt-3.5-turbo-instruct, you are probably looking for this page instead.

OpenAI offers a spectrum of models with different levels of power suitable for different tasks. This example goes over how to use LangChain to interact with OpenAI models

Overview

Integration details

Class	Package	Local	Serializable	JS support	Downloads	Version
`ChatOpenAI`	`langchain-openai`	❌	beta	✅

Setup

To access OpenAI models you’ll need to create an OpenAI account, get an API key, and install the langchain-openai integration package.

Credentials

Head to platform.openai.com to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

If you’re routing requests through a proxy or service emulator, you can set the base URL via env var instead of passing base_url. Resolution order (first match wins):

Explicit base_url (or openai_api_base) kwarg.
OPENAI_API_BASE — read by LangChain at init.
OPENAI_BASE_URL — read by the underlying openai SDK client.

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain OpenAI integration lives in the langchain-openai package:

pip install -qU langchain-openai

Should you need to specify your organization ID, you can use the following cell. However, it is not required if you are only part of a single organization or intend to use your default organization. You can check your default organization on the OpenAI API keys page. To specify your organization, you can use this:

OPENAI_ORGANIZATION = getpass()

os.environ["OPENAI_ORGANIZATION"] = OPENAI_ORGANIZATION

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_openai import OpenAI

llm = OpenAI()

Invocation

llm.invoke("Hello how are you?")

'\n\nI am an AI and do not have emotions like humans do, so I am always functioning at my optimal level. Thank you for asking! How can I assist you today?'

Chaining

from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate.from_template("How to say {input} in {output_language}:\n")

chain = prompt | llm
chain.invoke(
    {
        "output_language": "German",
        "input": "I love programming.",
    }
)

'\nIch liebe Programmieren.'

Using a proxy

If you are behind an explicit proxy, you can specify the http_client to pass through

pip install httpx

import httpx

openai = OpenAI(
    model_name="gpt-3.5-turbo-instruct",
    http_client=httpx.Client(proxies="http://proxy.yourcompany.com:8080"),
)

API reference

For detailed documentation of all OpenAI llm features and configurations head to the API reference: reference.langchain.com/python/langchain-openai/llms/base/OpenAI

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Overview

Integration details

Setup

Credentials

Installation

Instantiation

Invocation

Chaining

Using a proxy

API reference