ChatOpenAI

import getpass
import os

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

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

%pip install -qU langchain-openai

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # api_key="...",  # if you prefer to pass api key in directly instaed of using env vars
    # base_url="...",
    # organization="...",
    # other params...
)

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

print(ai_msg.content)

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

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

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


llm_with_tools = llm.bind_tools([GetWeather])

ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

llm_with_tools = llm.bind_tools([GetWeather], strict=True)
ai_msg = llm_with_tools.invoke(
    "what is the weather like in San Francisco",
)
ai_msg

ai_msg.tool_calls

fine_tuned_model = ChatOpenAI(
    temperature=0, model_name="ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR"
)

fine_tuned_model.invoke(messages)

import base64

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-audio-preview",
    temperature=0,
)

with open(
    "../../../../libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav",
    "rb",
) as f:
    # b64 encode it
    audio = f.read()
    audio_b64 = base64.b64encode(audio).decode()


output_message = llm.invoke(
    [
        (
            "human",
            [
                {"type": "text", "text": "Transcribe the following:"},
                # the audio clip says "I'm sorry, but I can't create..."
                {
                    "type": "input_audio",
                    "input_audio": {"data": audio_b64, "format": "wav"},
                },
            ],
        ),
    ]
)
output_message.content

code = """
/// <summary>
/// Represents a user with a first name, last name, and username.
/// </summary>
public class User
{
    /// <summary>
    /// Gets or sets the user's first name.
    /// </summary>
    public string FirstName { get; set; }

    /// <summary>
    /// Gets or sets the user's last name.
    /// </summary>
    public string LastName { get; set; }

    /// <summary>
    /// Gets or sets the user's username.
    /// </summary>
    public string Username { get; set; }
}
"""

llm = ChatOpenAI(model="gpt-4o")
query = (
    "Replace the Username property with an Email property. "
    "Respond only with code, and with no markdown formatting."
)
response = llm.invoke(
    [{"role": "user", "content": query}, {"role": "user", "content": code}],
    prediction={"type": "content", "content": code},
)
print(response.content)
print(response.response_metadata)

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-audio-preview",
    temperature=0,
    model_kwargs={
        "modalities": ["text", "audio"],
        "audio": {"voice": "alloy", "format": "wav"},
    },
)

output_message = llm.invoke(
    [
        ("human", "Are you made by OpenAI? Just answer yes or no"),
    ]
)

{
    'data': '<audio data b64-encoded',
    'expires_at': 1729268602,
    'id': 'audio_67127d6a44348190af62c1530ef0955a',
    'transcript': 'Yes.'
}

history = [
    ("human", "Are you made by OpenAI? Just answer yes or no"),
    output_message,
    ("human", "And what is your name? Just give your name."),
]
second_output_message = llm.invoke(history)