OpenAI

import { OpenAI } from 'openai';
import { wrapOpenAI } from '@wandb/weave';

const openai = wrapOpenAI(new OpenAI());

// Tous les appels à OpenAI sont désormais tracés
openai.chat.completions.create(
  {
    model: "gpt-4",
    messages: [
      {
        role: "system",
        content: "You are AGI. You will be provided with a message, and your task is to respond using emojis only."
      },
      {
        role: "user",
        content: "How are you?"
      }
    ]
  }
);

from openai import OpenAI
from pydantic import BaseModel
import weave

class UserDetail(BaseModel):
    name: str
    age: int

client = OpenAI()
weave.init('extract-user-details')

completion = client.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "Extract the user details from the message."},
        {"role": "user", "content": "My name is David and I am 30 years old."},
    ],
    response_format=UserDetail,
)

user_detail = completion.choices[0].message.parsed
print(user_detail)

from openai import AsyncOpenAI
import weave

client = AsyncOpenAI()
weave.init('async-emoji-bot')

async def call_openai():
    response = await client.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "system", 
                "content": "You are AGI. You will be provided with a message, and your task is to respond using emojis only."
            },
            {
                "role": "user",
                "content": "How are you?"
            }
        ]
    )
    return response

# Appeler la fonction asynchrone
result = await call_openai()

from openai import OpenAI
import weave

client = OpenAI()
weave.init('streaming-emoji-bot')

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
            "role": "system", 
            "content": "You are AGI. You will be provided with a message, and your task is to respond using emojis only."
        },
        {
            "role": "user",
            "content": "How are you?"
        }
    ],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

from openai import OpenAI
import weave

client = OpenAI()
weave.init('function-calling-bot')

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The location to get the weather for"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The unit to return the temperature in"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in New York?"
        }
    ],
    tools=tools
)

print(response.choices[0].message.tool_calls)

from openai import OpenAI
import weave

client = OpenAI()
weave.init('batch-processing')

# Créer un fichier de lot
batch_input = [
    {
        "custom_id": "request-1",
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "Hello, how are you?"}]
        }
    },
    {
        "custom_id": "request-2", 
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "What's the weather like?"}]
        }
    }
]

# Soumettre le lot
batch = client.batches.create(
    input_file_id="your-file-id",
    endpoint="/v1/chat/completions",
    completion_window="24h"
)

# Récupérer les résultats du lot
completed_batch = client.batches.retrieve(batch.id)

from openai import OpenAI
import weave

client = OpenAI()
weave.init('assistant-bot')

# Créer un assistant
assistant = client.beta.assistants.create(
    name="Math Assistant",
    instructions="You are a personal math tutor. Answer questions about math.",
    model="gpt-4"
)

# Créer un thread
thread = client.beta.threads.create()

# Ajouter un message au thread
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="What is 2+2?"
)

# Exécuter l’assistant
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)

# Obtenir la réponse de l’assistant
messages = client.beta.threads.messages.list(thread_id=thread.id)

from openai import OpenAI
import weave

client = OpenAI()
weave.init('custom-function-bot')

@weave.op
def generate_response(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "user",
                "content": prompt
            }
        ]
    )
    return response.choices[0].message.content

# Cet appel de fonction sera tracé
result = generate_response("Hello, how are you?")