openai-api-development

from openai import OpenAI
 
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    temperature=0.7,
    max_tokens=1000
)
 
message = response.choices[0].message.content

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)
 
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City and state, e.g., San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]
 
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

import json
 
def process_tool_calls(response, messages):
    tool_calls = response.choices[0].message.tool_calls
 
    if tool_calls:
        messages.append(response.choices[0].message)
 
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
 
            # Execute the function
            result = execute_function(function_name, function_args)
 
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })
 
        # Get final response
        return client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools
        )
 
    return response

assistant = client.beta.assistants.create(
    name="Data Analyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[
        {"type": "code_interpreter"},
        {"type": "file_search"}
    ],
    model="gpt-4o"
)

# Create a thread
thread = client.beta.threads.create()
 
# Add a message
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Analyze this data..."
)
 
# Run the assistant
run = client.beta.threads.runs.create_and_poll(
    thread_id=thread.id,
    assistant_id=assistant.id
)
 
# Get messages
if run.status == "completed":
    messages = client.beta.threads.messages.list(thread_id=thread.id)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Your text to embed",
    encoding_format="float"
)
 
embedding = response.data[0].embedding

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg",
                        "detail": "high"
                    }
                }
            ]
        }
    ]
)

from openai import RateLimitError, APIError
import time
 
def call_with_retry(func, max_retries=3, base_delay=1):
    for attempt in range(max_retries):
        try:
            return func()
        except RateLimitError:
            delay = base_delay * (2 ** attempt)
            time.sleep(delay)
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(base_delay)
    raise Exception("Max retries exceeded")