OpenAI Assistants API

# pip install openai
from openai import OpenAI

client = OpenAI()

# 1. Create an Assistant
assistant = client.beta.assistants.create(
    name="Math Tutor",
    instructions="You are a math tutor. Help students with math problems step by step.",
    model="gpt-4o",
    tools=[{"type": "code_interpreter"}]
)

# 2. Create a Thread
thread = client.beta.threads.create()

# 3. Add a Message
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Solve x² - 5x + 6 = 0"
)

# 4. Run the Assistant
run = client.beta.threads.runs.create_and_poll(
    thread_id=thread.id,
    assistant_id=assistant.id
)

# 5. Get the Response
if run.status == "completed":
    messages = client.beta.threads.messages.list(thread_id=thread.id)
    for msg in messages.data:
        if msg.role == "assistant":
            print(msg.content[0].text.value)

from openai import OpenAI

client = OpenAI()

# Create a Vector Store
vector_store = client.beta.vector_stores.create(
    name="Company Docs"
)

# Upload files to it
file = client.files.create(
    file=open("employee_handbook.pdf", "rb"),
    purpose="assistants"
)

client.beta.vector_stores.files.create(
    vector_store_id=vector_store.id,
    file_id=file.id
)

# Create Assistant with File Search
assistant = client.beta.assistants.create(
    name="HR Assistant",
    instructions="Answer employee questions based on company documents.",
    model="gpt-4o",
    tools=[{"type": "file_search"}],
    tool_resources={
        "file_search": {
            "vector_store_ids": [vector_store.id]
        }
    }
)

# Now ask questions about your docs!
thread = client.beta.threads.create()
client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="What is the vacation policy?"
)

run = client.beta.threads.runs.create_and_poll(
    thread_id=thread.id,
    assistant_id=assistant.id
)

import json
from openai import OpenAI

client = OpenAI()

# Define your functions
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }
    }
}]

# Create assistant with function
assistant = client.beta.assistants.create(
    name="Weather Bot",
    instructions="Help users get weather information.",
    model="gpt-4o",
    tools=tools
)

# ... create thread, add message, create run ...

# Handle function call
if run.status == "requires_action":
    tool_calls = run.required_action.submit_tool_outputs.tool_calls
    
    tool_outputs = []
    for call in tool_calls:
        if call.function.name == "get_weather":
            args = json.loads(call.function.arguments)
            # Call your actual weather API here
            result = {"temp": "22°C", "condition": "Sunny"}
            tool_outputs.append({
                "tool_call_id": call.id,
                "output": json.dumps(result)
            })
    
    # Submit results back
    run = client.beta.threads.runs.submit_tool_outputs_and_poll(
        thread_id=thread.id,
        run_id=run.id,
        tool_outputs=tool_outputs
    )

from openai import OpenAI

client = OpenAI()

# Stream the run
with client.beta.threads.runs.stream(
    thread_id=thread.id,
    assistant_id=assistant.id
) as stream:
    for text in stream.text_deltas:
        print(text, end="", flush=True)

# Or use event handlers for more control
from openai import AssistantEventHandler

class MyHandler(AssistantEventHandler):
    def on_text_delta(self, delta, snapshot):
        print(delta.value, end="", flush=True)
    
    def on_tool_call_created(self, tool_call):
        print(f"\n[Using {tool_call.type}...]")

with client.beta.threads.runs.stream(
    thread_id=thread.id,
    assistant_id=assistant.id,
    event_handler=MyHandler()
) as stream:
    stream.until_done()