OpenAI Assistants API와 스레드

무엇입니까

두 가지 큰 매력 포인트

Creating an Assistant

from openai import OpenAI
client = OpenAI()

assistant = client.beta.assistants.create(
    name='Support Bot',
    instructions='You help customers with orders.',
    model='gpt-4o-mini',
    tools=[{'type': 'file_search'}, {'type': 'code_interpreter'}]
)

Threads = Conversations

thread = client.beta.threads.create()

client.beta.threads.messages.create(
    thread_id=thread.id,
    role='user',
    content='Where is my order?'
)

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

완료 여부 확인

import time
while run.status not in ('completed', 'failed', 'requires_action'):
    time.sleep(1)
    run = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)

함수 호출

if run.status == 'requires_action':
    tool_outputs = []
    for tc in run.required_action.submit_tool_outputs.tool_calls:
        result = dispatch(tc)
        tool_outputs.append({'tool_call_id': tc.id, 'output': json.dumps(result)})
    client.beta.threads.runs.submit_tool_outputs(thread_id=thread.id, run_id=run.id, tool_outputs=tool_outputs)

File Search (Vector Store)

vector_store = client.beta.vector_stores.create(name='docs')
client.beta.vector_stores.file_batches.upload_and_poll(
    vector_store_id=vector_store.id,
    files=[open('handbook.pdf', 'rb')]
)
assistant = client.beta.assistants.update(assistant.id, tool_resources={'file_search': {'vector_store_ids': [vector_store.id]}})

코드 인터프리터

{'type': 'code_interpreter'}

Streaming

with client.beta.threads.runs.stream(thread_id=thread.id, assistant_id=assistant.id) as stream:
    for event in stream:
        print(event)

장단점

향후 계획

프로토타입에 사용하기

비용

Assistants에 가장 적합한 경우

복습