openai_retrieval.py

import os
from time import sleep
from openai import OpenAI

client = OpenAI(
    # defaults to 
    api_key=os.environ.get("OPENAI_API_KEY"),
)

# Step 1: Create an Assistant
# Upload a file with an "assistants" purpose
file = client.files.create(
  file=open("us_vs_sbf.txt", "rb"),
  purpose='assistants'
)

# Add the file to the assistant
assistant = client.beta.assistants.create(
  instructions="You are a history teacher. Use your knowledge base to best respond to student queries.",
  model="gpt-4-1106-preview",
  tools=[{"type": "retrieval"}],
  file_ids=[file.id]
)

# Step 2: Create a Thread
# A Thread represents a conversation. We recommend creating one
# Thread per user as soon as the user initiates the conversation.
# Pass any user-specific context and files in this thread by 
# creating Messages.
thread = client.beta.threads.create()

def ask_one_question(client, thread):
  # Step 3: Add a Message to a Thread
  # A Message contains the user's text, and optionally,
  # any files that the user uploads. 
  user_input = input("What is your question? ")
  message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content=user_input,
    file_ids=[file.id],
  )

  # Step 4: Run the Assistant
  # For the Assistant to respond to the user message, you need to create a Run.
  # This makes the Assistant read the Thread and decide whether to call tools
  # or simply use the model to best answer the user query. As the run
  # progresses, the assistant appends Messages to the thread with the role="assistant" .
  run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id,
    instructions="The user has a premium account."
  )


  # Step 5: Display the Assistant's Response
  # This creates a Run in a queued status. You can periodically 
  # retrieve the Run to check on its status to see if it has moved to completed.

  print(run.status)
  while (run.status != "completed"):
      sleep(1)
      print("Waiting for the Assistant to respond...")
      run = client.beta.threads.runs.retrieve(
          thread_id=thread.id,
          run_id=run.id
      )
      print(run.status)

  # Once the Run completes, you can retrieve the 
  # Messages added by the Assistant to the Thread.
  messages = client.beta.threads.messages.list(
    thread_id=thread.id
  )

  for m in messages:
      print(m.role + ": " + str(m.content[0].text))

while True:
  ask_one_question(client, thread)