OpenAI Assistants with File Search in Python

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This sample demonstrates how to use the OpenAI Assistants API with file search and streaming in a Python console application.

main.py
openai_assistants_file_search_streaming.py

How to generate this sample

Command

ai dev new openai-asst-streaming-with-file-search --python

Output

AI - Azure AI CLI, Version 1.0.0
Copyright (c) 2024 Microsoft Corporation. All Rights Reserved.

This PUBLIC PREVIEW version may change at any time.
See: https://aka.ms/azure-ai-cli-public-preview

Generating 'openai-asst-streaming-with-file-search' in 'openai-asst-streaming-with-file-search-py' (3 files)...

openai_assistants_file_search_streaming.py
main.py
requirements.txt

Generating 'openai-asst-streaming-with-file-search' in 'openai-asst-streaming-with-file-search-py' (3 files)... DONE!

main.py

STEP 1: Read the configuration settings from environment variables:

main.py

ASSISTANT_ID = os.getenv('ASSISTANT_ID') or "<insert your OpenAI assistant ID here>"
AZURE_OPENAI_API_KEY = os.getenv('AZURE_OPENAI_API_KEY', '<insert your Azure OpenAI API key here>')
AZURE_OPENAI_API_VERSION = os.getenv('AZURE_OPENAI_API_VERSION', '<insert your Azure OpenAI API version here>')
AZURE_OPENAI_ENDPOINT = os.getenv('AZURE_OPENAI_ENDPOINT', '<insert your Azure OpenAI endpoint here>')
AZURE_OPENAI_BASE_URL = f'{AZURE_OPENAI_ENDPOINT.rstrip("/")}/openai'

STEP 2: Initialize the helper class with the configuration settings:

main.py

openai = OpenAI(
    api_key = AZURE_OPENAI_API_KEY,
    base_url = AZURE_OPENAI_BASE_URL,
    default_query= { 'api-version': AZURE_OPENAI_API_VERSION },
    default_headers = { 'api-key': AZURE_OPENAI_API_KEY }
)
assistant = OpenAIAssistantsFileSearchStreamingClass(ASSISTANT_ID, openai)

STEP 3: Obtain user input, use the helper class to get the assistant's response, and display responses as they are received:

main.py

while True:
    user_input = input('User: ')
    if user_input == 'exit' or user_input == '':
        break
    print('\nAssistant: ', end='')
    assistant.get_response(user_input, lambda content: print(content, end=''))
    print('\n')

STEP 4: Handle thread retrieval and display existing messages:

main.py

threadId = sys.argv[1] if len(sys.argv) > 1 else None
if threadId is None:
    assistant.create_thread()
else:
    assistant.retrieve_thread(threadId)
    assistant.get_thread_messages(lambda role, content: print(f'{role.capitalize()}: {content}', end=''))

openai_assistants_file_search_streaming.py

STEP 1: Create the client and initialize chat message history with a system message:

openai_assistants_file_search_streaming.py

class OpenAIAssistantsFileSearchStreamingClass:

    def __init__(self, assistant_id, openai):
        self.assistant_id = assistant_id
        self.thread = None
        self.openai = openai

STEP 2: When the user provides input, add the user message to the chat message history:

openai_assistants_file_search_streaming.py

def get_response(self, user_input, callback) -> None:
    if self.thread == None:
        self.create_thread()
    message = self.openai.beta.threads.messages.create(
        thread_id=self.thread.id,
        role="user",
        content=user_input,
    )

STEP 3: Send the chat message history to the streaming OpenAI Assistants API and process each update:

openai_assistants_file_search_streaming.py

with self.openai.beta.threads.runs.stream(
    thread_id=self.thread.id,
    assistant_id=self.assistant_id,
    event_handler=EventHandler(self.openai, callback)
) as stream:
    stream.until_done()

STEP 4: For each non-empty update, accumulate the response, and invoke the callback for the update:

openai_assistants_file_search_streaming.py

def on_text_delta(self, delta, snapshot):
    content = delta.value
    if delta.annotations:
        for annotation in delta.annotations:
            content = content.replace(annotation.text, f"[{annotation.index}]")
    self.callback(content)

STEP 5: Finally, add the assistant's response to the chat message history, and return the response:

openai_assistants_file_search_streaming.py

message = self.openai.beta.threads.messages.create(
    thread_id=self.thread.id,
    role="user",
    content=user_input,
)

STEP 6: Create and retrieve threads, and get thread messages:

openai_assistants_file_search_streaming.py

def create_thread(self):
    self.thread = self.openai.beta.threads.create()
    return self.thread

def retrieve_thread(self, thread_id):
    self.thread = self.openai.beta.threads.retrieve(thread_id)
    return self.thread

def get_thread_messages(self, callback):
    messages = self.openai.beta.threads.messages.list(self.thread.id)
    messages.data.reverse()

    for message in messages.data:
        content = ''.join([item.text.value for item in message.content]) + '\n\n'
        callback(message.role, content)