Perform file search and code interpreter in same thread

API
1

I’m looking to build a app where I can upload multiple files in Assistants API these files get processed by both file search and code interpreter based on file type (pdf, csv, ppt) in the same thread. How do I do this? These are some of the functions I’ve created.

from openai import OpenAI
import config
import os

def createAssistant(file_ids, title):
#Create the OpenAI Client Instance
client = OpenAI(api_key=config.API_KEY)

instructions = """
You are a helpful assistant. Use your knowledge base to answer user questions.
"""

#The GPT Model for the Assistant (This can also be updated in the settings )
model = "gpt-4-turbo"

#Only Retireval Tool is relevant for our use case
tools = [{"type": "file_search"}]

##CREATE VECTOR STORE
vector_store = client.beta.vector_stores.create(name=title,file_ids=file_ids)
tool_resources = {"file_search": {"vector_store_ids": [vector_store.id]}}

#Create the Assistant
assistant = client.beta.assistants.create(
name=title,
instructions=instructions,
model=model,
tools=tools,
tool_resources=tool_resources
)

#Return the Assistant ID
return assistant.id,vector_store.id

def saveFileOpenAI(location):
#Create OpenAI Client
client = OpenAI(api_key=config.API_KEY)

#Send File to OpenAI
file = client.files.create(file=open(location, "rb"),purpose='assistants')

# Delete the temporary file
os.remove(location)

#Return FileID
return file.id

def startAssistantThread(prompt,vector_id):
#Initiate Messages
messages = [{“role”: “user”, “content”: prompt}]
#Create the OpenAI Client
client = OpenAI(api_key=config.API_KEY)
#Create the Thread
tool_resources = {“file_search”: {“vector_store_ids”: [vector_id]}}
thread = client.beta.threads.create(messages=messages,tool_resources=tool_resources)

return thread.id

def runAssistant(thread_id, assistant_id):
#Create the OpenAI Client
client = OpenAI(api_key=config.API_KEY)
run = client.beta.threads.runs.create(thread_id=thread_id,assistant_id=assistant_id)
return run.id

def checkRunStatus(thread_id, run_id):
client = OpenAI(api_key=config.API_KEY)
run = client.beta.threads.runs.retrieve(thread_id=thread_id,run_id=run_id)
return run.status

def retrieveThread(thread_id):
client = OpenAI(api_key=config.API_KEY)
thread_messages = client.beta.threads.messages.list(thread_id)
list_messages = thread_messages.data
thread_messages =
for message in list_messages:
obj = {}
obj[‘content’] = message.content[0].text.value
obj[‘role’] = message.role
thread_messages.append(obj)
return thread_messages[::-1]

def addMessageToThread(thread_id, prompt):
client = OpenAI(api_key=config.API_KEY)
thread_message = client.beta.threads.messages.create(thread_id,role=“user”,content=prompt)