Kevin Sylvestre

Kevin Sylvestre

Searching Files with ChatGPT Assistants

OpenAI’s ChatGPT offers a handful of document focused capabilities when using the agent APIs. While these APIs are in beta, they offer a powerful method of searching through a variety of files such as PDFs, HTML, DOCX, and more.

Step 1: The Setup

To get started both Python and the latest version of the openai PIP package are required. The package can be installed using:

pip install openai

It is important to verify that the latest version of the openai package is installed. The agent APIs used in this article changed significantly between beta v1 and beta v2. At the time of publication, the latest version is 1.34.0.

Step 2: Configuring an Assistant

To get started with OpenAI’s file searching tools an agent is required. Agent configuration is a fairly straightforward process in which some general instructions are provided and tools are specified. An ENV variable called OPENAI_API_KEY must be present to configure an agent. Alternatively, the api_key can be passed as a string to the client (e.g. OpenAI(api_key="sk-...")). For this use case the file_search tool is given:

from openai import OpenAI

client = OpenAI()

assistant = client.beta.assistants.create(
    name="Demo Assistant",
    instructions="You are an expert at searching PDFs. Use your knowledge base to answer questions.",
    model="gpt-4o",
    tools=[{"type": "file_search"}],
)

Step 3: Preparing any Files

It is possible to attach a PDF using the files API. As a test, this article looks at the "City of Vancouver Tourism Factsheet". To use this demo save that file locally as "vancouver.pdf".

file = client.files.create(file=open("vancouver.pdf", "rb"), purpose="assistants")

Step 4: Building a Thread

With the file ready it is time to prompt our agent using a thread. In this case the question is around the number of cruise ships in Vancouver for the 2017 / 2018 / 2019 years. If all goes well the data for that request exists on the 3rd page of the PDF in "FIGURE 5: CRUISE SHIP VISITS TO VANCOUVER". The data is embedded inside a chart.

thread = client.beta.threads.create(messages=[{
    "role": "user",
    "content": "How many cruise ships visited Vancouver in 2017 / 2018 / 2019?",
    "attachments": [{ "file_id": file.id, "tools": [{ "type": "file_search" }] }]
}])

Step 4: Running the Thread

Now that the thread is ready it is time to run and poll the thread:

run = client.beta.threads.runs.create_and_poll(assistant_id=assistant.id, thread_id=thread.id)
messages = client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id)
for message in messages:
    for content in message.content:
        if content.type == "text":
            print(content.text.value)
            print("\n")

            for annotation in content.text.annotations:
                print(annotation)

This run results in the expected counts along with annotations for the source:

The number of cruise ships that visited Vancouver were as follows:

- **2017**: 236 cruise ships
- **2018**: 243 cruise ships
- **2019**: 243 cruise ships

These numbers are detailed in the provided document which highlights Vancouver's tourism and economic impact【4:0†source】.

FileCitationAnnotation(file_citation=FileCitation(file_id='file-...'), start_index=282, end_index=294, text='【4:0†source】', type='file_citation')

Step 5: The Cleanup

With our result if the agent / file / thread aren't needed anymore it is easy to delete them:

client.files.delete(file.id)
client.beta.threads.delete(thread.id)
client.beta.assistants.delete(assistant.id)

Summary

That’s it! The agent is built and able to process a prompt with files. The combined example looks like this:

from openai import OpenAI

client = OpenAI()

assistant = client.beta.assistants.create(
    name="Demo Assistant",
    instructions="You are an expert at searching PDFs. Use your knowledge base to answer questions.",
    model="gpt-4o",
    tools=[{"type": "file_search"}],
)

file = client.files.create(file=open("vancouver.pdf", "rb"), purpose="assistants")

thread = client.beta.threads.create(messages=[{
    "role": "user",
    "content": "How many cruise ships visited Vancouver in 2017 / 2018 / 2019?",
    "attachments": [{ "file_id": file.id, "tools": [{ "type": "file_search" }] }]
}])

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

for message in messages:
    for content in message.content:
        if content.type == "text":
            print(content.text.value)
            print("\n")
            for annotation in content.text.annotations:
                print(annotation)

client.files.delete(file.id)
client.beta.threads.delete(thread.id)
client.beta.assistants.delete(assistant.id)

This article originally appeared on https://workflow.ing/blog/articles/searching-files-with-chat-gpt-assistants.