Vector store vs. file upload in assistant v2

API
,
1

I am a bit confused with this subject. What is the difference between creating a vector store and linking it to a thread/assistant, and uploading the file and linking it in the thread message directly like this:

message = client.beta.threads.messages.create(
	    thread_id=thread_id,
	    role="user",
	    content=[{"type": "image_file", "image_file": {"file_id": file_id}}]
	 )

My use case is the each of my users has their own thread and they upload images/files for assistant to help them read/summarize etc.
Why should I use vector store rather than simply uploading the file and attaching it to a message on the thread?

Any help is appreciated!

2

First things first, my explanation below is based on my understanding from OpenAI’s documentation, experimentation, and logical inference.
I am as much interested in a proper explanation as you are, so I’ll follow this thread with interest.
Please treat the following information as an informed guess rather than a definitive answer.

The main difference between using the Vector Store API and the File API lies in —I guess— how the assistant interacts with the data and how the data is stored, accessed, and queried over time.

When to use the Vector Store API

The Vector Store API is ideal for storing general-purpose or reusable knowledge that you want your assistant to refer to across multiple interactions. This works well when:

  1. You have documents that provide context for ongoing or future conversations.
    For example, if your assistant is designed to help with a software project, you might upload documentation, guidelines, or a codebase to the Vector Store. These files act as a knowledge base that the assistant can search through whenever needed.

  2. The assistant needs to perform semantic searches across multiple files.
    The Vector Store breaks the files into smaller chunks, organizes them semantically, and allows the assistant to find relevant information based on the context of the conversation, not just based on exact matches.

  3. You want to enable broader, context-aware interactions.
    If a user uploads a file to the Vector Store and later asks about a topic related to it (even across different threads or conversations), the assistant will search all relevant files in the store. However, it may not prioritize a specific file unless additional steps are taken (e.g., specifying the file ID).

    Example Use Case:

    • A user uploads two Python files (on different days) to the Vector Store. When they later ask about Python code, the assistant searches across all uploaded files to provide contextually relevant responses.

    Limitations:

    • It’s harder to target a specific file for immediate use ; for example both Python files you uploaded will be used to answer a question.

When to use the File API

The File API is designed for more direct and immediate file handling, especially when dealing with “one-shot” operations. It works well when:

  1. You want to attach a specific file to a conversation or thread for immediate use.
    For example, if a user uploads an image or document and wants the assistant to summarize it, you can attach that file directly to the thread and query the assistant to perform the task.

  2. You need precise control over which file the assistant uses.
    Each file uploaded via the File API has a unique file_id, which you can reference to target that specific file in your conversation.

    Example Use Case:

    • A user uploads a file with the file_id 12345 and asks the assistant to summarize it. The assistant processes only the specific file linked to the thread message.

    Advantages:

    • Provides a direct connection between the file and the conversation.
    • Easier to manage in applications with “upload-process-result” workflows.

    Limitations:

    • You need to manually manage the file_id of the files you upload, which implies another layer of complexity in your stateful app.
    • Files are tied to specific messages and threads, making it less suitable for long-term, reusable knowledge storage.

Comparison: File API vs Vector Store API

Feature File API Vector Store API
Purpose Immediate, file-specific operations. Long-term, context-aware knowledge storage.
Targeting Files Specific files via file_id. General semantic search across all stored files.
Usage Scope Per-thread or per-message basis. Cross-thread and reusable across conversations.
File Retrieval Manual reference to file IDs. Automatic semantic matching during queries.
Best For File-specific tasks like summarization, transcription. Building a reusable knowledge base for broader tasks.

Again, this comparison table is purely based on my understanding from the documentation, experimentation, and logical inference.
Don’t take it for granted ; you —and I— should gather more information or at least “more official” information on this topic.

What I recommend for your use case:

  • If users are uploading files to perform specific tasks (like summarizing or reading them), the File API is the better option. You can upload the file, attach it to a thread, and handle the query immediately.
  • If your assistant needs to handle long-term context or refer back to files over time, the Vector Store API would be more suitable. It allows the assistant to consider all uploaded files when generating responses, but you may need additional logic to prioritize specific files.

Or you could also end up using both APIs on demand, depending on your use case at the moment.

I will be interested in your feedback on this topic :slightly_smiling_face:

3

The above is nonsense bot talk.

Attaching a file to an assistants thread message, the only “attach” that there is, still creates a temporary vector store that is added to the same file search as a main vector store purposefully created. Then it is chunked and conflated with other search returns.

You cannot “reference that file”, or any other manufactured fabrication in the post.

4

If you create a file without using the Vector Store API, you need to attach it to your thread’s parameter by passing the ID of the created file.

Yes it seems to use the vector store applied to your thread —or create one under the hood— but it’s not clear whether there is a real difference between upload files to a vector store beforehand and creating files using the File API and then attaching them to your thread.

I mean, if both methods are doing the exact same thing, why not simply update the vector store with the new files instead of using 2 different methods? Hence my preface stating I was simply guessing there could be a difference of use between a direct vector store approach and a file upload one.

It seems I did not understand the docs properly then ; but still the author’s question remains and I’m equally interested as to whether there is room for a use case like the one he describes.

5

Assistants is multi-user. You can service 100 users with “bot that knows stuff” and still have unique data added by a user as a potential return to a search, via thread message.