As stated in the title, I would like to know if the file_search tool in the OpenAI Assistant API functions as a RAG (Retrieval-Augmented Generation) implementation.
While the internal workings of the tool might not be publicly disclosed, discussions with people around me have led to differing opinions—some believe it operates as a RAG, while others disagree.
I’m curious to hear the community’s perspective on this. Any insights or clarifications would be greatly appreciated!
Hi @whdms1107 and welcome to the community!
Yes, it can be considered RAG. It’s quite complex behind the scenes as it does the optimisation of the query, combination of both keyword and vector search, and re-ranking. You can see here for details on how it works.
RAG: retrieval-augmented generation
Not generation that calls for a search to be performed with an AI-written query, IMO.
Does a tool that gets the weather or the account’s remaining balance count as RAG then?
It is possible to do the RAG augmentation with no preliminary AI, just algorithms that search and provide the retrieval that augments the generation.
With file search enabled, you set the AI into motion to answer a question without any augmentation, and it has to do the decision-making and work.
OpenAI’s AI models want to fight me though.
The two scenarios you describe can both be considered forms of retrieval-augmented generation (RAG) because they incorporate a retrieval step that enhances the generation process. However, the distinction lies in where and how retrieval integrates with the generation workflow. Here’s the nomenclature decision for clarity:
Scenario 1: Tool-Based RAG
- Description: The AI model uses a function tool (
file_search) to emit a query, performing embeddings-based semantic search to retrieve ranked results, which are returned to the model asynchronously for further processing or another API call.- Classification: Tool-Based RAG.
- This approach is tool-centric, as retrieval occurs on-demand during inference and is initiated by the model itself (or its surrounding environment). The generation process adapts to retrieved results based on interactive steps between tools and the model.
Scenario 2: Pre-Contextualized RAG
- Description: User input and prior chat context are pre-processed into embeddings, which are used for semantic search. The ranked results are injected into the input context of the language model before inference begins.
- Classification: Pre-Contextualized RAG.
- This approach integrates retrieval directly into the context-building step before generation, ensuring the retrieved knowledge is always part of the initial input that the model uses to generate its response.
Key Differences
Feature Tool-Based RAG Pre-Contextualized RAG Retrieval Trigger Explicit, on-demand during inference Implicit, prior to inference Retrieval Timing Mid-inference or asynchronous Pre-inference Integration Model interacts with tools iteratively Retrieved data directly embedded Use Case Dynamic or adaptive retrieval needs Preemptive retrieval of context
Final Decision:
Both are valid RAG approaches, but Tool-Based RAG emphasizes dynamic, interactive retrieval during generation, while Pre-Contextualized RAG is structured around up-front retrieval to enrich the model’s input.
While I also dislike the term RAG applied to that, it’s technically not wrong, in either case.
Because after the initial tool call, you generate a second generation call that is the user query again, augmented with the tool response → which is “True RAG”
It’s just… …uncouth. Unrefined.
Let’s just call it Caveman RAG lol.