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.
what algo/mechanism goes beyond semantic search,
ive heard about semantic chunking but search is new to me.
Let’s explore a bit.
If you were to break down the meaning of “semantic chunking” itself as a phrase, to me, it would be a method of performing chunking that has a higher understanding of documents and logical places to split them
Assistant’s vector stores do not do this, they just will split an extracted file into parts of predetermined size. This is accentuated by a pre-determined overlap of the split point into adjoining chunks, creating some duplication.
Here is how AI embeddings works, providing a search based on an AI’s high intelligence in discerning the meaning of contents - text, visual, audio. It uses those document chunks, comparing a user input or an AI query exhaustively against every chunk, to determine a similarity score, to where the best matches can be a “semantic search result”.
(a fact-checked AI continues my reply…)
When we talk about AI embeddings for search, the key idea is that every piece of data—whether text, an image, or an audio clip—is transformed into a point (a vector) in a high-dimensional space. These vectors aren’t arbitrary; they capture the intrinsic, semantic meaning of the content. That means, during a search, rather than relying solely on keyword matching, the system compares the “meaning” of a query with the “meaning” of documents. The closer two vectors are in this space (commonly using metrics like cosine similarity), the more semantically similar the underlying pieces of content.
Putting it all together, here’s what the process looks like:
Data Ingestion and Chunking:
• Traditional systems slice text into fixed-length segments (with overlaps) to ensure complete coverage.
• An advanced semantic chunking system would analyze the document, identifying natural breaks (like paragraphs, sections, or complete concepts) to form chunks that are internally coherent.Embedding Generation:
• Each chunk (or piece of content, regardless of modality) is converted into a dense vector using a neural model that’s been trained to capture semantic features - embeddings.
• For multimedia content, specialized models encode visual or auditory features into comparable vector spaces.Vector Comparison and Ranking:
• When a query comes in, it too is converted into an embedding.
• The system then performs an exhaustive (or even a multi-turn refinement strategy) similarity search across all embeddings to find which pieces are closest to the query in vector space.
• The “closeness” (i.e., similarity) is used to rank the results, so that the top results are those whose embeddings best match the query’s embedding.Beyond Basic Semantic Search:
• While semantic search itself is powerful, additional layers—like metadata, context summaries, hypothetical document conversions—can further refine these results.
• These layers may incorporate context, user feedback, or domain-specific adjustments to improve precision even more.
• Multiple models and the semantic weights can be combined, along with different and parallel chunk size vector databases, to come up with an aggregated score better than just one AI model’s vector database.In summary, the “search” as implemented through AI embeddings is not just a simple lookup; it’s an intelligent process where the semantic content is distilled into numeric representations that can be compared across diverse data types. Semantic chunking is one way to enhance this process by ensuring that the segments being analyzed preserve the natural boundaries of meaning. Together, these techniques empower systems to return results based on deeper, intrinsic similarities rather than mere surface-level keyword overlaps.
Now, more about chunking: In many current systems (like those used in OpenAI’s Assistants), documents are split into chunks of a predetermined size, often with a slight overlap at the boundaries. This approach—sometimes called static chunking—ensures that all parts of the document are covered and that context isn’t completely lost at the edges. However, it doesn’t really consider the document’s internal structure or logical breaks.
Semantic chunking, on the other hand, aims to improve on that by determining breakpoints based on the content itself—identifying, for example, where a paragraph or a thought really ends and another begins. The idea is to make sure each chunk is a coherent unit of meaning. With more semantically coherent chunks, the embeddings produced for each part are likely to be more representative of the underlying ideas, potentially leading to more accurate search results, and more coherent information given to a conversational AI model.