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Indexed Collection (for RAG applications)

When should I use this?

When you want your bot's responses to be grounded in your uploaded documents.

Definition

Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant information from a set of documents to ground its answers in real data. Instead of relying solely on its built-in knowledge, the model uses indexes—specialized databases that store document content as vectors (numerical representations of meaning). This makes it easy for the model to find and use the most relevant parts of your uploaded files when answering questions.

Indexed collections will replace OpenAI Assistants' file search functionality in the future

If you’ve used the OpenAI Assistants’ file search capability in OCS, you’ve already interacted with an index behind the scenes.

TIP: You can create an indexed collection from an OpenAI assistant that uses file search by clicking the Create from assistant button

In OCS, indexed collections are managed through LLM services like OpenAI. When creating an indexed collection, you’ll select the LLM provider responsible for hosting and querying that index.

Currently supported providers

  • OpenAI

Chunking and Optimization

When you upload a document to an index, it’s broken up into smaller parts called chunks. These chunks are then converted into vectors and stored in the index. Chunking is a key part of how RAG works, as it affects how accurately the model can retrieve relevant information.

In most cases, it will not be necessary to change the default chunking strategy, but you’ll have the option to customize the chunking strategy for each set of uploaded files:

  • Chunk size – how large each chunk is (in tokens)
  • Chunk overlap – how much each chunk overlaps with the next, to preserve context

Choosing the right chunking strategy can improve retrieval accuracy, especially for technical documents.

This flexibility helps tailor the index to your use case—whether it’s short notes or long, complex reports.

Supported file types

Supported files are determined by the select provider: