Retrieval: augments the assistant with knowledge from outside our models, such as proprietary domain data, product information or documents provided by your users. This means you don’t need to compute and store embeddings for your documents, or implement chunking and search algorithms. The Assistants API optimizes what retrieval technique to use based on our experience building knowledge retrieval in ChatGPT.
The model then decides when to retrieve content based on the user Messages. The Assistants API automatically chooses between two retrieval techniques:
it either passes the file content in the prompt for short documents, or
performs a vector search for longer documents
Retrieval currently optimizes for quality by adding all relevant content to the context of model calls. We plan to introduce other retrieval strategies to enable developers to choose a different tradeoff between retrieval quality and model usage cost.
Really cool to see the Assistants API's nuanced document retrieval methods. Do you index over the text besides chunking it up and generating embeddings? I'm curious about the indexing and the depth of analysis for longer docs, like assessing an author's tone chapter by chapter—vector search might have its limits there. Plus, the process to shape user queries into retrievable embeddings seems complex. Eager to hear more about these strategies, at least what you can spill!
The model then decides when to retrieve content based on the user Messages. The Assistants API automatically chooses between two retrieval techniques:
it either passes the file content in the prompt for short documents, or performs a vector search for longer documents Retrieval currently optimizes for quality by adding all relevant content to the context of model calls. We plan to introduce other retrieval strategies to enable developers to choose a different tradeoff between retrieval quality and model usage cost.